# tslearn.metrics.gak¶

tslearn.metrics.gak(s1, s2, sigma=1.0)[source]

Compute Global Alignment Kernel (GAK) between (possibly multidimensional) time series and return it.

It is not required that both time series share the same size, but they must be the same dimension. GAK was originally presented in [1]. This is a normalized version that ensures that $$k(x,x)=1$$ for all $$x$$ and $$k(x,y) \in [0, 1]$$ for all $$x, y$$.

Parameters: s1 A time series s2 Another time series sigma : float (default 1.) Bandwidth of the internal gaussian kernel used for GAK float Kernel value

cdist_gak
Compute cross-similarity matrix using Global Alignment kernel

References

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

Examples

>>> gak([1, 2, 3], [1., 2., 2., 3.], sigma=2.)  # doctest: +ELLIPSIS
0.839...
>>> gak([1, 2, 3], [1., 2., 2., 3., 4.])  # doctest: +ELLIPSIS
0.273...