무회blog

0003-scikit-lear,방식으로 텍스트 ,코사인 유사도, 구하기-004¶ scikit-learn 001 방식 체크¶ In [1]: from sklearn.metrics.pairwise import cosine_similarity a=[[1,3,2],[2,2,1]] cosine_similarity(a) # 余弦相似度 Out[1]: array([[1. , 0.89087081], [0.89087081, 1. ]]) In [2]: from sklearn.metrics.pairwise import pairwise_distances pairwise_distances(a,metric="cosine") # 注意该方法返回的是余弦距离 Out[2]: array([[0. , 0.10912919], [0.10912919..

0003-Doc2vec ,방식으로 텍스트 ,코사인 유사도, 구하기-003-02¶ In [1]: # Word2Vec embedding # from gensim.models import Word2Vec from gensim.models.word2vec import Word2Vec from gensim.models import KeyedVectors from gensim.summarization import summarize from rank_bm25 import BM25Okapi from nltk import sent_tokenize, word_tokenize from konlpy.tag import Hannanum from konlpy.tag import Kkma hannanum = Hannanum() i..

002 - word2vec 방식으로 텍스트 코사인 유사도 구하기¶ In [1]: # Word2Vec embedding # from gensim.models import Word2Vec from gensim.models.word2vec import Word2Vec from gensim.models import KeyedVectors from gensim.summarization import summarize from rank_bm25 import BM25Okapi from nltk import sent_tokenize, word_tokenize from konlpy.tag import Hannanum from konlpy.tag import Kkma hannanum = Hannanum() import pa..

002 - word2vec 방식으로 텍스트 코사인 유사도 구하기¶ In [1]: # Word2Vec embedding # from gensim.models import Word2Vec from gensim.models.word2vec import Word2Vec from gensim.models import KeyedVectors from gensim.summarization import summarize from rank_bm25 import BM25Okapi from nltk import sent_tokenize, word_tokenize from konlpy.tag import Hannanum from konlpy.tag import Kkma hannanum = Hannanum() import pa..