From ef0ba913022c16510412c8c770d43c4ca72a9663 Mon Sep 17 00:00:00 2001 From: beiwang Date: Fri, 11 Nov 2016 20:14:44 +0000 Subject: [PATCH] gmm_creation --- GPy/core/parameterization/variational.py | 2 +- GPy/models/gmm_bayesian_gplvm.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py index 3137256f..bbc2a8dd 100644 --- a/GPy/core/parameterization/variational.py +++ b/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 diff --git a/GPy/models/gmm_bayesian_gplvm.py b/GPy/models/gmm_bayesian_gplvm.py index c6cdb0d3..a1066634 100644 --- a/GPy/models/gmm_bayesian_gplvm.py +++ b/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()