mirror of
https://github.com/fxsjy/jieba.git
synced 2025-07-10 00:01:33 +08:00
Sun Junyi
commit
4a93f21918
@ -17,14 +17,13 @@ import logging
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DICTIONARY = "dict.txt"
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DICT_LOCK = threading.RLock()
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trie = None # to be initialized
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pfdict = None # to be initialized
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FREQ = {}
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min_freq = 0.0
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total = 0.0
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user_word_tag_tab = {}
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initialized = False
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log_console = logging.StreamHandler(sys.stderr)
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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@ -34,84 +33,79 @@ def setLogLevel(log_level):
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global logger
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logger.setLevel(log_level)
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def gen_trie(f_name):
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def gen_pfdict(f_name):
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lfreq = {}
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trie = {}
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pfdict = set()
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ltotal = 0.0
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with open(f_name, 'rb') as f:
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lineno = 0
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for line in f.read().rstrip().decode('utf-8').split('\n'):
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lineno += 1
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try:
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word,freq,_ = line.split(' ')
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word,freq = line.split(' ')[:2]
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freq = float(freq)
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lfreq[word] = freq
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ltotal += freq
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p = trie
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for c in word:
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if c not in p:
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p[c] ={}
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p = p[c]
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p['']='' #ending flag
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for ch in xrange(len(word)):
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pfdict.add(word[:ch+1])
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except ValueError, e:
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logger.debug('%s at line %s %s' % (f_name, lineno, line))
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raise ValueError, e
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return trie, lfreq,ltotal
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return pfdict, lfreq, ltotal
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def initialize(*args):
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global trie, FREQ, total, min_freq, initialized
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if len(args)==0:
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global pfdict, FREQ, total, min_freq, initialized
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if not args:
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dictionary = DICTIONARY
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else:
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dictionary = args[0]
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with DICT_LOCK:
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if initialized:
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return
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if trie:
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del trie
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trie = None
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if pfdict:
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del pfdict
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pfdict = None
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_curpath = os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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abs_path = os.path.join(_curpath,dictionary)
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logger.debug("Building Trie..., from %s" % abs_path)
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logger.debug("Building prefix dict from %s ..." % abs_path)
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t1 = time.time()
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if abs_path == os.path.join(_curpath,"dict.txt"): #defautl dictionary
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if abs_path == os.path.join(_curpath, "dict.txt"): #default dictionary
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cache_file = os.path.join(tempfile.gettempdir(), "jieba.cache")
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else: #customer dictionary
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cache_file = os.path.join(tempfile.gettempdir(),"jieba.user."+str(hash(abs_path))+".cache")
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else: #custom dictionary
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cache_file = os.path.join(tempfile.gettempdir(), "jieba.user.%s.cache" % hash(abs_path))
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load_from_cache_fail = True
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if os.path.exists(cache_file) and os.path.getmtime(cache_file) > os.path.getmtime(abs_path):
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logger.debug("loading model from cache %s" % cache_file)
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logger.debug("Loading model from cache %s" % cache_file)
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try:
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trie,FREQ,total,min_freq = marshal.load(open(cache_file,'rb'))
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load_from_cache_fail = False
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pfdict,FREQ,total,min_freq = marshal.load(open(cache_file,'rb'))
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# prevent conflict with old version
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load_from_cache_fail = not isinstance(pfdict, set)
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except:
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load_from_cache_fail = True
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if load_from_cache_fail:
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trie,FREQ,total = gen_trie(abs_path)
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pfdict,FREQ,total = gen_pfdict(abs_path)
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FREQ = dict([(k,log(float(v)/total)) for k,v in FREQ.iteritems()]) #normalize
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min_freq = min(FREQ.itervalues())
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logger.debug("dumping model to file cache %s" % cache_file)
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logger.debug("Dumping model to file cache %s" % cache_file)
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try:
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tmp_suffix = "."+str(random.random())
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with open(cache_file+tmp_suffix,'wb') as temp_cache_file:
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marshal.dump((trie,FREQ,total,min_freq),temp_cache_file)
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marshal.dump((pfdict,FREQ,total,min_freq), temp_cache_file)
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if os.name == 'nt':
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import shutil
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replace_file = shutil.move
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from shutil import move as replace_file
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else:
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replace_file = os.rename
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replace_file(cache_file + tmp_suffix, cache_file)
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except:
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logger.error("dump cache file failed.")
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logger.exception("")
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logger.exception("Dump cache file failed.")
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initialized = True
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logger.debug("loading model cost %s seconds." % (time.time() - t1))
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logger.debug("Trie has been built succesfully.")
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logger.debug("Loading model cost %s seconds." % (time.time() - t1))
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logger.debug("Prefix dict has been built succesfully.")
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def require_initialized(fn):
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@ -151,30 +145,21 @@ def calc(sentence,DAG,idx,route):
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@require_initialized
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def get_DAG(sentence):
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N = len(sentence)
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i,j=0,0
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p = trie
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global pfdict, FREQ
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DAG = {}
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while i<N:
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c = sentence[j]
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if c in p:
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p = p[c]
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if '' in p:
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if i not in DAG:
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DAG[i]=[]
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DAG[i].append(j)
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j+=1
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if j>=N:
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N = len(sentence)
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for k in xrange(N):
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tmplist = []
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i = k
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frag = sentence[k]
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while i < N and frag in pfdict:
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if frag in FREQ:
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tmplist.append(i)
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i += 1
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j=i
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p=trie
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else:
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p = trie
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i+=1
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j=i
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for i in xrange(len(sentence)):
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if i not in DAG:
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DAG[i] =[i]
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frag = sentence[k:i+1]
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if not tmplist:
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tmplist.append(k)
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DAG[k] = tmplist
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return DAG
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def __cut_DAG_NO_HMM(sentence):
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@ -192,12 +177,12 @@ def __cut_DAG_NO_HMM(sentence):
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buf += l_word
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x = y
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else:
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if len(buf)>0:
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if buf:
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yield buf
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buf = u''
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yield l_word
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x = y
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if len(buf)>0:
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if buf:
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yield buf
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buf = u''
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@ -214,14 +199,14 @@ def __cut_DAG(sentence):
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if y-x == 1:
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buf += l_word
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else:
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if len(buf)>0:
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if buf:
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if len(buf) == 1:
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yield buf
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buf = u''
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else:
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if (buf not in FREQ):
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regognized = finalseg.cut(buf)
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for t in regognized:
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recognized = finalseg.cut(buf)
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for t in recognized:
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yield t
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else:
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for elem in buf:
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@ -230,13 +215,12 @@ def __cut_DAG(sentence):
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yield l_word
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x = y
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if len(buf)>0:
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if buf:
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if len(buf) == 1:
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yield buf
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else:
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if (buf not in FREQ):
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regognized = finalseg.cut(buf)
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for t in regognized:
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elif (buf not in FREQ):
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recognized = finalseg.cut(buf)
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for t in recognized:
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yield t
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else:
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for elem in buf:
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@ -246,31 +230,32 @@ def cut(sentence,cut_all=False,HMM=True):
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'''The main function that segments an entire sentence that contains
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Chinese characters into seperated words.
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Parameter:
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- sentence: The String to be segmented
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- cut_all: Model. True means full pattern, false means accurate pattern.
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- HMM: Whether use Hidden Markov Model.
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- sentence: The str/unicode to be segmented.
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- cut_all: Model type. True for full pattern, False for accurate pattern.
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- HMM: Whether to use the Hidden Markov Model.
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'''
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if not isinstance(sentence, unicode):
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try:
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sentence = sentence.decode('utf-8')
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except UnicodeDecodeError:
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sentence = sentence.decode('gbk', 'ignore')
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'''
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\u4E00-\u9FA5a-zA-Z0-9+#&\._ : All non-space characters. Will be handled with re_han
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\r\n|\s : whitespace characters. Will not be Handled.
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'''
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re_han, re_skip = re.compile(ur"([\u4E00-\u9FA5a-zA-Z0-9+#&\._]+)", re.U), re.compile(ur"(\r\n|\s)", re.U)
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# \u4E00-\u9FA5a-zA-Z0-9+#&\._ : All non-space characters. Will be handled with re_han
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# \r\n|\s : whitespace characters. Will not be handled.
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if cut_all:
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re_han, re_skip = re.compile(ur"([\u4E00-\u9FA5]+)", re.U), re.compile(ur"[^a-zA-Z0-9+#\n]", re.U)
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else:
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re_han, re_skip = re.compile(ur"([\u4E00-\u9FA5a-zA-Z0-9+#&\._]+)", re.U), re.compile(ur"(\r\n|\s)", re.U)
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blocks = re_han.split(sentence)
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if HMM:
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if cut_all:
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cut_block = __cut_all
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elif HMM:
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cut_block = __cut_DAG
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else:
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cut_block = __cut_DAG_NO_HMM
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if cut_all:
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cut_block = __cut_all
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for blk in blocks:
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if len(blk)==0:
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if not blk:
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continue
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if re_han.match(blk):
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for word in cut_block(blk):
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@ -312,37 +297,30 @@ def load_userdict(f):
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...
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Word type may be ignored
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'''
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global trie,total,FREQ
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if isinstance(f, (str, unicode)):
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f = open(f, 'rb')
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content = f.read().decode('utf-8')
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line_no = 0
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for line in content.split("\n"):
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line_no += 1
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if line.rstrip()=='': continue
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if not line.rstrip():
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continue
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tup = line.split(" ")
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word, freq = tup[0], tup[1]
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if freq.isdigit() is False: continue
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if freq.isdigit() is False:
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continue
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if line_no == 1:
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word = word.replace(u'\ufeff',u"") #remove bom flag if it exists
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if len(tup)==3:
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add_word(word, freq, tup[2])
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else:
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add_word(word, freq)
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add_word(*tup)
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@require_initialized
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def add_word(word, freq, tag=None):
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global FREQ, trie, total, user_word_tag_tab
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freq = float(freq)
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FREQ[word] = log(freq / total)
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global FREQ, pfdict, total, user_word_tag_tab
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FREQ[word] = log(float(freq) / total)
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if tag is not None:
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user_word_tag_tab[word] = tag.strip()
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p = trie
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for c in word:
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if c not in p:
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p[c] = {}
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p = p[c]
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p[''] = '' # ending flag
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for ch in xrange(len(word)):
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pfdict.add(word[:ch+1])
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__ref_cut = cut
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__ref_cut_for_search = cut_for_search
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@ -365,7 +343,7 @@ def enable_parallel(processnum=None):
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if sys.version_info[0]==2 and sys.version_info[1]<6:
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raise Exception("jieba: the parallel feature needs Python version>2.5")
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from multiprocessing import Pool, cpu_count
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if processnum==None:
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if processnum is None:
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processnum = cpu_count()
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pool = Pool(processnum)
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@ -373,8 +351,7 @@ def enable_parallel(processnum=None):
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parts = re.compile('([\r\n]+)').split(sentence)
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if cut_all:
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result = pool.map(__lcut_all, parts)
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else:
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if HMM:
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elif HMM:
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result = pool.map(__lcut, parts)
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else:
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result = pool.map(__lcut_no_hmm, parts)
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@ -415,9 +392,14 @@ def get_abs_path_dict():
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return abs_path
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def tokenize(unicode_sentence, mode="default", HMM=True):
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#mode ("default" or "search")
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"""Tokenize a sentence and yields tuples of (word, start, end)
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Parameter:
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- sentence: the unicode to be segmented.
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- mode: "default" or "search", "search" is for finer segmentation.
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- HMM: whether to use the Hidden Markov Model.
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"""
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if not isinstance(unicode_sentence, unicode):
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raise Exception("jieba: the input parameter should unicode.")
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raise Exception("jieba: the input parameter should be unicode.")
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start = 0
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if mode == 'default':
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for w in cut(unicode_sentence, HMM=HMM):
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@ -439,4 +421,3 @@ def tokenize(unicode_sentence,mode="default",HMM=True):
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yield (gram3, start+i, start+i+3)
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yield (w, start, start+width)
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start += width
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37
jieba/__main__.py
Normal file
37
jieba/__main__.py
Normal file
@ -0,0 +1,37 @@
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"""Jieba command line interface."""
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import sys
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import jieba
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from argparse import ArgumentParser
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parser = ArgumentParser(usage="%s -m jieba [options] filename" % sys.executable, description="Jieba command line interface.", epilog="If no filename specified, use STDIN instead.")
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parser.add_argument("-d", "--delimiter", metavar="DELIM", default=' / ',
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nargs='?', const=' ',
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help="use DELIM instead of ' / ' for word delimiter; use a space if it is without DELIM")
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parser.add_argument("-a", "--cut-all",
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action="store_true", dest="cutall", default=False,
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help="full pattern cutting")
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parser.add_argument("-n", "--no-hmm", dest="hmm", action="store_false",
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default=True, help="don't use the Hidden Markov Model")
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parser.add_argument("-q", "--quiet", action="store_true", default=False,
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help="don't print loading messages to stderr")
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parser.add_argument("-V", '--version', action='version',
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version="Jieba " + jieba.__version__)
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parser.add_argument("filename", nargs='?', help="input file")
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args = parser.parse_args()
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if args.quiet:
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jieba.setLogLevel(60)
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delim = unicode(args.delimiter)
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cutall = args.cutall
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hmm = args.hmm
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fp = open(args.filename, 'r') if args.filename else sys.stdin
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jieba.initialize()
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ln = fp.readline()
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while ln:
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l = ln.rstrip('\r\n')
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print(delim.join(jieba.cut(ln.rstrip('\r\n'), cutall, hmm)).encode('utf-8'))
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ln = fp.readline()
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fp.close()
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@ -9,9 +9,11 @@ except ImportError:
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_curpath = os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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abs_path = os.path.join(_curpath, "idf.txt")
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STOP_WORDS = set([
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"the","of","is","and","to","in","that","we","for","an","are","by","be","as","on","with","can","if","from","which","you","it","this","then","at","have","all","not","one","has","or","that"
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])
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STOP_WORDS = set((
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"the","of","is","and","to","in","that","we","for","an","are",
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"by","be","as","on","with","can","if","from","which","you","it",
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"this","then","at","have","all","not","one","has","or","that"
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))
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class IDFLoader:
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def __init__(self):
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@ -45,7 +47,6 @@ def set_idf_path(idf_path):
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if not os.path.exists(new_abs_path):
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raise Exception("jieba: path does not exist: " + new_abs_path)
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idf_loader.set_new_path(new_abs_path)
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return
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def set_stop_words(stop_words_path):
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global STOP_WORDS
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@ -56,7 +57,6 @@ def set_stop_words(stop_words_path):
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lines = content.split('\n')
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for line in lines:
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STOP_WORDS.add(line)
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return
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def extract_tags(sentence, topK=20):
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global STOP_WORDS
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@ -66,8 +66,10 @@ def extract_tags(sentence,topK=20):
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words = jieba.cut(sentence)
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freq = {}
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for w in words:
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if len(w.strip())<2: continue
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if w.lower() in STOP_WORDS: continue
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if len(w.strip()) < 2:
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continue
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if w.lower() in STOP_WORDS:
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continue
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freq[w] = freq.get(w, 0.0) + 1.0
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total = sum(freq.values())
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freq = [(k,v/total) for k,v in freq.iteritems()]
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@ -1,4 +1,4 @@
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#encoding=utf-8
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##encoding=utf-8
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from whoosh.analysis import RegexAnalyzer,LowercaseFilter,StopFilter,StemFilter
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from whoosh.analysis import Tokenizer,Token
|
||||
from whoosh.lang.porter import stem
|
||||
@ -19,10 +19,7 @@ class ChineseTokenizer(Tokenizer):
|
||||
words = jieba.tokenize(text, mode="search")
|
||||
token = Token()
|
||||
for (w,start_pos,stop_pos) in words:
|
||||
if not accepted_chars.match(w):
|
||||
if len(w)>1:
|
||||
pass
|
||||
else:
|
||||
if not accepted_chars.match(w) and len(w)<=1:
|
||||
continue
|
||||
token.original = token.text = w
|
||||
token.pos = start_pos
|
||||
@ -31,5 +28,6 @@ class ChineseTokenizer(Tokenizer):
|
||||
yield token
|
||||
|
||||
def ChineseAnalyzer(stoplist=STOP_WORDS, minsize=1, stemfn=stem, cachesize=50000):
|
||||
return ChineseTokenizer() | LowercaseFilter() | StopFilter(stoplist=stoplist,minsize=minsize)\
|
||||
|StemFilter(stemfn=stemfn, ignore=None,cachesize=cachesize)
|
||||
return (ChineseTokenizer() | LowercaseFilter() |
|
||||
StopFilter(stoplist=stoplist,minsize=minsize) |
|
||||
StemFilter(stemfn=stemfn, ignore=None,cachesize=cachesize))
|
||||
|
||||
@ -23,19 +23,19 @@ def load_model():
|
||||
|
||||
start_p = {}
|
||||
abs_path = os.path.join(_curpath, PROB_START_P)
|
||||
with open(abs_path, mode='rb') as f:
|
||||
with open(abs_path, mode='r') as f:
|
||||
start_p = marshal.load(f)
|
||||
f.closed
|
||||
|
||||
trans_p = {}
|
||||
abs_path = os.path.join(_curpath, PROB_TRANS_P)
|
||||
with open(abs_path, 'rb') as f:
|
||||
with open(abs_path, 'r') as f:
|
||||
trans_p = marshal.load(f)
|
||||
f.closed
|
||||
|
||||
emit_p = {}
|
||||
abs_path = os.path.join(_curpath, PROB_EMIT_P)
|
||||
with file(abs_path, 'rb') as f:
|
||||
with open(abs_path, 'r') as f:
|
||||
emit_p = marshal.load(f)
|
||||
f.closed
|
||||
|
||||
@ -53,7 +53,7 @@ def viterbi(obs, states, start_p, trans_p, emit_p):
|
||||
for y in states: #init
|
||||
V[0][y] = start_p[y] + emit_p[y].get(obs[0],MIN_FLOAT)
|
||||
path[y] = [y]
|
||||
for t in range(1,len(obs)):
|
||||
for t in xrange(1,len(obs)):
|
||||
V.append({})
|
||||
newpath = {}
|
||||
for y in states:
|
||||
@ -87,10 +87,10 @@ def __cut(sentence):
|
||||
yield sentence[next:]
|
||||
|
||||
def cut(sentence):
|
||||
if not ( type(sentence) is unicode):
|
||||
if not isinstance(sentence, unicode):
|
||||
try:
|
||||
sentence = sentence.decode('utf-8')
|
||||
except:
|
||||
except UnicodeDecodeError:
|
||||
sentence = sentence.decode('gbk', 'ignore')
|
||||
re_han, re_skip = re.compile(ur"([\u4E00-\u9FA5]+)"), re.compile(ur"(\d+\.\d+|[a-zA-Z0-9]+)")
|
||||
blocks = re_han.split(sentence)
|
||||
@ -101,5 +101,5 @@ def cut(sentence):
|
||||
else:
|
||||
tmp = re_skip.split(blk)
|
||||
for x in tmp:
|
||||
if x!="":
|
||||
if x:
|
||||
yield x
|
||||
|
||||
@ -18,10 +18,11 @@ def load_model(f_name,isJython=True):
|
||||
_curpath = os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
|
||||
|
||||
result = {}
|
||||
with file(f_name, "rb") as f:
|
||||
for line in open(f_name,"rb"):
|
||||
with open(f_name, "r") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line=="":continue
|
||||
if not line:
|
||||
continue
|
||||
word, _, tag = line.split(' ')
|
||||
result[word.decode('utf-8')] = tag
|
||||
f.closed
|
||||
@ -30,25 +31,25 @@ def load_model(f_name,isJython=True):
|
||||
|
||||
start_p = {}
|
||||
abs_path = os.path.join(_curpath, PROB_START_P)
|
||||
with open(abs_path, mode='rb') as f:
|
||||
with open(abs_path, mode='r') as f:
|
||||
start_p = marshal.load(f)
|
||||
f.closed
|
||||
|
||||
trans_p = {}
|
||||
abs_path = os.path.join(_curpath, PROB_TRANS_P)
|
||||
with open(abs_path, 'rb') as f:
|
||||
with open(abs_path, 'r') as f:
|
||||
trans_p = marshal.load(f)
|
||||
f.closed
|
||||
|
||||
emit_p = {}
|
||||
abs_path = os.path.join(_curpath, PROB_EMIT_P)
|
||||
with file(abs_path, 'rb') as f:
|
||||
with open(abs_path, 'r') as f:
|
||||
emit_p = marshal.load(f)
|
||||
f.closed
|
||||
|
||||
state = {}
|
||||
abs_path = os.path.join(_curpath, CHAR_STATE_TAB_P)
|
||||
with file(abs_path, 'rb') as f:
|
||||
with open(abs_path, 'r') as f:
|
||||
state = marshal.load(f)
|
||||
f.closed
|
||||
|
||||
@ -65,7 +66,7 @@ def makesure_userdict_loaded(fn):
|
||||
|
||||
@wraps(fn)
|
||||
def wrapped(*args,**kwargs):
|
||||
if len(jieba.user_word_tag_tab)>0:
|
||||
if jieba.user_word_tag_tab:
|
||||
word_tag_tab.update(jieba.user_word_tag_tab)
|
||||
jieba.user_word_tag_tab = {}
|
||||
return fn(*args,**kwargs)
|
||||
@ -78,7 +79,7 @@ class pair(object):
|
||||
self.flag = flag
|
||||
|
||||
def __unicode__(self):
|
||||
return self.word+u"/"+self.flag
|
||||
return u'%s/%s' % (self.word, self.flag)
|
||||
|
||||
def __repr__(self):
|
||||
return self.__str__()
|
||||
@ -117,7 +118,7 @@ def __cut_detail(sentence):
|
||||
else:
|
||||
tmp = re_skip.split(blk)
|
||||
for x in tmp:
|
||||
if x!="":
|
||||
if x:
|
||||
if re_num.match(x):
|
||||
yield pair(x, 'm')
|
||||
elif re_eng.match(x):
|
||||
@ -140,12 +141,12 @@ def __cut_DAG_NO_HMM(sentence):
|
||||
buf += l_word
|
||||
x = y
|
||||
else:
|
||||
if len(buf)>0:
|
||||
if buf:
|
||||
yield pair(buf,'eng')
|
||||
buf = u''
|
||||
yield pair(l_word, word_tag_tab.get(l_word, 'x'))
|
||||
x = y
|
||||
if len(buf)>0:
|
||||
if buf:
|
||||
yield pair(buf,'eng')
|
||||
buf = u''
|
||||
|
||||
@ -164,14 +165,14 @@ def __cut_DAG(sentence):
|
||||
if y-x == 1:
|
||||
buf += l_word
|
||||
else:
|
||||
if len(buf)>0:
|
||||
if buf:
|
||||
if len(buf) == 1:
|
||||
yield pair(buf, word_tag_tab.get(buf, 'x'))
|
||||
buf = u''
|
||||
else:
|
||||
if (buf not in jieba.FREQ):
|
||||
regognized = __cut_detail(buf)
|
||||
for t in regognized:
|
||||
recognized = __cut_detail(buf)
|
||||
for t in recognized:
|
||||
yield t
|
||||
else:
|
||||
for elem in buf:
|
||||
@ -180,23 +181,22 @@ def __cut_DAG(sentence):
|
||||
yield pair(l_word, word_tag_tab.get(l_word, 'x'))
|
||||
x = y
|
||||
|
||||
if len(buf)>0:
|
||||
if buf:
|
||||
if len(buf) == 1:
|
||||
yield pair(buf, word_tag_tab.get(buf, 'x'))
|
||||
else:
|
||||
if (buf not in jieba.FREQ):
|
||||
regognized = __cut_detail(buf)
|
||||
for t in regognized:
|
||||
elif (buf not in jieba.FREQ):
|
||||
recognized = __cut_detail(buf)
|
||||
for t in recognized:
|
||||
yield t
|
||||
else:
|
||||
for elem in buf:
|
||||
yield pair(elem, word_tag_tab.get(elem, 'x'))
|
||||
|
||||
def __cut_internal(sentence, HMM=True):
|
||||
if not ( type(sentence) is unicode):
|
||||
if not isinstance(sentence, unicode):
|
||||
try:
|
||||
sentence = sentence.decode('utf-8')
|
||||
except:
|
||||
except UnicodeDecodeError:
|
||||
sentence = sentence.decode('gbk', 'ignore')
|
||||
re_han, re_skip = re.compile(ur"([\u4E00-\u9FA5a-zA-Z0-9+#&\._]+)"), re.compile(ur"(\r\n|\s)")
|
||||
re_eng, re_num = re.compile(ur"[a-zA-Z0-9]+"), re.compile(ur"[\.0-9]+")
|
||||
@ -232,7 +232,7 @@ def __lcut_internal_no_hmm(sentence):
|
||||
|
||||
@makesure_userdict_loaded
|
||||
def cut(sentence, HMM=True):
|
||||
if (not hasattr(jieba,'pool')) or (jieba.pool==None):
|
||||
if (not hasattr(jieba, 'pool')) or (jieba.pool is None):
|
||||
for w in __cut_internal(sentence, HMM=HMM):
|
||||
yield w
|
||||
else:
|
||||
|
||||
@ -14,28 +14,27 @@ def viterbi(obs, states, start_p, trans_p, emit_p):
|
||||
for y in states.get(obs[0], all_states): #init
|
||||
V[0][y] = start_p[y] + emit_p[y].get(obs[0], MIN_FLOAT)
|
||||
mem_path[0][y] = ''
|
||||
for t in range(1,len(obs)):
|
||||
for t in xrange(1, len(obs)):
|
||||
V.append({})
|
||||
mem_path.append({})
|
||||
#prev_states = get_top_states(V[t-1])
|
||||
prev_states = [x for x in mem_path[t-1].keys() if len(trans_p[x]) > 0]
|
||||
|
||||
prev_states_expect_next = set((y for x in prev_states for y in trans_p[x].keys()))
|
||||
obs_states = states.get(obs[t],all_states)
|
||||
obs_states = set(obs_states) & set(prev_states_expect_next)
|
||||
obs_states = set(states.get(obs[t], all_states)) & prev_states_expect_next
|
||||
|
||||
if len(obs_states)==0: obs_states = prev_states_expect_next
|
||||
if len(obs_states)==0: obs_states = all_states
|
||||
if not obs_states:
|
||||
obs_states = prev_states_expect_next if prev_states_expect_next else all_states
|
||||
|
||||
for y in obs_states:
|
||||
(prob,state ) = max([(V[t-1][y0] + trans_p[y0].get(y,MIN_INF) + emit_p[y].get(obs[t],MIN_FLOAT) ,y0) for y0 in prev_states])
|
||||
prob, state = max([(V[t-1][y0] + trans_p[y0].get(y,MIN_INF) + emit_p[y].get(obs[t],MIN_FLOAT), y0) for y0 in prev_states])
|
||||
V[t][y] = prob
|
||||
mem_path[t][y] = state
|
||||
|
||||
last = [(V[-1][y], y) for y in mem_path[-1].keys()]
|
||||
#if len(last)==0:
|
||||
#print obs
|
||||
(prob, state) = max(last)
|
||||
prob, state = max(last)
|
||||
|
||||
route = [None] * len(obs)
|
||||
i = len(obs) - 1
|
||||
|
||||
16
test/demo.py
16
test/demo.py
@ -4,14 +4,14 @@ sys.path.append("../")
|
||||
|
||||
import jieba
|
||||
|
||||
seg_list = jieba.cut("我来到北京清华大学", cut_all=True)
|
||||
print "Full Mode:", "/ ".join(seg_list) # 全模式
|
||||
seg_list = jieba.cut(u"我来到北京清华大学", cut_all=True)
|
||||
print u"Full Mode:", u"/ ".join(seg_list) # 全模式
|
||||
|
||||
seg_list = jieba.cut("我来到北京清华大学", cut_all=False)
|
||||
print "Default Mode:", "/ ".join(seg_list) # 默认模式
|
||||
seg_list = jieba.cut(u"我来到北京清华大学", cut_all=False)
|
||||
print u"Default Mode:", u"/ ".join(seg_list) # 默认模式
|
||||
|
||||
seg_list = jieba.cut("他来到了网易杭研大厦")
|
||||
print ", ".join(seg_list)
|
||||
seg_list = jieba.cut(u"他来到了网易杭研大厦")
|
||||
print u", ".join(seg_list)
|
||||
|
||||
seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造") # 搜索引擎模式
|
||||
print ", ".join(seg_list)
|
||||
seg_list = jieba.cut_for_search(u"小明硕士毕业于中国科学院计算所，后在日本京都大学深造") # 搜索引擎模式
|
||||
print u", ".join(seg_list)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user