def gamma_soft_dtw(dataset, n_samples=100, random_state=None):
"""Compute gamma value to be used for GAK/Soft-DTW.
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 :math:`\\gamma` parameter for the Soft-DTW
Example
-------
>>> dataset = [[1, 2, 2, 3], [1., 2., 3., 4.]]
>>> gamma_soft_dtw(dataset=dataset, n_samples=200, random_state=0) # doctest: +ELLIPSIS
8.0...
See Also
--------
sigma_gak : Compute sigma parameter for Global Alignment kernel
References
----------
.. [1] M. Cuturi, "Fast global alignment kernels," ICML 2011.
"""
return 2. * sigma_gak(dataset=dataset, n_samples=n_samples, random_state=random_state) ** 2
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