diff --git a/doc/plot/plot_ISV.py b/doc/plot/plot_ISV.py
index 91b9ff61fdee5cc709c3797f3d5d753ddaf6780d..fc3f9c173916b7e545669ee30b014cfcf626435e 100644
--- a/doc/plot/plot_ISV.py
+++ b/doc/plot/plot_ISV.py
@@ -57,6 +57,10 @@ ubm = bob.learn.em.GMMMachine(n_gaussians).fit(X)
 gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
 isv_machine = bob.learn.em.ISVMachine(ubm, r_U).fit(gmm_stats, y)
 
+# gmm_stats = [ubm.acc_statistics(x) for x in [setosa, versicolor, virginica]]
+# isv_machine = bob.learn.em.ISVMachine(ubm, r_U).fit(gmm_stats, [0, 1, 2])
+
+
 # isvbase = isv_train([setosa, versicolor, virginica], ubm)
 
 # Variability direction
diff --git a/doc/plot/plot_JFA.py b/doc/plot/plot_JFA.py
index f77e6779c9f6f9b52ac78e72d76b498b8ec61d92..cae3e317df04b45f0f2890c96046194988bbc003 100644
--- a/doc/plot/plot_JFA.py
+++ b/doc/plot/plot_JFA.py
@@ -58,6 +58,9 @@ ubm = bob.learn.em.GMMMachine(n_gaussians).fit(X)
 gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
 jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V).fit(gmm_stats, y)
 
+# gmm_stats = [ubm.acc_statistics(x) for x in [setosa, versicolor, virginica]]
+# jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V).fit(gmm_stats, [0, 1, 2])
+
 
 # Variability direction U
 u0 = jfa_machine.U[0:2, 0] / np.linalg.norm(jfa_machine.U[0:2, 0])