rank

mvpy.math.rank(x: ndarray | Tensor) → ndarray | Tensor

Rank data in x along its final feature dimension. Ties are computed as averages.

Parameters:

xUnion[np.ndarray, torch.Tensor]

Unranked data ([samples x …] x features).

Returns:

rUnion[np.ndarray, torch.Tensor]

Ranked data ([samples x …] x features).

Examples

>>> import torch
>>> from mvpy.math import rank
>>> x = torch.tensor([2, 0.5, 1, 1])
>>> rank(x)
tensor([4.0000, 1.0000, 2.5000, 2.5000])