torch.trapz

torch.trapz(y, x, *, dim=-1) → Tensor

Estimate $\int y\,dx$ 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. If x is not in ascending order, intervals on which it is decreasing contribute negatively to the estimated integral (i.e., the convention $\int_a^b f = -\int_b^a f$ 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 $\int y\,dx$ along dim.

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

• dx (float) – The distance between points at which y is sampled.

• 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 $\int y\,dx$ along dim.