torch.trapz
-
torch.trapz(y, x, *, dim=-1) → Tensor
-
Estimate along
dim
, using the trapezoid rule.- Parameters
-
- y (Tensor) – The values of the function to integrate
-
x (Tensor) – The points at which the function
y
is sampled. Ifx
is not in ascending order, intervals on which it is decreasing contribute negatively to the estimated integral (i.e., the convention is followed). - dim (int) – The dimension along which to integrate. By default, use the last dimension.
- Returns
-
A Tensor with the same shape as the input, except with
dim
removed. Each element of the returned tensor represents the estimated integral alongdim
.
Example:
>>> y = torch.randn((2, 3)) >>> y tensor([[-2.1156, 0.6857, -0.2700], [-1.2145, 0.5540, 2.0431]]) >>> x = torch.tensor([[1, 3, 4], [1, 2, 3]]) >>> torch.trapz(y, x) tensor([-1.2220, 0.9683])
-
torch.trapz(y, *, dx=1, dim=-1) → Tensor
As above, but the sample points are spaced uniformly at a distance of
dx
.- Parameters
-
y (Tensor) – The values of the function to integrate
- Keyword Arguments
- Returns
-
A Tensor with the same shape as the input, except with
dim
removed. Each element of the returned tensor represents the estimated integral alongdim
.
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.8.0/generated/torch.trapz.html