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# 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): Raphaël Tinarrage, Yuichi Ike, Masatoshi Takenouchi
#
# Copyright (C) 2020 Inria, Copyright (C) 2020 FUjitsu Laboratories Ltd.
#
# Modification(s):
# - YYYY/MM Author: Description of the modification
from gudhi import SimplexTree
class WeightedRipsComplex:
"""
Class to generate a weighted Rips complex from a distance matrix and weights on vertices,
in the way described in the paper 'DTM-based filtrations' https://arxiv.org/abs/1811.04757.
Remark that the filtration value of a vertex is twice of its weight for the consistency with
RipsComplex, which is different from the definition in the paper.
"""
def __init__(self,
distance_matrix,
weights=None,
max_filtration=float('inf')):
"""
Args:
distance_matrix (Sequence[Sequence[float]]): distance matrix (full square or lower triangular).
weights (Sequence[float]): (one half of) weight for each vertex.
max_filtration (float): specifies the maximal filtration value to be considered.
"""
self.distance_matrix = distance_matrix
if weights is not None:
self.weights = weights
else:
self.weights = [0] * len(distance_matrix)
self.max_filtration = max_filtration
def create_simplex_tree(self, max_dimension):
"""
Args:
max_dimension (int): graph expansion until this given dimension.
"""
dist = self.distance_matrix
F = self.weights
num_pts = len(dist)
st = SimplexTree()
for i in range(num_pts):
if 2*F[i] <= self.max_filtration:
st.insert([i], 2*F[i])
for i in range(num_pts):
for j in range(i):
value = max(2*F[i], 2*F[j], dist[i][j] + F[i] + F[j])
# max is needed when F is not 1-Lipschitz
if value <= self.max_filtration:
st.insert([i,j], filtration=value)
st.expansion(max_dimension)
return st
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