from torch_scatter import scatter_add
[docs]def scatter_sub(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
r"""
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.. image:: https://raw.githubusercontent.com/rusty1s/pytorch_scatter/
master/docs/source/_figures/sub.svg?sanitize=true
:align: center
:width: 400px
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Subtracts all values from the :attr:`src` tensor into :attr:`out` at the
indices specified in the :attr:`index` tensor along a given axis
:attr:`dim`.If multiple indices reference the same location, their
**negated contributions add** (`cf.` :meth:`~torch_scatter.scatter_add`).
For one-dimensional tensors, the operation computes
.. math::
\mathrm{out}_i = \mathrm{out}_i - \sum_j \mathrm{src}_j
where :math:`\sum_j` is over :math:`j` such that
:math:`\mathrm{index}_j = i`.
Args:
src (Tensor): The source tensor.
index (LongTensor): The indices of elements to scatter.
dim (int, optional): The axis along which to index.
(default: :obj:`-1`)
out (Tensor, optional): The destination tensor. (default: :obj:`None`)
dim_size (int, optional): If :attr:`out` is not given, automatically
create output with size :attr:`dim_size` at dimension :attr:`dim`.
If :attr:`dim_size` is not given, a minimal sized output tensor is
returned. (default: :obj:`None`)
fill_value (int, optional): If :attr:`out` is not given, automatically
fill output tensor with :attr:`fill_value`. (default: :obj:`0`)
:rtype: :class:`Tensor`
.. testsetup::
import torch
.. testcode::
from torch_scatter import scatter_sub
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 = scatter_sub(src, index, out=out)
print(out)
.. testoutput::
tensor([[ 0., 0., -4., -3., -3., 0.],
[-2., -4., -4., 0., 0., 0.]])
"""
return scatter_add(src.neg(), index, dim, out, dim_size, fill_value)