def _split_into_words(args):
i, text = args
if i % 10000 == 0:
logger.info('row: %s' % i)
return model.infer_vector(split_into_words(text))
import MeCab
import doc2vec
from gensim import models
model = models.Doc2Vec.load('doc2vec.model')
# 似た文章を探す
def search_similar_texts(words):
x = model.infer_vector(words)
most_similar_texts = model.docvecs.most_similar([x])
for similar_text in most_similar_texts:
print(similar_text[0])
# 似た単語を探す
def search_similar_words(words):
for word in words:
print()
print(word + ':')
for result in model.most_similar(positive=word, topn=10):
print(result[0])
if __name__ == '__main__':
print('文字列入力:')
search_str = input()
words = doc2vec.split_into_words(search_str).words
search_similar_texts(words)
search_similar_words(words)