fixes in likelihoods

GPy/likelihoods/gaussian.py

@@ -35,12 +35,7 @@ class Gaussian(Likelihood):
         if gp_link is None:
             gp_link = link_functions.Identity()
 
-        if isinstance(gp_link, link_functions.Identity):
-            analytical_variance = True
-            analytical_mean = True
-        else:
-            analytical_variance = False
-            analytical_mean = False
+        assert isinstance(gp_link, link_functions.Identity), "the likelihood only implemented for the identity link"
 
         super(Gaussian, self).__init__(gp_link, name=name)
 
@@ -97,14 +92,10 @@ class Gaussian(Likelihood):
     def predictive_variance(self, mu, sigma, predictive_mean=None):
         return self.variance + sigma**2
 
-<<<<<<< HEAD
-    def pdf_link(self, link_f, y, Y_metadata=None):
-=======
     def predictive_quantiles(self, mu, var, quantiles, Y_metadata):
         return  [stats.norm.ppf(q/100.)*np.sqrt(var) + mu for q in quantiles]
 
-    def pdf_link(self, link_f, y, extra_data=None):
->>>>>>> a3287c38ea775155df4e90f7fe1883d12ffb54b9
+    def pdf_link(self, link_f, y, Y_metadata=None):
         """
         Likelihood function given link(f)
 

GPy/likelihoods/mixed_noise.py

@@ -27,6 +27,7 @@ class MixedNoise(Likelihood):
         return variance[:,None]
 
     def betaY(self,Y,Y_metadata):
+        #TODO not here.
         return Y/self.gaussian_variance(Y_metadata=Y_metadata)
 
     def update_gradients(self, gradients):
@@ -57,16 +58,6 @@ class MixedNoise(Likelihood):
         return _variance + sigma**2
 
 
-    def covariance_matrix(self, Y, Y_metadata):
-        #assert all([isinstance(l, Gaussian) for l in self.likelihoods_list])
-        #ind = Y_metadata['output_index'].flatten()
-        #variance = np.zeros(Y.shape[0])
-        #for lik, j in zip(self.likelihoods_list, range(len(self.likelihoods_list))):
-        #    variance[ind==j] = lik.variance
-        #return np.diag(variance)
-        return np.diag(self.gaussian_variance(Y_metadata).flatten())
-
-
     def samples(self, gp, Y_metadata):
         """
         Returns a set of samples of observations based on a given value of the latent variable.

GPy/likelihoods/student_t.py

@@ -246,9 +246,6 @@ class StudentT(Likelihood):
         return np.hstack((d2logpdf_dlink2_dvar, d2logpdf_dlink2_dv))
 
     def predictive_mean(self, mu, sigma, Y_metadata=None):
-        """
-        Compute mean of the prediction
-        """
         return self.gp_link.transf(mu) # only true in link is monotoci, which it is.
 
     def predictive_variance(self, mu,variance, predictive_mean=None, Y_metadata=None):
@@ -260,16 +257,8 @@ class StudentT(Likelihood):
     def conditional_mean(self, gp):
         return self.gp_link.transf(gp)
 
-<<<<<<< HEAD
-    def predictive_mean(self, mu, sigma):
-        """
-        Compute mean of the prediction
-        """
-        return mu
-=======
     def conditional_variance(self, gp):
         return self.deg_free/(self.deg_free - 2.)
->>>>>>> a3287c38ea775155df4e90f7fe1883d12ffb54b9
 
     def samples(self, gp, Y_metadata=None):
         """