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chol_inv added somewhere

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Ricardo 2013-05-15 18:12:50 +01:00
parent d0942a7bfd
commit df9700c412
1 changed files with 5 additions and 25 deletions
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30
GPy/models/FITC.py
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@ -3,8 +3,7 @@
import numpy as np
import pylab as pb
from ..util.linalg import mdot, jitchol, tdot, symmetrify,pdinv
#from ..util.linalg import mdot, jitchol, chol_inv, pdinv, trace_dot
from ..util.linalg import mdot, jitchol, chol_inv, tdot, symmetrify,pdinv
from ..util.plot import gpplot
from .. import kern
from scipy import stats, linalg
@ -57,7 +56,7 @@ class FITC(sparse_GP):
# 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.LBi = chol_inv(self.LB)
self.psi1V = np.dot(self.psi1, self.V_star)
Lmi_psi1V, info = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
@ -75,7 +74,7 @@ class FITC(sparse_GP):
psi1beta = self.psi1*self.beta_star.T
H = self.Kmm + mdot(self.psi1,psi1beta.T)
LH = jitchol(H)
LHi,info = linalg.lapack.flapack.dtrtrs(LH,np.asfortranarray(np.eye(self.M)),lower=1,trans=0)
LHi = chol_inv(LH)
Hi = np.dot(LHi.T,LHi)
betapsi1TLmiLBi = np.dot(psi1beta.T,LBiLmi.T)
@ -109,10 +108,10 @@ class FITC(sparse_GP):
for i,V_n,alpha_n,gamma_n,gamma_k in zip(range(self.N),self.V_star,alpha,gamma_2,gamma_3):
K_pp_K = np.dot(Kmmipsi1[:,i:(i+1)],Kmmipsi1[:,i:(i+1)].T)
#_dpsi1 = dA_dpsi1: yT*beta_star*y + Diag_dC_dpsi1 +Diag_dD_dpsi1
#Diag_dpsi1 = Diag_dA_dpsi1: yT*beta_star*y + Diag_dC_dpsi1 +Diag_dD_dpsi1
_dpsi1 = (-V_n**2 - alpha_n + 2.*gamma_k - gamma_n**2) * Kmmipsi1.T[i:(i+1),:]
#_dKmm = dA_dKmm: yT*beta_star*y +Diag_dC_dKmm +Diag_dD_dKmm
#Diag_dKmm = Diag_dA_dKmm: yT*beta_star*y +Diag_dC_dKmm +Diag_dD_dKmm
_dKmm = .5*(V_n**2 + alpha_n + gamma_n**2 - 2.*gamma_k) * K_pp_K #Diag_dD_dKmm
self._dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1,self.X[i:i+1,:],self.Z)
@ -121,25 +120,6 @@ class FITC(sparse_GP):
self._dKmm_dX += 2.*self.kern.dK_dX(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,self.X[i:i+1,:])
"""
for psi1_n,V_n,X_n,alpha_n,gamma_n,gamma_k in zip(self.psi1.T,self.V_star,self.X,alpha,gamma_2,gamma_3):
psin_K = np.dot(psi1_n[None,:],Kmmi)
K_pp_K = np.dot(psin_K.T,psin_K)
#_dpsi1 = dA_dpsi1: yT*beta_star*y + Diag_dC_dpsi1 +Diag_dD_dpsi1
_dpsi1 = (-V_n**2 - alpha_n + 2.*gamma_k - gamma_n**2) * psin_K
#_dKmm = dA_dKmm: yT*beta_star*y +Diag_dC_dKmm +Diag_dD_dKmm
_dKmm = .5*(V_n**2 + alpha_n + gamma_n**2 - 2.*gamma_k) * K_pp_K #Diag_dD_dKmm
self._dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1,X_n[None,:],self.Z)
self._dKmm_dtheta += self.kern.dK_dtheta(_dKmm,self.Z)
self._dKmm_dX += 2.*self.kern.dK_dX(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,X_n[None,:])
"""
# the partial derivative vector for the likelihood
if self.likelihood.Nparams == 0:
# save computation here.