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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-nao-supervisionada-testes.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Buscas não supervisionadas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 197,
"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 matplotlib.pyplot as plt\n",
"import random\n",
"import heapq\n",
"import math\n",
"import sys\n",
"from collections import defaultdict, deque, Counter\n",
"from itertools import combinations\n",
"\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": 198,
"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",
" #(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": 199,
"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\n",
" def find_min_edge(self):\n",
" \"\"\"Find minimum value of edges.\"\"\"\n",
" m = infinity\n",
" for d in self.graph.graph_dict.values():\n",
" local_min = min(d.values())\n",
" m = min(m, local_min)\n",
" \n",
" def h(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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Busca nao supervisionada: DFS - Teste 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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 = depth_first_graph_search(robot_problem)\n",
"print(node)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": 18,
"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": 19,
"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 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=(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",
"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 nao supervisionada: BFS - Teste 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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 = breadth_first_graph_search(robot_problem)\n",
"print(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": 18,
"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": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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jjz4aSY0ux8zqVHe4A+cDS4Dd8vRVwDcj4mZJ3wVmAt/pZP3RW7du5bnnnmPx4sWsW7euZxXbgJs/fz5nnHEG06ZNa3QpZlanuoZlJB0AfAi4Lk8LOA64NS8yGzili83sHRE9LNMaJSJ4/vnn+da3vsXChQsbXY6Z1aneMfergc8Bb+TpPYGNEbEtT68B9q+x7lTgA8Dhb7zxRo1FbEf19NNPs3z5ctauXcs111zDXXfd1eiSzKwOXYa7pJOAloh4pLK5g0VrnZYvAO4GftbU1J1RIGu0iKClpYUhQ4Ywfvx4AO67774GV2Vm9agnbY8BPiJpGjCCNOZ+NbC7pKZ89n4AsLaL7by+yy67cPjhh3Pqqady2GGH9apw639btmxhw4YN7LbbbrS/ML/88ssNrsrM6tFluEfEJcAlAJKOBT4bEdMl/SdwGukTMzOA27valiTGjBnDEUccwZQpU3pVuPW/zZs3M3bs2EaXYWY90JvPuV8EfEbSMtIY/PV9U5KZmfVWtwbBI+IB4IF8fwXg028zsx2Qv6FqZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBeoy3CWNkPSQpN9IelzSP+X2N0laKOkpSXMlDe//cs3MrB71nLm3AsdFxCRgMnCipKnAVcA3I+JgYAMws//KNDOz7ugy3CPZnCeH5b8AjgNuze2zgVP6pUIzM+u2pnoWkjQUeAR4C/BtYDmwMSK25UXWAPvXWH0qcAJAa2trr4o1M7P61BXuEdEGTJa0O3AbcGhHi9VYfQHQBtDc3Hx6T4o0M7Pu6danZSJiI/AA6Wx8d0ntLw4HAGv7tjQzM+upej4ts3c+Y0fSLqQhliXA/cBpebEZwO39VaSZmXVPPcMy44DZedx9CHBLRNwp6QngZkmXA48B1/djnWZm1g1dhntE/BZ4RwftK4Ap/VGUmZn1jr+hamZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgXqMtwlHSjpfklLJD0u6fzcPlbSvZKeyrd79H+5ZmZWj3rO3LcBF0bEocBU4BxJhwEXA/Mi4mBgXp42M7MdQJfhHhHrIuLRfH8TsATYHzgZmJ0Xmw2c0l9FmplZ9zR1Z2FJE4F3AAuBfSNiHaQXAEn71FhtKnACQGtra48LNTOz+tUd7pJGAT8GLoiIVyTVu+oCoA2gubn59G5XaGZm3VbXp2UkDSMF+5yI+Elufl7SuDx/HNDSPyWamVl31fNpGQHXA0si4hsVs+4AZuT7M4Db+748MzPriXqGZY4BzgR+J2lRbvs8cCVwi6SZwGrgo/1TopmZdVeX4R4RvwRqDbAf37flmJlZX/A3VM3MCuRwNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxAXYa7pO9LapG0uKJtrKR7JT2Vb/fo3zLNzKw76jlzvwE4sartYmBeRBwMzMvTO685c2DiRBgyJN3OmdPoisxsJ9dluEfEz4GXqppPBmbn+7OBU/q4rsFjzhyYNQtWrYKIdDtrlgPezBqqp2Pu+0bEOoB8u0/flTTIXHopbNmyfduWLandzKxBmgZgH1OBEwBaW1sHYHcDbPXq7rWbmQ2Anp65Py9pHEC+belk2QXA3cDdzc3NPdzdDmz8+O61m5kNgJ6G+x3AjHx/BnB735QzCF1xBYwcuX3byJGp3cysQer5KOSPgAeBQyStkTQTuBJ4n6SngPfl6Z3T9Olw7bUwYQJI6fbaa1O7mVmDdDnmHhGn15h1fB/XMnhNn+4wN7Mdir+hamZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFaipNytLOhG4BhgKXBcRV3awWLTfaWtrY9myZdx0003Mnz+/N7u2AdDa2sqSJUu2axs+fDhf+9rXGlSR2c5j0aJFSGLcuHGVzVFr+Wo9DndJQ4FvA+8D1gAPS7ojIp6oWnQ1MAXYbevWrbS0tLBmzRrWr1/f013bAIkIXn31VV577bU/tI0ZM4bHHnusgVWZ7Rw2bdrEhg0b2Lx5c3vTq8BL9a7fm2GZKcCyiFgREa8BNwMnd7DcPcAqYK+IYP/992fEiBG92K0NFEkcdNBBSAJg6NChTJgwocFVme0cxo8fz7Bhw3jxxRfbm26IiDfqXV8RdZ/lb7+idBpwYkR8Mk+fCRwdEZ+uWm4WMAvYCzgQ2NijHQ4OzUBro4voJyOArY0uoh+V/NyB+zeYCRgWEaO7s1JvxtzVQdufvFJExLXAtZJGA08C9/dinzu6DwB3N7qIflJy38D9G+xK7V8A/066ttktvQn3NaQz8XYHAGtrLRwRmyQ9C3wRGNOL/e7IJgNfb3QR/aTkvoH7N9iV2L83SEPfm9uHRrujN+H+MHCwpDcBzwIfBz7R1UoRsbwX+9yhSdoSEY82uo7+UHLfwP0b7ErvX0/0ONwjYpukT5PeCg0Fvh8Rj3ex2rU93d8gUXL/Su4buH+DnftXpccXVM3MbMflb6iamRXI4W5mVqABCXdJJ0paKmmZpIsHYp/9SdL3JbVIWlzRNlbSvZKeyrd7NLLG3pB0oKT7JS2R9Lik83N7EX2UNELSQ5J+k/v3T7n9TZIW5v7NlTS80bX2lKShkh6TdGeeLqlvKyX9TtIiSb/ObUUcmwCSdpd0q6Tf5//Bv+hJ//o93Ct+puCDwGHA6ZIO6+/99rMbgBOr2i4G5kXEwcC8PD1YbQMujIhDganAOfk5K6WPrcBxETGJ9BG6EyVNBa4Cvpn7twGY2cAae+t8oPKHgUrqG8BfRcTkiDgyT5dybEL6TPvPIuJtwCTS89j9/kVEv/4BfwHcXTF9CXBJf+93APo1EVhcMb0UGJfvjwOWNrrGPuzr7aTfECquj8BI4FHgaGA90JTbtztuB9Mf6Tsn84DjgDtJXzgsom+5/pXknzOpaCvi2AR2A54mf9ilN/0biGGZ/YFnKqbX5LbS7BsR6wDy7T4NrqdPSJoIvANYSEF9zMMWi4AW4F5gObAxIrblRQbzcXo18DnSl2AA9qScvkH61uY9kh7JP28C5RybBwEvAD/Iw2rXSdqVHvRvIMK9rp8psB2PpFHAj4ELIuKVRtfTlyKiLSImk85ypwCHdrTYwFbVe5JOAloi4pHK5g4WHXR9q3BMRLyTNNR7jqS/bHRBfagJeCfwnYh4B+mXIHs0xDQQ4d6tnykYxJ6XNA4g37Y0uJ5ekTSMFOxzIuInubmoPgJExEbgAdK1hd0ltX+xb7Aep8cAH5G0kvRLrceRzuRL6BsAEbE237YAt5FenEs5NtcAayJiYZ6+lRT23e7fQIT7H36mIF+h/zhwxwDsd6DdAczI92eQxqkHJaUfsrgeWBIR36iYVUQfJe0tafd8fxfgBNJFq/uB0/Jig7J/EXFJRBwQERNJ/2v3RcR0CugbgKRd848Qkocr3g8sppBjMyKeA56RdEhuOh54gp70b4AuEkwj/SLkcuDSRl+06IP+/AhYB7xOeqWdSRrXnAc8lW/HNrrOXvTvPaS37b8FFuW/aaX0EXg78Fju32LgS7n9IOAhYBnwn0Bzo2vtZT+PBe4sqW+5H7/Jf4+350kpx2buy2Tg1/n4/CmwR0/6558fMDMrkL+hamZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgX6/22QD2OCaFcIAAAAAElFTkSuQmCC\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=(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",
"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 nao supervisionada: BFS - Teste 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Node (50, 50)>\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(node)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"list_nodes = []\n",
"try:\n",
" for n in node.path():\n",
" list_nodes.append(n.state)\n",
"except:\n",
" pass\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": 28,
"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=(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",
"\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 nao supervisionada: DFS - Teste 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"566\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(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"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": 43,
"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": 45,
"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 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=(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,y)\n",
"plt.scatter(10,10,color='r')\n",
"plt.scatter(50,50,color='r')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Busca nao supervisionada: DFS - Teste 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"194\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(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"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": 49,
"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": 51,
"metadata": {
"scrolled": false
},
"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 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=(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 nao supervisionada: BFS - Teste 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 64,
"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 = breadth_first_graph_search(robot_problem)\n",
"print(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"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": 66,
"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": 67,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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xLLvsao6+5PwatzCZBs25u/sq4IgC018ETqhGo0Qku/Lz6+0jW9iyo2eP5Sa0t3H07PMhBPMDwp9ESj2hKiJSdX359b40zLrOLlqGGS1NRk+vv7ZcW0sTF598SL2a2RA0/ICIJEah/HrPbmev4c1MbG/DgIntbXzrzMOZeeTE+jSyQejIXUTqolB5Y7H8+stdPSy97P01bmFjU3AXkZorlH6Zc9sTA+bXpTxKy4hIzRUrb3SP8um5lF8fGgV3Eam5gdIv3zrzcOXXK0BpGRGpunLKG2ceOVHBvAIU3EWkqlTeWB9Ky4hIVam8sT505C4iVaXyxvpQcBeRiilUuz6hvY11BQK8yhurS2kZEamIYkPzHnfofipvrAMFdxGpiGK164uf3qTyxjpQWkZEylbO0AHrO7tU3lgHCu4iUhYNHdAYlJYRkbJo6IDGoOAuImXR0AGNQWkZERmQhg5oTAruIlKUhg5oXErLiEhRGjqgcenIXUSK0tABjUvBXUQADR2QNkrLiIiGDkghBXcR0dABKaS0jAyo0Fd1vbEbX/7rWij1Aho6oJEpuEtRxS4zB/Rmb2CFXlcDvMCyyq03LqVlpKhiX9WvWriiTi2SSij0ujpgecspt97YFNylqIFG+ZPGVez1c1BuPUVKTsuYWRPwR2Cdu59mZm8CbgLGAo8DZ7v7q9VpptRCOZeZKxffuIrl2Ce2t/H72cfXoUVSDeUcuV8ALM+5fyVwjbsfDGwBzqtkw6S2CpXCbd+5i5am/l/W21qaOO7Q/QqWzd2xZF1d2i7lOe7Q/cqaLo2ppOBuZpOADwLXh/sGHA/cGhaZD8ysRgOlNopdZt7WBDxxF5t/dQ3Nz9zPt848nMVPb1IuvoEtfnpTWdOlMZWalvku8CVgdLj/BqDT3XeF+2uBYt/JZwAnAnR3dw+xmVJJ5fyKzqrbv8O+21ayL7DrsZX8+a7RrN/xroLLKhffGHQuJRsGPXI3s9OADnd/LHdygUULVVIBPAQsBBa2traW30KpqGJXIraPbNlj2d27XmXXhuX9pt11113s07bnskDR6ZIshV7rgaZLYyrlyP0Y4HQzOxUYAexNdCTfbmbN4eh9ErC+es2USilW3tjaPIy2lqZ+81p7u9h7eP/P8e3btzOi0Ec7YEWmS7J4kcOwYtOlMQ165O7uc9x9krtPAT4O3OfunwQWAx8Oi80CFlStlVIx5fyKzldOncrI4Xt+/ncWqKAZaLoky8tdhV+nYtOlMcW5QvXLwE1mdgWwBLihMk2SSorzKzobN27kGwUeUyMFNja9ftlQ1kVM7n6/u58Wbq9y9+nu/hZ3/4i762xpwpRT3ljOlYgqpWtsev2yQVeopli1fkVHpXSNTa9fNmjgsBSr1q/oqJSusen1ywYF95So5a/oDPS4GpYg+ZRzzwalZVKg1r+ic/HJhxR8XA1L0BiUc88GBfcUqPWv6Mw8cmLBx9WwBI1BOfdsUFqmASXhV3QKPe7/unlpwWXXdXZxzNz7lKpJCOXcs0FH7g2mUAqm2IWhtc6hFtuegVI1CaLhB7JBwb3BJPlXdArl4gv9fJtSNfWl4QeyQcG9wST5V3QK5eKLxQulAOpHww9kg3LuCVZOeWNSfkUnPxd/zNz7VDaZMCqFzAYduSdUrcsbq0Vlk8mjUshsUHBPqFqXN1aLyiaTR6WQ2aC0TEINVK5WrfLGaimnbFK5+OpTKWQ2KLgnQC2HDkiKYvu3T1uLauKrLO19SyJKy9RZWnLr5SqUi28ZZrzy6i7l4atMOfdsUHCvs7Tk1stVKBc/akQzPb39iyeVh6885dyzQWmZGkvC0AFJkb9/b5r9q4LLre/sUtlkBSnnng06cq+hJA8dkATF9nmfthaVTVaQhh/IBgX3Gkry0AFJUKwm3gyVTVaQhh/IBgX3Gkry0AFJUKwmvrPAD3qD0ghDpeEHskE59yppxKEDkqDQeYarFq5Q2WQFqRQyG3TkXgVZLW+sFpVNVpZKIbNBwb0KslreWC0qm6wslUJmg9IyFaDyxupT2WTlqBQyG3TkHpPKG+tDZZNDp1LIbFBwj0nljfWhssmhUylkNii4x6TyxvpQ2eTQqRQyGwbNuZvZCOB3QGtY/lZ3v8zM3gTcBIwFHgfOdvdXq9nYJFJ5Y/2UUzapX356nUohs6GUI/du4Hh3PwKYBpxiZjOAK4Fr3P1gYAtwXvWamVwqK0sW/fLT4NRns2HQ4O6R7eFuS/hz4Hjg1jB9PjCzKi1MOJWVJYt++Wlw6rPZUFIppJk1AY8BbwG+DzwLdLr7rrDIWqDY99sZwIkA3d3dsRqbRCorSx798tPA1GezoaTg7u69wDQzawduB6YWWqzI6g8BvQCtra1nDaWRSab8ZWPQLz+9Tn02G8qqlnH3TuB+oqPxdjPr+3CYBKyvbNMag/KXjUFDGLxOfTYbBg3uZrZfOGLHzNqIUizLgcXAh8Nis4AF1Wpkkil/2Rg0hMHr1GezoZS0zHhgfsi7DwNucfe7zewp4CYzuwJYAtxQxXYmlvKXjUNDGETUZ7Nh0ODu7n8GjiwwfRUwvRqNaiTtI1vYUuDCGV3KnXwD5eHn3PbEa9U1fekaIBUBXn02G3SFaky6lLtxZXUIA/XZbFBwj0mXcjeurA5hoD6bDRryNyaVlTW2LP7yk/psNujIPSaVlaVP2ssm1WezQcE9JpWVpU/ayybVZ7NBaZmYVFaWTuWUTTYa9dlsUHCPSfnLbBjodW60mnj12WxQWiYm5S+zIU1DCavPZoOCe0zKX2ZDmoYSVp/NBqVlYlL+MjvKHUo4qeka9dls0JF7TPol+WwrlqfuG8Igieka9dlsUHCPSZdyZ1sjDmGgPpsNCu4x6VLubGvEIQzUZ7NBOfeYVFYmjTaEgfpsNujIPSaVlUkhSR7CQH02GxTcY1JZmRSS5CEM1GezQWmZmFRWJsUk9Zef1GezQUfuMamsTEqVlLJJ9dlsUHCPSWVlUqqklE2qz2aDgntMKiuTUiWlbFJ9NhuUc49JZWVSjiSUTarPZoOO3GNSWZnEVeuySfXZbFBwj0llZRJXrcsm1WezQWmZmFRWJpVQy19+Up/NBgX3mJS/lGqo5i8/qc9mg9IyMSl/KdVQzV9+Up/NBgX3mJS/lGqo5i8/qc9mw6BpGTObDPwUOADYDfzQ3a81s7HAzcAUYDXwUXffUr2mJpPyl1It5fzy07rOrj3KJq3I46rPZkMpR+67gC+6+1RgBvA5M3sbMBtY5O4HA4vC/czRpdxSS8Xy4gZ7pGqWrN+xx3IjR45Un82IQYO7u29w98fD7W3AcmAicAYwPyw2H5hZrUYmmS7llloqlIs3IL+7dfX08qt1wxk+fPhr05qbm5k2bZr6bEaUVS1jZlOAI4GHgf3dfQNEHwBmNq7IajOAEwG6u7uH3NCk0qXcUkt9aZrcaplClS8Am3eNYM6cOfz6179m9+7dnHzyyUyePJmXu/5ccHn12XQpObib2Sjgl8CF7r7VrFhGbw8PAb0Ara2tZ5XdwoRTWZnUWn4u/pi59xXtg8t3jOLW7iNY39nFbXd3cHHPOvXZjCipWsbMWogC+8/c/bYw+QUzGx/mjwc6qtPEZFNZmdRbuWWTxx26X8HlLz75kBq2Wqpt0OBu0SH6DcByd/9Ozqw7gVnh9ixgQeWbl3wqK5N6K7dscvHTmwouX+vfcpXqKiUtcwxwNvCEmfXVYX0FmAvcYmbnAc8DH6lOE5NNZWWSBOWUTa7v7Cq4vKTLoMHd3R+EoiWzJ1S2OY1H+UtJKvXNbNMVqjEp5y5JVSwXr9x6NmjgsJiUc5ekKlQ2Wc0f3pZkUXCPSTl3STLl1rNLaZmYdCm3iCSRgntMupRbRJJIwT0mDT8gIkmk4B5TsbIylZuJSD0puMekUkgRSSIF95hUCikiSaTgHpNKIUUkiRTcY1IppIgkkYJ7TCqFFJEkUnCPSaWQIpJECu4xqRRSRJJIwT0mlUKKSBIpuMekUkgRSSIF95hUCikiSaTgHpNy7iKSRAruMSnnLiJJpOAek3LuIpJECu4xKecuIkmk4B6Thh8QkSRScI9Jww+ISBIpuMek4QdEJIkU3GNSKaSIJJGCe0wqhRSRJFJwj0mlkCKSRIMGdzP7sZl1mNmynGljzexeM3sm/B9T3WYml0ohRSSJSjlynweckjdtNrDI3Q8GFoX7mTShvY3Tn1zMgz84l1VXfogHf3Aupz+5WDl3EamrQYO7u/8OeClv8hnA/HB7PjCzwu1qGBe88Ahzf3sdk7ZuYhjOpK2bmPvb67jghUfq3TQRybCh5tz3d/cNAOH/uMo1qbG8b941jNzV3W/ayF3dvG/eNXVqkYgINNdgGzOAEwG6u7sHWbTxjOssfOK02HQRkVoY6pH7C2Y2HiD87xhg2YeAhcDC1tbWIW4uuTraC5c8FpsuIlILQw3udwKzwu1ZwILKNKfxrLnoUrpa+n9odbW0suaiS+vUIhGR0kohfwH8ATjEzNaa2XnAXOAkM3sGOCncz6SjLzmfZZddzcb2cezG2Ng+jmWXXc3Rl5xf76aJSIYNmnN397OKzDqhwm1pWEdfcj6EYH5A+BMRqSddoSoikkIK7iIiKaTgLiKSQgruIiIppOAuIpJCCu4iIimk4C4ikkIK7iIiKaTgLiKSQgruIiIppOAuIpJCCu4iIimk4C4ikkIK7iIiKaTgLiKSQgruIiIppOAuIpJCCu4iIimk4C4ikkIK7iIiKaTgLiKSQgruIiIppOAuIpJCCu4iIimk4C4ikkIK7iIiKaTgLiKSQgruIiIp1BxnZTM7BbgWaAKud/e5BRbzvhu9vb2sXLmSG2+8kQceeCDOpqUGtm7dyssvv9xv2o4dO7jqqqvq1CKR7Fi6dClmxvjx43Mne7Hl8w05uJtZE/B94CRgLfComd3p7k/lLfo8MB3Ye+fOnXR0dLB27Vo2b9481E1LjezevRt3p6en57Vpra2tLFmypI6tEsmGbdu2sWXLFrZv39436RXgpVLXj5OWmQ6sdPdV7v4qcBNwRoHl7gGeA/Z1dyZOnMiIESNibFZqZdiwYUydOhUzA6CpqYnDDjuszq0SyYYDDzyQlpYWXnzxxb5J89x9d6nrm3vJR/n9VzT7MHCKu3863D8beLe7n5+33GeAzwD7ApOBziFtsDG0At31bkSVtAFd9W5EFaX5tQPtXyMzoMXdR5ezUpycuxWYtscnhbv/EPihmY0G/gIsjrHNpDsZWFjvRlRJmvcNtH+NLq3758C/E53bLEuc4L6W6Ei8zyRgfbGF3X2bma0DLgX2ibHdJJsGXF3vRlRJmvcNtH+NLo37t5so9b29LzVajjjB/VHgYDN7E7AO+DjwicFWcvdnY2wz0cxsh7s/Xu92VEOa9w20f40u7fs3FEMO7u6+y8zOJ/oq1AT82N2fHGS1Hw51ew0izfuX5n0D7V+j0/7lGfIJVRERSS5doSoikkIK7iIiKVST4G5mp5jZCjNbaWaza7HNajKzH5tZh5kty5k21szuNbNnwv8x9WxjHGY22cwWm9lyM3vSzC4I01Oxj2Y2wsweMbM/hf37Rpj+JjN7OOzfzWY2vN5tHSozazKzJWZ2d7ifpn1bbWZPmNlSM/tjmJaKvglgZu1mdquZPR3eg38zlP2renDPGabgA8DbgLPM7G3V3m6VzQNOyZs2G1jk7gcDi8L9RrUL+KK7TwVmAJ8Lr1la9rEbON7djyAqoTvFzGYAVwLXhP3bApxXxzbGdQGwPOd+mvYN4Dh3n+buR4X7aembENW0/9bdDwWOIHody98/d6/qH/A3wMKc+3OAOdXebg32awqwLOf+CmB8uD0eWFHvNlZwXxcQjSGUun0ERgKPA+8GNgPNYXq/fttIf0TXnCwCjgfuJrrgMBX7Ftq/mjCcSc60VPRNYG/gr4Rilzj7V4u0zERgTc79tWFa2uzv7hsAwv9xdW5PRZjZFOBI4GFStI8hbbEU6ADuBZ4FOt19V1ikkfvpd4EvEV0EA/AG0rNvEF21eY+ZPRaGN4H09M2DgE3AT0Ja7Xoz24sh7F8tgntJwxRI8pjZKOCXwIXuvrXe7akkd+9192lER7nTgamFFqttq+ISJs+EAAABoklEQVQzs9OADnd/LHdygUUbbt9yHOPu7yRK9X7OzN5X7wZVUDPwTuAH7n4k0UiQQ0ox1SK4lzVMQQN7wczGA4T/HXVuTyxm1kIU2H/m7reFyanaRwB37wTuJzq30G5mfRf2NWo/PQY43cxWE43UejzRkXwa9g0Ad18f/ncAtxN9OKelb64F1rr7w+H+rUTBvuz9q0Vwf22YgnCG/uPAnTXYbq3dCcwKt2cR5akbkkUDWdwALHf37+TMSsU+mtl+ZtYebrcBJxKdtFoMfDgs1pD75+5z3H2Su08heq/d5+6fJAX7BmBme4VBCAnpivcDy0hJ33T3jcAaMzskTDoBeIqh7F+NThKcSjQi5LPAJfU+aVGB/fkFsAHoIfqkPY8or7kIeCb8H1vvdsbYv/cQfW3/M7A0/J2aln0E3gEsCfu3DPhamH4Q8AiwEvgvoLXebY25n8cCd6dp38J+/Cn8PdkXT9LSN8O+TAP+GPrnHcCYoeyfhh8QEUkhXaEqIpJCCu4iIimk4C4ikkIK7iIiKaTgLiKSQgruIiIppOAuIpJC/x+SVvdlTiFZzgAAAABJRU5ErkJggg==\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=(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 nao supervisionada: Uniform Cost Search"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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 = uniform_cost_search(robot_problem)\n",
"print(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": 12,
"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": 13,
"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 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=(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 nao supervisionada: Best First Search"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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 = best_first_graph_search(robot_problem, robot_problem.h)\n",
"print(node.path_cost)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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": 26,
"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": 30,
"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",
"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": 179,
"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": 180,
"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": 181,
"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Busca nao supervisionada: DFS - Printa todos os nos visitados"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 185,
"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 = best_first_search(robot_problem,f=lambda n: -len(n))\n",
"# node = depth_first_graph_search(robot_problem)"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"scrolled": false
},
"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": 177,
"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": 178,
"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": 187,
"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 nao supervisionada: BFS - Printa todos os nos visitados"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculo do custo da busca e o caminho percorrido"
]
},
{
"cell_type": "code",
"execution_count": 188,
"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 = best_first_search(robot_problem,len)\n",
"# node = breadth_first_graph_search(robot_problem)"
]
},
{
"cell_type": "code",
"execution_count": 189,
"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": 166,
"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": 167,
"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": 195,
"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_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()"
]
}
],
"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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