MultiquadricKernel#

class datafold.pcfold.MultiquadricKernel(epsilon=1.0, distance=None)[source]#

Bases: RadialBasisKernel

Multiquadric radial basis kernel.

K = \sqrt(\frac{1}{2 \varepsilon} \cdot D + 1)

where D is the squared euclidean distance matrix.

See also super classes RadialBasisKernel and PCManifoldKernel for more functionality and documentation.

Parameters:

epsilon (float) – Positive float to scale the kernel weights.

Methods Summary

evaluate(distance_matrix)

Evaluate the kernel on pre-computed distance matrix.

Methods Documentation

evaluate(distance_matrix)[source]#

Evaluate the kernel on pre-computed distance matrix.

Parameters:

distance_matrix (Union[ndarray, csr_matrix]) – Matrix of pairwise distances of shape (n_samples_Y, n_samples_X).

Returns:

Kernel matrix of same shape and type as distance_matrix.

Return type:

Union[np.ndarray, scipy.sparse.csr_matrix]