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05 April 2024, 20:18:59 UTC
Revision cf4b6590c0550a6163992c1bac87ab5c8181a2e7 authored by James Hensman on 26 October 2016, 21:03:30 UTC, committed by James Hensman on 26 October 2016, 21:03:30 UTC 
pep8ifying test_variational
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Tip revision: cf4b6590c0550a6163992c1bac87ab5c8181a2e7 authored by James Hensman on 26 October 2016, 21:03:30 UTC
pep8ifying test_variational
Tip revision: cf4b659
reference.py 
import numpy as np

def referenceRbfKernel( X, lengthScale, signalVariance ):
    (nDataPoints, inputDimensions ) = X.shape
    kernel = np.zeros( (nDataPoints, nDataPoints ) )
    for row_index in range( nDataPoints ):
        for column_index in range( nDataPoints ):
            vecA = X[row_index,:]
            vecB = X[column_index,:]
            delta = vecA - vecB
            distanceSquared = np.dot( delta.T, delta )
            kernel[row_index, column_index ] = signalVariance * np.exp( -0.5*distanceSquared / lengthScale** 2)
    return kernel


def referencePeriodicKernel( X, lengthScale, signalVariance, period ):
    # Based on the GPy implementation of standard_period kernel
    base = np.pi * (X[:, None, :] - X[None, :, :]) / period
    exp_dist = np.exp( -0.5* np.sum( np.square(  np.sin( base ) / lengthScale ), axis = -1 ) )
    return signalVariance * exp_dist
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