Added HLLE and LTSA to swissroll graphical example

examples/undocumented/python_modular/graphical/dimreduct/swissroll.py

@@ -7,7 +7,7 @@
 from shogun.Preprocessor import LocallyLinearEmbedding
 lle = LocallyLinearEmbedding()
 lle.set_k(9)
-preprocs.append((lle, "Locally Linear Embedding with k=%d" % lle.get_k()))
+preprocs.append((lle, "LLE with k=%d" % lle.get_k()))
 
 from shogun.Preprocessor import ClassicMDS
 mds = ClassicMDS()
@@ -16,26 +16,36 @@
 from shogun.Preprocessor import LandmarkMDS
 lmds = LandmarkMDS()
 lmds.set_landmark_number(50)
-preprocs.append((lmds,"Landmark MDS with %d landmarks" % lmds.get_landmark_number()))
+preprocs.append((lmds,"LMDS with %d landmarks" % lmds.get_landmark_number()))
 
 from shogun.Preprocessor import ClassicIsomap, KISOMAP
 cisomap = ClassicIsomap()
 cisomap.set_type(KISOMAP)
 cisomap.set_k(9)
-preprocs.append((cisomap,"Classic K-Isomap with k=%d" % cisomap.get_k()))
+preprocs.append((cisomap,"K-Isomap with k=%d" % cisomap.get_k()))
 
 from shogun.Preprocessor import LandmarkIsomap
 lisomap = LandmarkIsomap()
 lisomap.set_landmark_number(50)
 lisomap.set_type(KISOMAP)
 lisomap.set_k(9)
-preprocs.append((lisomap,"L-K-Isomap with k=%d, %d landmarks" % (lisomap.get_k(),lisomap.get_landmark_number())))
+preprocs.append((lisomap,"K-LIsomap with k=%d, %d landmarks" % (lisomap.get_k(),lisomap.get_landmark_number())))
+
+from shogun.Preprocessor import HessianLocallyLinearEmbedding
+hlle = HessianLocallyLinearEmbedding()
+hlle.set_k(6)
+preprocs.append((hlle,"Hessian LLE with k=%d" % (hlle.get_k())))
+
+from shogun.Preprocessor import LocalTangentSpaceAlignment
+ltsa = LocalTangentSpaceAlignment()
+ltsa.set_k(6)
+preprocs.append((ltsa,"LTSA with k=%d" % (ltsa.get_k())))
 
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 
 fig = plt.figure()
-swiss_roll_fig = fig.add_subplot(len(preprocs)/2+1,len(preprocs)/2,1,projection='3d')
+swiss_roll_fig = fig.add_subplot(3,3,1,projection='3d')
 swiss_roll_fig.scatter(X[0], X[1], X[2], s=10, c=tt, cmap=plt.cm.Spectral)
 plt.subplots_adjust(hspace=0.4)
 
@@ -46,7 +56,7 @@
 	features = RealFeatures(X)
 	preproc.set_target_dim(2)
 	new_feats = preproc.apply_to_feature_matrix(features)
-	preproc_subplot = fig.add_subplot(len(preprocs)/2+1,len(preprocs)/2,i+2)
+	preproc_subplot = fig.add_subplot(3,3,i+2)
 	preproc_subplot.scatter(new_feats[0],new_feats[1], c=tt, cmap=plt.cm.Spectral)
 	plt.title(label)
 	print preproc.get_name(), 'done'