euclidean

mvpy.math.euclidean(x: ndarray | Tensor, y: ndarray | Tensor, *args: Any) → ndarray | Tensor

Computes euclidean distances between x and y.

Parameters:

xUnion[np.ndarray, torch.Tensor]

Matrix ([samples …] x features)

yUnion[np.ndarray, torch.Tensor]

Matrix ([samples …] x features)

Returns:

Union[np.ndarray, torch.Tensor]

Vector or matrix of euclidean distances

Notes

Euclidean distances are defined as:

\[d(x, y) = \sqrt{\sum_{i=1}^n (x_i - y_i)^2}\]

where \(x_i\) and \(y_i\) are the \(i\)-th elements of \(x\) and \(y\), respectively.

Examples

>>> import torch
>>> import mvpy as mv
>>> x, y = torch.normal(0, 1, (10, 50)), torch.normal(0, 1, (10, 50))
>>> d = mv.math.euclidean(x, y)
>>> d.shape
torch.Size([10])