# dgl.sparse.spmatrixΒΆ

`dgl.sparse.``spmatrix`(indices: torch.Tensor, val: Optional[torch.Tensor] = None, shape: Optional[Tuple[int, int]] = None)dgl.sparse.sparse_matrix.SparseMatrix[source]ΒΆ

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,))
```