def build_paths(self):
aut = self.aut
self.paths = defaultdict(set)
self.paths[()].add(("^",))
needed = set(self.corpus.keys())
top_sort_needed = set(closure_and_top_sort(self.corpus.keys()))
# iterate through all the paths with DFS-like algorithm
# and prune if needed
while len(needed) > 0:
for s, paths in self.paths.items():
for path in paths.copy():
last = path[-1]
if last not in aut.m:
continue
for tgt in aut.m[last]:
new_path = path + (tgt,)
emission = (aut.emissions[tgt] if tgt in aut.emissions
else ())
new_s = s + emission
if new_s not in top_sort_needed:
continue
self.paths[new_s].add(new_path)
if s in needed:
needed.remove(s)
needed = set(self.corpus.keys())
self.paths = dict((s, set([p for p in paths if p[-1] == "$"]))
for s, paths in self.paths.iteritems() if s in needed)
def probability_of_strings(self, strings):
"""
Expects a list of strings.
Outputs a map from those strings to probabilities.
"""
topsorted = closure_and_top_sort(strings)
# remove empty string
topsorted = topsorted[1:]
memo = self.init_memo()
output = {}
for string in topsorted:
self.update_probability_of_string(string, memo)
output[string] = memo[string][self.state_indices["$"]]
return output
