diff options
Diffstat (limited to 'src/python/gudhi/weighted_rips_complex.py')
| -rw-r--r-- | src/python/gudhi/weighted_rips_complex.py | 19 |
1 files changed, 11 insertions, 8 deletions
diff --git a/src/python/gudhi/weighted_rips_complex.py b/src/python/gudhi/weighted_rips_complex.py index 7d14ac65..9df2ddf9 100644 --- a/src/python/gudhi/weighted_rips_complex.py +++ b/src/python/gudhi/weighted_rips_complex.py @@ -1,6 +1,6 @@ # 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 and Yuichi Ike +# Author(s): Raphaël Tinarrage, Yuichi Ike, Masatoshi Takenouchi # # Copyright (C) 2020 Inria, Copyright (C) 2020 FUjitsu Laboratories Ltd. # @@ -12,23 +12,26 @@ from gudhi import SimplexTree class WeightedRipsComplex: """ class to generate a weighted Rips complex - from a distance matrix and filtration value + from a distance matrix and weights on vertices """ def __init__(self, - distance_matrix=None, - filtration_values=None, + distance_matrix, + weights=None, max_filtration=float('inf')): """ Parameters: distance_matrix: list of list of float, distance matrix (full square or lower triangular) filtration_values: list of float, - flitration value for each index + weight for each vertex max_filtration: float, specifies the maximal filtration value to be considered """ self.distance_matrix = distance_matrix - self.filtration_values = filtration_values + 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): @@ -38,7 +41,7 @@ class WeightedRipsComplex: graph expansion until this given dimension """ dist = self.distance_matrix - F = self.filtration_values + F = self.weights num_pts = len(dist) st = SimplexTree() @@ -47,7 +50,7 @@ class WeightedRipsComplex: if F[i] < self.max_filtration: st.insert([i], F[i]) for i in range(num_pts): - for j in range(num_pts): + for j in range(i): value = (dist[i][j] + F[i] + F[j]) / 2 if value < self.max_filtration: st.insert([i,j], filtration=value) |