class FeatureContainer:
def __init__(self, word_dict_path):
# Load word list
self.word_dict_path = word_dict_path
self.word_list = []
with open(word_dict_path, 'r') as ins:
for line in ins.readlines():
self.word_list.append(line.split()[1])
self.word_dict = {}
for idx, ascword in enumerate(self.word_list):
self.word_dict[ascword.decode('utf8')] = idx
self.fb = FeatureBuilder(self.word_dict)
self.smb = SimhashBuilder(self.word_list)
print 'Loaded ', len(self.word_list), 'words'
def compute_feature(self, token_list):
new_words = []
for token in token_list:
if not token in self.word_dict:
new_words.append(token)
if len(new_words) != 0:
# Update word_list and word_dict
self.fb.update_words(new_words)
self.smb.update_words([word.encode('utf8') for word in new_words])
self.word_dict = self.fb.word_dict
self.word_list.extend([word.encode('utf8') for word in new_words])
feature_vec = self.fb.compute(token_list)
return feature_vec, self.smb.sim_hash(feature_vec)
class FeatureContainer:
def __init__(self, word_dict_path):
# Load word list
self.word_dict_path = word_dict_path
self.word_list = []
with open(word_dict_path, "r") as ins:
for line in ins.readlines():
self.word_list.append(line.split()[1])
self.word_dict = {}
for idx, ascword in enumerate(self.word_list):
self.word_dict[ascword.decode("utf8")] = idx
self.fb = FeatureBuilder(self.word_dict)
self.smb = SimhashBuilder(self.word_list)
print "Loaded ", len(self.word_list), "words"
def compute_feature(self, token_list):
new_words = []
for token in token_list:
if not token in self.word_dict:
new_words.append(token)
if len(new_words) != 0:
# Update word_list and word_dict
self.fb.update_words(new_words)
self.smb.update_words([word.encode("utf8") for word in new_words])
self.word_dict = self.fb.word_dict
self.word_list.extend([word.encode("utf8") for word in new_words])
feature_vec = self.fb.compute(token_list)
return feature_vec, self.smb.sim_hash(feature_vec)
with open(sys.argv[4], 'r') as ins:
for line in ins.readlines():
doc_list.append(line.strip())
# Detection process begins
min_sim = 64
min_docid = 0
with open(sys.argv[5], 'r') as ins:
for lineidx, line in enumerate(ins.readlines()):
if lineidx != 642:
continue
# Tokenize
tokens = jt.tokens(line.strip().decode('utf8'))
# Compute text feature
feature = fb.compute(tokens)
# Compute simhash
fingerprint = smb.sim_hash(feature)
result_list = []
for idx, fp in enumerate(fingerprint_list):
sim = hamming_distance(fingerprint, fp, 64)
result_list.append((sim, idx))
result_list = sorted(result_list, cmp=lambda x,y: cmp(x[0],y[0]))
if result_list[0][0] < min_sim:
min_sim, min_docid = result_list[0][0], lineidx
#'''
with open(sys.argv[6], 'w') as outs:
outs.write(line.strip()+os.linesep)
for sim, idx in result_list:
outs.write('%s\t%s%s' %(sim, doc_list[idx], os.linesep))
#'''
#if lineidx == 2:
# break
with open(sys.argv[4], 'r') as ins:
for line in ins.readlines():
doc_list.append(line.strip())
# Detection process begins
min_sim = 64
min_docid = 0
with open(sys.argv[5], 'r') as ins:
for lineidx, line in enumerate(ins.readlines()):
if lineidx != 642:
continue
# Tokenize
tokens = jt.tokens(line.strip().decode('utf8'))
# Compute text feature
feature = fb.compute(tokens)
# Compute simhash
fingerprint = smb.sim_hash(feature)
result_list = []
for idx, fp in enumerate(fingerprint_list):
sim = hamming_distance(fingerprint, fp, 64)
result_list.append((sim, idx))
result_list = sorted(result_list, cmp=lambda x, y: cmp(x[0], y[0]))
if result_list[0][0] < min_sim:
min_sim, min_docid = result_list[0][0], lineidx
#'''
with open(sys.argv[6], 'w') as outs:
outs.write(line.strip() + os.linesep)
for sim, idx in result_list:
outs.write('%s\t%s%s' % (sim, doc_list[idx], os.linesep))
#'''
#if lineidx == 2:
# break
