better GPLVM oil flow demo

GPy/examples/dimensionality_reduction.py

@@ -44,12 +44,14 @@ def GPLVM_oil_100():
     data = GPy.util.datasets.oil_100()
 
     # create simple GP model
-    m = GPy.models.GPLVM(data['X'], 2)
+    kernel = GPy.kern.rbf(6, ARD = True) + GPy.kern.bias(6)
+    m = GPy.models.GPLVM(data['X'], 6, kernel = kernel)
 
     # optimize
     m.ensure_default_constraints()
-    m.optimize()
+    m.optimize(messages=1)
 
     # plot
     print(m)
+    m.plot_latent(labels=data['Y'].argmax(axis=1))
     return m

GPy/models/GPLVM.py

@@ -66,6 +66,8 @@ class GPLVM(GP):
         :param resolution: the resolution of the grid on which to evaluate the predictive variance
         """
 
+        util.plot.Tango.reset()
+        
         if labels is None:
             labels = np.ones(self.N)
         if which_indices is None:
@@ -88,7 +90,7 @@ class GPLVM(GP):
         #first, plot the output variance as a function of the latent space
         Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(self.X[:,[input_1, input_2]],resolution=resolution)
 	Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
-	Xtest_full[:, :2] = Xtest        
+	Xtest_full[:, :2] = Xtest
 	mu, var, low, up = self.predict(Xtest_full)
 	var = var[:, :2]
         pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear')