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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: Mathieu Carriere
*
* Copyright (C) 2017 INRIA
*
* 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 GIC_H_
#define GIC_H_
#include <gudhi/Debug_utils.h>
#include <gudhi/graph_simplicial_complex.h>
#include <gudhi/reader_utils.h>
#include <gudhi/Simplex_tree.h>
#include <gudhi/Rips_complex.h>
#include <gudhi/Points_off_io.h>
#include <gudhi/distance_functions.h>
#include <boost/graph/adjacency_list.hpp>
#include <iostream>
#include <vector>
#include <map>
#include <string>
#include <limits> // for numeric_limits
#include <utility> // for pair<>
#include <functional> // for greater<>
#include <stdexcept>
#include <initializer_list>
#include <algorithm> // for std::max
#include <cstdint> // for std::uint32_t
using Simplex_tree = Gudhi::Simplex_tree<>;
using Filtration_value = Simplex_tree::Filtration_value;
using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>;
using Point = std::vector<float>;
bool simplex_comp(const std::vector<int>& s1, const std::vector<int>& s2){
if(s1.size() == s2.size()){
return s1[0] < s2[0];
}
else return s1.size() < s2.size();
}
namespace Gudhi {
namespace graph_induced_complex {
/**
* \class Graph_induced_complex
* \brief Graph induced complex data structure.
*
* \ingroup graph_induced_complex
*
* \details
*
*
*/
class Graph_induced_complex {
private:
typedef int Cover_t;
private:
std::vector<std::vector<Cover_t> > cliques;
private:
std::map<int, std::vector<Cover_t> > Cov;
private:
int maximal_dim;
private:
Simplex_tree<> st;
public:
void set_cover(const std::string& cover_file_name){
int vertex_id = 0; Cover_t cov; std::vector<Cover_t> cov_elts, cov_number;
std::ifstream input(cover_file_name); std::string line;
while(std::getline(input,line)){
cov_elts.clear(); std::stringstream stream(line);
while(stream >> cov){cov_elts.push_back(cov); cov_number.push_back(cov);}
Cov.insert(std::pair<int, std::vector<Cover_t> >(vertex_id, cov_elts)); vertex_id++;
}
std::vector<Cover_t>::iterator it;
std::sort(cov_number.begin(),cov_number.end()); it = std::unique(cov_number.begin(),cov_number.end());
cov_number.resize(std::distance(cov_number.begin(),it)); maximal_dim = cov_number.size();
return;
}
public:
void set_graph_simplex_tree(const double& threshold, const std::string& off_file_name){
Points_off_reader<Point> off_reader(off_file_name);
Rips_complex rips_complex_from_points(off_reader.get_point_cloud(), threshold, Euclidean_distance());
rips_complex_from_points.create_complex(st, 1);
return;}
public:
template<typename SimplicialComplexForGIC>
void create_complex(SimplicialComplexForGIC & complex) {
size_t sz = cliques.size(); int dimension = 0;
for(int i = 0; i < sz; i++){
complex.insert_simplex_and_subfaces(cliques[i]);
if(dimension < cliques[i].size()-1) dimension = cliques[i].size()-1;
}
complex.set_dimension(dimension);
}
public:
void find_all_simplices(std::vector<std::vector<Cover_t> > & cliques, const std::vector<std::vector<Cover_t> > & cover_elts,\
int & token, std::vector<Cover_t> & simplex_tmp){
int num_nodes = cover_elts.size();
if(token == num_nodes-1){
int num_clus = cover_elts[token].size();
for(int i = 0; i < num_clus; i++){
std::vector<Cover_t> simplex = simplex_tmp; simplex.push_back(cover_elts[token][i]);
std::vector<Cover_t>::iterator it;
std::sort(simplex.begin(),simplex.end()); it = std::unique(simplex.begin(),simplex.end());
simplex.resize(std::distance(simplex.begin(),it));
cliques.push_back(simplex);
}
}
else{
int num_clus = cover_elts[token].size();
for(int i = 0; i < num_clus; i++){
std::vector<Cover_t> simplex = simplex_tmp; simplex.push_back(cover_elts[token][i]);
find_all_simplices(cliques, cover_elts, ++token, simplex);
}
}
}
public:
void find_simplices() {
// Find IDs of edges to remove
std::vector<int> simplex_to_remove; int simplex_id = 0;
for (auto simplex : st.complex_simplex_range()) {
if(st.dimension(simplex) == 1){
std::vector<std::vector<Cover_t> > comp;
for(auto vertex : st.simplex_vertex_range(simplex)) comp.push_back(Cov[vertex]);
if(comp[0].size() == 1 && comp[1].size() == 1 && comp[0][0] == comp[1][0]) simplex_to_remove.push_back(simplex_id);
}
simplex_id++;
}
// Remove edges
int current_id = 0; auto simplex_tmp = st.complex_simplex_range().begin();
if(simplex_to_remove[current_id] == 0){st.remove_maximal_simplex(*simplex_tmp); current_id++;}
auto simplex = st.complex_simplex_range().begin();
for(int i = 1; i < --simplex_id; i++){
int j = i+1; auto simplex_tmp = simplex; simplex_tmp++;
if(j == simplex_to_remove[current_id]){st.remove_maximal_simplex(*simplex_tmp); current_id++; i++;}
simplex++;
}
// Build the Simplex Tree corresponding to the graph
st.expansion(maximal_dim);
// Find simplices of GIC
cliques.clear();
for (auto simplex : st.complex_simplex_range()) {
std::vector<std::vector<Cover_t> > cover_elts; int token = 0; std::vector<Cover_t> sim;
for (auto vertex : st.simplex_vertex_range(simplex)) cover_elts.push_back(Cov[vertex]);
find_all_simplices(cliques,cover_elts,token,sim);
}
std::vector<std::vector<Cover_t> >::iterator it;
std::sort(cliques.begin(),cliques.end()); it = std::unique(cliques.begin(),cliques.end());
cliques.resize(std::distance(cliques.begin(),it));
}
};
} // namespace graph_induced_complex
} // namespace Gudhi
#endif // GIC_H_
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