diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 3e6ad841..b3320ca9 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -304,75 +304,72 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
         m.plot_scales("MRD Scales")
     return m
 
-# # Commented out because dataset is missing
-# def brendan_faces():
-#     from GPy import kern
-#     data = GPy.util.datasets.brendan_faces()
-#     Q = 2
-#     Y = data['Y'][0:-1:10, :]
-#     # Y = data['Y']
-#     Yn = Y - Y.mean()
-#     Yn /= Yn.std()
+def brendan_faces():
+    from GPy import kern
+    data = GPy.util.datasets.brendan_faces()
+    Q = 2
+    Y = data['Y'][0:-1:10, :]
+    # Y = data['Y']
+    Yn = Y - Y.mean()
+    Yn /= Yn.std()
 
-#     m = GPy.models.GPLVM(Yn, Q)
-#     # m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
+    m = GPy.models.GPLVM(Yn, Q)
+    # m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
 
-#     # optimize
-#     m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
+    # optimize
+    m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
 
-#     m.ensure_default_constraints()
-#     m.optimize('scg', messages=1, max_f_eval=10000)
+    m.ensure_default_constraints()
+    m.optimize('scg', messages=1, max_f_eval=10000)
 
-#     ax = m.plot_latent(which_indices=(0, 1))
-#     y = m.likelihood.Y[0, :]
-#     data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
-#     lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
-#     raw_input('Press enter to finish')
-#     plt.close('all')
+    ax = m.plot_latent(which_indices=(0, 1))
+    y = m.likelihood.Y[0, :]
+    data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
+    lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
+    raw_input('Press enter to finish')
+    plt.close('all')
 
-#     return m
+    return m
 
-# # Commented out because dataset is missing
-# def stick():
-#     data = GPy.util.datasets.stick()
-#     m = GPy.models.GPLVM(data['Y'], 2)
+def stick():
+    data = GPy.util.datasets.stick()
+    m = GPy.models.GPLVM(data['Y'], 2)
 
-#     # optimize
-#     m.ensure_default_constraints()
-#     m.optimize(messages=1, max_f_eval=10000)
-#     m._set_params(m._get_params())
+    # optimize
+    m.ensure_default_constraints()
+    m.optimize(messages=1, max_f_eval=10000)
+    m._set_params(m._get_params())
 
-#     ax = m.plot_latent()
-#     y = m.likelihood.Y[0, :]
-#     data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
-#     lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
-#     raw_input('Press enter to finish')
-#     plt.close('all')
+    ax = m.plot_latent()
+    y = m.likelihood.Y[0, :]
+    data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
+    lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
+    raw_input('Press enter to finish')
+    plt.close('all')
 
-#     return m
+    return m
 
-# # Commented out because dataset is missing
-# def cmu_mocap(subject='35', motion=['01'], in_place=True):
+def cmu_mocap(subject='35', motion=['01'], in_place=True):
 
-#     data = GPy.util.datasets.cmu_mocap(subject, motion)
-#     Y = data['Y']
-#     if in_place:
-#         # Make figure move in place.
-#         data['Y'][:, 0:3] = 0.0
-#     m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
+    data = GPy.util.datasets.cmu_mocap(subject, motion)
+    Y = data['Y']
+    if in_place:
+        # Make figure move in place.
+        data['Y'][:, 0:3] = 0.0
+    m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
 
-#     # optimize
-#     m.ensure_default_constraints()
-#     m.optimize(messages=1, max_f_eval=10000)
+    # optimize
+    m.ensure_default_constraints()
+    m.optimize(messages=1, max_f_eval=10000)
 
-#     ax = m.plot_latent()
-#     y = m.likelihood.Y[0, :]
-#     data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
-#     lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
-#     raw_input('Press enter to finish')
-#     plt.close('all')
+    ax = m.plot_latent()
+    y = m.likelihood.Y[0, :]
+    data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
+    lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
+    raw_input('Press enter to finish')
+    plt.close('all')
 
-#     return m
+    return m
 
 # def BGPLVM_oil():
 #     data = GPy.util.datasets.oil()