more tidying in EP, removed examples from _module_ ( and opened discussion on github

GPy/__init__.py

@@ -6,5 +6,5 @@ import kern
 import models
 import inference
 import util
-import examples
+#import examples TODO: discuss!
 from core import priors

GPy/inference/Expectation_Propagation.py

@@ -128,7 +128,7 @@ class FITC(EP_base):
     :param epsilon: Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
     :param powerep: Power-EP parameters (eta,delta) - 2x1 numpy array (floats)
     """
-    def __init__(self,likelihood,Knn_diag,Kmn,Kmm,*args,**kwargs)
+    def __init__(self,likelihood,Knn_diag,Kmn,Kmm,*args,**kwargs):
         self.Knn_diag = Knn_diag
         self.Kmn = Kmn
         self.Kmm = Kmm

GPy/models/GP_EP.py

@@ -6,7 +6,7 @@ import numpy as np
 import pylab as pb
 from scipy import stats, linalg
 from .. import kern
-from ..inference.Expectation_Propagation import EP,Full
+from ..inference.Expectation_Propagation import Full
 from ..inference.likelihoods import likelihood,probit#,poisson,gaussian
 from ..core import model
 from ..util.linalg import pdinv,jitchol

GPy/models/generalized_FITC.py

@@ -9,7 +9,7 @@ from .. import kern
 from ..core import model
 from ..util.linalg import pdinv,mdot
 from ..util.plot import gpplot
-from ..inference.Expectation_Propagation import EP,Full,FITC
+from ..inference.Expectation_Propagation import FITC
 from ..inference.likelihoods import likelihood,probit
 
 class generalized_FITC(model):

GPy/models/uncollapsed_sparse_GP.py

@@ -39,7 +39,8 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
                 M = Z.shape[0]
             else:
                 M=M
-            self.set_vb_param(np.hstack((np.ones(M*D)),np.eye(M).flatten()))
+            q_u = np.hstack((np.ones(M*D)),np.eye(M).flatten())
+        self.set_vb_param(q_u)
         sparse_GP_regression.__init__(self, X, Y, *args, **kwargs)
 
     def _computations(self):