tslearn.metrics.sigma_gak

tslearn.metrics.sigma_gak(dataset, n_samples=100, random_state=None)[source]

Compute sigma value to be used for GAK.

This method was originally presented in [1].

Parameters:
dataset

A dataset of time series

n_samples : int (default: 100)

Number of samples on which median distance should be estimated

random_state : integer or numpy.RandomState or None (default: None)

The generator used to draw the samples. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.

Returns:
float

Suggested bandwidth (\(\sigma\)) for the Global Alignment kernel

See also

gak
Compute Global Alignment kernel
cdist_gak
Compute cross-similarity matrix using Global Alignment kernel

References

[1]
  1. Cuturi, “Fast global alignment kernels,” ICML 2011.

Examples

>>> dataset = [[1, 2, 2, 3], [1., 2., 3., 4.]]
>>> sigma_gak(dataset=dataset,
...           n_samples=200,
...           random_state=0)  # doctest: +ELLIPSIS
2.0...