from IPython.display import Image Image("SlidesGudhi/GeneralPipeLine_ML.png",width= 1000)
import pandas as pd import numpy as np import pickle as pickle import gudhi as gd from persistence_graphical_tools_Bertrand import * %matplotlib inline
f = open("data_acc","rb") data = pickle.load(f) f.close() data_A = data data_B = data data_C = data label = data print(label) data_A_sample = data_A
The persistence landscape has been introduced Bubenik etal. JMLR 2015 as an alternative to persistence diagrams. This approach aims at representing topological features in an Hilbert space, for which statistical learning methods can be directly applied.
Note that many other alternatives have been proposed: silhouettes, persistence images, cumulative peristence intensity function etc. Coming soon in Gudhi...
Image("SlidesGudhi/Landscapes.png",width = 800)