[dim_reduce examples] updated swiss roll

GPy/examples/dimensionality_reduction.py

@@ -99,7 +99,7 @@ def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_induci
         m.kern.plot_ARD()
     return m
 
-def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4, sigma=.2):
+def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=25, Q=4, sigma=.2):
     import GPy
     from GPy.util.datasets import swiss_roll_generated
     from GPy.models import BayesianGPLVM
@@ -144,16 +144,15 @@ def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4
     m = BayesianGPLVM(Y, Q, X=X, X_variance=S, num_inducing=num_inducing, Z=Z, kernel=kernel)
     m.data_colors = c
     m.data_t = t
-    m['noise_variance'] = Y.var() / 100.
-
+    
     if optimize:
-        m.optimize('scg', messages=verbose, max_iters=2e3)
+        m.optimize('bfgs', messages=verbose, max_iters=2e3)
 
     if plot:
         fig = plt.figure('fitted')
         ax = fig.add_subplot(111)
         s = m.input_sensitivity().argsort()[::-1][:2]
-        ax.scatter(*m.X.T[s], c=c)
+        ax.scatter(*m.X.mean.T[s], c=c)
 
     return m
 

GPy/util/datasets.py

@@ -635,7 +635,7 @@ def osu_run1(data_set='osu_run1', sample_every=4):
     return data_details_return({'Y': Y, 'connect' : connect}, data_set)
 
 def swiss_roll_generated(num_samples=1000, sigma=0.0):
-    with open(os.path.join(data_path, 'swiss_roll.pickle')) as f:
+    with open(os.path.join(os.path.dirname(__file__), 'datasets', 'swiss_roll.pickle')) as f:
         data = pickle.load(f)
     Na = data['Y'].shape[0]
     perm = np.random.permutation(np.r_[:Na])[:num_samples]