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dd_k_div_best.py
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dd_k_div_best.py
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# /usr/bin/python
from __future__ import division
import sys, math
import fst_search, viterbi
def init_dd_param(dd_u, n, tagset):
for i in range(n):
dd_u[i] = {}#defaultdict()
for t in tagset:
dd_u[i][t] = 0
# can be made faster, use dictionary shallow copying
def compute_indicators(seq, tagset):
ind = {}
for i in range(len(seq)):
z = {}
for t in tagset:
if seq[i] == t:
z[t] = 1
else:
z[t] = 0
ind[i] = z
return ind
'''
Executes the dual decomposition algorithm to get the k-best
list of sequences
'''
def run(sentence, tagset, hmm, k_best_list):
max_iter = len(k_best_list)*200
n = len(sentence)
k = len(k_best_list)
u = {} # dual decomposition parameter
init_dd_param(u, n, tagset)
iteration = 1
while iteration <= max_iter:
#print iteration
step_size = 1.0 / math.sqrt(iteration)
#print "step size", step_size
seq1, score1, score2 = viterbi.run(sentence, tagset, hmm, u)
y = compute_indicators(seq1, tagset)
#print 0, ' '.join(seq1)
seq2, fst_score = fst_search.run(k_best_list, u, tagset)
z = compute_indicators(seq2, tagset)
#print j+1, ' '.join(seq)
# check for agreement
if seq1 != seq2:
update(y, z, u, step_size)
else:
return seq1, iteration
iteration += 1
return seq1, -1
'''
Dual decomposition update
'''
def update(indi1, indi2, u, step_size):
for i in range(len(indi1)):
for t in u[i].iterkeys():
u[i][t] -= (indi2[i][t] - indi1[i][t])*step_size