def show(ded, antvalues):
if ded.rule:
value = rule.subst(ded.rule.erhs, antvalues)
else:
value = antvalues[0]
return ("[%.3f" %
cost.prob(ded.dcost['posterior']), ) + value + ("]", )
def transition(self, r, antstates, i, j, j1=None):
enums = [self.map_word(e) for e in rule.subst(r.erhs, antstates)]
score = 0.
for i, j in self.xngrams(enums):
score += self.ngram.lookup_ngrams(enums, i + self.order - 1, j)
state = self.make_state(enums)
return (state, self.unit * -score)
def transition(self, r, antstates, i, j, j1=None):
enums = [self.map_word(e) for e in rule.subst(r.erhs, antstates)]
score = 0.
for i, j in self.xngrams(enums):
score += self.ngram.lookup_ngrams(enums, i+self.order-1, j)
state = self.make_state(enums)
return (state, self.unit * -score)
def compute_nbest(self, item, n):
"""Assumes that the 1-best has already been found
and stored in Deduction.viterbi"""
if id(item) not in self.nbinfos:
self.nbinfos[id(item)] = NBestInfo(item)
nb = self.nbinfos[id(item)]
while len(nb.nbest) < n and len(nb.cands) > 0:
# Get the next best and add it to the list
(cost, ded, ranks) = heapq.heappop(nb.cands)
if self.ambiguity_limit:
# compute English string
antes = []
for ant, rank in itertools.izip(ded.ants, ranks):
self.compute_nbest(ant, rank + 1)
antes.append(self.nbinfos[id(ant)].english[rank])
if ded.rule is not None:
e = rule.subst(ded.rule.erhs, antes)
elif len(antes) == 1: # this is used by the Hiero goal item
e = antes[0]
# don't want more than ambiguity_limit per english
nb.ecount[tuple(e)] += 1
if nb.ecount[tuple(e)] <= self.ambiguity_limit:
nb.nbest.append((cost, ded, ranks))
nb.english.append(e)
else:
nb.nbest.append((cost, ded, ranks))
# Replenish the candidate pool
for ant_i in xrange(len(ded.ants)):
ant, rank = ded.ants[ant_i], ranks[ant_i]
if self.compute_nbest(ant, rank + 2) >= rank + 2:
ant_nb = self.nbinfos[id(ant)]
nextranks = list(ranks)
nextranks[ant_i] += 1
nextranks = tuple(nextranks)
if (ded, nextranks) not in nb.index:
nextcost = cost - ant_nb.nbest[rank][0] + ant_nb.nbest[
rank + 1][0]
heapq.heappush(nb.cands, (nextcost, ded, nextranks))
nb.index.add((ded, nextranks))
return len(nb.nbest)
def compute_nbest(self, item, n):
"""Assumes that the 1-best has already been found
and stored in Deduction.viterbi"""
if id(item) not in self.nbinfos:
self.nbinfos[id(item)] = NBestInfo(item)
nb = self.nbinfos[id(item)]
while len(nb.nbest) < n and len(nb.cands) > 0:
# Get the next best and add it to the list
(cost,ded,ranks) = heapq.heappop(nb.cands)
if self.ambiguity_limit:
# compute English string
antes = []
for ant, rank in itertools.izip (ded.ants, ranks):
self.compute_nbest(ant, rank+1)
antes.append(self.nbinfos[id(ant)].english[rank])
if ded.rule is not None:
e = rule.subst(ded.rule.erhs, antes)
elif len(antes) == 1: # this is used by the Hiero goal item
e = antes[0]
# don't want more than ambiguity_limit per english
nb.ecount[tuple(e)] += 1
if nb.ecount[tuple(e)] <= self.ambiguity_limit:
nb.nbest.append((cost,ded,ranks))
nb.english.append(e)
else:
nb.nbest.append((cost,ded,ranks))
# Replenish the candidate pool
for ant_i in xrange(len(ded.ants)):
ant, rank = ded.ants[ant_i], ranks[ant_i]
if self.compute_nbest(ant, rank+2) >= rank+2:
ant_nb = self.nbinfos[id(ant)]
nextranks = list(ranks)
nextranks[ant_i] += 1
nextranks = tuple(nextranks)
if (ded, nextranks) not in nb.index:
nextcost = cost - ant_nb.nbest[rank][0] + ant_nb.nbest[rank+1][0]
heapq.heappush(nb.cands, (nextcost, ded, nextranks))
nb.index.add((ded,nextranks))
return len(nb.nbest)
def show(ded, antvalues):
if ded.rule:
value = rule.subst(ded.rule.erhs, antvalues)
else:
value = antvalues[0]
return ("[%.3f" % cost.prob(ded.dcost["posterior"]),) + value + ("]",)
def english(self):
return self.value(lambda ded, antvalues: rule.subst(
ded.rule.erhs, antvalues) if ded.rule else antvalues[0])
def french(self):
return self.value(lambda ded, antvalues: rule.subst(
ded.rule.frhs, antvalues) if ded.rule else antvalues[0])
def english(self):
return self.value(lambda ded, antvalues: rule.subst(ded.rule.erhs, antvalues) if ded.rule else antvalues[0])
def french(self):
return self.value(lambda ded, antvalues: rule.subst(ded.rule.frhs, antvalues) if ded.rule else antvalues[0])