diff --git a/GPy/inference/SGD.py b/GPy/inference/SGD.py
index 2f2d1ef0..7f452a57 100644
--- a/GPy/inference/SGD.py
+++ b/GPy/inference/SGD.py
@@ -18,7 +18,7 @@ class opt_SGD(Optimizer):
 
     """
 
-    def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, **kwargs):
+    def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, center = True, **kwargs):
         self.opt_name = "Stochastic Gradient Descent"
 
         self.model = model
@@ -30,6 +30,7 @@ class opt_SGD(Optimizer):
         self.messages = messages
         self.batch_size = batch_size
         self.self_paced = self_paced
+        self.center = center
 
         num_params = len(self.model._get_params())
         if isinstance(self.learning_rate, float):
@@ -48,7 +49,7 @@ class opt_SGD(Optimizer):
         return status
 
     def non_null_samples(self, data):
-        return np.isnan(data).sum(axis=1) == 0
+        return (np.isnan(data).sum(axis=1) == 0)
 
     def check_for_missing(self, data):
         return np.isnan(data).sum() > 0
@@ -138,8 +139,9 @@ class opt_SGD(Optimizer):
 
         j = self.subset_parameter_vector(self.x_opt, samples, shapes)
         self.model.X = X[samples]
-        # self.model.Y -= self.model.Y.mean() # <----------------- WARNING!!!!
-        # self.model.Y /= self.model.Y.std()
+        if self.center:
+            self.model.Y -= self.model.Y.mean()
+            self.model.Y /= self.model.Y.std()
         model_name = self.model.__class__.__name__
 
         if model_name == 'Bayesian_GPLVM':
@@ -161,7 +163,7 @@ class opt_SGD(Optimizer):
         return f, step, self.model.N
 
     def opt(self, f_fp=None, f=None, fp=None):
-        self.x_opt = self.model._get_params()
+        self.x_opt = self.model._get_params_transformed()
         X, Y = self.model.X.copy(), self.model.Y.copy()
         N, Q = self.model.X.shape
         D = self.model.Y.shape[1]
@@ -172,9 +174,14 @@ class opt_SGD(Optimizer):
             missing_data = self.check_for_missing(self.model.Y)
         self.model.Youter = None # this is probably not very efficient
         self.model.YYT = None
-        step = np.zeros_like(self.model._get_params())
+        num_params = self.model._get_params()
+
+        if self.center:
+            print "WARNING: centering the data"
 
         for it in range(self.iterations):
+            step = np.zeros_like(num_params)
+
             if it == 0 or self.self_paced is False:
                 features = np.random.permutation(Y.shape[1])
             else:
@@ -189,12 +196,9 @@ class opt_SGD(Optimizer):
             for j in features:
                 count += 1
                 self.model.D = len(j)
-                self.model.Y = Y[:, j:j+1]
-
+                self.model.Y = Y[:, j]
                 # self.model.trYYT = np.sum(np.square(self.model.Y))
                 if missing_data or sparse_matrix:
-                    # if self.model.Y.std() == 0.0 or self.model.Y.shape[0] == 0: <--- not sure about this
-                    #     continue
                     shapes = self.get_param_shapes(N, Q)
                     f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes, sparse_matrix)
                 else:
@@ -206,6 +210,12 @@ class opt_SGD(Optimizer):
 
                 if self.messages == 2:
                     status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d}\r".format(feature = count, tot = len(features), f = f, nm = Nj)
+
+                    # TODO: remove this, it's only for debugging
+                    if self.model.__class__.__name__ == 'Bayesian_GPLVM':
+                        beta = np.exp(self.x_opt)[-7]
+                        status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d} \t inv_bbeta: {beta: 1.5f}\r".format(feature = count, tot = len(features), f = f, nm = Nj, beta = 1./beta)
+
                     sys.stdout.write(status)
                     sys.stdout.flush()
                     last_printed_count = count