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EPEM is running.
This commit is contained in:
Ricardo Andrade
parent
f941d629e6
commit
0a8686d7c0
5 changed files with 117 additions and 112 deletions
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@ -65,8 +65,8 @@ class EP(likelihood):
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self.tau_tilde = np.zeros(self.N)
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self.v_tilde = np.zeros(self.N)
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#Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
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self.mu = np.zeros(self.N)
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self.Sigma = K.copy()
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mu = np.zeros(self.N)
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Sigma = K.copy()
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"""
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Initial values - Cavity distribution parameters:
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@ -94,29 +94,27 @@ class EP(likelihood):
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update_order = np.random.permutation(self.N)
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for i in update_order:
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#Cavity distribution parameters
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self.tau_[i] = 1./self.Sigma[i,i] - self.eta*self.tau_tilde[i]
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self.v_[i] = self.mu[i]/self.Sigma[i,i] - self.eta*self.v_tilde[i]
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print 1./self.Sigma[i,i],self.tau_tilde[i]
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self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i]
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self.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i]
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#Marginal moments
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self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self.data[i],self.tau_[i],self.v_[i])
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#Site parameters update
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Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./self.Sigma[i,i])
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Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - self.mu[i]/self.Sigma[i,i])
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Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
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Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
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self.tau_tilde[i] = self.tau_tilde[i] + Delta_tau
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self.v_tilde[i] = self.v_tilde[i] + Delta_v
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#Posterior distribution parameters update
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si=self.Sigma[:,i].reshape(self.N,1)
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self.Sigma = self.Sigma - Delta_tau/(1.+ Delta_tau*self.Sigma[i,i])*np.dot(si,si.T)
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self.mu = np.dot(self.Sigma,self.v_tilde)
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si=Sigma[:,i].reshape(self.N,1)
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Sigma = Sigma - Delta_tau/(1.+ Delta_tau*Sigma[i,i])*np.dot(si,si.T)
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mu = np.dot(Sigma,self.v_tilde)
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self.iterations += 1
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print self.tau_tilde[i]
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#Sigma recomptutation with Cholesky decompositon
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Sroot_tilde_K = np.sqrt(self.tau_tilde)[:,None]*K
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B = np.eye(self.N) + np.sqrt(self.tau_tilde)[None,:]*Sroot_tilde_K
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L = jitchol(B)
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V,info = linalg.flapack.dtrtrs(L,Sroot_tilde_K,lower=1)
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self.Sigma = K - np.dot(V.T,V)
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self.mu = np.dot(self.Sigma,self.v_tilde)
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Sigma = K - np.dot(V.T,V)
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mu = np.dot(Sigma,self.v_tilde)
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epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N
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epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
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self.np1.append(self.tau_tilde.copy())
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@ -139,7 +137,7 @@ class EP(likelihood):
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"""
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Kmmi, Lm, Lmi, Kmm_logdet = pdinv(Kmm)
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KmnKnm = np.dot(Kmn, Kmn.T)
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KmmiKmn = np.dot(Kmmi,self.Kmn)
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KmmiKmn = np.dot(Kmmi,Kmn)
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Qnn_diag = np.sum(Kmn*KmmiKmn,-2)
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LLT0 = Kmm.copy()
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@ -221,13 +219,13 @@ class EP(likelihood):
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q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)
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Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn
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"""
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self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
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self.P0 = self.Kmn.T
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self.KmnKnm = np.dot(self.P0.T, self.P0)
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self.KmmiKmn = np.dot(self.Kmmi,self.P0.T)
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self.Qnn_diag = np.sum(self.P0.T*self.KmmiKmn,-2)
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self.Diag0 = self.Knn_diag - self.Qnn_diag
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self.R0 = jitchol(self.Kmmi).T
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Kmmi, self.Lm, self.Lmi, Kmm_logdet = pdinv(Kmm)
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P0 = Kmn.T
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KmnKnm = np.dot(P0.T, P0)
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KmmiKmn = np.dot(Kmmi,P0.T)
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Qnn_diag = np.sum(P0.T*KmmiKmn,-2)
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Diag0 = Knn_diag - Qnn_diag
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R0 = jitchol(Kmmi).T
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"""
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Posterior approximation: q(f|y) = N(f| mu, Sigma)
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@ -236,11 +234,11 @@ class EP(likelihood):
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"""
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self.w = np.zeros(self.N)
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self.gamma = np.zeros(self.M)
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self.mu = np.zeros(self.N)
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self.P = self.P0.copy()
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self.R = self.R0.copy()
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self.Diag = self.Diag0.copy()
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self.Sigma_diag = self.Knn_diag
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mu = np.zeros(self.N)
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P = P0.copy()
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R = R0.copy()
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Diag = Diag0.copy()
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Sigma_diag = Knn_diag
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"""
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Initial values - Cavity distribution parameters:
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@ -268,41 +266,41 @@ class EP(likelihood):
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update_order = np.random.permutation(self.N)
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for i in update_order:
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#Cavity distribution parameters
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self.tau_[i] = 1./self.Sigma_diag[i] - self.eta*self.tau_tilde[i]
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self.v_[i] = self.mu[i]/self.Sigma_diag[i] - self.eta*self.v_tilde[i]
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self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
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self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
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#Marginal moments
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self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(data[i],self.tau_[i],self.v_[i])
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#Site parameters update
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Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./self.Sigma_diag[i])
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Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - self.mu[i]/self.Sigma_diag[i])
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Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
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Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])
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self.tau_tilde[i] = self.tau_tilde[i] + Delta_tau
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self.v_tilde[i] = self.v_tilde[i] + Delta_v
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#Posterior distribution parameters update
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dtd1 = Delta_tau*self.Diag[i] + 1.
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dii = self.Diag[i]
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self.Diag[i] = dii - (Delta_tau * dii**2.)/dtd1
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pi_ = self.P[i,:].reshape(1,self.M)
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self.P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_
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Rp_i = np.dot(self.R,pi_.T)
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RTR = np.dot(self.R.T,np.dot(np.eye(self.M) - Delta_tau/(1.+Delta_tau*self.Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),self.R))
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self.R = jitchol(RTR).T
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dtd1 = Delta_tau*Diag[i] + 1.
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dii = Diag[i]
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Diag[i] = dii - (Delta_tau * dii**2.)/dtd1
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pi_ = P[i,:].reshape(1,self.M)
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P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_
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Rp_i = np.dot(R,pi_.T)
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RTR = np.dot(R.T,np.dot(np.eye(self.M) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))
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R = jitchol(RTR).T
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self.w[i] = self.w[i] + (Delta_v - Delta_tau*self.w[i])*dii/dtd1
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self.gamma = self.gamma + (Delta_v - Delta_tau*self.mu[i])*np.dot(RTR,self.P[i,:].T)
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self.RPT = np.dot(self.R,self.P.T)
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self.Sigma_diag = self.Diag + np.sum(self.RPT.T*self.RPT.T,-1)
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self.mu = self.w + np.dot(self.P,self.gamma)
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self.gamma = self.gamma + (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)
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RPT = np.dot(R,P.T)
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Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)
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mu = self.w + np.dot(P,self.gamma)
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self.iterations += 1
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#Sigma recomptutation with Cholesky decompositon
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self.Diag = self.Diag0/(1.+ self.Diag0 * self.tau_tilde)
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self.P = (self.Diag / self.Diag0)[:,None] * self.P0
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self.RPT0 = np.dot(self.R0,self.P0.T)
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L = jitchol(np.eye(self.M) + np.dot(self.RPT0,(1./self.Diag0 - self.Diag/(self.Diag0**2))[:,None]*self.RPT0.T))
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self.R,info = linalg.flapack.dtrtrs(L,self.R0,lower=1)
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self.RPT = np.dot(self.R,self.P.T)
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self.Sigma_diag = self.Diag + np.sum(self.RPT.T*self.RPT.T,-1)
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self.w = self.Diag * self.v_tilde
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self.gamma = np.dot(self.R.T, np.dot(self.RPT,self.v_tilde))
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self.mu = self.w + np.dot(self.P,self.gamma)
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Diag = Diag0/(1.+ Diag0 * self.tau_tilde)
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P = (Diag / Diag0)[:,None] * P0
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RPT0 = np.dot(R0,P0.T)
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L = jitchol(np.eye(self.M) + np.dot(RPT0,(1./Diag0 - Diag/(Diag0**2))[:,None]*RPT0.T))
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R,info = linalg.flapack.dtrtrs(L,R0,lower=1)
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RPT = np.dot(R,P.T)
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Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)
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self.w = Diag * self.v_tilde
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self.gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))
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mu = self.w + np.dot(P,self.gamma)
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epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N
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epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
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self.np1.append(self.tau_tilde.copy())
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@ -7,7 +7,6 @@ from scipy import stats
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import scipy as sp
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import pylab as pb
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from ..util.plot import gpplot
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#from . import EP
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class likelihood_function:
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"""
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