dgl.sparse.SparseMatrix.smax

SparseMatrix.smax(dim: int | None = None)

计算稀疏矩阵 input 在给定维度 dim 上的非零值的最大值。

减少操作不计算零值。如果要减少的行或列没有任何非零值，结果将为0。

Parameters:

• input (SparseMatrix) – The input sparse matrix

• dim (int, optional) –

  The dimension to reduce, must be either 0 (by rows) or 1 (by columns) or None (on both rows and columns simultaneously)

  If dim is None, it reduces both the rows and the columns in the sparse matrix, producing a tensor of shape input.val.shape[1:]. Otherwise, it reduces on the row (dim=0) or column (dim=1) dimension, producing a tensor of shape (input.shape[1],) + input.val.shape[1:] or (input.shape[0],) + input.val.shape[1:].

Returns:

简化张量

Return type:

torch.Tensor

示例

案例1：标量值稀疏矩阵

>>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
>>> val = torch.tensor([1, 1, 2])
>>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
>>> dglsp.smax(A)
tensor(2)
>>> dglsp.smax(A, 0)
tensor([1, 0, 2])
>>> dglsp.smax(A, 1)
tensor([1, 2, 0, 0])

案例2：向量值稀疏矩阵

>>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
>>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
>>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
>>> dglsp.smax(A)
tensor([2, 2])
>>> dglsp.smax(A, 1)
tensor([[1, 2],
        [2, 2],
        [0, 0],
        [0, 0]])