print '5', doc.getSentenceOrginal(sent[4])
with open(str(filename) + '_output.txt', 'w') as file:
for i in [0,1,2,3,4]:
file.write(doc.getSentenceOrginal(sent[i]) + ' ')
print ' '
## LDA
from collections import Counter
## get MAP sentences by lda topic i
popular = Counter()
for n in range(1):
print n
doc.getLDA(6)
for i in range(doc.topics):
sentences = doc.setencesByLDAFreqCloseness(i)
popular.update(Counter([doc.getSentenceOrginal(sentence) for sentence in sentences[0:1]]))
# for j in range(3):
# sentence = doc.getSentenceOrginal(sentences[j])
# if sentence in popular_sentences:
# popular_sentences[sentence] += 1
# else:
# popular_sentences[sentence] = 1
popular = [elem[0] for elem in popular.most_common(6)]
# popular = [sent for sent in set(popular_sentences)]
# popular = sorted(popular_sentences, key=popular_sentences.get, reverse = True)
for sent in popular:
print sent
from Project import Document
from collections import Counter
import numpy as np
# filename = raw_input("file name ")
filename = 'economist1'
doc = Document(filename + '.txt')
## Get key sentence
sent = doc.setencesByFreqCloseness()
maxSent = sent[0]
print doc.getSentenceOrginal(maxSent)
doc.getLDA(5)
topicAndScore = doc.getTopicAndScore()
maxTopic, maxScore = topicAndScore[maxSent]
print topicAndScore[maxSent]
sentByTopics = {}
for key in topicAndScore:
value = topicAndScore[key]
topic = value[0]
if topic in sentByTopics:
sentByTopics[topic] += [key]
else:
sentByTopics[topic] = [key]
