fixed bug in RBF, added inducing inputs to BGPLVM plots

GPy/kern/rbf.py

@@ -55,7 +55,6 @@ class rbf(kernpart):
         self._X, self._X2, self._params = np.empty(shape=(3,1))
 
     def _get_params(self):
-        foo
         return np.hstack((self.variance,self.lengthscale))
 
     def _set_params(self,x):

GPy/models/Bayesian_GPLVM.py

@@ -83,3 +83,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
 
     def _log_likelihood_gradients(self):
         return np.hstack((self.dL_dmuS().flatten(), sparse_GP._log_likelihood_gradients(self)))
+
+    def plot_latent(self, *args, **kwargs):
+        input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
+        pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')

GPy/models/GPLVM.py

@@ -117,6 +117,4 @@ class GPLVM(GP):
         pb.xlim(xmin[0],xmax[0])
         pb.ylim(xmin[1],xmax[1])
 
-
-
-        
+        return input_1, input_2

GPy/models/sparse_GPLVM.py

@@ -55,3 +55,7 @@ class sparse_GPLVM(sparse_GP_regression, GPLVM):
         #passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
         mu, var, upper, lower = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
         pb.plot(mu[:, 0] , mu[:, 1], 'ko')
+
+    def plot_latent(self, *args, **kwargs):
+        input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
+        pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')