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GPy Kernel Cheatsheet:: All the kernels in GPy (example realizations, covariance matrix, kernel equation)
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%pylab inline | |
import numpy as np | |
import pylab as plt | |
import GPy | |
import re | |
def get_equation(kern): | |
match = re.search(r'(math::)(\r\n|\r|\n)*(?P<equation>.*)(\r\n|\r|\n)*', kern.__doc__) | |
return '' if match is None else match.group('equation').strip() | |
for kernel_name in dir(GPy.kern): | |
Kernel = getattr(GPy.kern, kernel_name) | |
if Kernel.__class__ == GPy.kern.Exponential.__class__ == GPy.kern._src.kernel_slice_operations.KernCallsViaSlicerMeta: | |
# Try plotting sample paths here | |
try: | |
k = Kernel(input_dim=1) | |
X = np.linspace(0.,1.,500) # define X to be 500 points evenly spaced over [0,1] | |
X = X[:,None] # reshape X to make it n*p --- we try to use 'design matrices' in GPy | |
mu = np.zeros((500))# vector of the means --- we could use a mean function here, but here it is just zero. | |
C = k.K(X,X) # compute the covariance matrix associated with inputs X | |
# Generate 20 separate samples paths from a Gaussian with mean mu and covariance C | |
Z = np.random.multivariate_normal(mu,C,20) | |
kernel_equation = get_equation(k) | |
#print kernel_equation | |
from IPython.display import Math, display | |
display(Math(kernel_equation)) | |
fig = plt.figure() # open a new plotting window | |
plt.subplot(121) | |
for i in range(3): | |
plt.plot(X[:],Z[i,:]) | |
plt.title('{} samples'.format(kernel_name)) | |
plt.subplot(122) | |
plt.imshow(C, interpolation='nearest') | |
plt.title('{} covariance'.format(kernel_name)) | |
except: | |
continue |
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