Author: Martin Royer Copyright: INRIA
atol is a python package implementing an automatic measure vectorisation procedure for topological learning problems. It is based on the ATOL paper https://hal.archives-ouvertes.fr/hal-02296513. It contains a notebook with short usage demonstration 'atol-demo.ipynb'. Install with:
$ git clone https://github.com/martinroyer/atol
$ cd atol
$ (sudo) pip install .
import numpy as np
a, b, c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6], [7.7, 8.8, 9.9]]), np.array([[-4, -5, -6], [-7, -8, -9]])
from sklearn.cluster import KMeans
from atol import Atol
atol_vectoriser = Atol(quantiser=KMeans(n_clusters=10))
atol_vectoriser.fit(X=[a, b, c])
atol_vectoriser.transform(X=[a, b, c])
atol_vectoriser(a)
atol_vectoriser(c)
For experiments on graphs we use the data folder and functions from the 'perslay' repository: https://github.com/MathieuCarriere/perslay, so this package should be installed at the same level as atol for instance.