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changes to FITC

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This commit is contained in:
Ricardo 2013-05-09 19:04:31 +01:00
parent 3aca6e4683
commit 00230d7b02
2 changed files with 23 additions and 15 deletions
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37
GPy/models/FITC.py
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@ -57,7 +57,6 @@ class FITC(sparse_GP):
self.Diag0 = self.psi0 - np.diag(self.Qnn)
self.beta_star = self.likelihood.precision/(1. + self.likelihood.precision*self.Diag0[:,None]) #Includes Diag0 in the precision
self.V_star = self.beta_star * self.likelihood.Y
@ -72,12 +71,10 @@ class FITC(sparse_GP):
self.A = tdot(tmp)
# factor B
self.B = np.eye(self.M) + self.A
self.LB = jitchol(self.B)
self.LBi,info = linalg.lapack.flapack.dtrtrs(self.LB,np.eye(self.M),lower=1)
self.psi1V = np.dot(self.psi1, self.V_star)
@ -93,10 +90,8 @@ class FITC(sparse_GP):
dlogB_dpsi0 = -.5*self.kern.dKdiag_dtheta(self.beta_star,X=self.X)
dlogB_dpsi1 = self.kern.dK_dtheta(b_psi1_Ki,self.X,self.Z)
dlogB_dKmm = -.5*self.kern.dK_dtheta(Ki_pbp_Ki,X=self.Z)
self.dlogB_dtheta = dlogB_dpsi0 + dlogB_dpsi1 + dlogB_dKmm
# dyby_dtheta
Kmmi = np.dot(self.Lmi.T,self.Lmi)
VVT = np.outer(self.V_star,self.V_star)
@ -116,11 +111,22 @@ class FITC(sparse_GP):
tmp = np.dot(psin_K.T,psin_K)
dyby_dKmm = .5*V_n**2 * tmp
dyby_dtheta += self.kern.dK_dtheta(dyby_dKmm,self.Z)
#self.dyby_dtheta = dyby_dpsi0 #+ dyby_dpsi1 + dyby_dKmm
self.dyby_dtheta = dyby_dtheta
# dlogB_dtheta
#C_B
dlogKi_dKmm = -.5 * Kmmi
dlogB_dtheta = self.kern.dK_dtheta(dlogKi_dKmm,self.Z)
#C_A
#C_AA
LBiLmi = np.dot(self.LBi,self.Lmi)
partial = .5*np.dot(LBiLmi.T,LBiLmi)
dlogB_dtheta += self.kern.dK_dtheta(partial,self.Z)
#C_AB
# the partial derivative vector for the likelihood
@ -131,7 +137,7 @@ class FITC(sparse_GP):
raise NotImplementedError, "heteroscedatic derivates not implemented"
else:
# likelihood is not heterscedatic
self.partial_for_likelihood = 0
self.partial_for_likelihood = 0 #FIXME
#self.partial_for_likelihood = -0.5 * self.N * self.D * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
#self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
#self.partial_for_likelihood += self.likelihood.precision * (0.5 * np.sum(self.A * self.DBi_plus_BiPBi) - np.sum(np.square(self._LBi_Lmi_psi1V)))
@ -186,8 +192,8 @@ class FITC(sparse_GP):
"""
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
#A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star))
A = - 0.5 * np.sum(self.V_star * self.likelihood.Y)
#A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
C = -self.D * (np.sum(np.log(np.diag(self.LB))))
"""
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
#B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
@ -195,7 +201,7 @@ class FITC(sparse_GP):
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + C + D # +B
"""
return A
return C
def _log_likelihood_gradients(self):
@ -203,8 +209,9 @@ class FITC(sparse_GP):
return np.hstack((self.dL_dZ().flatten(), self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))
def dL_dtheta(self):
#dL_dtheta = self.dlogB_dtheta
dL_dtheta = self.dyby_dtheta
dL_dtheta = self.dlogB_dtheta
#dL_dtheta = self.dyby_dtheta
dL_dtheta = self.dlogB_dtheta + self.dyby_dtheta
"""
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
if self.has_uncertain_inputs:

1
GPy/models/__init__.py
View file

@ -12,3 +12,4 @@ from sparse_GPLVM import sparse_GPLVM
from Bayesian_GPLVM import Bayesian_GPLVM
from mrd import MRD
from generalized_FITC import generalized_FITC
from FITC import FITC