def delta_weights_old(self, updates, weights):
self.set_weights(weights)
#
self.full_create_weight_fst(updates)
if self.non_neg:
print "Intersecting"
#openfst.ArcSortInput(self.weight_fst)
self.temp_fst = fsa.rho_compose(self.fst, False, self.just_weight_fst, True, False)
else:
self.create_weight_fst(updates)
self.temp_fst = fsa.rho_compose(self.temp_fst, False, self.weight_fst, True, True)
sent_fst1.SetInputSymbols(s_table) sent_fst1.SetOutputSymbols(s_table) lm_fsa.SetInputSymbols(s_table) lm_fsa.SetOutputSymbols(s_table) openfst.ArcSortInput(lm_fsa) inter_fst = openfst.StdVectorFst() short_fst = openfst.StdVectorFst() inter_det_fst = openfst.StdVectorFst() #openfst.Intersect(fst1, fst2, inter_fst) inter_fst = fsa.rho_compose(sent_fst1, False, lm_fsa, True, True) openfst.Determinize(inter_fst, inter_det_fst) print inter_det_fst.NumStates() fsa.print_fst(inter_det_fst) openfst.ShortestPath(inter_det_fst, short_fst, 1) print short_fst.NumStates() openfst.TopSort(short_fst) fsa.print_fst(short_fst) #----- fst1 = openfst.StdVectorFst() fst2 = openfst.StdVectorFst()
def set_heuristic_fsa(self, simple_tree, orig_uni_tree, simple_count, step_count, nts, last_sym):
"""
simple_tree - FSA of trans tree with zero weights
orig_uni_tree - FSA of trans tree with unigram weights
simple_count - FSA with counting
step_count - FSA with 1.0 penalty for each step
nts- map of all non-terms
"""
uni_tree = orig_uni_tree
#openfst.RmEpsilon(orig_uni_tree)
#openfst.Determinize(orig_uni_tree, uni_tree)
#openfst.Minimize(uni_tree)
t1 = fsa.rho_compose(uni_tree, False, simple_count, True, True)
t2 = fsa.rho_compose(simple_tree, False, step_count, True, True)
assert uni_tree.NumStates() == simple_tree.NumStates(), str(uni_tree.NumStates()) + " "+ str(simple_tree.NumStates())
assert step_count.NumStates() == simple_count.NumStates()
self.heuristic_count_fst = openfst.StdVectorFst()
self.heuristic_fst = openfst.StdVectorFst()
openfst.Determinize(t1, self.heuristic_fst)
openfst.Determinize(t2, self.heuristic_count_fst)
openfst.Connect(self.heuristic_count_fst)
openfst.Connect(self.heuristic_fst)
openfst.TopSort(self.heuristic_count_fst)
openfst.TopSort(self.heuristic_fst)
assert self.heuristic_count_fst.NumStates() == self.heuristic_fst.NumStates()
self.reverse_heuristic_fst = openfst.StdVectorFst()
openfst.Reverse(self.heuristic_fst, self.reverse_heuristic_fst)
self.nts = set([nt[1] for nt in nts])
print "creating tables"
# heuristic = {}
# for step in range(1,self.output_bound+1):
# for nt in self.nts:
# heuristic.setdefault((nt, step), 1e10)
# #print nt, step, heuristic[nt, step]
# def unzip3(m):
# n = len(m)
# a = openfst.IntVector(n)
# b = openfst.IntVector(n)
# c = openfst.FloatVector(n)
# for i,k in enumerate(m):
# #print k
# a[i] = int(k[0])
# b[i] = int(k[1])
# #print i, k[0], k[1], self.heuristic_count_fst.InputSymbols().Find(k[0]), float(m[k])
# c[i] = float(m[k])
# return (a,b,c)
# self.blanks = unzip3(heuristic)
# self.orig_n = len(self.blanks[0])
self.topo_dist = openfst.FloatVector()
openfst.ShortestDistance(self.heuristic_count_fst, self.topo_dist, False)
self.topo_dist = [int(uninf(u))+1 for u in self.topo_dist]
self.topo_dist = openfst.IntVector(self.topo_dist)
n = len(self.topo_dist)
self.cache_step = [None] * n
# for i in range(n):
# for j in range(self.heuristic_count_fst.NumArcs(i)):
# out = self.heuristic_count_fst.GetOutput(i,j)
# next = self.heuristic_count_fst.GetNext(i,j)
# if next >= n: continue
# if self.topo_dist[next] > self.output_bound: continue
# if out not in self.nts: continue
# self.cache_heu.append((next, out, int(self.output_bound - self.topo_dist[next])+1))
self.state_symbol = openfst.IntVector(n)
for i in range(n):
for j in range(self.heuristic_count_fst.NumArcs(i)):
out = self.heuristic_count_fst.GetOutput(i,j)
next = self.heuristic_count_fst.GetNext(i,j)
if next >= n: continue
if self.topo_dist[next] > self.output_bound+ 1: continue
if out not in self.nts: continue
self.cache_step[next] = (self.topo_dist[next], out)
self.state_symbol[next] = out - tree_extractor.SRC_NODE
self.heuristic_pruner = openfst.BeamPrune(self.reverse_heuristic_fst.NumStates(),
self.output_bound+1,
tree_extractor.SRC_NODE,
self.last_sym)
#openfst.Minimize(det_lm_fsa)
else:
det_fsa = fsa
openfst.ArcSortInput(fsa)
openfst.ArcSortInput(lm_fsa)
det_lm_fsa = lm_fsa
print "Intersecting %s %s %s" % (det_fsa.NumStates(), det_lm_fsa.NumStates(), count_fsa.NumStates())
#openfst.Prune(det_lm_fsa, 3.0)
fsa2 = openfst.StdVectorFst()
fsa3 = openfst.StdVectorFst()
print "Intersect 1"
#FSA.Intersect(det_fsa, det_lm_fsa, fsa2)
fsa2 = FSA.rho_compose(det_fsa, False, det_lm_fsa, True, True)
# tree_count_fsa = FSA.rho_compose(det_fsa, False, count_fsa, True, True)
shortest = openfst.StdVectorFst()
bests = {}
totals = {}
if minimize:
print "Minimizing FSA2"
#det_fsa2 = openfst.StdVectorFst()
#det_fsa2 = fsa2
det_fsa2 = openfst.StdVectorFst()
openfst.Determinize(fsa2, det_fsa2)
openfst.Connect(det_fsa2)
