From 4e827e913d2dc7e7187ce7b5be435f3c6b959cba Mon Sep 17 00:00:00 2001 From: Ricardo Date: Wed, 15 May 2013 02:01:08 +0100 Subject: [PATCH] Some changes --- GPy/models/FITC.py | 104 +++++++++++++-------------------------------- 1 file changed, 30 insertions(+), 74 deletions(-) diff --git a/GPy/models/FITC.py b/GPy/models/FITC.py index 95cf1352..c02f470e 100644 --- a/GPy/models/FITC.py +++ b/GPy/models/FITC.py @@ -62,8 +62,9 @@ class FITC(sparse_GP): self.psi1V = np.dot(self.psi1, self.V_star) # back substutue C into psi1V - tmp, info1 = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0) - self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(tmp), lower=1, trans=0) + Lmi_psi1V, info1 = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0) + self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(Lmi_psi1V), lower=1, trans=0) + Kmmipsi1 = np.dot(self.Lmi.T,Lmipsi1) b_psi1_Ki = self.beta_star * Kmmipsi1.T @@ -76,100 +77,50 @@ class FITC(sparse_GP): VV_p_Ki = np.dot(VVT,Kmmipsi1.T) Ki_pVVp_Ki = np.dot(Kmmipsi1,VV_p_Ki) psi1beta = self.psi1*self.beta_star.T - H = self.Kmm + mdot(self.psi1,self.beta_star*self.psi1.T) + H = self.Kmm + mdot(self.psi1,psi1beta.T) Hi, LH, LHi, logdetH = pdinv(H) betapsi1TLmiLBi = np.dot(psi1beta.T,LBiLmi.T) alpha = np.array([np.dot(a.T,a) for a in betapsi1TLmiLBi])[:,None] gamma_1 = mdot(VVT,self.psi1.T,Hi) pHip = mdot(self.psi1.T,Hi,self.psi1) gamma_2 = mdot(self.beta_star*pHip,self.V_star) - gamma_3 = self.V_star * mdot(self.V_star.T,pHip*self.beta_star).T + #gamma_3 = self.V_star * mdot(self.V_star.T,pHip*self.beta_star).T + gamma_3 = self.V_star * gamma_2 - - self._dL_dpsi0 = .5 * self.V_star**2 #dA_psi0???? - self._dL_dpsi0 += -0.5 * self.beta_star#dA_dpsi0 + self._dL_dpsi0 = -0.5 * self.beta_star#dA_dpsi0: logdet(self.beta_star) + self._dL_dpsi0 += .5 * self.V_star**2 #dA_psi0: yT*beta_star*y self._dL_dpsi0 += .5 *alpha #dC_dpsi0 self._dL_dpsi0 += 0.5*mdot(self.beta_star*pHip,self.V_star)**2 - self.V_star * mdot(self.V_star.T,pHip*self.beta_star).T #dD_dpsi0 - self._dL_dpsi1 = b_psi1_Ki.copy() #dA_dpsi1 + self._dL_dpsi1 = b_psi1_Ki.copy() #dA_dpsi1: logdet(self.beta_star) self._dL_dpsi1 += -np.dot(psi1beta.T,LBL_inv) #dC_dpsi1 self._dL_dpsi1 += gamma_1 - mdot(psi1beta.T,Hi,self.psi1,gamma_1) #dD_dpsi1 - self._dL_dKmm = -0.5 * np.dot(Kmmipsi1,b_psi1_Ki) #dA_dKmm + self._dL_dKmm = -0.5 * np.dot(Kmmipsi1,b_psi1_Ki) #dA_dKmm: logdet(self.beta_star) self._dL_dKmm += -.5*Kmmi + .5*LBL_inv + mdot(LBL_inv,psi1beta,Kmmipsi1.T) #dC_dKmm self._dL_dKmm += -.5 * mdot(Hi,self.psi1,gamma_1) #dD_dKmm - _dpsi1_dtheta = 0 - _dpsi1_dX = 0 - _dKmm_dtheta = 0 - _dKmm_dX = 0 + self._dpsi1_dtheta = 0 + self._dpsi1_dX = 0 + self._dKmm_dtheta = 0 + self._dKmm_dX = 0 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) - _dpsi1 = -V_n**2 * psin_K #Diag_dA_dpsi1 + _dpsi1 = -V_n**2 * psin_K #dA_dpsi1: yT*beta_star*y _dpsi1 += - alpha_n * psin_K #Diag_dC_dpsi1 _dpsi1 += - gamma_n**2 * psin_K + 2. * gamma_k * psin_K #Diag_dD_dpsi1 - _dKmm = .5*V_n**2 * np.dot(psin_K.T,psin_K) #Diag_dA_dKmm + _dKmm = .5*V_n**2 * np.dot(psin_K.T,psin_K) #dA_dKmm: yT*beta_star*y _dKmm += .5 * alpha_n * np.dot(psin_K.T,psin_K) #Diag_dC_dKmm - _dKmm += .5*gamma_n**2 * np.dot(psin_K.T,psin_K) - gamma_n * np.dot(psin_K.T,psin_K) #Diag_dD_dKmm + _dKmm += .5*gamma_n**2 * np.dot(psin_K.T,psin_K) - gamma_k * np.dot(psin_K.T,psin_K) #Diag_dD_dKmm - _dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1,X_n[None,:],self.Z) - _dKmm_dtheta += self.kern.dK_dtheta(_dKmm,self.Z) - - _dKmm_dX += 2.*self.kern.dK_dX(_dKmm ,self.Z) - _dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,X_n[None,:]) - - - #_dA_dpsi1 = -V_n**2 * np.dot(psi1_n[None,:],Kmmi) - #_dC_dpsi1 = - alpha_n * np.dot(psi1_n[None,:],Kmmi) - #_dD_dpsi1_1 = - gamma_n**2 * np.dot(psi1_n[None,:],Kmmi) - #_dD_dpsi1_2 = 2. * gamma_k * np.dot(psi1_n[None,:],Kmmi) - - - #dA_dpsi1_theta += self.kern.dK_dtheta(_dA_dpsi1,X_n[None,:],self.Z) - #_dC_dpsi1_dtheta += self.kern.dK_dtheta(_dC_dpsi1,X_n[None,:],self.Z) - #_dD_dpsi1_dtheta_1 += self.kern.dK_dtheta(_dD_dpsi1_1,X_n[None,:],self.Z) - #_dD_dpsi1_dtheta_2 += self.kern.dK_dtheta(_dD_dpsi1_2,X_n[None,:],self.Z) - - - #dA_dpsi1_X += self.kern.dK_dX(_dA_dpsi1.T,self.Z,X_n[None,:]) - #_dC_dpsi1_dX += self.kern.dK_dX(_dC_dpsi1.T,self.Z,X_n[None,:]) - #_dD_dpsi1_dX_1 += self.kern.dK_dX(_dD_dpsi1_1.T,self.Z,X_n[None,:]) - #_dD_dpsi1_dX_2 += self.kern.dK_dX(_dD_dpsi1_2.T,self.Z,X_n[None,:]) - - - - #_dA_dKmm = .5*V_n**2 * np.dot(psin_K.T,psin_K) - #_dC_dKmm = .5 * alpha_n * np.dot(psin_K.T,psin_K) - #_dD_dKmm_1 = .5*gamma_n**2 * np.dot(psin_K.T,psin_K) - #_dD_dKmm_2 = - gamma_n * np.dot(psin_K.T,psin_K) - - - #dA_dKmm_theta += self.kern.dK_dtheta(_dA_dKmm,self.Z) - #_dC_dKmm_dtheta += self.kern.dK_dtheta(_dC_dKmm,self.Z) - #_dD_dKmm_dtheta_1 += self.kern.dK_dtheta(_dD_dKmm_1,self.Z) - #_dD_dKmm_dtheta_2 += self.kern.dK_dtheta(_dD_dKmm_2,self.Z) - - - #dA_dKmm_X += 2.*self.kern.dK_dX(_dA_dKmm,self.Z) - #_dC_dKmm_dX += 2.*self.kern.dK_dX(_dC_dKmm,self.Z) - #_dD_dKmm_dX_1 += 2.*self.kern.dK_dX(_dD_dKmm_1,self.Z) - #_dD_dKmm_dX_2 += 2.*self.kern.dK_dX(_dD_dKmm_2,self.Z) - - - self._dL_dtheta = self.kern.dKdiag_dtheta(self._dL_dpsi0,self.X) - self._dL_dtheta += self.kern.dK_dtheta(self._dL_dpsi1,self.X,self.Z) - self._dL_dtheta += self.kern.dK_dtheta(self._dL_dKmm,X=self.Z) - self._dL_dtheta += _dKmm_dtheta - self._dL_dtheta += _dpsi1_dtheta - - self._dL_dX = self.kern.dK_dX(self._dL_dpsi1.T,self.Z,self.X) - self._dL_dX += 2. * self.kern.dK_dX(self._dL_dKmm,X=self.Z) - self._dL_dX += _dpsi1_dX - self._dL_dX += _dKmm_dX + 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 @@ -217,16 +168,21 @@ class FITC(sparse_GP): if self.has_uncertain_inputs: raise NotImplementedError, "FITC approximation not implemented for uncertain inputs" else: - #dL_dtheta = self.dA_dtheta + self.dlogB_dtheta + self.dD_dtheta - dL_dtheta = self._dL_dtheta #FIXME + dL_dtheta = self.kern.dKdiag_dtheta(self._dL_dpsi0,self.X) + dL_dtheta += self.kern.dK_dtheta(self._dL_dpsi1,self.X,self.Z) + dL_dtheta += self.kern.dK_dtheta(self._dL_dKmm,X=self.Z) + dL_dtheta += self._dKmm_dtheta + dL_dtheta += self._dpsi1_dtheta return dL_dtheta def dL_dZ(self): if self.has_uncertain_inputs: raise NotImplementedError, "FITC approximation not implemented for uncertain inputs" else: - #dL_dZ = self.dA_dX + self.dlogB_dX + self.dD_dX - dL_dZ = self._dL_dX #FIXME + dL_dZ = self.kern.dK_dX(self._dL_dpsi1.T,self.Z,self.X) + dL_dZ += 2. * self.kern.dK_dX(self._dL_dKmm,X=self.Z) + dL_dZ += self._dpsi1_dX + dL_dZ += self._dKmm_dX return dL_dZ def _raw_predict(self, Xnew, which_parts, full_cov=False):