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luizcartolano2/proj1-busca-nao-supervisionada-testes.ipynb

Last active November 13, 2019 18:44
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Código dos testes feitos no Projeto de MC906
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proj1-busca-supervisionada-testes.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Buscas supervisionadas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 94,
"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",
"import heapq\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": 81,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# make the dict where the key is associated with his neighbors\n",
"# 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",
"# (i+1,j+1):sqrt(2), (i-1,j-1):sqrt(2), (i-1,j+1):sqrt(2), (i+1,j-1):sqrt(2),}\n",
" \n",
"grafo = UndirectedGraph(mapa)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modelagem da classe problema"
]
},
{
"cell_type": "code",
"execution_count": 71,
"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",
"# elif (act[1] == 40 and ((0<= act[0] < 18) or (19<= act[0] <= 18))):\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\n",
" \n",
" def heuristic_1(self, node):\n",
" \"\"\"h function is straight-line distance from a node's state to goal.\"\"\"\n",
" locs = getattr(self.graph, 'locations', None)\n",
" if locs:\n",
" if type(node) is str:\n",
" return int(distance(locs[node], locs[self.goal]))\n",
"\n",
" return int(distance(locs[node.state], locs[self.goal]))\n",
" else:\n",
" return infinity\n",
" \n",
" def heuristic_2(self,node):\n",
" \"\"\" Manhattan Heuristic Function \"\"\"\n",
" x1,y1 = node.state[0], node.state[1]\n",
" x2,y2 = self.goal[0], self.goal[1]\n",
" \n",
" return abs(x2 - x1) + abs(y2 - y1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Busca supervisionada A*: Heuristica 1 - Teste 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n"
]
}
],
"source": [
"init_pos = (10,10)\n",
"goal_pos = (50,50)\n",
"\n",
"robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
"node = astar_search(robot_problem, h=robot_problem.heuristic_1)\n",
"print(node)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": 10,
"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 A* com a primeira heuristica')\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=(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.annotate(\"\",\n",
" xy=(20,40), xycoords='data',\n",
" xytext=(0, 40), textcoords='data',\n",
" arrowprops=dict(arrowstyle=\"-\",\n",
" edgecolor = \"black\",\n",
" linewidth=5,\n",
" alpha=0.65,\n",
" connectionstyle=\"arc3,rad=0.\"),\n",
" )\n",
"\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": [
"## Busca supervisionada A*: Heuristica 2 - Teste 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n"
]
}
],
"source": [
"init_pos = (10,10)\n",
"goal_pos = (50,50)\n",
"\n",
"robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
"node = astar_search(robot_problem, h=robot_problem.heuristic_2)\n",
"print(node)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 14,
"metadata": {
"scrolled": true
},
"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 A* com a segunda heuristica')\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=(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.annotate(\"\",\n",
" xy=(20,40), xycoords='data',\n",
" xytext=(0, 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": [
"## Busca supervisionada A*: Heuristica 1 - Teste 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"132\n"
]
}
],
"source": [
"init_pos = (10,10)\n",
"goal_pos = (50,50)\n",
"\n",
"robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
"node = astar_search(robot_problem, h=robot_problem.heuristic_1)\n",
"print(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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": 21,
"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 A* com a primeira heuristica')\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=(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.annotate(\"\",\n",
" xy=(20,40), xycoords='data',\n",
" xytext=(19, 40), 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=(17,40), xycoords='data',\n",
" xytext=(0, 40), 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.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": [
"## Busca supervisionada A*: Heuristica 2 - Teste 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"80\n"
]
}
],
"source": [
"init_pos = (10,10)\n",
"goal_pos = (50,50)\n",
"\n",
"robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
"node = astar_search(robot_problem, h=robot_problem.heuristic_2)\n",
"print(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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": 25,
"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 A* com a primeira heuristica')\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=(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.annotate(\"\",\n",
" xy=(20,40), xycoords='data',\n",
" xytext=(19, 40), 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=(17,40), xycoords='data',\n",
" xytext=(0, 40), 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.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": [
"## Busca supervisionada A*: Heuristica 1 - Teste 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"136\n"
]
}
],
"source": [
"init_pos = (10,10)\n",
"goal_pos = (50,50)\n",
"\n",
"robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
"node = astar_search(robot_problem, h=robot_problem.heuristic_1)\n",
"print(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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": 31,
"metadata": {
"scrolled": true
},
"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 A* com a primeira heuristica')\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=(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.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",
"\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": [
"## Busca supervisionada A*: Heuristica 2 - Teste 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"84\n"
]
}
],
"source": [
"init_pos = (10,10)\n",
"goal_pos = (50,50)\n",
"\n",
"robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
"node = astar_search(robot_problem, h=robot_problem.heuristic_2)\n",
"print(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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": 35,
"metadata": {
"scrolled": true
},
"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 A* com a primeira heuristica')\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=(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.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",
"\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": [
"# Marcando todos os nós visitados pelos algoritmos"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"# class Node:\n",
"# \"A Node in a search tree.\"\n",
"# def __init__(self, state, parent=None, action=None, path_cost=0):\n",
"# self.__dict__.update(state=state, parent=parent, action=action, path_cost=path_cost)\n",
"\n",
"# def __repr__(self): return '<{}>'.format(self.state)\n",
"# def __len__(self): return 0 if self.parent is None else (1 + len(self.parent))\n",
"# def __lt__(self, other): return self.path_cost < other.path_cost"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"FIFOQueue = deque\n",
"\n",
"LIFOQueue = list\n",
"\n",
"class PriorityQueue:\n",
" \"\"\"A queue in which the item with minimum f(item) is always popped first.\"\"\"\n",
"\n",
" def __init__(self, items=(), key=lambda x: x): \n",
" self.key = key\n",
" self.items = [] # a heap of (score, item) pairs\n",
" for item in items:\n",
" self.add(item)\n",
" \n",
" def add(self, item):\n",
" \"\"\"Add item to the queuez.\"\"\"\n",
" pair = (self.key(item), item)\n",
" heapq.heappush(self.items, pair)\n",
"\n",
" def pop(self):\n",
" \"\"\"Pop and return the item with min f(item) value.\"\"\"\n",
" return heapq.heappop(self.items)[1]\n",
" \n",
" def top(self): return self.items[0][1]\n",
"\n",
" def __len__(self): return len(self.items)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"def expand(problem, node):\n",
" \"Expand a node, generating the children nodes.\"\n",
" s = node.state\n",
" for action in problem.actions(s):\n",
" s1 = problem.result(s, action)\n",
" cost = node.path_cost + 1\n",
" yield Node(s1, node, action, cost)\n",
" \n",
"\n",
"def path_actions(node):\n",
" \"The sequence of actions to get to this node.\"\n",
" if node.parent is None:\n",
" return [] \n",
" return path_actions(node.parent) + [node.action]\n",
"\n",
"\n",
"def path_states(node):\n",
" \"The sequence of states to get to this node.\"\n",
" if node in (cutoff, failure, None): \n",
" return []\n",
" return path_states(node.parent) + [node.state]\n",
"\n",
"def best_first_search(problem, f):\n",
" \"Search nodes with minimum f(node) value first; make `reached` global.\"\n",
" global reached # <<<<<<<<<<< Only change here\n",
" frontier = PriorityQueue([Node(problem.initial)], key=f)\n",
" reached = {}\n",
" while frontier:\n",
" node = frontier.pop()\n",
" if problem.goal_test(node.state):\n",
" return node\n",
" for child in expand(problem, node):\n",
" s = child.state\n",
" if s not in reached or child.path_cost < reached[s].path_cost:\n",
" reached[s] = child\n",
" frontier.add(child)\n",
" return failure\n",
"\n",
"def g(n): return n.path_cost"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Busca supervisionada: A* Heurística 1 - Printa todos os nos visitados"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"init_pos = (10,10)\n",
"goal_pos = (50,50)\n",
"\n",
"robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
"# node = best_first_search(robot_problem,f=lambda n: g(n) + robot_problem.heuristic_1(n))\n",
"node = astar_search(robot_problem, h=robot_problem.heuristic_1)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": [
"x_reach = []\n",
"y_reach = []\n",
"for nod in list(reached):\n",
" x_reach.append(nod[0])\n",
" y_reach.append(nod[1])"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 97,
"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": 98,
"metadata": {
"scrolled": true
},
"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.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=(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.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",
"\n",
"\n",
"plt.scatter(x_reach,y_reach,color='g')\n",
"plt.scatter(x,y,color='yellow')\n",
"plt.scatter(10,10,color='r')\n",
"plt.scatter(50,50,color='r')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Busca supervisionada: A* Heurística 2 - Printa todos os nos visitados"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"init_pos = (10,10)\n",
"goal_pos = (50,50)\n",
"\n",
"robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
"# node, l = breadth_first_graph_search_mod(robot_problem)\n",
"# node = node = best_first_search(robot_problem,f=lambda n: g(n) + robot_problem.heuristic_2(n))\n",
"node = astar_search(robot_problem, h=robot_problem.heuristic_2)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"x_reach2 = []\n",
"y_reach2 = []\n",
"for nod in list(reached):\n",
" x_reach2.append(nod[0])\n",
" y_reach2.append(nod[1])"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 101,
"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": 102,
"metadata": {
"scrolled": true
},
"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.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=(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.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",
"\n",
"\n",
"plt.scatter(x_reach2,y_reach2,color='g')\n",
"plt.scatter(x,y,color='yellow')\n",
"plt.scatter(10,10,color='r')\n",
"plt.scatter(50,50,color='r')\n",
"plt.show()"
]
},
{
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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