dgl.sparse.SparseMatrix.sumο
- SparseMatrix.sum(dim: int | None = None)ο
Computes the sum of non-zero values of the
input
sparse matrix along the given dimensiondim
.- Parameters:
input (SparseMatrix) β The input sparse matrix
dim (int, optional) β
The dimension to reduce, must be either 0 (by rows) or 1 (by columns) or None (on both rows and columns simultaneously)
If
dim
is None, it reduces both the rows and the columns in the sparse matrix, producing a tensor of shapeinput.val.shape[1:]
. Otherwise, it reduces on the row (dim=0
) or column (dim=1
) dimension, producing a tensor of shape(input.shape[1],) + input.val.shape[1:]
or(input.shape[0],) + input.val.shape[1:]
.
- Returns:
Reduced tensor
- Return type:
torch.Tensor
Examples
Case1: scalar-valued sparse matrix
>>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]]) >>> val = torch.tensor([1, 1, 2]) >>> A = dglsp.spmatrix(indices, val, shape=(4, 3)) >>> dglsp.sum(A) tensor(4) >>> dglsp.sum(A, 0) tensor([2, 0, 2]) >>> dglsp.sum(A, 1) tensor([1, 3, 0, 0])
Case2: vector-valued sparse matrix
>>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]]) >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]]) >>> A = dglsp.spmatrix(indices, val, shape=(4, 3)) >>> dglsp.sum(A) tensor([5, 5]) >>> dglsp.sum(A, 0) tensor([[3, 3], [0, 0], [2, 2]])