r2

mvpy.math.r2(y: ndarray | Tensor, y_h: ndarray | Tensor) → ndarray | Tensor

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

Parameters:

yUnion[np.ndarray, torch.Tensor]

True outcomes of shape ([n_samples, ...,] n_features).

y_hUnion[np.ndarray, torch.Tensor]

Predicted outcomes of shape ([n_samples, ...,] n_features).

Returns:

rUnion[np.ndarray, torch.Tensor]

R2 scores of shape ([n_samples, ...]).

Examples

>>> import torch
>>> from mvpy.math import rank
>>> y = torch.tensor([1.0, 2.0, 3.0])
>>> y_h = torch.tensor([1.0, 2.0, 3.0])
>>> r2(x)
tensor([1.0])