git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/Nerve_GIC@2411 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 92552c44d0a2369735bd29495c3504f53010a06c

9 files changed, 181 insertions, 23 deletions

diff --git a/src/Nerve_GIC/doc/COPYRIGHT b/src/Nerve_GIC/doc/COPYRIGHT
new file mode 100644
index 00000000..0c36a526
--- /dev/null
+++ b/src/Nerve_GIC/doc/COPYRIGHT
@@ -0,0 +1,19 @@
+The files of this directory are part of the Gudhi Library. The Gudhi library
+(Geometric Understanding in Higher Dimensions) is a generic C++ library for
+computational topology.
+
+Author(s):       Mathieu Carrière
+
+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/>.
diff --git a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
index 8c31b262..f30d51a4 100644
--- a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
+++ b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
@@ -33,23 +33,31 @@ namespace graph_induced_complex {
  * 
  * @{
  * 
+ * \section covers Covers
+ *
+ * Nerves and Graph Induced Complexes require a cover C of the input point cloud P,
+ * that is a set of subsets of P whose union is P itself.
+ * Very often, this cover is obtained from the preimage of a family of intervals covering
+ * the image of some scalar-valued function f defined on P. This family is parameterized
+ * by its resolution, which can be either the number or the length of the intervals,
+ * and its gain, which is the overlap percentage between consecutive intervals (ordered by their first values).
+ *
  * \section nerves Nerves
  *
  * \subsection nervedefinition Nerve definition
  *
- * Assume you are given a cover C of your point cloud P, that is a set of subsets of P
- * whose union is P itself. Then, the Nerve of this cover
+ * Assume you are given a cover C of your point cloud P. Then, the Nerve of this cover
  * is the simplicial complex that has one k-simplex per k-fold intersection of cover elements.
  * See also <a target="_blank" href="https://en.wikipedia.org/wiki/Nerve_of_a_covering"> Wikipedia </a>.
  *
+ * \image html "nerve.png" "Nerve of a double torus"
+ *
  * \subsection nerveexample Example
  *
  * This example builds the Nerve of a point cloud sampled on a 3D human shape (human.off).
  * The cover C comes from the preimages of intervals (10 intervals with gain 0.3)
  * covering the height function (coordinate 2),
  * which are then refined into their connected components using the triangulation of the .OFF file.
- * All intervals have the resolution (either the length or the number of the intervals)
- * and gain (overlap percentage).
  *
  * \include Nerve_GIC/Nerve.cpp
  *
@@ -62,6 +70,13 @@ namespace graph_induced_complex {
  *
  * \include Nerve_GIC/Nerve.txt
  *
+ * The first three lines are requirements for visualization with Kepler-Mapper.
+ * The fourth line contains the number of vertices nv and edges ne of the Nerve.
+ * The next nv lines represent the vertices. Each line contains the vertex ID,
+ * the number of data points it contains, and their average color function value.
+ * Finally, the next ne lines represent the edges, characterized by the ID of their vertices.
+ *
+ *
  * \section gic Graph Induced Complexes (GIC)
  *
  * \subsection gicdefinition GIC definition
@@ -73,6 +88,8 @@ namespace graph_induced_complex {
  * See <a target="_blank" href="https://arxiv.org/abs/1304.0662"> this article </a>
  * for more details.
  *
+ * \image html "gic_complex.png" "GIC of a point cloud."
+ *
  * \subsection gicexample Example
  *
  * This example builds the GIC of a point cloud sampled on a 3D human shape (human.off).
diff --git a/src/Nerve_GIC/doc/gic_complex.png b/src/Nerve_GIC/doc/gic_complex.png
new file mode 100644
index 00000000..fb4b20ad
--- /dev/null
+++ b/src/Nerve_GIC/doc/gic_complex.png
diff --git a/src/Nerve_GIC/doc/nerve.png b/src/Nerve_GIC/doc/nerve.png
new file mode 100644
index 00000000..b66da4a4
--- /dev/null
+++ b/src/Nerve_GIC/doc/nerve.png
diff --git a/src/Nerve_GIC/include/gudhi/GIC.h b/src/Nerve_GIC/include/gudhi/GIC.h
index c373b25c..e9b7a1f1 100644
--- a/src/Nerve_GIC/include/gudhi/GIC.h
+++ b/src/Nerve_GIC/include/gudhi/GIC.h
@@ -162,10 +162,21 @@ class Graph_induced_complex {
     *
     */
    void set_graph_from_file(const std::string& graph_file_name){
-     int neighb; int vid; std::ifstream input(graph_file_name); std::string line; std::vector<int> edge(2);
+     int neighb; int vid; std::ifstream input(graph_file_name); std::string line; std::vector<int> edge(2); int n = 0;
      while(std::getline(input,line)){
        std::stringstream stream(line); stream >> vid; edge[0] = vid;
        while(stream >> neighb){edge[1] = neighb; st.insert_simplex_and_subfaces(edge);}
+       n++;
+     }
+
+     std::vector<int> empty;
+     for(int i = 0; i < n; i++)  adjacency_matrix.insert(std::pair<int,std::vector<int> >(i,empty));
+     for (auto simplex : st.complex_simplex_range()) {
+       if(st.dimension(simplex) == 1){
+         std::vector<int> vertices;
+         for(auto vertex : st.simplex_vertex_range(simplex))  vertices.push_back(vertex);
+         adjacency_matrix[vertices[0]].push_back(vertices[1]); adjacency_matrix[vertices[1]].push_back(vertices[0]);
+       }
      }
    }
 
@@ -191,6 +202,16 @@ class Graph_induced_complex {
        }
        i++;
      }
+
+     std::vector<int> empty;
+     for(int i = 0; i < numpts; i++)  adjacency_matrix.insert(std::pair<int,std::vector<int> >(i,empty));
+     for (auto simplex : st.complex_simplex_range()) {
+       if(st.dimension(simplex) == 1){
+         std::vector<int> vertices;
+         for(auto vertex : st.simplex_vertex_range(simplex))  vertices.push_back(vertex);
+         adjacency_matrix[vertices[0]].push_back(vertices[1]); adjacency_matrix[vertices[1]].push_back(vertices[0]);
+       }
+     }
    }
 
  public: // Set graph from Rips complex.
@@ -204,6 +225,17 @@ class Graph_induced_complex {
      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); data_dimension = off_reader.get_point_cloud()[0].size();
+
+     std::vector<int> empty; int n = off_reader.get_point_cloud().size();
+     for(int i = 0; i < n; i++)  adjacency_matrix.insert(std::pair<int,std::vector<int> >(i,empty));
+     for (auto simplex : st.complex_simplex_range()) {
+       if(st.dimension(simplex) == 1){
+         std::vector<int> vertices;
+         for(auto vertex : st.simplex_vertex_range(simplex))  vertices.push_back(vertex);
+         adjacency_matrix[vertices[0]].push_back(vertices[1]); adjacency_matrix[vertices[1]].push_back(vertices[0]);
+       }
+     }
+
    }
 
  public: // Automatic tuning of Rips complex.
@@ -277,6 +309,16 @@ class Graph_induced_complex {
      Rips_complex rips_complex_from_points(off_reader.get_point_cloud(), delta, Euclidean_distance());
      rips_complex_from_points.create_complex(st, 1);
 
+     std::vector<int> empty;
+     for(int i = 0; i < n; i++)  adjacency_matrix.insert(std::pair<int,std::vector<int> >(i,empty));
+     for (auto simplex : st.complex_simplex_range()) {
+       if(st.dimension(simplex) == 1){
+         std::vector<int> vertices;
+         for(auto vertex : st.simplex_vertex_range(simplex))  vertices.push_back(vertex);
+         adjacency_matrix[vertices[0]].push_back(vertices[1]); adjacency_matrix[vertices[1]].push_back(vertices[0]);
+       }
+     }
+
    }
 
 
@@ -337,7 +379,7 @@ class Graph_induced_complex {
      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);}
+       while(stream >> cov){cov_elts.push_back(cov); cov_number.push_back(cov); cover_fct.insert(std::pair<Cover_t,int>(cov,cov));}
        cover.insert(std::pair<int, std::vector<Cover_t> >(vertex_id, cov_elts)); vertex_id++;
      }
      std::vector<Cover_t>::iterator it;
@@ -442,17 +484,6 @@ class Graph_induced_complex {
      std::vector<int> points(num_pts); for(int i = 0; i < num_pts; i++)  points[i] = i;
      std::sort(points.begin(),points.end(),functional_comp);
 
-     // Build adjacency matrix
-     std::vector<int> empty;
-     for(int i = 0; i < num_pts; i++)  adjacency_matrix.insert(std::pair<int,std::vector<int> >(points[i],empty));
-     for (auto simplex : st.complex_simplex_range()) {
-       if(st.dimension(simplex) == 1){
-         std::vector<int> vertices;
-         for(auto vertex : st.simplex_vertex_range(simplex))  vertices.push_back(vertex);
-         adjacency_matrix[vertices[0]].push_back(vertices[1]); adjacency_matrix[vertices[1]].push_back(vertices[0]);
-       }
-     }
-
      int id = 0; int pos = 0; double min_prop_int; double max_prop_int;
 
      for(int i = 0; i < res; i++){       
@@ -627,7 +658,6 @@ class Graph_induced_complex {
  public:
    /** \brief Creates the simplicial complex.
     *
-    * \tparam SimplicialComplexForGIC must meet `SimplicialComplexForRips` concept.
     * @param[in] complex SimplicialComplexForGIC to be created.
     *
     */
@@ -685,14 +715,16 @@ class Graph_induced_complex {
      }
 
      // Remove edges
-     int current_id = 1;
-     auto simplex = st.complex_simplex_range().begin(); int num_rem = 0;
-     for(int i = 0; i < simplex_id-1; i++){
+     if(simplex_to_remove.size() > 1){
+       int current_id = 1;
+       auto simplex = st.complex_simplex_range().begin(); int num_rem = 0;
+       for(int i = 0; i < simplex_id-1; 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++; num_rem++;}
          else  simplex++;
-     } simplex = st.complex_simplex_range().begin();
-     for(int i = 0; i < simplex_to_remove[0]; i++)  simplex++;  st.remove_maximal_simplex(*simplex);
+       } simplex = st.complex_simplex_range().begin();
+       for(int i = 0; i < simplex_to_remove[0]; i++)  simplex++;  st.remove_maximal_simplex(*simplex);
+     }
 
      // Build the Simplex Tree corresponding to the graph
      st.expansion(maximal_dim);
diff --git a/src/Nerve_GIC/test/data/cloud b/src/Nerve_GIC/test/data/cloud
new file mode 100644
index 00000000..82fc5c79
--- /dev/null
+++ b/src/Nerve_GIC/test/data/cloud
@@ -0,0 +1,5 @@
+OFF
+3 0 0
+0 0 0
+2 1 0
+4 0 0
\ No newline at end of file
diff --git a/src/Nerve_GIC/test/data/cover b/src/Nerve_GIC/test/data/cover
new file mode 100644
index 00000000..5f5fbe75
--- /dev/null
+++ b/src/Nerve_GIC/test/data/cover
@@ -0,0 +1,3 @@
+1
+2
+3
\ No newline at end of file
diff --git a/src/Nerve_GIC/test/data/graph b/src/Nerve_GIC/test/data/graph
new file mode 100644
index 00000000..37142800
--- /dev/null
+++ b/src/Nerve_GIC/test/data/graph
@@ -0,0 +1,3 @@
+0 1
+0 2
+1 2
\ No newline at end of file
diff --git a/src/Nerve_GIC/test/test.cpp b/src/Nerve_GIC/test/test.cpp
new file mode 100644
index 00000000..20e5ee4d
--- /dev/null
+++ b/src/Nerve_GIC/test/test.cpp
@@ -0,0 +1,79 @@
+/*    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):       Mathieu Carrière
+ *
+ *    Copyright (C) 2017  INRIA Saclay (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/>.
+ */
+
+#define BOOST_TEST_DYN_LINK
+#define BOOST_TEST_MODULE "graph_induced_complex"
+
+#include <boost/test/unit_test.hpp>
+#include <cmath>  // float comparison
+#include <limits>
+#include <string>
+#include <vector>
+#include <algorithm>    // std::max
+#include <gudhi/GIC.h>
+#include <gudhi/distance_functions.h>
+#include <gudhi/reader_utils.h>
+
+bool are_almost_the_same(float a, float b) {
+  return std::fabs(a - b) < std::numeric_limits<float>::epsilon();
+}
+
+BOOST_AUTO_TEST_CASE(check_nerve) {
+
+  Gudhi::graph_induced_complex::Graph_induced_complex GIC;
+  std::string graph_file_name("data/graph"); GIC.set_graph_from_file(graph_file_name);
+  std::string cover_file_name("data/cover"); GIC.set_cover_from_file(cover_file_name);
+  std::string cloud_file_name("data/cloud"); GIC.set_color_from_file(cloud_file_name);
+  GIC.find_Nerve_simplices(); Simplex_tree stree; GIC.create_complex(stree);
+
+  BOOST_CHECK(stree.num_vertices() == 3);
+  BOOST_CHECK((stree.num_simplices()-stree.num_vertices()) == 0);
+  BOOST_CHECK(stree.dimension() == 0);
+}
+
+BOOST_AUTO_TEST_CASE(check_GICMAP) {
+
+  Gudhi::graph_induced_complex::Graph_induced_complex GIC; GIC.set_verbose(1);
+  std::string graph_file_name("data/graph"); GIC.set_graph_from_file(graph_file_name);
+  std::string cover_file_name("data/cover"); GIC.set_cover_from_file(cover_file_name);
+  std::string cloud_file_name("data/cloud"); GIC.set_color_from_file(cloud_file_name);
+  GIC.find_GICMAP_simplices_with_functional_minimal_cover(); Simplex_tree stree; GIC.create_complex(stree);
+
+  BOOST_CHECK(stree.num_vertices() == 3);
+  BOOST_CHECK((stree.num_simplices()-stree.num_vertices()) == 2);
+  BOOST_CHECK(stree.dimension() == 1);
+}
+
+BOOST_AUTO_TEST_CASE(check_GIC) {
+
+  Gudhi::graph_induced_complex::Graph_induced_complex GIC;
+  std::string graph_file_name("data/graph"); GIC.set_graph_from_file(graph_file_name);
+  std::string cover_file_name("data/cover"); GIC.set_cover_from_file(cover_file_name);
+  std::string cloud_file_name("data/cloud"); GIC.set_color_from_file(cloud_file_name);
+  GIC.find_GIC_simplices(); Simplex_tree stree; GIC.create_complex(stree);
+
+  BOOST_CHECK(stree.num_vertices() == 3);
+  BOOST_CHECK((stree.num_simplices()-stree.num_vertices()) == 4);
+  BOOST_CHECK(stree.dimension() == 2);
+}
+
+