gmm_creation

GPy/core/parameterization/variational.py

@@ -58,11 +58,22 @@ 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
@@ -77,11 +88,19 @@ 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
@@ -91,6 +110,7 @@ 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)
@@ -108,6 +128,36 @@ class GmmNormalPrior(VariationalPrior):
         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)
+        cita_3 = np.zeros_like(pi)
+        for i in range(self.n_component):
+            
+            print("About to change the gradient")            
+            print pi.values[i]
+            print mu
+            print self.px_mu.values[i]
+            print self.px_var.values[i]
+            
+            cita_0 += pi.values[i] * (mu - self.px_mu.values[i]) / self.px_var.values[i]
+            print "Has this helped?"
+            self.px_mu[i].gradient += pi[i] * (mu - self.px_mu[i]) / self.px_var[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_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]) - pi[i] * np.log(self.pi[i] / np.square(pi[i]))
+            self.variational_pi[i].gradient += cita_3[i]
+
+        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
@@ -117,6 +167,10 @@ 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/__init__.py

@@ -26,3 +26,4 @@ from .state_space_model import StateSpace
 from .ibp_lfm import IBPLFM
 from .gp_offset_regression import GPOffsetRegression
 from .gp_grid_regression import GPRegressionGrid
+from .gmm_bayesian_gplvm import GmmBayesianGPLVM

GPy/models/gmm_bayesian_gplvm.py

@@ -52,6 +52,7 @@ 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)
@@ -62,6 +63,26 @@ class GmmBayesianGPLVM(SparseGP_MPI):
             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()
+        # px_mu = np.zeros(n_component)
+        # px_var = np.ones(n_component)
+        px_mu = [[]] * n_component
+        px_var = [[]] * n_component
+        for i in range(n_component):
+            px_mu[i] = np.zeros_like(X_variance)
+            px_var[i] = np.ones_like(X_variance)
+        
+
+        # print("Should print")
+        # print(pi)
+        # print(px_mu)
+        # 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)