Source code for padasip.filters.gmcc
"""
.. versionadded:: 1.2.0
The generalized maximum correntropy criterion (GMCC)
is implemented according https://doi.org/10.1109/TSP.2016.2539127.
The GMCC adaptive filter can be created as follows
>>> import padasip as pa
>>> pa.filters.FilterGMCC(n)
where :code:`n` is the size (number of taps) of the filter.
Content of this page:
.. contents::
:local:
:depth: 1
.. seealso:: :ref:`filters`
Minimal Working Examples
======================================
If you have measured data you may filter it as follows
.. code-block:: python
import numpy as np
import matplotlib.pylab as plt
import padasip as pa
# creation of data
N = 500
x = np.random.normal(0, 1, (N, 4)) # input matrix
v = np.random.normal(0, 0.1, N) # noise
d = 2*x[:,0] + 0.1*x[:,1] - 4*x[:,2] + 0.5*x[:,3] + v # target
# identification
f = pa.filters.FilterGMCC(n=4, mu=0.1, w="random")
y, e, w = f.run(d, x)
# show results
plt.figure(figsize=(15,9))
plt.subplot(211);plt.title("Adaptation");plt.xlabel("samples - k")
plt.plot(d,"b", label="d - target")
plt.plot(y,"g", label="y - output");plt.legend()
plt.subplot(212);plt.title("Filter error");plt.xlabel("samples - k")
plt.plot(10*np.log10(e**2),"r", label="e - error [dB]");plt.legend()
plt.tight_layout()
plt.show()
Code Explanation
====================
"""
import numpy as np
from padasip.filters.base_filter import AdaptiveFilter
[docs]class FilterGMCC(AdaptiveFilter):
"""
This class represents an adaptive GMCC filter.
"""
kind = "GMCC"
def __init__(self, n, mu=0.01, lambd=0.03, alpha=2, **kwargs):
"""
**Kwargs:**
* `lambd` : kernel parameter (float) commonly known as lambda.
* `alpha` : shape parameter (float). `alpha = 2` make the filter LMS
"""
super().__init__(n, mu, **kwargs)
self.lambd = lambd
self.alpha = alpha
[docs] def learning_rule(self, e, x):
"""
Override the parent class.
"""
return self.mu * self.lambd * self.alpha * \
np.exp(-self.lambd * (np.abs(e) ** self.alpha)) * \
(np.abs(e) ** (self.alpha - 1)) * np.sign(e) * x