我计算了我的LDA模型，我检索了我的主题，现在我正在寻找计算语料库中每个主题的权重/百分比的方法。令人惊讶的是我找不到这样做的方法，到目前为止我的代码看起来像：
delete到目前为止，我在其他论坛上看到的是：
A(A const& ) = delete;
A& operator=(A const& ) = delete;
但是我收到了群集2中的错误：## Libraries to download
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim import corpora, models
import gensim
## Tokenizing
tokenizer = RegexpTokenizer(r'\w+')
# create English stop words list
en_stop = stopwords.words('english')
# Create p_stemmer of class PorterStemmer
p_stemmer = PorterStemmer()
import json
import nltk
import re
import pandas
appended_data = []
#for i in range(20014,2016):
# df0 = pandas.DataFrame([json.loads(l) for l in open('SDM_%d.json' % i)])
# appended_data.append(df0)
for i in range(2005,2016):
if i > 2013:
df0 = pandas.DataFrame([json.loads(l) for l in open('SDM_%d.json' % i)])
appended_data.append(df0)
df1 = pandas.DataFrame([json.loads(l) for l in open('Scot_%d.json' % i)])
df2 = pandas.DataFrame([json.loads(l) for l in open('APJ_%d.json' % i)])
df3 = pandas.DataFrame([json.loads(l) for l in open('TH500_%d.json' % i)])
df4 = pandas.DataFrame([json.loads(l) for l in open('DRSM_%d.json' % i)])
appended_data.append(df1)
appended_data.append(df2)
appended_data.append(df3)
appended_data.append(df4)
appended_data = pandas.concat(appended_data)
# doc_set = df1.body
doc_set = appended_data.body
# list for tokenized documents in loop
texts = []
# loop through document list
for i in doc_set:
# clean and tokenize document string
raw = i.lower()
tokens = tokenizer.tokenize(raw)
# remove stop words from tokens
stopped_tokens = [i for i in tokens if not i in en_stop]
# add tokens to list
texts.append(stopped_tokens)
# turn our tokenized documents into a id <-> term dictionary
dictionary = corpora.Dictionary(texts)
# convert tokenized documents into a document-term matrix
corpus = [dictionary.doc2bow(text) for text in texts]
# generate LDA model
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=15, id2word = dictionary, passes=50)
ldamodel.save("model.lda0")
。知道为什么吗？
答案 0 :(得分：4)
您需要在lda函数中声明最小概率为零：
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=15, id2word = dictionary, passes=50, minimum_probability=0)
此外，您可以通过以下方式获取所有文章的主题分发：
for i in range(len(doc_set)):
print(ldamodel[corpus[i]])