models.py 文件源码

def _sparse_dot(self, tst_mat, i2i_mat):
    # scipy always returns sparse result, even if dot product is actually dense
    # this function offers solution to this problem
    # it also takes care on sparse result w.r.t. to further processing
        if self.dense_output: # calculate dense result directly
        # TODO implement matmat multiplication instead of iteration with matvec
            res_type = np.result_type(i2i_mat.dtype, tst_mat.dtype)
            scores = np.empty((tst_mat.shape[0], i2i_mat.shape[1]), dtype=res_type)
            for i in xrange(tst_mat.shape[0]):
                v = tst_mat.getrow(i)
                scores[i, :] = csc_matvec(i2i_mat, v, dense_output=True, dtype=res_type)
        else:
            scores = tst_mat.dot(i2i_mat.T)
            # NOTE even though not neccessary for symmetric i2i matrix,
            # transpose helps to avoid expensive conversion to CSR (performed by scipy)
            if scores.nnz > NNZ_MAX:
                # too many nnz lead to undesired memory overhead in downvote_seen_items
                scores = scores.toarray(order='C')
        return scores