dgl.sparse.from_coo

dgl.sparse.from_coo(row: Tensor, col: Tensor, val: Tensor | None = None, shape: Tuple[int, int] | None = None) → SparseMatrix[source]

从坐标列表（COO）创建一个稀疏矩阵，该列表存储一组（行，列，值）元组。

See COO in Wikipedia.

Parameters:

行 (torch.Tensor) – 形状为 (nnz) 的行索引
col (torch.Tensor) – 形状为 (nnz) 的列索引
val (torch.Tensor, optional) – 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) – 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:

稀疏矩阵

Return type:

SparseMatrix

示例

案例1：带有行和列索引但没有值的稀疏矩阵。

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

案例2：带有标量值的稀疏矩阵。

>>> 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,))

案例3：具有向量值的稀疏矩阵。

>>> dst = torch.tensor([1, 1, 2])
>>> src = torch.tensor([2, 4, 3])
>>> val = torch.tensor([[1., 1.], [2., 2.], [3., 3.]])
>>> A = dglsp.from_coo(dst, src, 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,))