Scatter Min¶
-
torch_scatter.
scatter_min
(src, index, dim=-1, out=None, dim_size=None, fill_value=None)[source]¶ Minimizes all values from the
src
tensor intoout
at the indices specified in theindex
tensor along a given axisdim
.If multiple indices reference the same location, their contributions minimize (cf.scatter_add()
). The second return tensor contains index location insrc
of each minimum value (known as argmin).For one-dimensional tensors, the operation computes
\[\mathrm{out}_i = \min(\mathrm{out}_i, \min_j(\mathrm{src}_j))\]where \(\min_j\) is over \(j\) such that \(\mathrm{index}_j = i\).
Parameters: - src (Tensor) – The source tensor.
- index (LongTensor) – The indices of elements to scatter.
- dim (int, optional) – The axis along which to index.
(default:
-1
) - out (Tensor, optional) – The destination tensor. (default:
None
) - dim_size (int, optional) – If
out
is not given, automatically create output with sizedim_size
at dimensiondim
. Ifdim_size
is not given, a minimal sized output tensor is returned. (default:None
) - fill_value (int, optional) – If
out
is not given, automatically fill output tensor withfill_value
. (default:None
) - fill_value – If
out
is not given, automatically fill output tensor withfill_value
. If set toNone
, the output tensor is filled with the greatest possible value ofsrc.dtype
. (default:None
)
Return type: (
Tensor
,LongTensor
)from torch_scatter import scatter_min src = torch.Tensor([[-2, 0, -1, -4, -3], [0, -2, -1, -3, -4]]) index = torch.tensor([[ 4, 5, 4, 2, 3], [0, 0, 2, 2, 1]]) out = src.new_zeros((2, 6)) out, argmin = scatter_min(src, index, out=out) print(out) print(argmin)
tensor([[ 0., 0., -4., -3., -2., 0.], [-2., -4., -3., 0., 0., 0.]]) tensor([[-1, -1, 3, 4, 0, 1], [ 1, 4, 3, -1, -1, -1]])