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Changes in FITC approximation computation

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This commit is contained in:
Ricardo Andrade 2013-04-05 17:27:44 +01:00
parent d42b731146
commit 22ad5d94e9
2 changed files with 19 additions and 19 deletions
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12
GPy/likelihoods/EP.py
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@ -302,10 +302,16 @@ class EP(likelihood):
mu = self.w + np.dot(P,self.gamma) mu = self.w + np.dot(P,self.gamma)
self.iterations += 1 self.iterations += 1
#Sigma recomptutation with Cholesky decompositon #Sigma recomptutation with Cholesky decompositon
Diag = Diag0/(1.+ Diag0 * self.tau_tilde) Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)
P = (Diag / Diag0)[:,None] * P0 Diag = Diag0 * Iplus_Dprod_i
P = Iplus_Dprod_i[:,None] * P0
#Diag = Diag0/(1.+ Diag0 * self.tau_tilde)
#P = (Diag / Diag0)[:,None] * P0
RPT0 = np.dot(R0,P0.T) RPT0 = np.dot(R0,P0.T)
L = jitchol(np.eye(M) + np.dot(RPT0,(1./Diag0 - Diag/(Diag0**2))[:,None]*RPT0.T)) L = jitchol(np.eye(M) + np.dot(RPT0,((1. - Iplus_Dprod_i)/Diag0)[:,None]*RPT0.T))
#L = jitchol(np.eye(M) + np.dot(RPT0,(1./Diag0 - Iplus_Dprod_i/Diag0)[:,None]*RPT0.T))
#L = jitchol(np.eye(M) + np.dot(RPT0,(1./Diag0 - Diag/(Diag0**2))[:,None]*RPT0.T))
R,info = linalg.lapack.flapack.dtrtrs(L,R0,lower=1) R,info = linalg.lapack.flapack.dtrtrs(L,R0,lower=1)
RPT = np.dot(R,P.T) RPT = np.dot(R,P.T)
Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1) Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)

26
GPy/models/generalized_FITC.py
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@ -76,11 +76,17 @@ class generalized_FITC(sparse_GP):
# Compute generalized FITC's diagonal term of the covariance # Compute generalized FITC's diagonal term of the covariance
self.Qnn = mdot(self.psi1.T,self.Kmmi,self.psi1) self.Qnn = mdot(self.psi1.T,self.Kmmi,self.psi1)
self.Diag0 = self.psi0 - np.diag(self.Qnn) self.Diag0 = self.psi0 - np.diag(self.Qnn)
self.Diag = self.Diag0/(1.+ self.Diag0 * self._precision.flatten()) Iplus_Dprod_i = 1./(1.+ self.Diag0 * self._precision.flatten())
self.Diag = self.Diag0 * Iplus_Dprod_i
#self.Diag = self.Diag0/(1.+ self.Diag0 * self._precision.flatten())
self.P = (self.Diag / self.Diag0)[:,None] * self.psi1.T
self.P = Iplus_Dprod_i[:,None] * self.psi1.T
#self.P = (self.Diag / self.Diag0)[:,None] * self.psi1.T
self.RPT0 = np.dot(self.Lmi,self.psi1) self.RPT0 = np.dot(self.Lmi,self.psi1)
self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,(1./self.Diag0 - self.Diag/(self.Diag0**2))[:,None]*self.RPT0.T)) self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,((1. - Iplus_Dprod_i)/self.Diag0)[:,None]*self.RPT0.T))
#self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,(1./self.Diag0 - Iplus_Dprod_i/self.Diag0)[:,None]*self.RPT0.T))
#self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,(1./self.Diag0 - self.Diag/(self.Diag0**2))[:,None]*self.RPT0.T))
self.R,info = linalg.flapack.dtrtrs(self.L,self.Lmi,lower=1) self.R,info = linalg.flapack.dtrtrs(self.L,self.Lmi,lower=1)
self.RPT = np.dot(self.R,self.P.T) self.RPT = np.dot(self.R,self.P.T)
self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT) self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT)
@ -89,7 +95,7 @@ class generalized_FITC(sparse_GP):
self.mu = self.w + np.dot(self.P,self.gamma) self.mu = self.w + np.dot(self.P,self.gamma)
# Remove extra term from dL_dpsi1 # Remove extra term from dL_dpsi1
self.dL_dpsi1 += -mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB self.dL_dpsi1 -= mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB
else: else:
raise NotImplementedError, "homoscedastic fitc not implemented" raise NotImplementedError, "homoscedastic fitc not implemented"
# Remove extra term from dL_dpsi1 # Remove extra term from dL_dpsi1
@ -180,18 +186,6 @@ class generalized_FITC(sparse_GP):
var_ = Kxx_ + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T)) var_ = Kxx_ + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T))
var = (Kxx + np.sum(KR0T.T*np.dot(Sigma_H - np.eye(self.M),KR0T.T),0))[:,None] var = (Kxx + np.sum(KR0T.T*np.dot(Sigma_H - np.eye(self.M),KR0T.T),0))[:,None]
return mu_star[:,None],var return mu_star[:,None],var
"""
Kx = self.kern.K(self.Z, Xnew)
mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)
if full_cov:
Kxx = self.kern.K(Xnew)
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE this won't work for plotting
else:
Kxx = self.kern.Kdiag(Xnew)
var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.C/self.scale_factor**2, Kx),0)
a = kjk
return mu,var[:,None]
"""
else: else:
raise NotImplementedError, "homoscedastic fitc not implemented" raise NotImplementedError, "homoscedastic fitc not implemented"
""" """