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import heapq
class PriorityQueue(object):
Basic priority queue. Calculates priority based on the provided function.
Does not let change the priority of an existing item, simply adds another one.
def __init__(self, priority_function):
self.heap = []
self.priority_function = priority_function
def push(self, item):
heapq.heappush(self.heap, (self.priority_function(item), item))
def pop(self):
priority, item = heapq.heappop(self.heap)
return item
def is_empty(self):
return len(self.heap) == 0
class Node(object):
def __init__(self, state, parent=None, path_cost=0):
self.state = state
self.parent = parent
self.path_cost = path_cost
def expand(self, dictionary):
return (Node(s, self, self.path_cost+1) for s in generate_words(self.state) if s in dictionary)
def chain(self):
node, trail = self, []
while node:
node = node.parent
return reversed(trail)
def load_words(length):
return frozenset(line.strip() for line in open("/usr/share/dict/words") if len(line) == length+1 and line.islower())
LOWER_LETTERS = [chr(i) for i in xrange(97, 123)]
def generate_words(word):
new_words = []
for i in xrange(len(word)):
new_words.extend([word[:i] + c + word[i+1:] for c in LOWER_LETTERS if c != word[i]])
return new_words
def heuristic(item, goal):
return sum([c == d for c, d in zip(item.state, goal)])
def search(start_word, goal_word, dictionary):
assert(len(start_word) == len(goal_word))
frontier = PriorityQueue(lambda n: n.path_cost + heuristic(n, goal_word))
explored = set()
while not frontier.is_empty():
node = frontier.pop()
if node.state in explored:
if node.state == goal_word:
return node.chain()
for child in node.expand(dictionary):
if child.state not in explored:
return []
if __name__ == '__main__':
for word in search("head", "tail", load_words(len("head"))):
print word
for word in search("white", "black", load_words(len("white"))):
print word