resolve conflictions

GPy/core/parameterization/variational.py

@@ -58,22 +58,15 @@ class GmmNormalPrior(VariationalPrior):
         self.link_parameter(self.variational_pi)
         self.variational_pi.constrain_bounded(0.0, 1.0)
 
-<<<<<<< HEAD
-        # self.stop = 5
-=======
         self.stop = 5
->>>>>>> a43d4b074832a791b7453c454f304eacd478c624
 
     def KL_divergence(self, variational_posterior):
         # Lagrange multiplier maybe also needed here
 
-<<<<<<< HEAD
-=======
         # var_mean = np.square(variational_posterior.mean).sum()
         # var_S = (variational_posterior.variance - np.log(variational_posterior.variance)).sum()
         # return 0.5 * (var_mean + var_S) - 0.5 * variational_posterior.input_dim * variational_posterior.num_data
 
->>>>>>> a43d4b074832a791b7453c454f304eacd478c624
         mu = variational_posterior.mean
         S = variational_posterior.variance
         pi = self.variational_pi
@@ -88,19 +81,13 @@ class GmmNormalPrior(VariationalPrior):
         return 0.5 * (cita[0] - (np.log(S)).sum() + cita[1]) + 0.5 * (cita[2] - total_n) + cita[3]
 
     def update_gradients_KL(self, variational_posterior):
-<<<<<<< HEAD
+
-        # import pdb; pdb.set_trace() # breakpoint 1
-        # print("Updating Gradients")
-        # if self.stop<1:
-        #     return
-        # self.stop-=1
-=======
         import pdb; pdb.set_trace() # breakpoint 1
         print("Updating Gradients")
         if self.stop<1:
             return
         self.stop-=1
->>>>>>> a43d4b074832a791b7453c454f304eacd478c624
+
         #dL:
         #variational_posterior.mean.gradient -= variational_posterior.mean
         #variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5
@@ -110,25 +97,6 @@ class GmmNormalPrior(VariationalPrior):
         S = variational_posterior.variance
         pi = self.variational_pi
 
-<<<<<<< HEAD
-        cita_0 = np.zeros(mu.shape)
-        cita_1 = np.zeros(mu.shape)
-        cita_2 = np.zeros(mu.shape)
-        cita_3 = np.zeros(pi.shape)
-        for i in range(self.n_component):        
-            cita_0 += pi.values[i] * (mu - self.px_mu.values[i]) / self.px_var.values[i]     
-            cita_1 += (pi[i] / self.px_var[i])
-            cita_2 += pi[i] * (S + np.square(mu - self.px_mu[i])) / np.square(self.px_var[i])
-            self.px_mu[i].gradient += pi[i] * (mu - self.px_mu[i]) / self.px_var[i]
-            self.px_var[i].gradient += (pi[i] * (S + np.square(mu - self.px_mu[i])) / np.square(self.px_var[i]) - (pi[i] / self.px_var[i])) * 0.5
-            cita_3[i] = (np.log(self.px_var[i]).sum() + (S / self.px_var[i]).sum()+ (np.square(mu - self.px_mu[i]) / self.px_var[i]).sum() )* (-0.5) + np.log(self.pi[i] / pi[i]) - 1
-            # self.variational_pi[i].gradient += cita_3[i]
-
-        variational_posterior.mean.gradient -= cita_0
-        variational_posterior.variance.gradient += (1. / (S) - cita_1) * 0.5
-        self.variational_pi.gradient +=cita_3
-
-=======
         cita_0 = np.zeros_like(mu)
         cita_1 = np.zeros_like(mu)
         cita_2 = np.zeros_like(mu)
@@ -155,9 +123,6 @@ class GmmNormalPrior(VariationalPrior):
         variational_posterior.mean.gradient -= cita_0
         variational_posterior.variance.gradient += (1. / (S) - cita_1) * 0.5
 
-        
-    
->>>>>>> a43d4b074832a791b7453c454f304eacd478c624
     def check_weights(self, weights):
         assert weights.min() >= 0.0
         assert weights.max() <= 1.0
@@ -167,10 +132,7 @@ class GmmNormalPrior(VariationalPrior):
         self.check_weights(self.variational_pi)
         self.check_weights(self.pi)
 
-<<<<<<< HEAD
-=======
 
->>>>>>> a43d4b074832a791b7453c454f304eacd478c624
 class SpikeAndSlabPrior(VariationalPrior):
     def __init__(self, pi=None, learnPi=False, variance = 1.0, group_spike=False, name='SpikeAndSlabPrior', **kw):
         super(SpikeAndSlabPrior, self).__init__(name=name, **kw)

GPy/models/gmm_bayesian_gplvm.py

@@ -52,18 +52,6 @@ class GmmBayesianGPLVM(SparseGP_MPI):
         if likelihood is None:
             likelihood = Gaussian()
 
-<<<<<<< HEAD
-
-        # Need to define what the model is initialised like
-        pi = np.ones(n_component) / float(n_component) # p(k)
-        variational_pi = pi.copy()
-        px_mu = [[]] * n_component
-        px_var = [[]] * n_component
-        for i in range(n_component):
-            px_mu[i] = np.zeros(X_variance.shape)
-            px_var[i] = np.ones(X_variance.shape)
-        
-=======
         # Need to define what the model is initialised like
         pi = np.ones(n_component) / float(n_component) # p(k)
         variational_pi = pi.copy()
@@ -82,7 +70,6 @@ class GmmBayesianGPLVM(SparseGP_MPI):
         # print(px_var)
         # print(variational_pi)
         # print("Didnt print")
->>>>>>> a43d4b074832a791b7453c454f304eacd478c624
         self.variational_prior = GmmNormalPrior(px_mu=px_mu, px_var=px_var, pi=pi,
                                 n_component=n_component, variational_pi=variational_pi)