EM algorithm

GPy/examples/ep_fix.py

@@ -35,5 +35,6 @@ print m.checkgrad()
 m.optimize()
 #m.em(plot_all=False) # EM algorithm
 m.plot(samples=3)
+m.EM()
 
 print(m)

GPy/models/GP.py

@@ -229,6 +229,33 @@ class GP(model):
         phi = None if not self.EP else self.likelihood.predictive_mean(mu,var)
         return mu, var, phi
 
+    def EM(self,max_f_eval=20,epsilon=.1,plot_all=False): #TODO check this makes sense
+        """
+        Fits sparse_EP and optimizes the hyperparametes iteratively until convergence is achieved.
+        """
+        self.epsilon_em = epsilon
+        log_likelihood_change = self.epsilon_em + 1.
+        self.parameters_path = [self._get_params()]
+        self.approximate_likelihood()
+        self.site_approximations_path = [[self.ep_approx.tau_tilde,self.ep_approx.v_tilde]]
+        self.log_likelihood_path = [self.log_likelihood()]
+        iteration = 0
+        while log_likelihood_change > self.epsilon_em:
+            print 'EM iteration', iteration
+            self.optimize(max_f_eval = max_f_eval)
+            log_likelihood_new = self.log_likelihood()
+            log_likelihood_change = log_likelihood_new - self.log_likelihood_path[-1]
+            if log_likelihood_change < 0:
+                print 'log_likelihood decrement'
+                self._set_params(self.parameters_path[-1])
+                self.kern._set_params(self.parameters_path[-1])
+            else:
+                self.approximate_likelihood()
+                self.log_likelihood_path.append(self.log_likelihood())
+                self.parameters_path.append(self._get_params())
+                self.site_approximations_path.append([self.ep_approx.tau_tilde,self.ep_approx.v_tilde])
+            iteration += 1
+
     def plot(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None):
         """
         :param samples: the number of a posteriori samples to plot