complete the setup.py, fix #202 problem in posseg

jieba/__init__.py

@@ -1,5 +1,5 @@
 from __future__ import with_statement
-__version__ = '0.34'
+__version__ = '0.35'
 __license__ = 'MIT'
 
 import re
@@ -78,7 +78,8 @@ def initialize(dictionary=None):
         if os.path.exists(cache_file) and os.path.getmtime(cache_file) > os.path.getmtime(abs_path):
             logger.debug("Loading model from cache %s" % cache_file)
             try:
-                pfdict,FREQ,total,min_freq = marshal.load(open(cache_file,'rb'))
+                with open(cache_file, 'rb') as cf:
+                    pfdict,FREQ,total,min_freq = marshal.load(cf)
                 # prevent conflict with old version
                 load_from_cache_fail = not isinstance(pfdict, set)
             except:

jieba/posseg/__init__.py

@@ -46,7 +46,7 @@ def load_model(f_name, isJython=True):
 
     state = {}
     abs_path = os.path.join(_curpath, CHAR_STATE_TAB_P)
-    with open(abs_path, 'r') as f:
+    with open(abs_path, 'rb') as f:
         state = marshal.load(f)
     f.closed
 
@@ -126,7 +126,7 @@ def __cut_detail(sentence):
 def __cut_DAG_NO_HMM(sentence):
     DAG = jieba.get_DAG(sentence)
     route = {}
-    jieba.calc(sentence, DAG, 0, route=route)
+    jieba.calc(sentence, DAG, route)
     x = 0
     N = len(sentence)
     buf = u''
@@ -151,7 +151,7 @@ def __cut_DAG(sentence):
     DAG = jieba.get_DAG(sentence)
     route = {}
 
-    jieba.calc(sentence,DAG,0,route=route)
+    jieba.calc(sentence, DAG, route)
 
     x = 0
     buf = u''

jieba/posseg/viterbi.py

@@ -3,9 +3,7 @@ MIN_FLOAT = -3.14e100
 MIN_INF = float("-inf")
 
 def get_top_states(t_state_v, K=4):
-    items = t_state_v.items()
+    return sorted(t_state_v, key=t_state_v.__getitem__, reverse=True)[:K]
-    topK = sorted(items, key=operator.itemgetter(1), reverse=True)[:K]
-    return [x[0] for x in topK]
 
 def viterbi(obs, states, start_p, trans_p, emit_p):
     V = [{}] #tabular
@@ -27,7 +25,7 @@ def viterbi(obs, states, start_p, trans_p, emit_p):
             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
 

setup.py

@@ -1,10 +1,86 @@
+# -*- coding: utf-8 -*-
 from distutils.core import setup
+LONGDOC = u"""
+jieba
+=====
+
+“结巴”中文分词：做最好的 Python 中文分词组件
+
+"Jieba" (Chinese for "to stutter") Chinese text segmentation: built to
+be the best Python Chinese word segmentation module.
+
+完整文档见 ``README.md``
+
+GitHub: https://github.com/fxsjy/jieba
+
+特点
+====
+
+-  支持三种分词模式：
+
+   -  精确模式，试图将句子最精确地切开，适合文本分析；
+   -  全模式，把句子中所有的可以成词的词语都扫描出来,
+      速度非常快，但是不能解决歧义；
+   -  搜索引擎模式，在精确模式的基础上，对长词再次切分，提高召回率，适合用于搜索引擎分词。
+
+-  支持繁体分词
+-  支持自定义词典
+
+在线演示： http://jiebademo.ap01.aws.af.cm/
+
+安装说明
+========
+
+Python 2.x
+----------
+
+-  全自动安装： ``easy_install jieba`` 或者 ``pip install jieba``
+-  半自动安装：先下载 https://pypi.python.org/pypi/jieba/ ，解压后运行
+   python setup.py install
+-  手动安装：将 jieba 目录放置于当前目录或者 site-packages 目录
+-  通过 ``import jieba`` 来引用
+
+Python 3.x
+----------
+
+见 https://pypi.python.org/pypi/jieba3k/
+
+-  目前 master 分支是只支持 Python 2.x 的
+-  Python 3.x 版本的分支也已经基本可用：
+   https://github.com/fxsjy/jieba/tree/jieba3k
+
+.. code:: bash
+
+    git clone https://github.com/fxsjy/jieba.git
+    git checkout jieba3k
+    python setup.py install
+
+-  或使用pip3安装： pip3 install jieba3k
+
+"""
+
 setup(name='jieba',
       version='0.35',
       description='Chinese Words Segementation Utilities',
+      long_description=LONGDOC,
       author='Sun, Junyi',
       author_email='ccnusjy@gmail.com',
-      url='http://github.com/fxsjy',
+      url='https://github.com/fxsjy/jieba',
+      license="MIT",
+      classifiers=[
+        'Intended Audience :: Developers',
+        'License :: OSI Approved :: MIT License',
+        'Operating System :: OS Independent',
+        'Natural Language :: Chinese (Simplified)',
+        'Natural Language :: Chinese (Traditional)',
+        'Programming Language :: Cython',
+        'Programming Language :: Python',
+        'Programming Language :: Python :: 2',
+        'Topic :: Text Processing',
+        'Topic :: Text Processing :: Indexing',
+        'Topic :: Text Processing :: Linguistic',
+      ],
+      keywords='NLP,tokenizing,Chinese word segementation',
       packages=['jieba'],
       package_dir={'jieba':'jieba'},
       package_data={'jieba':['*.*','finalseg/*','analyse/*','posseg/*']}