# dgl.sparse.from_cscο

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

Creates a sparse matrix from compress sparse column (CSC) format.

See CSC in Wikipedia.

For column i of the sparse matrix

• the row indices of the non-zero elements are stored in `indices[indptr[i]: indptr[i+1]]`

• the corresponding values are stored in `val[indptr[i]: indptr[i+1]]`

Parameters:
• indptr (torch.Tensor) β Pointer to the row indices of shape N + 1, where N is the number of columns

• indices (torch.Tensor) β The row indices of shape nnz

• val (torch.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 `indptr` and `indices`, i.e., `(indices.max() + 1, len(indptr) - 1)`. Otherwise, `shape` should be no smaller than this.

Returns:

Sparse matrix

Return type:

SparseMatrix

Examples

Case1: Sparse matrix without values

```[[0, 1, 0],
[0, 0, 1],
[1, 1, 1]]
```
```>>> indptr = torch.tensor([0, 1, 3, 5])
>>> indices = torch.tensor([2, 0, 2, 1, 2])
>>> A = dglsp.from_csc(indptr, indices)
SparseMatrix(indices=tensor([[2, 0, 2, 1, 2],
[0, 1, 1, 2, 2]]),
values=tensor([1., 1., 1., 1., 1.]),
shape=(3, 3), nnz=5)
>>> # Specify shape
>>> A = dglsp.from_csc(indptr, indices, shape=(5, 3))
SparseMatrix(indices=tensor([[2, 0, 2, 1, 2],
[0, 1, 1, 2, 2]]),
values=tensor([1., 1., 1., 1., 1.]),
shape=(5, 3), nnz=5)
```

Case2: Sparse matrix with scalar/vector values. Following example is with vector data.

```>>> indptr = torch.tensor([0, 1, 3, 5])
>>> indices = torch.tensor([2, 0, 2, 1, 2])
>>> val = torch.tensor([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]])
>>> A = dglsp.from_csc(indptr, indices, val)
SparseMatrix(indices=tensor([[2, 0, 2, 1, 2],
[0, 1, 1, 2, 2]]),
values=tensor([[1, 1],
[2, 2],
[3, 3],
[4, 4],
[5, 5]]),
shape=(3, 3), nnz=5, val_size=(2,))
```