chg: fixed naming in variational priors

:

GPy/core/parameterization/variational.py

@@ -10,7 +10,7 @@ from .param import Param
 from paramz.transformations import Logexp, Logistic,__fixed__
 
 class VariationalPrior(Parameterized):
-    def __init__(self, name='latent space', **kw):
+    def __init__(self, name='latent prior', **kw):
         super(VariationalPrior, self).__init__(name=name, **kw)
 
     def KL_divergence(self, variational_posterior):
@@ -23,6 +23,9 @@ class VariationalPrior(Parameterized):
         raise NotImplementedError("override this for variational inference of latent space")
 
 class NormalPrior(VariationalPrior):
+    def __init__(self, name='normal_prior', **kw):
+        super(VariationalPrior, self).__init__(name=name, **kw)
+
     def KL_divergence(self, variational_posterior):
         var_mean = np.square(variational_posterior.mean).sum()
         var_S = (variational_posterior.variance - np.log(variational_posterior.variance)).sum()
@@ -58,7 +61,7 @@ class SpikeAndSlabPrior(VariationalPrior):
             pi = self.pi[idx]
         else:
             pi = self.pi
-            
+
         var_mean = np.square(mu)/self.variance
         var_S = (S/self.variance - np.log(S))
         var_gamma = (gamma*np.log(gamma/pi)).sum()+((1-gamma)*np.log((1-gamma)/(1-pi))).sum()
@@ -163,12 +166,12 @@ class NormalPosterior(VariationalPosterior):
         """Compute the KL divergence to another NormalPosterior Object. This only holds, if the two NormalPosterior objects have the same shape, as we do computational tricks for the multivariate normal KL divergence.
         """
         return .5*(
-            np.sum(self.variance/other.variance) 
-            + ((other.mean-self.mean)**2/other.variance).sum() 
+            np.sum(self.variance/other.variance)
+            + ((other.mean-self.mean)**2/other.variance).sum()
             - self.num_data * self.input_dim
             + np.sum(np.log(other.variance)) - np.sum(np.log(self.variance))
             )
-    
+
 class SpikeAndSlabPosterior(VariationalPosterior):
     '''
     The SpikeAndSlab distribution for variational approximations.
@@ -190,11 +193,11 @@ class SpikeAndSlabPosterior(VariationalPosterior):
         else:
             self.gamma = Param("binary_prob",binary_prob,Logistic(1e-10,1.-1e-10))
             self.link_parameter(self.gamma)
-            
+
     def propogate_val(self):
         if self.group_spike:
             self.gamma.values[:] = self.gamma_group.values
-    
+
     def collate_gradient(self):
         if self.group_spike:
             self.gamma_group.gradient = self.gamma.gradient.reshape(self.gamma.shape).sum(axis=0)