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SearchAlgorithms.py
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SearchAlgorithms.py
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from collections import deque
from Graph import Node
import logging
logging.basicConfig(filename='search_algorithms.log', level=logging.DEBUG)
# function used to sort a list
def sortFunction(val):
return val[1]
def pretty_print_table(table):
for i in range(len(table)):
row = ''
for j in range(len(table[0])):
row += f'{table[i][j]} '
print(row)
#
# Implements search algorithms:
# 1) Breadth-first search (BuscaLargura)
# 2) Depth-first search (BuscaProfundidade)
# 3) Iterative deepening search (BPI)
# 4) Uniform cost search (CustoUniforme)
# 5) Greddy search algorithm (BuscaGananciosa)
# 6) A* search algorithm (AEstrela)
# 7) hill-climing search algorithms
#
class SearchAlgorithm:
def search(self):
pass
#
# This class implements the Breadth-first search
#
class BuscaLargura (SearchAlgorithm):
def search (self, initialState):
#Creating a Queue
open = deque()
open.append(Node(initialState, None))
while (len(open) > 0):
n = open.popleft()
if (n.state.is_goal()):
return n
for i in n.state.sucessors():
open.append(Node(i,n))
return None
#
# This class implements the Depth-first search (limited)
#
class BuscaProfundidade (SearchAlgorithm):
def search (self, initialState, m):
#Using list as stack
open = []
open.append(Node(initialState, None))
while (len(open) > 0):
n = open.pop()
if (n.state.is_goal()):
return n
if (n.depth < m):
for i in n.state.sucessors():
open.append(Node(i,n))
return None
#
# This class implements Iterative Deepening Depth-first search
#
class BuscaProfundidadeIterativa (SearchAlgorithm):
def search (self, initialState):
n = 1
algorithm = BuscaProfundidade()
while True:
result = algorithm.search(initialState, n)
if (result != None):
return result
n = n+1
#
# This class implements a Uniform cost search algorithm
#
class BuscaCustoUniforme (SearchAlgorithm):
def search (self, initialState):
open = []
new_n = Node(initialState, None)
open.append((new_n, new_n.g))
while (len(open) > 0):
#list sorted by g()
open.sort(key = sortFunction, reverse = True)
n = open.pop()[0]
if (n.state.is_goal()):
return n
for i in n.state.sucessors():
new_n = Node(i,n)
open.append((new_n,new_n.g))
return None
#
# This class implements a Greddy search algorithm
#
class BuscaGananciosa (SearchAlgorithm):
def search (self, initialState):
open = []
new_n = Node(initialState, None)
open.append((new_n, new_n.h()))
while (len(open) > 0):
#list sorted by h()
open.sort(key = sortFunction, reverse = True)
n = open.pop()[0]
if (n.state.is_goal()):
return n
for i in n.state.sucessors():
new_n = Node(i,n)
open.append((new_n, new_n.h()))
return None
#
# This class implements a A* search algorithm
#
class AEstrela (SearchAlgorithm):
def search (self, initialState):
states = []
open = []
new_n = Node(initialState, None)
open.append((new_n, new_n.f()))
while (len(open) > 0):
#list sorted by f()
open.sort(key = sortFunction, reverse = True)
n = open.pop()[0]
logging.debug(n.state.env()+" -- "+str(n.f())+" -- "+str(n.h()))
if (n.state.is_goal()):
return n
for i in n.state.sucessors():
new_n = Node(i,n)
# eh necessario descrever o conteudo do estado
# para verificar se ele já foi instanciado ou nao
if (new_n.state.env() not in states):
open.append((new_n,new_n.f()))
# nao eh adiciona o estado ao vetor.
# eh adicionado o conteudo
states.append(new_n.state.env())
logging.debug(len(states))
else:
logging.debug('nao entrou')
return None
class SubidaMontanha (SearchAlgorithm):
def best(self, successors):
best_state = successors[0]
for i in successors:
if i.h() < best_state.h():
best_state = i
return best_state
def search (self, initialState):
atual = initialState
while True:
prox = self.best(atual.sucessors())
if prox.h() >= atual.h():
return atual
atual = prox
class SubidaMontanha2 (SearchAlgorithm):
def best(self, successors):
best_state = successors[0]
for i in successors:
if i.h() < best_state.h():
best_state = i
return best_state
def search (self, initialState):
atual = initialState
while True:
prox = self.best(atual.sucessors())
if prox.h() >= atual.h():
if atual.is_goal():
return atual
else:
atual.randomBoard()
else:
atual = prox