Function val_like

python/dgl/sparse/sparse_matrix.py:1108–1143

Creates a sparse matrix from an existing sparse matrix using new values. The new sparse matrix will have the same non-zero indices as the given sparse matrix and use the given values as the new non-zero values. Parameters ---------- mat : SparseMatrix An existing sparse

(mat: SparseMatrix, val: torch.Tensor)

def val_like(mat: SparseMatrix, val: torch.Tensor) -> SparseMatrix:
    """Creates a sparse matrix from an existing sparse matrix using new values.

    The new sparse matrix will have the same non-zero indices as the given
    sparse matrix and use the given values as the new non-zero values.

    Parameters
    ----------
    mat : SparseMatrix
        An existing sparse matrix with non-zero values
    val : torch.Tensor
        The new values of the non-zero elements, a tensor of shape ``(nnz)`` or
        ``(nnz, D)``

    Returns
    -------
    SparseMatrix
        New sparse matrix

    Examples
    --------

    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
    >>> val = torch.ones(3)
    >>> A = dglsp.spmatrix(indices, val)
    >>> A = dglsp.val_like(A, torch.tensor([2, 2, 2]))
    SparseMatrix(indices=tensor([[1, 1, 2],
                                 [2, 4, 3]]),
                 values=tensor([2, 2, 2]),
                 shape=(3, 5), nnz=3)
    """
    assert (
        val.dim() <= 2
    ), "The values of a SparseMatrix can only be scalars or vectors."

    return SparseMatrix(torch.ops.dgl_sparse.val_like(mat.c_sparse_matrix, val))


def diag(