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Ricardo
parent
14b449bcfd
commit
6d65e2adf8
1 changed files with 100 additions and 19 deletions
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@ -24,11 +24,18 @@ class FITC(sparse_GP):
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#ERASEME
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#N = likelihood.Y.size
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#self.beta_star = np.random.rand(N,1)
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#self.Kmm_ = kernel.K(self.Z).copy()
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#self.Kmmi_,a,b,c = pdinv(self.Kmm_)
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#self.psi1_ = kernel.K(self.Z,X).copy()
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N = likelihood.Y.size
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self.beta_star = np.random.rand(N,1)
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self.Kmm_ = kernel.K(self.Z).copy()
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self.Kmmi_,a,b,c = pdinv(self.Kmm_)
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self.psi1_ = kernel.K(self.Z,X).copy()
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Haux = np.random.rand(self.M,self.M)
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self.matA = np.dot(Haux,Haux.T) + np.eye(self.M)*100.
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self.matV = np.random.rand(N,1)
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self.H_ = np.dot(Haux,Haux.T) + np.eye(self.M)*3.
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self.Hi_, l1,l2,l3 = pdinv(self.H_)
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#self.V_star_ = np.random.rand(N,1)
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sparse_GP.__init__(self, X, likelihood, kernel=kernel, Z=self.Z, X_variance=None, normalize_X=False)
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@ -63,28 +70,28 @@ class FITC(sparse_GP):
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self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.M),lower=1)
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Lmipsi1 = np.dot(self.Lmi,self.psi1)
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self.Qnn = np.dot(Lmipsi1.T,Lmipsi1)
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self.Qnn = np.dot(Lmipsi1.T,Lmipsi1).copy()
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self.Diag0 = self.psi0 - np.diag(self.Qnn)
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#TODO eraseme
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"""
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self.psi1 = self.psi1_
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self.Lm = jitchol(self.Kmm_)
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self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.M),lower=1)
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Lmipsi1 = np.dot(self.Lmi,self.psi1)
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#self.true_psi1 = self.kern.K(self.Z,self.X)
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self.true_psi1 = self.kern.K(self.Z,self.X)
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#self.Qnn = mdot(self.true_psi1.T,self.Lmi.T,self.Lmi,self.true_psi1)
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self.Kmmi, a,b,c = pdinv(self.Kmm)
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self.Qnn = mdot(self.psi1.T,self.Kmmi,self.psi1)
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#self.Kmmi, a,b,c = pdinv(self.Kmm)
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#self.Qnn = mdot(self.psi1.T,self.Kmmi,self.psi1)
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#self.Diag0 = self.psi0 #- np.diag(self.Qnn)
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self.Diag0 = - np.diag(self.Qnn)
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#self.Diag0 = - np.diag(self.Qnn)
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#Kmmi,Lm,Lmi,logdetK = pdinv(self.Kmm)
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#self.Lambda = self.Kmmi_ + mdot(self.Kmmi_,self.psi1_,self.beta_star*self.psi1_.T,self.Kmmi_) + np.eye(self.M)*100
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#self.Lambdai, LLm, LLmi, self.logdetLambda = pdinv(self.Lambda)
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"""
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#TODO uncomment
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self.beta_star = self.likelihood.precision/(1. + self.likelihood.precision*self.Diag0[:,None]) #Includes Diag0 in the precision
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#self.beta_star = self.likelihood.precision/(1. + self.likelihood.precision*self.Diag0[:,None]) #Includes Diag0 in the precision
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self.true_beta_star = self.likelihood.precision/(1. + self.likelihood.precision*self.Diag0[:,None]) #Includes Diag0 in the precision
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self.true_V_star = self.true_beta_star * self.likelihood.Y
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self.V_star = self.beta_star * self.likelihood.Y
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# The rather complex computations of self.A
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@ -138,7 +145,6 @@ class FITC(sparse_GP):
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self.dyby_dtheta = dyby_dtheta
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# dlogB_dtheta : C
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#C_B
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dC_B = -.5*Kmmi
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C_B = self.kern.dK_dtheta(dC_B,self.Z) #check
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@ -176,6 +182,77 @@ class FITC(sparse_GP):
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self.dlogB_dtheta = C_B + C_AA + C_ABA + C_ABB + C_ABCA + C_ABCB + C_ABCC
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# dD_dtheta
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#FIXME
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H = self.Kmm + mdot(self.psi1,self.beta_star*self.psi1.T)
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H = self.Kmm_ + mdot(self.psi1,self.beta_star*self.psi1.T)
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H = self.Kmm_ + mdot(self.psi1,self.true_beta_star*self.psi1.T)
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Hi, LH, LHi, logdetH = pdinv(H)
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self.q1 = .5* mdot(self.V_star.T,self.true_psi1.T,Hi,self.true_psi1,self.V_star)
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self.q1 = .5* mdot(self.true_V_star.T,self.psi1.T,Hi,self.psi1,self.true_V_star)
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#self.q1 = .5* mdot(self.true_V_star.T,self.true_psi1.T,Hi,self.true_psi1,self.true_V_star)
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#self.q2 = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
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# D_B
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gamma_1 = mdot(VVT,self.psi1.T,Hi) #TODO restore
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#gamma_1 = mdot(VVT,self.true_psi1.T,Hi)
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dD_B = gamma_1
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D_B = self.kern.dK_dtheta(dD_B,self.X,self.Z) #check
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# D_C
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#dD_CA = -.5 * mdot(Hi,self.psi1,gamma_1) #TODO restore
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dD_CA = -.5 * mdot(Hi,self.true_psi1,gamma_1)
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D_CA = self.kern.dK_dtheta(dD_CA,self.Z) #check
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# D_CB
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dD_CBA = - mdot(psi1beta.T,Hi,self.psi1,gamma_1)
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D_CBA = self.kern.dK_dtheta(dD_CBA,self.X,self.Z)
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# D_CBB
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pHip = mdot(self.psi1.T,Hi,self.psi1)
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gamma_2 = mdot(self.beta_star*pHip,self.V_star)
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D_CBBA = .5 * self.kern.dKdiag_dtheta(gamma_2**2,self.X)
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D_CBBB = 0
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for psi1_n,gamma_n,X_n in zip(self.psi1.T,gamma_2,self.X):
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dD_CBBB = - gamma_n**2 * np.dot(psi1_n[None,:],Kmmi)
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D_CBBB += self.kern.dK_dtheta(dD_CBBB,X_n[:,None],self.Z)
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D_CBBC = 0
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for psi1_n,gamma_n,X_n in zip(self.psi1.T,gamma_2,self.X):
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psin_K = np.dot(psi1_n[None,:],Kmmi)
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tmp = np.dot(psin_K.T,psin_K)
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dD_CBBC = .5*gamma_n**2 * tmp
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D_CBBC += self.kern.dK_dtheta(dD_CBBC,self.Z)
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# D_A
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pHip = mdot(self.psi1.T,Hi,self.psi1) #TODO remove defined above
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gamma_3 = self.true_V_star * mdot(self.true_V_star.T,pHip*self.true_beta_star).T
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#gamma_3 = self.V_star * mdot(self.V_star.T,pHip*self.beta_star).T
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dD_AA = - gamma_3
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D_AA = self.kern.dKdiag_dtheta(dD_AA,self.X) #check
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D_AB = 0
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#for psi1_n,gamma_n,X_n in zip(self.psi1.T,gamma_3,self.X):
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for psi1_n,gamma_n,X_n in zip(self.true_psi1.T,gamma_3,self.X):
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dD_AB = 2. * gamma_n * np.dot(psi1_n[None,:],Kmmi)
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D_AB += self.kern.dK_dtheta(dD_AB,X_n[:,None],self.Z) #check
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D_AC = 0
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#for psi1_n,gamma_n,X_n in zip(self.psi1.T,gamma_3,self.X):
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for psi1_n,gamma_n,X_n in zip(self.true_psi1.T,gamma_3,self.X):
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psin_K = np.dot(psi1_n[None,:],Kmmi)
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tmp = np.dot(psin_K.T,psin_K)
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dD_AC = - gamma_n * tmp
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D_AC += self.kern.dK_dtheta(dD_AC,self.Z) #check
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#self.dD_dtheta = D_AA + D_AB + D_AC + D_B + D_CA + D_CBA + D_CBBA + D_CBBB + D_CBBC
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self.dD_dtheta = D_AA + D_AB + D_AC
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#self.q1 = .5* mdot(self.V_star_.T,self.true_psi1.T,self.Hi_,self.true_psi1,self.V_star_)
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# the partial derivative vector for the likelihood
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if self.likelihood.Nparams == 0:
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@ -194,8 +271,10 @@ class FITC(sparse_GP):
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def log_likelihood(self):
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""" Compute the (lower bound on the) log marginal likelihood """
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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)
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C = -self.D * (np.sum(np.log(np.diag(self.LB))))
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#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)
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#C = -self.D * (np.sum(np.log(np.diag(self.LB))))
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#D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
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D = self.q1
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"""
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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)
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#B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
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@ -203,7 +282,8 @@ class FITC(sparse_GP):
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D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
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return A + C + D # +B
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"""
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return A+C
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#return A+C
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return D
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def _log_likelihood_gradients(self):
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@ -214,8 +294,9 @@ class FITC(sparse_GP):
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#dL_dtheta = self.dlogB_dtheta
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#dL_dtheta = self.dyby_dtheta
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#dL_dtheta = self.dlogbeta_dtheta + self.dyby_dtheta
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dL_dtheta = self.dlogB_dtheta
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dL_dtheta = self.dlogbeta_dtheta + self.dyby_dtheta + self.dlogB_dtheta
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#dL_dtheta = self.dlogbeta_dtheta + self.dyby_dtheta + self.dlogB_dtheta
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dL_dtheta = self.dD_dtheta
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"""
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dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
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if self.has_uncertain_inputs:
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