dgl.sparse.from_csc

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

从压缩稀疏列（CSC）格式创建稀疏矩阵。

See CSC in Wikipedia.

对于稀疏矩阵的第i列

• 非零元素的行索引存储在 indices[indptr[i]: indptr[i+1]]

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

Parameters:

• indptr (torch.Tensor) – 指向形状为 N + 1 的行索引的指针，其中 N 是列的数量

• indices (torch.Tensor) – 形状为 nnz 的行索引

• 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) – 如果未指定，它将从 indptr 和 indices 推断，即 (indices.max() + 1, len(indptr) - 1)。 否则，shape 不应小于此值。

Returns:

稀疏矩阵

Return type:

SparseMatrix

示例

案例1：没有值的稀疏矩阵

[[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)

案例2：带有标量/向量值的稀疏矩阵。以下示例是带有向量数据的。

>>> 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,))