trivial merge resolution

GPy/models/GP_EP.py

@@ -26,6 +26,9 @@ class GP_EP(model):
         :param powerep: power-EP parameters [$\eta$,$\delta$], defaults to [1.,1.] (list)
         :rtype: GPy model class.
         """
+        if kernel is None:
+            kernel = kern.rbf(X.shape[1]) + kern.bias(X.shape[1]) + kern.white(X.shape[1])
+
         assert isinstance(kernel,kern.kern), 'kernel is not a kern instance'
         self.likelihood = likelihood
         self.Y = self.likelihood.Y

GPy/models/GP_regression.py

@@ -29,7 +29,7 @@ class GP_regression(model):
 
     def __init__(self,X,Y,kernel=None,normalize_X=False,normalize_Y=False, Xslices=None):
         if kernel is None:
-            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1]) + kern.bias(X.shape[1])
+            kernel = kern.rbf(X.shape[1]) + kern.bias(X.shape[1]) + kern.white(X.shape[1])
 
         # parse arguments
         self.Xslices = Xslices
@@ -87,7 +87,7 @@ class GP_regression(model):
         if self.Youter is None:
             return -0.5*np.trace(mdot(self.Y.T,self.Ki,self.Y))
         else:
-            return -0.5*np.sum(np.multiply(self.Ki, self.Y))
+            return -0.5*np.sum(np.multiply(self.Ki, self.Youter))
 
     def log_likelihood(self):
         complexity_term = -0.5*self.N*self.D*np.log(2.*np.pi) - self.D*self.hld