some work on ep, and some messing with wher ethe derivatives are computed (in the model, not the inference

GPy/inference/latent_function_inference/ep.py

@@ -23,11 +23,26 @@ class EP(object):
         self.old_mutilde, self.old_vtilde = None, None
 
     def inference(self, kern, X, likelihood, Y, Y_metadata=None):
+        num_data, output_dim = X.shape
+        assert output_dim ==1, "ep in 1D only (for now!)"
 
         K = kern.K(X)
 
         mu_tilde, tau_tilde = self.expectation_propagation()
 
+        Wi, LW, LWi, W_logdet = pdinv(K + np.diag(1./tau_tilde)
+
+        alpha, _ = dpotrs(LW, mu_tilde, lower=1)
+
+        log_marginal =  0.5*(-num_data * log_2_pi - W_logdet - np.sum(alpha * mu_tilde))
+
+        dL_dK = 0.5 * (tdot(alpha[:,None]) - Wi)
+
+        #TODO: what abot derivatives of the likelihood parameters?
+
+        return Posterior(woodbury_inv=Wi, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK}
+
+
 
     def expectation_propagation(self, K, Y, Y_metadata, likelihood)
 

GPy/inference/latent_function_inference/exact_gaussian_inference.py

@@ -49,8 +49,7 @@ class ExactGaussianInference(object):
 
         dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
 
-        kern.update_gradients_full(dL_dK, X)
-
+        #TODO: does this really live here?
         likelihood.update_gradients(np.diag(dL_dK))
 
         return Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK}