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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(s): Clément Maria
*
* Copyright (C) 2014 INRIA Sophia Antipolis-Méditerranée (France)
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef GRAPH_SIMPLICIAL_COMPLEX_H_
#define GRAPH_SIMPLICIAL_COMPLEX_H_
#include <boost/graph/adjacency_list.hpp>
#include <utility> // for pair<>
#include <vector>
#include <map>
/* Edge tag for Boost PropertyGraph. */
struct edge_filtration_t {
typedef boost::edge_property_tag kind;
};
/* Vertex tag for Boost PropertyGraph. */
struct vertex_filtration_t {
typedef boost::vertex_property_tag kind;
};
typedef int Vertex_handle;
typedef double Filtration_value;
typedef boost::adjacency_list < boost::vecS, boost::vecS, boost::undirectedS
, boost::property < vertex_filtration_t, Filtration_value >
, boost::property < edge_filtration_t, Filtration_value >
> Graph_t;
typedef std::pair< Vertex_handle, Vertex_handle > Edge_t;
/** \brief Output the proximity graph of the points.
*
* If points contains n elements, the proximity graph is the graph
* with n vertices, and an edge [u,v] iff the distance function between
* points u and v is smaller than threshold.
*
* The type PointCloud furnishes .begin() and .end() methods, that return
* iterators with value_type Point.
*/
template< typename PointCloud
, typename Point >
Graph_t compute_proximity_graph(PointCloud &points
, Filtration_value threshold
, Filtration_value distance(Point p1, Point p2)) {
std::vector< Edge_t > edges;
std::vector< Filtration_value > edges_fil;
std::map< Vertex_handle, Filtration_value > vertices;
Vertex_handle idx_u, idx_v;
Filtration_value fil;
idx_u = 0;
for (auto it_u = points.begin(); it_u != points.end(); ++it_u) {
idx_v = idx_u + 1;
for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) {
fil = distance(*it_u, *it_v);
if (fil <= threshold) {
edges.emplace_back(idx_u, idx_v);
edges_fil.push_back(fil);
}
}
++idx_u;
}
Graph_t skel_graph(edges.begin()
, edges.end()
, edges_fil.begin()
, idx_u); // number of points labeled from 0 to idx_u-1
auto vertex_prop = boost::get(vertex_filtration_t(), skel_graph);
boost::graph_traits<Graph_t>::vertex_iterator vi, vi_end;
for (std::tie(vi, vi_end) = boost::vertices(skel_graph);
vi != vi_end; ++vi) {
boost::put(vertex_prop, *vi, 0.);
}
return skel_graph;
}
#endif // GRAPH_SIMPLICIAL_COMPLEX_H_
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