changes to rbf and white to allow new parameter gradient structure

GPy/inference/latent_function_inference/exact_gaussian_inference.py

@@ -24,7 +24,7 @@ class ExactGaussianInference(object):
         """
         find a matrix L which satisfies LL^T = YY^T.
 
-        Note that L may have fewer columns than Y, else L=Y. 
+        Note that L may have fewer columns than Y, else L=Y.
         """
         N, D = Y.shape
         if (N>D):
@@ -33,22 +33,26 @@ class ExactGaussianInference(object):
             #if Y in self.cache, return self.Cache[Y], else store Y in cache and return L.
             raise NotImplementedError, 'TODO' #TODO
 
-    def inference(self, K, likelihood, Y, Y_metadata=None):
+    def inference(self, kern, X, likelihood, Y, Y_metadata=None):
         """
         Returns a Posterior class containing essential quantities of the posterior
         """
         YYT_factor = self.get_YYTfactor(Y)
 
+        K = kern.K(X)
+
         Wi, LW, LWi, W_logdet = pdinv(K + likelihood.covariance_matrix(Y, Y_metadata))
 
         alpha, _ = dpotrs(LW, YYT_factor, lower=1)
 
-        dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
-
         log_marginal =  0.5*(-Y.size * log_2_pi - Y.shape[1] * W_logdet - np.sum(alpha * YYT_factor))
 
-        dL_dtheta_lik = likelihood._gradients(np.diag(dL_dK))
+        dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
 
-        return Posterior(log_marginal, dL_dK, dL_dtheta_lik, LW, alpha, K)
+        kern.update_gradients_full(dL_dK)
+
+        likelihood.update_gradients(np.diag(dL_dK))
+
+        return Posterior(log_marginal, dL_dK, LW, alpha, K)
 
 

GPy/inference/latent_function_inference/laplace.py

@@ -17,7 +17,7 @@ from ..util.linalg import mdot, jitchol, pddet, dpotrs
 from functools import partial as partial_func
 import warnings
 
-class Laplace(likelihood):
+class LaplaceInference(object):
     """Laplace approximation to a posterior"""
 
     def __init__(self, data, noise_model, extra_data=None):

GPy/inference/latent_function_inference/posterior.py

@@ -14,7 +14,7 @@ class Posterior(object):
     schemes and the model classes. 
 
     """
-    def __init__(self, log_marginal, dL_dK, dL_dtheta_lik, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None):
+    def __init__(self, log_marginal, dL_dK, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None):
         """
         log_marginal: log p(Y|X)
         dL_dK: d/dK log p(Y|X)
@@ -56,7 +56,7 @@ class Posterior(object):
         if ((woodbury_chol is not None) and (woodbury_vector is not None) and (K is not None)) or ((mean is not None) and (cov is not None) and (K is not None)):
             pass # we have sufficient to compute the posterior
         else:
-            raise ValueError, "insufficient onformation to compute the posterior"
+            raise ValueError, "insufficient information to compute the posterior"
 
         #option 1:
         self._woodbury_chol = woodbury_chol