def ModelInit(self, filename):
Docs = LoadData.LoadDataFromFile(os.getcwd() + "/" + filename)
self.D = len(Docs)
print "Load ", self.D, " docs from the file"
StopWordList = LoadData.LoadStopWords()
WordListSet = [
Preprocess.PreprocessText(doc, StopWordList) for doc in Docs
if type(doc) != unicode
]
self.Dictionary = Preprocess.ConstructDictionary(WordListSet)
self.W = len(self.Dictionary)
print "Total number of words is: ", self.W
print "Begin to save the dictionary..."
self.SaveDictionary()
print "Done!!"
print "Begin to map the word to ID"
self.IDListSet = []
inv_dict = {v: k for k, v in self.Dictionary.iteritems()}
for wdl in WordListSet:
IdList = Preprocess.Word2Id(wdl, inv_dict)
self.IDListSet.append(IdList)
print "Done!!"
self.ndsum = ListUtil.Initial(self.D)
self.theta = ListUtil.InitialMat(self.D, self.K, 0.0)
self.phi = ListUtil.InitialMat(self.K, self.W, 0.0)
self.nd = ListUtil.InitialMat(self.D, self.K, 0)
self.nw = ListUtil.InitialMat(self.W, self.K, 0)
self.Z = []
print "Begin to initialize the LDA model..."
self.RandomAssignTopic()
print "Topic assignment done!!"
def get_file_list():
"""
获取一个doc里面所有txt的内容，
一个句子一行
一个txt文档一个列表
"""
doc_path_list = LoadData.get_doc_path_list()
# for i in xrange(0, len(doc_path_list)):
file_list = []
for i in xrange(0, 1):
doc_path = doc_path_list[i]
file_path_list = LoadData.get_file_path_list(doc_path)
for j in xrange(0, len(file_path_list)):
file_path = file_path_list[j]
file = LoadData.LoadDataFromFile(file_path)
file_list.append(file)
return file_list
