dgl.sparse.div¶

dgl.sparse.div(A: Union[dgl.sparse.sparse_matrix.SparseMatrix, dgl.sparse.diag_matrix.DiagMatrix], B: Union[dgl.sparse.diag_matrix.DiagMatrix, numbers.Number, torch.Tensor]) → Union[dgl.sparse.sparse_matrix.SparseMatrix, dgl.sparse.diag_matrix.DiagMatrix][source]¶

Elementwise division for DiagMatrix and SparseMatrix, equivalent to A / B.

The supported combinations are shown as follows.

A \ B	DiagMatrix	SparseMatrix	scalar
DiagMatrix	✅	🚫	✅
SparseMatrix	🚫	🚫	✅
scalar	🚫	🚫	🚫

Parameters

A (SparseMatrix or DiagMatrix) – Sparse or diagonal matrix
B (DiagMatrix or Scalar) – Diagonal matrix or scalar value

Returns

Diagonal matrix

Return type

DiagMatrix

Examples

>>> A = dglsp.diag(torch.arange(1, 4))
>>> B = dglsp.diag(torch.arange(10, 13))
>>> dglsp.div(A, B)
DiagMatrix(val=tensor([0.1000, 0.1818, 0.2500]),
           shape=(3, 3))

>>> A = dglsp.diag(torch.arange(1, 4))
>>> dglsp.div(A, 2)
DiagMatrix(val=tensor([0.5000, 1.0000, 1.5000]),
           shape=(3, 3))

>>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
>>> val = torch.tensor([1, 2, 3])
>>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
>>> dglsp.div(A, 2)
SparseMatrix(indices=tensor([[1, 0, 2],
                             [0, 3, 2]]),
             values=tensor([0.5000, 1.0000, 1.5000]),
             shape=(3, 4), nnz=3)