`sklvq.distances`.Euclidean

class sklvq.distances.Euclidean[source]

Euclidean distance function

Class that holds the euclidean distance function and its gradient.

Parameters:

force_all_finite{True, False, “allow-nan”}: Parameter to indicate that NaNLVQ distance variant should be used. If true no nans are allowed. If False or “allow-nan” nans are allowed.

See also

SquaredEuclidean, AdaptiveSquaredEuclidean, LocalAdaptiveSquaredEuclidean

Notes

Compatible with the GLVQ algorithm (only).

__call__(data: ndarray, model: GLVQ) → ndarray[source]

Computes the Euclidean distance:: $d(\mathbf{w}, \mathbf{x}) = \sqrt{(\mathbf{x} - \mathbf{w})^{\top} (\mathbf{x} - \mathbf{w})},$

with $\mathbf{w}$ a prototype and $\mathbf{x}$ a sample.

Parameters:

datandarray with shape (n_samples, n_features): The data for which the distances to the prototypes of the model need to be computed.
modelGLVQ: A GLVQ model instance, containing the prototypes.

Returns:

ndarray with shape (n_samples, n_prototypes): Evaluation of the distance between each sample and prototype of the model.

gradient(data: ndarray, model: GLVQ, i_prototype: int) → ndarray[source]

Computes the gradient of the euclidean distance with respect to a single prototype:

$\frac{\partial d}{\partial \mathbf{w}_i} = -2 \cdot (\mathbf{x} - \mathbf{w}_i)$

Parameters:

datandarray with shape (n_samples, n_features): The data for which the distance gradient to the prototypes of the model need to be computed.
modelGLVQ: A GLVQ model instance.
i_prototypeint: Index of the prototype to compute the gradient for.

Returns:

ndarray with shape (n_samples, n_features): The gradient of the prototype with respect to every sample in the data.

sklvq.distances.Euclidean