corrected the predictive variance for Gaussian likelihoods

GPy/likelihoods/Gaussian.py

@@ -51,7 +51,7 @@ class Gaussian(likelihood):
         true_var = (var + self._variance)*self._std**2
         _5pc = mean + - 2.*np.sqrt(true_var)
         _95pc = mean + 2.*np.sqrt(true_var)
-        return mean, _5pc, _95pc
+        return mean, true_var, _5pc, _95pc
 
     def fit_full(self):
         """

GPy/likelihoods/likelihood.py

@@ -25,11 +25,16 @@ class likelihood:
     def _get_param_names(self):
         raise NotImplementedError
 
-    def _set_params(self,x):
+    def _set_params(self, x):
         raise NotImplementedError
 
     def fit(self):
         raise NotImplementedError
 
-    def _gradients(self,partial):
+    def _gradients(self, partial):
         raise NotImplementedError
+
+    def predictive_values(self, mu, var):
+        raise NotImplementedError
+
+

GPy/likelihoods/likelihood_functions.py

@@ -48,14 +48,14 @@ class probit(likelihood_function):
 
     def predictive_values(self,mu,var):
         """
-        Compute  mean, and conficence interval (percentiles 5 and 95) of the  prediction
+        Compute  mean, variance and conficence interval (percentiles 5 and 95) of the  prediction
         """
         mu = mu.flatten()
         var = var.flatten()
         mean = stats.norm.cdf(mu/np.sqrt(1+var))
         p_025 = np.zeros(mu.shape)
         p_975 = np.ones(mu.shape)
-        return mean, p_025, p_975
+        return mean, np.nan*var, p_025, p_975 # TODO: better values here (mean is okay)
 
 class Poisson(likelihood_function):
     """
@@ -131,4 +131,4 @@ class Poisson(likelihood_function):
         tmp = stats.poisson.ppf(np.array([.025,.975]),mean)
         p_025 = tmp[:,0]
         p_975 = tmp[:,1]
-        return mean,p_025,p_975
+        return mean,np.nan*mean,p_025,p_975 # better variance here TODO