qfeval_functions.functions.ma

ma(x, span, dim=-1)[source]

Compute the moving (sliding window) average of a tensor.

This function calculates the average of elements within a sliding window of size span along the specified dimension. The output tensor has the same shape as the input tensor. For positions where the sliding window cannot fully cover preceding elements (i.e., the first span - 1 elements along the selected dimension), the result is nan.

The moving average is computed as:

\[\text{MA}[i] = \frac{1}{\text{span}} \sum_{j=i-\text{span}+1}^{i} x[j]\]

This is a simple moving average (SMA) where all values in the window have equal weight.

Parameters:

x (Tensor) – The input tensor containing values to be averaged.
span (int) – The size of the sliding window. Must be positive.
dim (int) – The dimension along which to compute the moving average. Default is -1 (the last dimension).

Returns:

A tensor of the same shape as the input, containing the moving averages. The first span - 1 elements along the specified dimension are nan.

Return type:

Tensor

Example

>>> # Simple moving average with window size 3
>>> x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
>>> QF.ma(x, span=3)
tensor([nan, nan, 2., 3., 4.])

>>> # 2D tensor with moving average along columns
>>> x = torch.tensor([[1.0, 2.0, 3.0, 4.0],
...                   [5.0, 6.0, 7.0, 8.0],
...                   [9.0, 10.0, 11.0, 12.0]])
>>> QF.ma(x, span=2, dim=1)
tensor([[    nan,  1.5000,  2.5000,  3.5000],
        [    nan,  5.5000,  6.5000,  7.5000],
        [    nan,  9.5000, 10.5000, 11.5000]])

>>> # Moving average along the first dimension
>>> x = torch.tensor([[1.0, 2.0],
...                   [3.0, 4.0],
...                   [5.0, 6.0],
...                   [7.0, 8.0]])
>>> QF.ma(x, span=3, dim=0)
tensor([[nan, nan],
        [nan, nan],
        [3., 4.],
        [5., 6.]])