Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel

GPy/inference/latent_function_inference/inferenceX.py

@@ -27,12 +27,19 @@ def infer_newX(model, Y_new, optimize=True, init='L2'):
 
 class InferenceX(Model):
     """
-    The class for inference of new X with given new Y. (do_test_latent)
+    The model class for inference of new X with given new Y. (replacing the "do_test_latent" in Bayesian GPLVM)
+    It is a tiny inference model created from the original GP model. The kernel, likelihood (only Gaussian is supported at the moment) 
+    and posterior distribution are taken from the original model.
+    For Regression models and GPLVM, a point estimate of the latent variable X will be inferred. 
+    For Bayesian GPLVM, the variational posterior of X will be inferred. 
+    X is inferred through a gradient optimization of the inference model.
 
     :param model: the GPy model used in inference
     :type model: GPy.core.Model
     :param Y: the new observed data for inference
     :type Y: numpy.ndarray
+    :param init: the distance metric of Y for initializing X with the nearest neighbour.
+    :type init: 'L2', 'NCC' and 'rand'
     """
     def __init__(self, model, Y, name='inferenceX', init='L2'):
         if np.isnan(Y).any() or getattr(model, 'missing_data', False):

GPy/inference/latent_function_inference/laplace.py

@@ -139,10 +139,6 @@ class Laplace(LatentFunctionInference):
 
         f_hat, Ki_fhat = self.rasm_mode(K, Y, likelihood, Ki_f_init, Y_metadata=Y_metadata)
 
-        self.f_hat = f_hat
-        #self.Ki_fhat =  Ki_fhat
-        #self.K = K.copy()
-
         #Compute hessian and other variables at mode
         log_marginal, woodbury_inv, dL_dK, dL_dthetaL = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, kern, Y_metadata)
 
@@ -298,6 +294,11 @@ class Laplace(LatentFunctionInference):
         else:
             dL_dthetaL = np.zeros(likelihood.size)
 
+        #Cache some things for speedy LOO
+        self.Ki_W_i = Ki_W_i
+        self.K = K
+        self.W = W
+        self.f_hat = f_hat
         return log_marginal, K_Wi_i, dL_dK, dL_dthetaL
 
     def _compute_B_statistics(self, K, W, log_concave, *args, **kwargs):