def testSimilarities(self):
        # The model is trained using CBOW
        model = word2vec.Word2Vec(size=2, min_count=1, sg=0, hs=0, negative=2)
        model.build_vocab(sentences)     
        model.train(sentences)
        
        self.assertTrue(model.n_similarity(['graph', 'trees'], ['trees', 'graph']))     
        self.assertTrue(model.n_similarity(['graph'], ['trees']) == model.similarity('graph', 'trees'))
Sort the indices of this matrix *in place*
dataset/ETHPy150Open largelymfs/topical_word_embeddings/TWE-1/gensim/test/test_word2vec.py/TestWord2VecModel.testSimilarities--crossover with--dataset/ETHPy150Open scipy/scipy/scipy/sparse/compressed.py/_cs_matrix.sort_indices