added BGPLVM oil flow demo and changed default X_variance init

GPy/examples/dimensionality_reduction.py

@@ -55,3 +55,31 @@ def GPLVM_oil_100():
     print(m)
     m.plot_latent(labels=data['Y'].argmax(axis=1))
     return m
+
+
+def BGPLVM_oil():
+    data = GPy.util.datasets.oil()
+    Y, X = data['Y'], data['X']
+    X -= X.mean(axis=0)
+    # X /= X.std(axis=0)
+
+    Q = 10
+    M = 30
+
+    kernel = GPy.kern.rbf(Q, ARD = True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
+    m = GPy.models.Bayesian_GPLVM(X, Q, kernel=kernel, M=M)
+    m.scale_factor = 10000.0
+    m.constrain_positive('(white|noise|bias|X_variance|rbf_variance|rbf_length)')
+    from sklearn import cluster
+    km = cluster.KMeans(M, verbose=10)
+    Z = km.fit(m.X).cluster_centers_
+    # Z = GPy.util.misc.kmm_init(m.X, M)
+    m.set('iip', Z)
+    # optimize
+    # m.ensure_default_constraints()
+
+    import pdb; pdb.set_trace()
+    m.optimize('tnc', messages=1)
+    print m
+    m.plot_latent(labels=data['Y'].argmax(axis=1))
+    return m

GPy/inference/SGD.py

@@ -242,8 +242,8 @@ class opt_SGD(Optimizer):
                     f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes, sparse_matrix)
                 else:
                     Nj = N
-                    # momentum_term = self.momentum * step # compute momentum using update(t-1)
                     f, fp = f_fp(self.x_opt)
+                    # momentum_term = self.momentum * step # compute momentum using update(t-1)
                     # step = self.learning_rate * fp # compute update(t)
                     # self.x_opt -= step + momentum_term
                     step = self.momentum * step + self.learning_rate * fp

GPy/models/Bayesian_GPLVM.py

@@ -27,7 +27,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
             X = self.initialise_latent(init, Q, Y)
 
         if S is None:
-            S = np.ones_like(X) * 1e-2#
+            S = np.ones_like(X) * 0.5 + np.random.randn(*X.shape) * 1e-3
 
         if Z is None:
             Z = np.random.permutation(X.copy())[:M]