2bb8e1dfeadfef943c888f7e4e735fd161535475,gensim/sklearn_api/lsimodel.py,LsiTransformer,transform,#LsiTransformer#Any#,56

Before Change

        // The input as array of array
        check = lambda x: [x] if isinstance(x[0], tuple) else x
        docs = check(docs)
        X = [[] for i in range(0, len(docs))]
        for k, v in enumerate(docs):
            doc_topics = self.gensim_model[v]
            // returning dense representation for compatibility with sklearn but we should go back to sparse representation in the future
            probs_docs = matutils.sparse2full(doc_topics, self.num_topics)
            X[k] = probs_docs
        return np.reshape(np.array(X), (len(docs), self.num_topics))

    def partial_fit(self, X):
        

After Change

            )

        // The input as array of array
        if isinstance(docs[0], tuple):
            docs = [docs]
        // returning dense representation for compatibility with sklearn but we should go back to sparse representation in the future
        distribution = [matutils.sparse2full(self.gensim_model[doc], self.num_topics) for doc in docs]
        return np.reshape(np.array(distribution), (len(docs), self.num_topics))

    def partial_fit(self, X):
        

Instances