some updates for params changes and likelihood fixes

GPy/core/parameterization/array_core.py

@@ -37,10 +37,26 @@ class ObservableArray(np.ndarray, Observable):
         if obj is None: return
         self._observers_ = getattr(obj, '_observers_', None)
 
+    def _s_not_empty(self, s):
+        # this checks whether there is something picked by this slice.
+        return True
+        # TODO:  disarmed, for performance increase,
+        if not isinstance(s, (list,tuple,np.ndarray)):
+            return True
+        if isinstance(s, (list,tuple)):
+            return len(s)!=0
+        if isinstance(s, np.ndarray):
+            if s.dtype is bool:
+                return np.all(s)
+            else:
+                return s.size != 0
+
     def __setitem__(self, s, val, update=True):
+        if self._s_not_empty(s):
             super(ObservableArray, self).__setitem__(s, val)
             if update:
                 self._notify_observers()
+                
     def __getslice__(self, start, stop):
         return self.__getitem__(slice(start, stop))
     def __setslice__(self, start, stop, val):

GPy/core/parameterization/param.py

@@ -146,7 +146,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
     def __setitem__(self, s, val, update=True):
         super(Param, self).__setitem__(s, val, update=update)
         #self._notify_tied_parameters()
-        if update:
+        if update and self._s_not_empty(s):
             self._notify_parameters_changed()
 
     #===========================================================================

GPy/inference/latent_function_inference/laplace.py

@@ -216,6 +216,8 @@ class Laplace(object):
         """
         if not log_concave:
             #print "Under 1e-10: {}".format(np.sum(W < 1e-6))
+            # W[W<1e-6] = 1e-6
+            # NOTE: when setting a parameter inside parameters_changed it will allways come to closed update circles!!!
             W.__setitem__(W < 1e-6, 1e-6, update=False)  # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
                                 # If the likelihood is non-log-concave. We wan't to say that there is a negative variance
                                 # To cause the posterior to become less certain than the prior and likelihood,