spkit.mean_minSE

spkit.mean_minSE(x, y, W=5, show=False, compare_mse=True, plot_log=False, esp=0.001, show_legend=True)

Mean of Minimum Squared Error (MMSE) under temporal shift

Mean of Minimum Squared Error (MMSE) is the metric computed the closeness of two signals. MMSE ignores the a little

for t = -W to W

\[MminSE = 1/K \sum_0^K-1 min( (x(k) - y(k-t))**2 ) \]

Parameters:

x: 1d-array

y: 1d-array

Returns:

See also

spkit

#TODO

Notes

#TODO

References

• wikipedia -

Examples

>>> import numpy as np
>>> import spkit as sp
>>> #TODO