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/* This file is part of the Gudhi Library. The Gudhi library
* (Geometric Understanding in Higher Dimensions) is a generic C++
* library for computational topology.
*
* Author: Francois Godi
*
* Copyright (C) 2015 Inria
*
* Modification(s):
* - YYYY/MM Author: Description of the modification
* - 2019/06 Vincent Rouvreau : Fix doxygen warning.
*/
#ifndef BOTTLENECK_H_
#define BOTTLENECK_H_
#include <gudhi/Graph_matching.h>
#include <vector>
#include <algorithm> // for max
#include <limits> // for numeric_limits
#include <cmath>
#include <cfloat> // FLT_EVAL_METHOD
namespace Gudhi {
namespace persistence_diagram {
inline double bottleneck_distance_approx(Persistence_graph& g, double e) {
double b_lower_bound = 0.;
double b_upper_bound = g.diameter_bound();
const double alpha = std::pow(g.size(), 1. / 5.);
Graph_matching m(g);
Graph_matching biggest_unperfect(g);
while (b_upper_bound - b_lower_bound > 2 * e) {
double step = b_lower_bound + (b_upper_bound - b_lower_bound) / alpha;
#if !defined FLT_EVAL_METHOD || FLT_EVAL_METHOD < 0 || FLT_EVAL_METHOD > 1
// On platforms where double computation is done with excess precision,
// we force it to its true precision so the following test is reliable.
volatile double drop_excess_precision = step;
step = drop_excess_precision;
// Alternative: step = CGAL::IA_force_to_double(step);
#endif
if (step <= b_lower_bound || step >= b_upper_bound) // Avoid precision problem
break;
m.set_r(step);
while (m.multi_augment()) {} // compute a maximum matching (in the graph corresponding to the current r)
if (m.perfect()) {
m = biggest_unperfect;
b_upper_bound = step;
} else {
biggest_unperfect = m;
b_lower_bound = step;
}
}
return (b_lower_bound + b_upper_bound) / 2.;
}
inline double bottleneck_distance_exact(Persistence_graph& g) {
std::vector<double> sd = g.sorted_distances();
long lower_bound_i = 0;
long upper_bound_i = sd.size() - 1;
const double alpha = std::pow(g.size(), 1. / 5.);
Graph_matching m(g);
Graph_matching biggest_unperfect(g);
while (lower_bound_i != upper_bound_i) {
long step = lower_bound_i + static_cast<long> ((upper_bound_i - lower_bound_i - 1) / alpha);
m.set_r(sd.at(step));
while (m.multi_augment()) {} // compute a maximum matching (in the graph corresponding to the current r)
if (m.perfect()) {
m = biggest_unperfect;
upper_bound_i = step;
} else {
biggest_unperfect = m;
lower_bound_i = step + 1;
}
}
return sd.at(lower_bound_i);
}
/** \brief Function to compute the Bottleneck distance between two persistence diagrams.
*
* \tparam Persistence_diagram1,Persistence_diagram2
* models of the concept `PersistenceDiagram`.
*
* \param[in] diag1 The first persistence diagram.
* \param[in] diag2 The second persistence diagram.
*
* \param[in] e
* \parblock
* If `e` is 0, this uses an expensive algorithm to compute the exact distance.
*
* If `e` is not 0, it asks for an additive `e`-approximation, and currently
* also allows a small multiplicative error (the last 2 or 3 bits of the
* mantissa may be wrong). This version of the algorithm takes advantage of the
* limited precision of `double` and is usually a lot faster to compute,
* whatever the value of `e`.
*
* Thus, by default, `e` is the smallest positive double.
* \endparblock
*
* \ingroup bottleneck_distance
*/
template<typename Persistence_diagram1, typename Persistence_diagram2>
double bottleneck_distance(const Persistence_diagram1 &diag1, const Persistence_diagram2 &diag2,
double e = (std::numeric_limits<double>::min)()) {
Persistence_graph g(diag1, diag2, e);
if (g.bottleneck_alive() == std::numeric_limits<double>::infinity())
return std::numeric_limits<double>::infinity();
return (std::max)(g.bottleneck_alive(), e == 0. ? bottleneck_distance_exact(g) : bottleneck_distance_approx(g, e));
}
} // namespace persistence_diagram
} // namespace Gudhi
#endif // BOTTLENECK_H_
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