Revision b08f3062c96677de266af26767634fd7c6e6611d authored by Alexander G. de G. Matthews on 09 September 2016, 10:59:46 UTC, committed by James Hensman on 09 September 2016, 10:59:46 UTC
* Renaming tf_hacks to tf_wraps * Changing tf_hacks to tf_wraps in code. * adding a tf_hacks file that raises deprecationwarnings * release notes * bumpng version on docs * importing tf_hacks, tf_wraps
1 parent 61b0659
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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