dgl.sparse.softmax¶

dgl.sparse.softmax(input: dgl.sparse.sparse_matrix.SparseMatrix) → dgl.sparse.sparse_matrix.SparseMatrix[source]¶

Applies row-wise softmax to the non-zero elements of the sparse matrix.

Equivalently, applies softmax to the non-zero elements of the sparse matrix along the column (dim=1) dimension.

If input.val takes shape (nnz, D), then the output matrix output and output.val take the same shape as input and input.val. output.val[:, i] is calculated based on input.val[:, i].

Parameters: input (SparseMatrix) – The input sparse matrix
Returns: The output sparse matrix
Return type: SparseMatrix

Examples

Case1: matrix with values of shape (nnz)

>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
>>> nnz = len(row)
>>> val = torch.arange(nnz).float()
>>> A = dglsp.spmatrix(indices, val)
>>> dglsp.softmax(A)
SparseMatrix(indices=tensor([[0, 0, 1, 2],
                             [1, 2, 2, 0]]),
             values=tensor([0.2689, 0.7311, 1.0000, 1.0000]),
             shape=(3, 3), nnz=4)

Case2: matrix with values of shape (nnz, D)

>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
>>> val = torch.tensor([[0., 7.], [1., 3.], [2., 2.], [3., 1.]])
>>> A = dglsp.spmatrix(indices, val)
>>> dglsp.softmax(A)
SparseMatrix(indices=tensor([[0, 0, 1, 2],
                             [1, 2, 2, 0]]),
             values=tensor([[0.2689, 0.9820],
                            [0.7311, 0.0180],
                            [1.0000, 1.0000],
                            [1.0000, 1.0000]]),
             shape=(3, 3), nnz=4, val_size=(2,))