From 54568898173f514bf6e069df23cef0fd711211dd Mon Sep 17 00:00:00 2001
From: Nicolas <n.durrande@sheffield.ac.uk>
Date: Wed, 5 Jun 2013 18:51:26 +0100
Subject: [PATCH] removed examples with non public datasets

---
 GPy/examples/dimensionality_reduction.py | 78 ++++++++++++------------
 GPy/examples/tutorials.py                |  4 +-
 2 files changed, 43 insertions(+), 39 deletions(-)

diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index b3320ca9..1cad6340 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -304,32 +304,33 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
         m.plot_scales("MRD Scales")
     return m
 
-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()
+# # 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()
 
-    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
 
 def stick():
     data = GPy.util.datasets.stick()
@@ -349,27 +350,28 @@ def stick():
 
     return m
 
-def cmu_mocap(subject='35', motion=['01'], in_place=True):
+# # Commented out because dataset is missing
+# 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()
diff --git a/GPy/examples/tutorials.py b/GPy/examples/tutorials.py
index e610fcf4..6950af37 100644
--- a/GPy/examples/tutorials.py
+++ b/GPy/examples/tutorials.py
@@ -143,5 +143,7 @@ def model_interaction():
     X = np.random.randn(20,1)
     Y = np.sin(X) + np.random.randn(*X.shape)*0.01 + 5.
     k = GPy.kern.rbf(1) + GPy.kern.bias(1)
-    return GPy.models.GPRegression(X, Y, kernel=k)
+    m = GPy.models.GPRegression(X, Y, kernel=k)
+    m.ensure_default_constraints()
+    return m