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2d498eb231
...
7f5313608e
19
cidades.py
19
cidades.py
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@ -16,7 +16,6 @@ class Cidades(SearchDomain):
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def __init__(self,connections, coordinates):
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def __init__(self,connections, coordinates):
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self.connections = connections
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self.connections = connections
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self.coordinates = coordinates
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self.coordinates = coordinates
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def actions(self,city):
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def actions(self,city):
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actlist = []
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actlist = []
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for (C1,C2,D) in self.connections:
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for (C1,C2,D) in self.connections:
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@ -25,26 +24,14 @@ class Cidades(SearchDomain):
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elif (C2==city):
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elif (C2==city):
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actlist += [(C2,C1)]
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actlist += [(C2,C1)]
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return actlist
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return actlist
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def result(self,city,action):
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def result(self,city,action):
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(C1,C2) = action
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(C1,C2) = action
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if C1==city:
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if C1==city:
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return C2
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return C2
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def cost(self, city, action):
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def cost(self, city, action):
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(C1,C2) = action
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pass
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for (X,Y,D) in self.connections:
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if X==C1 and Y==C2:
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return D
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if X==C2 and Y==C1:
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return D
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def heuristic(self, city, goal_city):
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def heuristic(self, city, goal_city):
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coords_city = cidades_portugal.coordinates[city]
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pass
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coords_goal_city = cidades_portugal.coordinates[goal_city]
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return ((coords_city[0] - coords_goal_city[0])**2 + (coords_city[1] - coords_goal_city[1])**2)**0.5
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def satisfies(self, city, goal_city):
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def satisfies(self, city, goal_city):
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return goal_city==city
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return goal_city==city
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@ -128,3 +115,5 @@ def search_path(c1,c2,strategy):
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my_tree.strategy = strategy
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my_tree.strategy = strategy
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return my_tree.search()
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return my_tree.search()
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@ -62,16 +62,12 @@ class SearchProblem:
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# Nos de uma arvore de pesquisa
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# Nos de uma arvore de pesquisa
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class SearchNode:
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class SearchNode:
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def __init__(self,state,parent, depth, cost=0, heuristic=0):
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def __init__(self,state,parent, depth):
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self.state = state
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self.state = state
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self.parent = parent
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self.parent = parent
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self.depth = depth
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self.depth = depth
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self.cost = cost
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self.heuristic = heuristic
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def __str__(self):
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def __str__(self):
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return "no(" + str(self.state) + "," + str(self.parent) + ")"
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return "no(" + str(self.state) + "," + str(self.parent) + ")"
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def __repr__(self):
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def __repr__(self):
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return str(self)
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return str(self)
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@ -87,8 +83,6 @@ class SearchTree:
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self.solution = None
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self.solution = None
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self.terminals = 0
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self.terminals = 0
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self.non_terminals = 0
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self.non_terminals = 0
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self.highest_cost_nodes = [root]
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self.average_depth = 0
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@property
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@property
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def length(self):
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def length(self):
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@ -98,10 +92,6 @@ class SearchTree:
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def avg_branching(self):
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def avg_branching(self):
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return ((self.terminals + self.non_terminals) - 1) / self.non_terminals if self.non_terminals > 0 else None
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return ((self.terminals + self.non_terminals) - 1) / self.non_terminals if self.non_terminals > 0 else None
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@property
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def cost(self):
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return self.solution.cost if self.solution else None
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# obter o caminho (sequencia de estados) da raiz ate um no
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# obter o caminho (sequencia de estados) da raiz ate um no
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def get_path(self,node):
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def get_path(self,node):
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if node.parent == None:
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if node.parent == None:
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@ -115,10 +105,8 @@ class SearchTree:
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while self.open_nodes != []:
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while self.open_nodes != []:
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self.terminals = len(self.open_nodes)
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self.terminals = len(self.open_nodes)
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node = self.open_nodes.pop(0)
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node = self.open_nodes.pop(0)
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if self.problem.goal_test(node.state):
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if self.problem.goal_test(node.state):
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self.solution = node
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self.solution = node
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self.average_depth = self.average_depth / (self.terminals + self.non_terminals)
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return self.get_path(node)
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return self.get_path(node)
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self.non_terminals += 1
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self.non_terminals += 1
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@ -126,22 +114,13 @@ class SearchTree:
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for a in self.problem.domain.actions(node.state):
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for a in self.problem.domain.actions(node.state):
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newstate = self.problem.domain.result(node.state,a)
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newstate = self.problem.domain.result(node.state,a)
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if newstate not in self.get_path(node):
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if newstate not in self.get_path(node):
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newnode = SearchNode(
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newnode = SearchNode(newstate,node,node.depth+1)
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newstate,
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if limit != None and self.strategy == 'depth':
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node,
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if newnode.depth <= limit:
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node.depth+1,
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lnewnodes.append(newnode)
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node.cost+self.problem.domain.cost(node.state,a),
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else:
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self.problem.domain.heuristic(newstate,self.problem.goal)
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)
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if not (limit != None and self.strategy == 'depth' and newnode.depth > limit):
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lnewnodes.append(newnode)
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lnewnodes.append(newnode)
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if newnode.cost > self.highest_cost_nodes[0].cost:
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self.highest_cost_nodes = [newnode]
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elif newnode.cost == self.highest_cost_nodes[0].cost:
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self.highest_cost_nodes.append(newnode)
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self.average_depth += newnode.depth
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self.add_to_open(lnewnodes)
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self.add_to_open(lnewnodes)
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return None
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return None
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# juntar novos nos a lista de nos abertos de acordo com a estrategia
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# juntar novos nos a lista de nos abertos de acordo com a estrategia
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@ -151,9 +130,5 @@ class SearchTree:
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elif self.strategy == 'depth':
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elif self.strategy == 'depth':
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self.open_nodes[:0] = lnewnodes
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self.open_nodes[:0] = lnewnodes
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elif self.strategy == 'uniform':
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elif self.strategy == 'uniform':
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self.open_nodes = sorted(self.open_nodes + lnewnodes, key=lambda node: node.cost)
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pass
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elif self.strategy == 'greedy':
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self.open_nodes = sorted(self.open_nodes + lnewnodes, key=lambda node: node.heuristic)
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elif self.strategy == 'a*':
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self.open_nodes = sorted(self.open_nodes + lnewnodes, key=lambda node: node.cost + node.heuristic)
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