srcdir + 'relation_train.binsum-%d.txt' % bin_num,
srcdir + 'relation_valid.binsum-%d.txt' % bin_num,
srcdir + 'relation_test.binsum-%d.txt' % bin_num
]
embed_dict = read_embedding(filename = embedfile)
print('read embedding finished ...')
_PAD_ = len(embed_dict)
embed_size = len(list(embed_dict.values())[0])
embed_dict[_PAD_] = np.zeros((embed_size, ), dtype=np.float32)
embed = np.float32(np.random.uniform(-0.2, 0.2, [_PAD_+1, embed_size]))
embed = convert_embed_2_numpy(embed_dict, embed = embed)
corpus, _ = read_data(corpusfile)
print('read corpus finished....')
for idx, relfile in enumerate(relfiles):
binfile = binfiles[idx]
rel = read_relation(relfile)
fout = open(binfile, 'w')
for label, d1, d2 in rel:
assert d1 in corpus
assert d2 in corpus
qnum = len(corpus[d1])
d1_embed = embed[corpus[d1]]
d2_embed = embed[corpus[d2]]
curr_bin_sum = cal_binsum(d1_embed, d2_embed, qnum, bin_num)
curr_bin_sum = curr_bin_sum.tolist()
fout.write(' '.join(map(str, curr_bin_sum)))
fout.write('\n')
fout.close()
print('generate bin sum finished ...')
srcdir + 'relation_train.binsum-%d.txt' % bin_num,
srcdir + 'relation_valid.binsum-%d.txt' % bin_num,
srcdir + 'relation_test.binsum-%d.txt' % bin_num
]
embed_dict = read_embedding(filename=embedfile)
print('read embedding finished ...')
_PAD_ = len(embed_dict)
embed_size = len(embed_dict[embed_dict.keys()[0]])
embed_dict[_PAD_] = np.zeros((embed_size, ), dtype=np.float32)
embed = np.float32(np.random.uniform(-0.2, 0.2, [_PAD_ + 1, embed_size]))
embed = convert_embed_2_numpy(embed_dict, embed=embed)
corpus, _ = read_data(corpusfile)
print('read corpus finished....')
for idx, relfile in enumerate(relfiles):
binfile = binfiles[idx]
rel = read_relation(relfile)
fout = open(binfile, 'w')
for label, d1, d2 in rel:
assert d1 in corpus
assert d2 in corpus
qnum = len(corpus[d1])
d1_embed = embed[corpus[d1]]
d2_embed = embed[corpus[d2]]
curr_bin_sum = cal_binsum(d1_embed, d2_embed, qnum, bin_num)
curr_bin_sum = curr_bin_sum.tolist()
fout.write(' '.join(map(str, curr_bin_sum)))
fout.write('\n')
fout.close()
print 'generate bin sum finished ...'
print('read corpus finished....')
for idx, relfile in enumerate(relfiles):
binfile = binfiles[idx]
rel = read_relation_linear(relfile)
fout_tit = open(binfile + "_title.txt", 'w')
fout_ques = open(binfile + "_question.txt", 'w')
fout_ans = open(binfile + "_answer.txt", "w")
for label, d1, d2, d3, d4 in rel:
assert d1 in corpus
assert d2 in corpus
assert d3 in corpus
assert d4 in corpus
qnum = len(corpus[d1])
d1_embed = embed[corpus[d1]]
d2_embed = embed[corpus[d2]]
d3_embed = embed[corpus[d3]]
d4_embed = embed[corpus[d4]]
curr_bin_sum_title = cal_binsum(d1_embed, d2_embed, qnum, bin_num)
curr_bin_sum_ques = cal_binsum(d1_embed, d3_embed, qnum, bin_num)
curr_bin_sum_ans = cal_binsum(d1_embed, d4_embed, qnum, bin_num)
fout_tit.write(' '.join(map(str, curr_bin_sum_title.tolist())))
fout_ques.write(' '.join(map(str, curr_bin_sum_ques.tolist())))
fout_ans.write(' '.join(map(str, curr_bin_sum_ans.tolist())))
fout_tit.write('\n')
fout_ques.write('\n')
fout_ans.write('\n')
fout_tit.close()
fout_ques.close()
fout_ans.close()
print 'generate bin sum finished ...'