Warning: this is a side-project in progress so many bugs could arise. Please raise an issue if this happens.

Kernel Adaptive Filtering for Python

This package implements several Kernel Adaptive Filtering algorithms for research purposes. It aims to be easily extendable.

Features

Adaptive Kernel Filters

• Kernel Least Mean Squares (KLMS) - KlmsFilter
• Exogenous Kernel Least Mean Squares (KLMS-X) - KlmsxFilter
• Kernel Recursive Least Squares (KRLS) - KrlsFilter

Sparsification Criteria

• Novelty (KLMS)
• Approximate Linear Dependency (KLRS)

Additional Features

• Delayed input support (KLMS)
• Adaptive kernel parameter learning (KLMS)

For a more visual comparison, check the latest features sheet.

Quickstart

Let's do a simple example using a KLMS Filter over given input and target arrays:

from kaftools.filters import KlmsFilter
from kaftools.kernels import GaussianKernel

klms = KlmsFilter(input, target)
klms.fit(learning_rate=0.1, kernel=GaussianKernel(sigma=0.1))

And that's it!