本文整理汇总了Python中State.State.print_state方法的典型用法代码示例。如果您正苦于以下问题：Python State.print_state方法的具体用法？Python State.print_state怎么用？Python State.print_state使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类State.State的用法示例。
在下文中一共展示了State.print_state方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: depth_first
# 需要导入模块: from State import State [as 别名]
# 或者: from State.State import print_state [as 别名]
def depth_first(file_name):
tile_set = read_file(file_name) #list of tiles
start_state = State([],tile_set) #State with no placed tiles
root = Node(start_state, 0.0, None) #First Node, no cost, no parent
frontier = [root] #list of nodes
best_solution = State([],[]) #stores best solution
best_cost = float("inf") #set to infinity
nodes_generated = len(frontier)
#runs while frontier is not empty
while frontier:
current_node = frontier.pop() #removes last node in frontier, stores in current_node
#checks if current_node is a solution
#compares current_node to best_solution
#stores new best solution
if current_node.get_state().is_goal() and current_node.get_cost() < best_cost:
best_cost = current_node.get_cost()
best_solution = current_node.get_state()
#creates child_nodes to search
#adds child_nodes to frontier
child_nodes = current_node.expand()
nodes_generated = len(child_nodes) + nodes_generated
for c_n in child_nodes:
frontier.append(c_n)
#prints best solution to console
print "HERE IS THE BEST"
print best_cost
print ""
best_solution.print_state()
print "Nodes Generated: " + str(nodes_generated)示例2: uniform_cost
# 需要导入模块: from State import State [as 别名]
# 或者: from State.State import print_state [as 别名]
def uniform_cost(file_name):
tile_set = read_file(file_name) #list of tiles
start_state = State([],tile_set) #State with no placed tiles
root = Node(start_state, 0.0, None) #First Node, no cost, no parent
frontier = [root] #list of nodes
best_solution = State([],[]) #stores best solution
best_cost = float("inf") #set to infinity
nodes_generated = len(frontier)
keep_running = True
nodes_generated = len(frontier)
#runs while frontier is not empty
while keep_running:
best_cost_index = None
current_best_cost = float("inf")
frontier_length = range(len(frontier))
for f in frontier_length:
if frontier[f].get_cost() < current_best_cost:
best_cost_index = f
current_best_cost = frontier[f].get_cost()
current_node = frontier.pop(best_cost_index) #removes best_cost_index node in frontier, stores in current_node
#checks if current_node is a solution
#compares current_node to best_solution
#stores new best solution
if current_node.get_state().is_goal() and current_node.get_cost() < best_cost:
best_cost = current_node.get_cost()
best_solution = current_node.get_state()
#creates child_nodes to search
#adds child_nodes to frontier
child_nodes = current_node.expand()
nodes_generated = len(child_nodes) + nodes_generated
for c_n in child_nodes:
frontier.append(c_n)
frontier_length = range(len(frontier))
keep_running = False
for f in frontier_length:
if keep_running == False and frontier[f].get_cost() < best_cost:
keep_running = True
#prints best solution to console
print "HERE IS THE BEST"
print best_cost
print ""
best_solution.print_state()
print "Nodes Generated: " + str(nodes_generated)