Adaptive Kernel Methods following scikit-learn API
Here we mainly test the Kernel LMS algorithm for R^n vectors and spike trains. KernelLMS deals with Rˆn vectors and SpikeKLMS with spike times series To test those methods either run 'test_KLMS.py' or 'test_SpikeKLMS.py'. This will show how the methods work for regression. There also an example of KLMS for classification at 'test_klms_classify.py'
Spike_KLMS algorithm used SLASH library for spike train signal processing, which is included here. SLASH provides spike-spike, population-spike and population-population inner products. To test SLASH for unserpervised learning, we included Paiva's PCA at 'test_paiva.py' and a modification for populations at 'test_populationPCA.py'