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Merge branch 'params' of github.com:SheffieldML/GPy into params

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This commit is contained in:
Alan Saul 2014-03-04 10:56:36 +00:00
commit ca40b9803a
2 changed files with 12 additions and 13 deletions
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11
GPy/kern/_src/rbf.py
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@ -62,13 +62,18 @@ class RBF(Stationary):
#from psi1 #from psi1
self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance) self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance)
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0) if self.ARD:
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
else:
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).sum()
#from psi2 #from psi2
self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum() self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum()
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0) if self.ARD:
return self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
else:
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).sum()
elif isinstance(variational_posterior, variational.NormalPosterior): elif isinstance(variational_posterior, variational.NormalPosterior):

14
GPy/kern/_src/rbf_psi_comp/ssrbf_psi_comp.py
View file

@ -6,22 +6,15 @@ The package for the psi statistics computation
""" """
import numpy as np import numpy as np
from GPy.util.caching import Cache_this
@Cache_this(limit=1)
def _Z_distances(Z): def _Z_distances(Z):
Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q
Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q
return Zhat, Zdist return Zhat, Zdist
# def _psi1computations(self, Z, vp): @Cache_this(limit=1)
# mu, S = vp.mean, vp.variance
# l2 = lengthscale **2
# denom = S[:, None, :] / l2 + 1. # N,1,Q
# dist = Z[None, :, :] - mu[:, None, :] # N,M,Q
# dist_sq = np.square(dist) / l2 / denom # N,M,Q
# exponent = -0.5 * np.sum(dist_sq + np.log(denom), -1)#N,M
# psi1 = self.variance * np.exp(exponent) # N,M
# return denom, dist, dist_sq, psi1
def _psi1computations(variance, lengthscale, Z, mu, S, gamma): def _psi1computations(variance, lengthscale, Z, mu, S, gamma):
""" """
Z - MxQ Z - MxQ
@ -64,6 +57,7 @@ def _psi1computations(variance, lengthscale, Z, mu, S, gamma):
return _psi1, _dpsi1_dvariance, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _dpsi1_dZ, _dpsi1_dlengthscale return _psi1, _dpsi1_dvariance, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _dpsi1_dZ, _dpsi1_dlengthscale
@Cache_this(limit=1)
def _psi2computations(variance, lengthscale, Z, mu, S, gamma): def _psi2computations(variance, lengthscale, Z, mu, S, gamma):
""" """
Z - MxQ Z - MxQ