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# rikrd/gpy_kernel_cheatsheet.py

Last active July 5, 2022 20:59
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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.*)(\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

### ThomasScragg commented Feb 21, 2019

Hi rikrd,
very useful program- I had to make some changes to run in a jupyter notebook on my machine:

#%pylab inline
import numpy as np

```````import matplotlib`
import pylab as pl   # the matplotlib for plotting
`from matplotlib import pyplot as plt`
``````

#import pylab as plt
import GPy
import re
from IPython.display import Math, display #This will display the equation of the kernel in picture form

if Kernel.class == GPy.kern.Exponential.class == GPy.kern.src.kernel_slice_operations.KernCallsViaSlicerMeta:
(removed the ._src and replaced it with .src)

1. ``````      plt.subplot(122)
plt.imshow(C, interpolation='nearest')
plt.title('{} covariance'.format(kernel_name))
``````

``````This now displays the kernel plots and equations together, eg