From 1aa060439f09305741f7ed60d6cfc7d429da72b8 Mon Sep 17 00:00:00 2001 From: mu Date: Fri, 10 Jan 2014 18:19:05 +0000 Subject: [PATCH] xt --- GPy/models/state_space_xt_sep.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/GPy/models/state_space_xt_sep.py b/GPy/models/state_space_xt_sep.py index d4d0e9df..3e257cee 100644 --- a/GPy/models/state_space_xt_sep.py +++ b/GPy/models/state_space_xt_sep.py @@ -66,12 +66,31 @@ class StateSpace_1(Model): return self.kern._get_param_names_transformed() + ['noise_variance'] def log_likelihood(self): + + X=self.X + + # Sort the matrix (save the order) + _, return_index, return_inverse = np.unique(X,True,True) + X = X[return_index] + # Get the model matrices from the kernel (F,L,Qc,H,Pinf) = self.kern.sde() + n=X.shape[0] + F1 = np.kron(np.eye(n),F) + L1 = np.kron(np.eye(n),L) + K1=self.spacekern.K(X) + Qc1 = K1*Qc #kron(K,Qc1); + H1 = np.kron(np.eye(n),H) + Pinf1 = np.kron(K1,Pinf) + + + # Use the Kalman filter to evaluate the likelihood return self.kf_likelihood(F,L,Qc,H,self.sigma2,Pinf,self.X.T,self.Y.T) + #return self.kf_likelihood(F1,L1,Qc1,H1,self.sigma2,Pinf1,self.X.T,self.Y.T) + def _log_likelihood_gradients(self):