def addNodeandEdge(self, a, b, nextLayer, model):
score = 0
if self.p.__len__() > 0 and (self.p[-1] == ' ' or self.q[-1] == ' '):
ascore = 0
bscore = 0
if self.p[-1] == ' ':
ascore = model.score(a, context=tokenlize(' '))
else:
ascore = model.score(a, context=tokenlize(self.p))
if self.q[-1] == ' ':
bscore = model.score(b, context=tokenlize(' '))
else:
bscore = model.score(b, context=tokenlize(self.q))
score = log_base2(ascore * bscore)
else:
score = log_base2(
model.score(a, context=tokenlize(self.p)) *
model.score(b, context=tokenlize(self.q)))
# print(self.p + a + "," + self.q + b + ": " + str(score))
if score > POSSIBLE_BRANCH_THRESHOLD or (self.p.__len__() < 3
and score > -20):
print(self.p + a + "," + self.q + b + ": " + str(score))
newNode = Node(self.p + a, self.q + b)
newEdge = Edge(a, b, self, newNode, score)
newNode.inEdge.append(newEdge)
newNode.sumWeight = newEdge.prevNode.sumWeight + score
self.possibleEdges.add(newEdge)
nextLayer.nodeList.add(newNode)
def logscore(self, word, context=None):
"""Evaluate the log score of this word in this context.
The arguments are the same as for `score` and `unmasked_score`.
"""
return log_base2(self.score(word, context))
def logscore(self, word, context=None):
"""Evaluate the log score of this word in this context.
The arguments are the same as for `score` and `unmasked_score`.
"""
return log_base2(self.score(word, context))
