def greedy_best_first(board, heuristic):
"""
an implementation of the greedy best first search algorithm. it uses a heuristic function to find the quickest
way to the destination
:param board: (Board) the board you start at
:param heuristic: (function) the heuristic function
:return: (list) path to solution, (int) number of explored boards
"""
frontier = PriorityQueue()
node = Node(board)
frontier.add(node, heuristic(node.data))
explored = []
while frontier.has_next():
node = frontier.pop()
if node.data.is_solved():
return node.path(), len(explored) + 1
for move in node.data.legal_moves():
child = Node(node.data.forecast(move), node)
if (not frontier.has(child)) and (child.data not in explored):
frontier.add(child, heuristic(child.data))
explored.append(node.data)
return None, len(explored)
def a_star(board, heuristic):
"""
A*算法主题
:param board: 要解决的游戏
:param heuristic: 选择的启发函数
:return: 返回的解的路径
"""
frontier = PriorityQueue()
node = Node(board)
frontier.add(node, heuristic(node.data) + len(node.path()) - 1)
explored = []
while frontier.has_next():
node = frontier.pop()
if node.data.is_solved():
return node.path()
for move in node.data.legal_moves():
child = Node(node.data.forecast(move), node)
if (not frontier.has(child)) and (child.data not in explored):
frontier.add(child,
heuristic(child.data) + len(child.path()) - 1)
elif frontier.has(child):
child_value = heuristic(child.data) + len(child.path()) - 1
if child_value < frontier.get_value(child):
frontier.remove(child)
frontier.add(child, child_value)
explored.append(node.data)
return None
def a_star(board, heuristic):
"""
solves the board using the A* approach accompanied by the heuristic function
:param board: board to solve
:param heuristic: heuristic function
:return: path to solution, and number of explored nodes
"""
frontier = PriorityQueue()
node = Node(board)
frontier.add(node, heuristic(node.data) + len(node.path()) - 1)
explored = []
while frontier.has_next():
node = frontier.pop()
# check if solved
if node.data.is_solved():
return node.path(), len(explored) + 1
# add children to frontier
for move in node.data.legal_moves():
child = Node(node.data.forecast(move), node)
# child must not have already been explored
if (not frontier.has(child)) and (child.data not in explored):
frontier.add(child,
heuristic(child.data) + len(child.path()) - 1)
# if the child is already in the frontier, it can be added only if it's better
elif frontier.has(child):
child_value = heuristic(child.data) + len(child.path()) - 1
if child_value < frontier.get_value(child):
frontier.remove(child)
frontier.add(child, child_value)
explored.append(node.data)
return None, len(explored)
