def read_matrix_and_idmap_from_file(fn, dic=dict(), ecd='utf-8'):
idx_map = dic.copy()
with codecs.open(fn, encoding=ecd, errors='ignore') as inf:
row, column = inf.readline().rstrip().split(' ')
dim = int(column)
multiplier = 1 #np.sqrt(1.0/3) #(2.0/dim)
#print row, column
# note: here +3 include OOV, BOS and EOS
content = []
not_in_dict = 0
for line in inf:
elems = line.rstrip().split(' ')
content.append(elems)
if elems[0] not in idx_map:
idx_map[elems[0]] = len(idx_map)
not_in_dict += 1
print 'load embedding! %s words, %d not in the dictionary. Dictionary size: %d' % (
row, not_in_dict, len(dic))
assert len(idx_map) == len(dic) + not_in_dict
M = ArrayInit(ArrayInit.onesided_uniform,
multiplier=1.0 / dim).initialize(len(idx_map) + 2,
dim) #*2)
for elems in content:
if elems[0] in idx_map:
idx = idx_map[elems[0]]
vec_elem = elems[1:] #.split(',')
#print len(vec_elem), elems
r = np.array([float(_e) for _e in vec_elem])
M[idx, :dim] = (r / np.linalg.norm(r)) * multiplier
else:
print 'error in load embedding matrix!!'
print 'embedding matrix shape:', M.shape, 'word map size:', len(idx_map)
return M, idx_map
def read_matrix_from_pkl(fn, dic):
'''
Assume that the file contains words in first column,
and embeddings in the rest and that dic maps words to indices.
'''
voc, data = pickle.load(codecs.open(fn))
dim = len(data[0])
# NOTE: The norm of onesided_uniform rv is sqrt(n)/sqrt(3)
# Since the expected value of X^2 = 1/3 where X ~ U[0, 1]
# => sum(X_i^2) = dim/3
# => norm = sqrt(dim/3)
# => norm/dim = sqrt(1/3dim)
multiplier = np.sqrt(1.0 / (3 * dim))
M = ArrayInit(ArrayInit.onesided_uniform,
multiplier=1.0 / dim).initialize(len(dic), dim)
not_in_dict = 0
for i, e in enumerate(data):
r = np.array([float(_e) for _e in e])
if voc[i] in dic:
idx = dic[voc[i]]
M[idx] = (r / np.linalg.norm(r)) * multiplier
else:
not_in_dict += 1
print 'load embedding! %d words, %d not in the dictionary. Dictionary size: %d' % (
len(voc), not_in_dict, len(dic))
return M
def read_matrix_from_file(fn, dic, ecd='utf-8'):
not_in_dict = 0
with codecs.open(fn, encoding=ecd, errors='ignore') as inf:
row, column = inf.readline().rstrip().split()
dim = int(column)
multiplier = np.sqrt(1.0 / 3)
#print row, column
idx_map = dict()
line_count = 0
M = ArrayInit(ArrayInit.onesided_uniform,
multiplier=1.0 / dim).initialize(len(dic) + 2, dim)
for line in inf:
elems = line.rstrip().split(' ')
if elems[0] in dic:
idx = dic[elems[0]]
vec_elem = elems[1:] #.split(',')
r = np.array([float(_e) for _e in vec_elem])
M[idx] = (r / np.linalg.norm(r)) * multiplier
idx_map[idx] = line_count
else:
not_in_dict += 1
line_count += 1
print 'load embedding! %s words, %d not in the dictionary. Dictionary size: %d' % (
row, not_in_dict, len(dic))
print 'embedding matrix shape:', M.shape, 'word map size:', len(idx_map)
return M, idx_map
