plt.xlabel('Index')
plt.ylabel('Latent Variable 1')
plt.title('Metric MDS 1-Dimension Plot with Observation Class')
plt.scatter(mds_dim[:, 0], list(alldata.index), c=y)
plt.colorbar()
plt.show()
'''
-------------------------------------------------------------------------------
-----------------------------------Isomap--------------------------------------
-------------------------------------------------------------------------------
'''
#Apply isomap embedding, keeping n components < the number of original features
iso_model = Isomap(n_neighbors=5, n_components=2)
iso_model.fit_transform(x_std)
print(iso_model.get_params())
iso_dim = iso_model.embedding_
print(iso_dim.shape) #There should be 2 latent variables represented
#Plot first 2 extracted features and the observation class
plt.figure(figsize=(10, 5))
plt.xlabel('Latent Variable 1 (explains most variance)')
plt.ylabel('Latent Variable 2 (explains second most variance)')
plt.title('Isomap 2-Dimension Plot with Observation Class')
plt.scatter(iso_dim[:, 0], iso_dim[:, 1], c=y)
plt.colorbar()
plt.show()
#Apply isomap for many different choices of dimensions
#Limitation is that nbr dimensions must be < the number of original features
nbr_dim = range(3)
