Using Kernels

Out-of-sample normalized and centered kernels

import kerch
import numpy as np
from matplotlib import pyplot as plt

sample = np.sin(np.arange(0,15) / np.pi) + .1
oos = np.sin(np.arange(15,30) / np.pi) + .1

k = kerch.kernel.factory(type="polynomial", sample=sample, center=True, normalize=True)

fig, axs = plt.subplots(2,2)

axs[0,0].imshow(k.K, vmin=-1, vmax=1)
axs[0,0].set_title("Sample -Sample")

axs[0,1].imshow(k.k(y=oos), vmin=-1, vmax=1)
axs[0,1].set_title("Sample - OOS")

axs[1,0].imshow(k.k(x=oos), vmin=-1, vmax=1)
axs[1,0].set_title("OOS - Sample")

im = axs[1,1].imshow(k.k(x=oos, y=oos), vmin=-1, vmax=1)
axs[1,1].set_title("OOS - OOS")

for ax in axs.flat:
    ax.set_xticks([])
    ax.set_yticks([])

fig.colorbar(im, ax=axs.ravel().tolist())

(Source code, png, hires.png, pdf)