http://blog.christianperone.com/2013/09/machine-learning-cosine-similarity-for-vector-space-models-part-iii/

 

documents = ("The sky is blue","The sun is bright","The sun in the sky is bright","We can see the shining sun, the bright sun")from sklearn.feature_extraction.text import TfidfVectorizertfidf_vectorizer = TfidfVectorizer()tfidf_matrix = tfidf_vectorizer.fit_transform(documents)# print tfidf_matrixfrom sklearn.metrics.pairwise import cosine_similarityprint cosine_similarity(tfidf_matrix[0], tfidf_matrix)import math# This was already calculated on the previous step, so we just use the valuecos_sim = 0.52305744angle_in_radians = math.acos(cos_sim)print math.degrees(angle_in_radians)