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torch.cumulative_trapezoid
torch.cumulative_trapezoid(y, x=None, *, dx=None, dim=-1) → Tensor
-
Cumulatively computes the trapezoidal rule along
dim
. By default the spacing between elements is assumed to be 1, butdx
can be used to specify a different constant spacing, andx
can be used to specify arbitrary spacing alongdim
.For more details, please read
torch.trapezoid()
. The difference betweentorch.trapezoid()
and this function is that,torch.trapezoid()
returns a value for each integration, where as this function returns a cumulative value for every spacing within the integration. This is analogous to how.sum
returns a value and.cumsum
returns a cumulative sum.- Parameters
- Keyword Arguments
Examples:
>>> # Cumulatively computes the trapezoidal rule in 1D, spacing is implicitly 1. >>> y = torch.tensor([1, 5, 10]) >>> torch.cumulative_trapezoid(y) tensor([3., 10.5]) >>> # Computes the same trapezoidal rule directly up to each element to verify >>> (1 + 5) / 2 3.0 >>> (1 + 10 + 10) / 2 10.5 >>> # Cumulatively computes the trapezoidal rule in 1D with constant spacing of 2 >>> # NOTE: the result is the same as before, but multiplied by 2 >>> torch.cumulative_trapezoid(y, dx=2) tensor([6., 21.]) >>> # Cumulatively computes the trapezoidal rule in 1D with arbitrary spacing >>> x = torch.tensor([1, 3, 6]) >>> torch.cumulative_trapezoid(y, x) tensor([6., 28.5]) >>> # Computes the same trapezoidal rule directly up to each element to verify >>> ((3 - 1) * (1 + 5)) / 2 6.0 >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 28.5 >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 matrix >>> y = torch.arange(9).reshape(3, 3) tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) >>> torch.cumulative_trapezoid(y) tensor([[ 0.5, 2.], [ 3.5, 8.], [ 6.5, 14.]]) >>> # Cumulatively computes the trapezoidal rule for each column of the matrix >>> torch.cumulative_trapezoid(y, dim=0) tensor([[ 1.5, 2.5, 3.5], [ 6.0, 8.0, 10.0]]) >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix >>> # with the same arbitrary spacing >>> y = torch.ones(3, 3) >>> x = torch.tensor([1, 3, 6]) >>> torch.cumulative_trapezoid(y, x) tensor([[2., 5.], [2., 5.], [2., 5.]]) >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix >>> # with different arbitrary spacing per row >>> y = torch.ones(3, 3) >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) >>> torch.cumulative_trapezoid(y, x) tensor([[1., 2.], [2., 4.], [3., 6.]])
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