EP plots samples now for the phi transformation.

GPy/examples/poisson.py

@@ -43,6 +43,6 @@ print m.checkgrad()
 # Optimize and plot
 m.optimize()
 #m.em(plot_all=False) # EM algorithm
-m.plot()
+m.plot(samples=4)
 
 print(m)

GPy/examples/sparse_ep_fix.py

@@ -14,6 +14,7 @@ pb.ion()
 N = 500
 M = 5
 
+pb.close('all')
 ######################################
 ## 1 dimensional example
 
@@ -42,6 +43,7 @@ print m.checkgrad()
 #check gradient FIXME unit test please
 # optimize and plot
 #m.optimize('tnc', messages = 1)
+m.EM()
 m.plot(samples=3,full_cov=False)
 # print(m)
 

GPy/inference/likelihoods.py

@@ -83,12 +83,20 @@ class probit(likelihood):
         var = var.flatten()
         return stats.norm.cdf(mu/np.sqrt(1+var))
 
+    def predictive_var(self,mu,var):
+        p=self.predictive_mean(mu,var)
+        return p*(1-p)
+
     def _log_likelihood_gradients():
         raise NotImplementedError
 
-    def plot(self,X,phi,X_obs,Z=None):
+    def plot(self,X,mu,var,phi,X_obs,Z=None,samples=0):
         assert X_obs.shape[1] == 1, 'Number of dimensions must be 1'
-        gpplot(X,phi,np.zeros(X.shape[0]))
+        phi_var = self.predictive_var(mu,var)
+        gpplot(X,phi,phi_var)
+        if samples:
+            phi_samples = np.vstack([np.random.binomial(1,phi.flatten()) for s in range(samples)])
+            pb.plot(X,phi_samples.T,'x', alpha = 0.4, c='#3465a4' )
         pb.plot(X_obs,(self.Y+1)/2,'kx',mew=1.5)
         if Z is not None:
             pb.plot(Z,Z*0+.5,'r|',mew=1.5,markersize=12)
@@ -164,16 +172,22 @@ class poisson(likelihood):
         sigma2_hat = m2 - mu_hat**2 # Second central moment
         return float(Z_hat), float(mu_hat), float(sigma2_hat)
 
-    def predictive_mean(self,mu,variance):
+    def predictive_mean(self,mu,var):
         return np.exp(mu*self.scale + self.location)
 
+    def predictive_var(self,mu,var):
+        return predictive_mean(mu,var)
+
     def _log_likelihood_gradients():
         raise NotImplementedError
 
-    def plot(self,X,phi,X_obs,Z=None):
+    def plot(self,X,mu,var,phi,X_obs,Z=None,samples=0):
         assert X_obs.shape[1] == 1, 'Number of dimensions must be 1'
-        gpplot(X,phi,np.zeros(X.shape[0]))
+        gpplot(X,phi,phi.flatten())
         pb.plot(X_obs,self.Y,'kx',mew=1.5)
+        if samples:
+            phi_samples = np.vstack([np.random.poisson(phi.flatten(),phi.size) for s in range(samples)])
+            pb.plot(X,phi_samples.T, alpha = 0.4, c='#3465a4', linewidth = 0.8)
         if Z is not None:
             pb.plot(Z,Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12)
 

GPy/models/GP.py

@@ -285,7 +285,7 @@ class GP(model):
 
             if self.EP:
                 pb.subplot(212)
-                self.likelihood.plot(Xnew,phi,self.X)
+                self.likelihood.plot(Xnew,m,v,phi,self.X,samples=samples)
                 pb.xlim(xmin,xmax)
 
         elif self.X.shape[1]==2:

GPy/models/sparse_GP.py

@@ -151,7 +151,6 @@ class sparse_GP(GP):
         else:
             self.ep_approx = Full(self.X,self.likelihood,self.kernel,inducing=None,epsilon=self.epsilon_ep,power_ep=[self.eta,self.delta])
         self.beta, self.Y, self.Z_ep = self.ep_approx.fit_EP()
-        print "Aqui toy"
         self.trbetaYYT = np.sum(np.square(self.Y)*self.beta)
         self._computations()