gmm_creation

GPy/core/parameterization/variational.py

@@ -58,7 +58,7 @@ class GmmNormalPrior(VariationalPrior):
         self.link_parameter(self.variational_pi)
         self.variational_pi.constrain_bounded(0.0, 1.0)
 
-        self.stop = 5
+        # self.stop = 5
 
     def KL_divergence(self, variational_posterior):
         # Lagrange multiplier maybe also needed here

GPy/models/gmm_bayesian_gplvm.py

@@ -52,6 +52,7 @@ class GmmBayesianGPLVM(SparseGP_MPI):
         if likelihood is None:
             likelihood = Gaussian()
 
+
         # Need to define what the model is initialised like
         pi = np.ones(n_component) / float(n_component) # p(k)
         variational_pi = pi.copy()