def txt_list(txts, N, lda):
# 获取推荐的文章列表
a = FeatureSimilarity.rank_tp(txts, lda)
b = to_article(a, lda)
texts = sorted(b, reverse=True)[0 : N + 1]
rec_list = []
for i in texts:
rec_list.append(b.index(i))
# print rec_list
# for j in rec_list:
# title = t[j].split(',')[3][1:-1]
return rec_list # 推荐的文章列表
def txt_list(txts,N,lda):
#获取推荐的文章列表
a = FeatureSimilarity.rank_tp(txts,lda)
b = to_article(a,lda)
texts = sorted(b,reverse = True)[0:N+1]
rec_list = []
for i in texts:
rec_list.append(b.index(i))
#print rec_list
#for j in rec_list:
#title = t[j].split(',')[3][1:-1]
return rec_list #推荐的文章列表
# coding:utf-8
# 计算每篇文章top-n主题下的所有单词概率总和
import gensim
import stopword
import FeatureSimilarity
txt = open(r'D:\citeulike\temp.dat', 'r').readlines()
diction = gensim.corpora.Dictionary.load('citeulike.dict')
batch_lda = gensim.models.LdaModel.load(r'D:\citeulike\dic\batch_lda_100.lda')
topics = FeatureSimilarity.rank_tp()
def word(page):
#计算推荐的文章page在给定主题topics下的单词概率和
topic_rate_new = 0
content_list = stopword.get_txt(txt[page])
doc_bow = diction.doc2bow(content_list)
doc_lda = batch_lda[doc_bow]
for j in range(len(doc_lda)):
dic = {}
word_rate = 0
tp_id = doc_lda[j][0]
if tp_id in topics:
word_list = batch_lda.show_topic(tp_id, 500000)
dic = dict(word_list) # 将showtopic的[(a,b),(),()]形式转换成字典
for w in list(set(content_list)):
# print dic[w]
word_rate += dic[w] # 计算总的主题下单词概率
# 乘以该主题的概率并除以文章内单词总个数，防止有些文章词很多，值也就很大
topic_rate = doc_lda[j][1] * word_rate / len(set(content_list))
topic_rate_new += topic_rate
#print topic_rate_new
return topic_rate_new
# coding:utf-8
# 计算每篇文章top-n主题下的所有单词概率总和
import gensim
import stopword
import FeatureSimilarity
txt = open(r'D:\citeulike\temp.dat', 'r').readlines()
diction = gensim.corpora.Dictionary.load('citeulike.dict')
batch_lda = gensim.models.LdaModel.load(r'D:\citeulike\dic\batch_lda_100.lda')
topics = FeatureSimilarity.rank_tp()
def word(page):
#计算推荐的文章page在给定主题topics下的单词概率和
topic_rate_new = 0
content_list = stopword.get_txt(txt[page])
doc_bow = diction.doc2bow(content_list)
doc_lda = batch_lda[doc_bow]
for j in range(len(doc_lda)):
dic = {}
word_rate = 0
tp_id = doc_lda[j][0]
if tp_id in topics:
word_list = batch_lda.show_topic(tp_id, 500000)
dic = dict(word_list) # 将showtopic的[(a,b),(),()]形式转换成字典
for w in list(set(content_list)):
# print dic[w]
word_rate += dic[w] # 计算总的主题下单词概率
# 乘以该主题的概率并除以文章内单词总个数，防止有些文章词很多，值也就很大
topic_rate = doc_lda[j][1] * word_rate / len(set(content_list))
topic_rate_new += topic_rate
