From be8417315c9e141ee664bf86e8c8a53126628d04 Mon Sep 17 00:00:00 2001
From: Nicolo Fusi <nicolo.fusi@gmail.com>
Date: Thu, 31 Jan 2013 13:38:05 +0000
Subject: [PATCH] minor changes

---
 GPy/inference/SGD.py               | 4 ++--
 GPy/models/sparse_GP_regression.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/GPy/inference/SGD.py b/GPy/inference/SGD.py
index 79ddf605..25f9feae 100644
--- a/GPy/inference/SGD.py
+++ b/GPy/inference/SGD.py
@@ -132,7 +132,6 @@ class opt_SGD(Optimizer):
         self.model.Y = self.model.Y[samples]
         model_name = self.model.__class__.__name__
 
-        import pdb; pdb.set_trace()
         if model_name == 'Bayesian_GPLVM':
             self.model.trYYT = np.sum(np.square(self.model.Y))
 
@@ -159,6 +158,7 @@ class opt_SGD(Optimizer):
         self.trace = []
         missing_data = self.check_for_missing(self.model.Y)
         self.model.Youter = None # this is probably not very efficient
+        self.model.YYT = None
 
         for it in range(self.iterations):
             if it == 0 or self.self_paced is False:
@@ -176,7 +176,7 @@ class opt_SGD(Optimizer):
                 count += 1
                 self.model.D = len(j)
                 self.model.Y = Y[:, j]
-
+                self.model.trYYT = np.sum(np.square(self.model.Y))
                 if missing_data:
                     shapes = self.get_param_shapes(N, Q)
                     f, step, Nj = self.step_with_missing_data(f_fp, X, Y, step, shapes)
diff --git a/GPy/models/sparse_GP_regression.py b/GPy/models/sparse_GP_regression.py
index fc8a9014..6b958dae 100644
--- a/GPy/models/sparse_GP_regression.py
+++ b/GPy/models/sparse_GP_regression.py
@@ -120,7 +120,7 @@ class sparse_GP_regression(GP_regression):
     def _get_param_names(self):
         return sum([['iip_%i_%i'%(i,j) for i in range(self.Z.shape[0])] for j in range(self.Z.shape[1])],[]) + ['noise_precision']+self.kern._get_param_names_transformed()
 
-        
+
     def log_likelihood(self):
         """ Compute the (lower bound on the) log marginal likelihood """
         sf2 = self.scale_factor**2
@@ -132,7 +132,7 @@ class sparse_GP_regression(GP_regression):
 
     def _log_likelihood_gradients(self):
         return np.hstack([self.dL_dZ().flatten(), self.dL_dbeta(), self.dL_dtheta()])
-    
+
     def dL_dbeta(self):
         """
         Compute the gradient of the log likelihood wrt beta.