Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel

Conflicts:
	GPy/examples/dimensionality_reduction.py

GPy/examples/dimensionality_reduction.py

@@ -82,7 +82,7 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
         m.ensure_default_constraints()
 
     y = m.likelihood.Y[0, :]
-    fig,(latent_axes,sense_axes) = plt.subplots(1,2)
+    fig, (latent_axes, hist_axes) = plt.subplots(1, 2)
     plt.sca(latent_axes)
     m.plot_latent()
     data_show = GPy.util.visualize.vector_show(y)
@@ -181,15 +181,26 @@ def bgplvm_simulation_matlab_compare():
     from GPy.models import mrd
     from GPy import kern
     reload(mrd); reload(kern)
-    k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
+    # k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
+    k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
     m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
 #                        X=mu,
 #                        X_variance=S,
                        _debug=True)
     m.ensure_default_constraints()
     m.auto_scale_factor = True
-    m['noise'] = .01  # Y.var() / 100.
-    m['{}_variance'.format(k.parts[0].name)] = .01
+    m['noise'] = Y.var() / 100.
+    m['linear_variance'] = .01
+
+#     lscstr = 'X_variance'
+#     m[lscstr] = .01
+#     m.unconstrain(lscstr); m.constrain_fixed(lscstr, .1)
+
+#     cstr = 'white'
+#     m.unconstrain(cstr); m.constrain_bounded(cstr, .01, 1.)
+
+#     cstr = 'noise'
+#     m.unconstrain(cstr); m.constrain_bounded(cstr, .01, 1.)
     return m
 
 def bgplvm_simulation(burnin='scg', plot_sim=False,
@@ -385,7 +396,7 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True):
     Y = data['Y']
     if in_place:
         # Make figure move in place.
-        data['Y'][:, 0:3]=0.0
+        data['Y'][:, 0:3] = 0.0
     m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
 
     # optimize