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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, Pawel Dlotko, Vincent Rouvreau
*
* Copyright (C) 2016 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 DOC_GRAPH_INDUCED_COMPLEX_INTRO_GRAPH_INDUCED_COMPLEX_H_
#define DOC_GRAPH_INDUCED_COMPLEX_INTRO_GRAPH_INDUCED_COMPLEX_H_
namespace Gudhi {
namespace graph_induced_complex {
/** \defgroup graph_induced_complex Graph induced complex
*
* \author Mathieu Carrière
*
* @{
*
* Visualizations of the simplicial complexes can be done with either
* <a target="_blank" href=""> neato </a>,
* <a target="_blank" href=""> geomview </a>, or
* <a target="_blank" href=""> python </a> + <a target="_blank" href=""> firefox </a>.
*
* \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. 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.
*
* \include Nerve_GIC/Nerve.cpp
*
* When launching:
*
* \code $> ./Nerve ../../../../data/points/human.off 2 10 0.3 --v
* \endcode
*
* the program output is:
*
* \include Nerve_GIC/Nerve.txt
*
* The program also writes a file SC.txt.
* The first three lines in this file 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.
* Using e.g.
*
* \code $> python visu.py && firefox SC.html
* \endcode
*
* one can obtain the following visualization:
*
* \image html "nervevisu.png" "Visualization with Kepler Mapper"
*
* \section gic Graph Induced Complexes (GIC)
*
* \subsection gicdefinition GIC definition
*
* Again, assume you are given a cover C of your point cloud P. Moreover, assume
* you are also given a graph G built on top of P. Then, for any clique in G
* whose nodes all belong to different elements of C, the GIC includes a corresponding
* simplex, whose dimension is the number of nodes in the clique minus one.
* 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. Image taken from
* <a target="_blank" href="https://arxiv.org/abs/1304.0662"> this article </a>"
*
* \subsection gicexample Example with cover from function
*
* This example builds the GIC of a point cloud sampled on a 3D human shape (human.off).
* The cover C comes from the preimages of intervals (with length 0.075 and gain 0)
* covering the height function (coordinate 2),
* and the graph G comes from a Rips complex built with threshold 0.075.
* Note that if the gain is too big, the number of cliques increases a lot,
* which make the computation time much larger.
*
* \include Nerve_GIC/GIC.cpp
*
* When launching:
*
* \code $> ./GIC ../../../../data/points/human.off 0.075 2 0.075 0 --v
* \endcode
*
* the program outputs SC.txt, which can be visualized with python and firefox as before:
*
* \image html "gicvisu.png" "Visualization with Kepler Mapper"
*
* \subsection gicexamplevor Example with cover from Voronoï
*
* This example builds the GIC of a point cloud sampled on a 3D human shape (human.off).
* We randomly subsampled 100 points in the point cloud, which act as seeds of
* a geodesic Voronoï diagram. Each cell of the diagram is then an element of C.
* The graph G (used to compute both the geodesics for Voronoï and the GIC)
* comes from the triangulation of the human shape.
*
* \include Nerve_GIC/GICvoronoi.cpp
*
* When launching:
*
* \code $> ./GICvoronoi ../../../../data/points/human.off 100 --v
* \endcode
*
* the program outputs SC.off. Using e.g.
*
* \code $> geomview SC.off
* \endcode
*
* one can obtain the following visualization:
*
* \image html "gicvoronoivisu.png" "Visualization with Geomview"
*
* \subsection mapperdeltadefinition Mapper Delta
*
* If one restricts to the cliques in G whose nodes all belong to preimages of consecutive
* intervals (assuming the cover of the height function is minimal, i.e. no more than
* two intervals can intersect at a time), the GIC is of dimension one, i.e. a graph.
* We call this graph the Mapper Delta, since it is related to the usual Mapper. See
* <a target="_blank" href="https://arxiv.org/abs/1511.05823"> this article </a> for more details.
*
* \subsection mapperdeltaexample Example
*
* Mapper Delta comes with optimal selection for the Rips threshold,
* the resolution and the gain of the function cover. In this example,
* we compute the Mapper Delta of a Klein bottle embedded in R^5,
* where the graph G comes from a Rips complex with optimal threshold,
* and the cover C comes from the preimages of intervals covering the first coordinate,
* with optimal resolution and gain. Note that optimal threshold, resolution and gain
* also exist for the Nerve of this cover.
*
* \include Nerve_GIC/MapperDeltaCoord.cpp
*
* When launching:
*
* \code $> ./MapperDeltaCoord ../../../../data/points/KleinBottle5D.off 0 --v
* \endcode
*
* the program outputs SC.dot. Using e.g.
*
* \code $> neato SC.dot -Tpdf -o SC.pdf
* \endcode
*
* one can obtain the following visualization:
*
* \image html "mapperdeltacoordvisu2.pdf" "Visualization with Neato"
*
* We also provide an example on a set of 72 pictures taken around the same object (lucky_cat.off).
* The function is now the first eigenfunction given by PCA, whose values
* are written in a file (lucky_cat_PCA1). Threshold, resolution and gain are automatically selected as before.
*
* \include Nerve_GIC/MapperDeltaFunc.cpp
*
* When launching:
*
* \code $> ./MapperDeltaFunc ../../../../data/points/COIL_database/lucky_cat.off ../../../../data/points/COIL_database/lucky_cat_PCA1 --v
* \endcode
*
* the program outputs again SC.dot which gives the following visualization after using neato:
*
* \image html "mapperdeltafuncvisu.pdf" "Visualization with Neato"
*
* \copyright GNU General Public License v3.
* \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
*/
/** @} */ // end defgroup graph_induced_complex
} // namespace graph_induced_complex
} // namespace Gudhi
#endif // DOC_GRAPH_INDUCED_COMPLEX_INTRO_GRAPH_INDUCED_COMPLEX_H_
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