dgl.sparse.spmatrixο

dgl.sparse.spmatrix(indices: Tensor, val: Tensor | None = None, shape: Tuple[int, int] | None = None) SparseMatrix[source]ο

Creates a sparse matrix from Coordinate format indices.

Parameters:
• indices (tensor.Tensor) β The indices are the coordinates of the non-zero elements in the matrix, which should have shape of (2, N) where the first row is the row indices and the second row is the column indices of non-zero elements.

• val (tensor.Tensor, optional) β The values of shape (nnz) or (nnz, D). If None, it will be a tensor of shape (nnz) filled by 1.

• shape (tuple[int, int], optional) β If not specified, it will be inferred from row and col, i.e., (row.max() + 1, col.max() + 1). Otherwise, shape should be no smaller than this.

Returns:

Sparse matrix

Return type:

SparseMatrix

Examples

Case1: Sparse matrix with row and column indices without values.

>>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
>>> A = dglsp.spmatrix(indices)
SparseMatrix(indices=tensor([[1, 1, 2],
[2, 4, 3]]),
values=tensor([1., 1., 1.]),
shape=(3, 5), nnz=3)
>>> # Specify shape
>>> A = dglsp.spmatrix(indices, shape=(5, 5))
SparseMatrix(indices=tensor([[1, 1, 2],
[2, 4, 3]]),
values=tensor([1., 1., 1.]),
shape=(5, 5), nnz=3)

Case2: Sparse matrix with scalar values.

>>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
>>> val = torch.tensor([[1.], [2.], [3.]])
>>> A = dglsp.spmatrix(indices, val)
SparseMatrix(indices=tensor([[1, 1, 2],
[2, 4, 3]]),
values=tensor([[1.],
[2.],
[3.]]),
shape=(3, 5), nnz=3, val_size=(1,))

Case3: Sparse matrix with vector values.

>>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
>>> val = torch.tensor([[1., 1.], [2., 2.], [3., 3.]])
>>> A = dglsp.spmatrix(indices, val)
SparseMatrix(indices=tensor([[1, 1, 2],
[2, 4, 3]]),
values=tensor([[1., 1.],
[2., 2.],
[3., 3.]]),
shape=(3, 5), nnz=3, val_size=(2,))