tslearn.metrics.cdist_gak

tslearn.metrics.cdist_gak(dataset1, dataset2=None, sigma=1.0, n_jobs=None, verbose=0)[source]

Compute cross-similarity matrix using Global Alignment kernel (GAK).

GAK was originally presented in [1].

Parameters:
dataset1

A dataset of time series

dataset2

Another dataset of time series

sigma : float (default 1.)

Bandwidth of the internal gaussian kernel used for GAK

n_jobs : int or None, optional (default=None)

The number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See scikit-learns’ Glossary for more details.

verbose : int, optional (default=0)

The verbosity level: if non zero, progress messages are printed. Above 50, the output is sent to stdout. The frequency of the messages increases with the verbosity level. If it more than 10, all iterations are reported. Glossary for more details.

Returns:
numpy.ndarray

Cross-similarity matrix

See also

gak
Compute Global Alignment kernel

References

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

Examples

>>> cdist_gak([[1, 2, 2, 3], [1., 2., 3., 4.]], sigma=2.)
array([[1.        , 0.65629661],
       [0.65629661, 1.        ]])
>>> cdist_gak([[1, 2, 2], [1., 2., 3., 4.]],
...           [[1, 2, 2, 3], [1., 2., 3., 4.], [1, 2, 2, 3]],
...           sigma=2.)
array([[0.71059484, 0.29722877, 0.71059484],
       [0.65629661, 1.        , 0.65629661]])