blob: 63c9b1387e24d68d1e874db0599d807f156a65ab (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
|
# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
# Author(s): Yuichi Ike, Raphaël Tinarrage
#
# Copyright (C) 2020 Inria, Copyright (C) 2020 FUjitsu Laboratories Ltd.
#
# Modification(s):
# - YYYY/MM Author: Description of the modification
from gudhi.weighted_rips_complex import WeightedRipsComplex
from gudhi.point_cloud.dtm import DistanceToMeasure
from scipy.spatial.distance import cdist
class DTMRipsComplex(WeightedRipsComplex):
"""
Class to generate a DTM Rips complex from a distance matrix or a point set,
in the way described in :cite:`dtmfiltrations`.
Remark that all the filtration values are doubled compared to the definition in the paper
for the consistency with RipsComplex.
:Requires: `SciPy <installation.html#scipy>`_
"""
def __init__(self,
points=None,
distance_matrix=None,
k=1,
q=2,
max_filtration=float('inf')):
"""
Args:
points (numpy.ndarray): array of points.
distance_matrix (numpy.ndarray): full distance matrix.
k (int): number of neighbors for the computation of DTM. Defaults to 1, which is equivalent to the usual Rips complex.
q (float): order used to compute the distance to measure. Defaults to 2.
max_filtration (float): specifies the maximal filtration value to be considered.
"""
if distance_matrix is None:
if points is None:
# Empty Rips construction
points=[]
distance_matrix = cdist(points,points)
self.distance_matrix = distance_matrix
# TODO: address the error when k is too large
if k <= 1:
self.weights = [0] * len(distance_matrix)
else:
dtm = DistanceToMeasure(k, q=q, metric="precomputed")
self.weights = dtm.fit_transform(distance_matrix)
self.max_filtration = max_filtration
|
