通过@excray注释的帮助，我设法弄清楚答案，实际上，我们需要编写一个简单的for循环，以迭代表示火车数据和测试数据的两个数组。
首先实现一个简单的lambda函数来保存用于余弦计算的公式：
cosine_function = lambda a, b : round(np.inner(a, b)/(LA.norm(a)*LA.norm(b)), 3)
然后只需编写一个简单的for循环以遍历to向量，每个逻辑都是“对于trainVectorizerArray中的每个向量，必须在testVectorizerArray中找到与向量的余弦相似度。”
from sklearn.feature_extraction.text import CountVectorizerfrom sklearn.feature_extraction.text import TfidfTransformerfrom nltk.corpus import stopwordsimport numpy as npimport numpy.linalg as LAtrain_set = ["The sky is blue.", "The sun is bright."] #documentstest_set = ["The sun in the sky is bright."] #QuerystopWords = stopwords.words('english')vectorizer = CountVectorizer(stop_words = stopWords)#print vectorizertransformer = TfidfTransformer()#print transformertrainVectorizerArray = vectorizer.fit_transform(train_set).toarray()testVectorizerArray = vectorizer.transform(test_set).toarray()print 'Fit Vectorizer to train set', trainVectorizerArrayprint 'Transform Vectorizer to test set', testVectorizerArraycx = lambda a, b : round(np.inner(a, b)/(LA.norm(a)*LA.norm(b)), 3)for vector in trainVectorizerArray: print vector for testV in testVectorizerArray: print testV cosine = cx(vector, testV) print cosinetransformer.fit(trainVectorizerArray)printprint transformer.transform(trainVectorizerArray).toarray()transformer.fit(testVectorizerArray)print tfidf = transformer.transform(testVectorizerArray)print tfidf.todense()这是输出：
Fit Vectorizer to train set [[1 0 1 0] [0 1 0 1]]Transform Vectorizer to test set [[0 1 1 1]][1 0 1 0][0 1 1 1]0.408[0 1 0 1][0 1 1 1]0.816[[ 0.70710678 0. 0.70710678 0. ] [ 0. 0.70710678 0. 0.70710678]][[ 0. 0.57735027 0.57735027 0.57735027]]
