roundtrip error fixed for likelihood tests

GPy/core/model.py

@@ -154,8 +154,7 @@ class Model(Parameterized):
         x = np.random.randn(self.size_transformed)
         x = self._untransform_params(x)
         # now draw from prior where possible
-        if self.priors is not None and len(self.priors):
-            [np.put(x, i, p.rvs(1)) for i, p in enumerate(self.priors) if not p is None]
+        [np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.iteritems() if not p is None]
         self._set_params(x)
         # self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
 

GPy/core/parameterization/param.py

@@ -145,7 +145,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
         return new_arr
     def __setitem__(self, s, val, update=True):
         super(Param, self).__setitem__(s, val, update=update)
-        self._notify_tied_parameters()
+        #self._notify_tied_parameters()
         if update:
             self._highest_parent_.parameters_changed()
 
@@ -201,11 +201,11 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
     @property
     def is_fixed(self):
         return self._highest_parent_._is_fixed(self)
-    def round(self, decimals=0, out=None):
-        view = super(Param, self).round(decimals, out).view(Param)
-        view.__array_finalize__(self)
-        return view
-    round.__doc__ = numpy.round.__doc__
+    #def round(self, decimals=0, out=None):
+    #    view = super(Param, self).round(decimals, out).view(Param)
+    #    view.__array_finalize__(self)
+    #    return view
+    #round.__doc__ = numpy.round.__doc__
     def _get_original(self, param):
         return self
 
@@ -337,8 +337,8 @@ class ParamConcatenation(object):
     def __setitem__(self, s, val, update=True):
         ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
         vals = self._vals(); vals[s] = val; del val
-        [numpy.place(p, ind[ps], vals[ps]) and p._notify_tied_parameters()
-        for p, ps in zip(self.params, self._param_slices_)]
+        [numpy.place(p, ind[ps], vals[ps])# and p._notify_tied_parameters()
+         for p, ps in zip(self.params, self._param_slices_)]
         if update:
             self.params[0]._highest_parent_.parameters_changed()
     def _vals(self):

GPy/inference/latent_function_inference/laplace.py

@@ -216,7 +216,7 @@ class Laplace(object):
         """
         if not log_concave:
             #print "Under 1e-10: {}".format(np.sum(W < 1e-6))
-            W[W < 1e-6] = 1e-6  # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
+            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,
                                 # This is a property only held by non-log-concave likelihoods