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/*
* $Revision: 3521 $
*
* last checkin:
* $Author: gutwenger $
* $Date: 2013-05-31 14:52:33 +0200 (Fri, 31 May 2013) $
***************************************************************/
/** \file
* \brief Declaration of interface for clustering algorithms that
* compute a clustering for a given graph based on some
* structural or semantical properties.
*
* Precondition:
* Input graph has to be connected
*
* \author Karsten Klein
*
* \par License:
* This file is part of the Open Graph Drawing Framework (OGDF).
*
* \par
* Copyright (C)<br>
* See README.txt in the root directory of the OGDF installation for details.
*
* \par
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License
* Version 2 or 3 as published by the Free Software Foundation;
* see the file LICENSE.txt included in the packaging of this file
* for details.
*
* \par
* 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.
*
* \par
* You should have received a copy of the GNU General Public
* License along with this program; if not, write to the Free
* Software Foundation, Inc., 51 Franklin Street, Fifth Floor,
* Boston, MA 02110-1301, USA.
*
* \see http://www.gnu.org/copyleft/gpl.html
***************************************************************/
#ifdef _MSC_VER
#pragma once
#endif
#ifndef OGDF_CLUSTERER_MODULE_H
#define OGDF_CLUSTERER_MODULE_H
#include <ogdf/basic/Graph.h>
#include <ogdf/cluster/ClusterGraph.h>
#include <ogdf/basic/simple_graph_alg.h>
namespace ogdf {
//Helper classes to code a clustering, used as an interface to applications that
//need to build the clustergraph structure themselves
class SimpleCluster
{
public:
SimpleCluster(SimpleCluster* parent = 0) : m_size(0), m_parent(parent), m_index(-1) { }
//insert vertices and children
void pushBackVertex(node v) {m_nodes.pushBack(v);}
void pushBackChild(SimpleCluster* s) {m_children.pushBack(s);}
void setParent(SimpleCluster* parent) {m_parent = parent;}
SimpleCluster* getParent() {return m_parent;}
void setIndex(int index) {m_index = index;}
int getIndex() {return m_index;}
SList<node> &nodes() {return m_nodes;}
SList<SimpleCluster*> &children() {return m_children;}
int m_size; //preliminary: allowed to be inconsistent with real vertex number to
//allow lazy vertex addition (should therefore be local Array?)
private:
SList<node> m_nodes;
SList<SimpleCluster*> m_children;
SimpleCluster* m_parent;
int m_index; //index of the constructed cluster
};//class SimpleCluster
/**
* \brief Interface for algorithms that compute a clustering for a
* given graph
*
* The class ClustererModule is the base class for clustering
* classes that allow to compute some hierarchical clustering
*/
class OGDF_EXPORT ClustererModule
{
public:
//Constructor taking a graph G to be clustered
explicit ClustererModule(const Graph &G) : m_pGraph(&G) {OGDF_ASSERT(isConnected(G));}
//! Default constructor, initializes a clustering module.
// Allows to cluster multiple
// graphs with the same instance of the Clusterer
ClustererModule() {}
/**
* \brief Sets the graph to be clustered
*
* @param G is the input graph
*
*/
void setGraph(const Graph &G) {OGDF_ASSERT(isConnected(G));m_pGraph = &G;}
//! Returns the graph to be clustered
const Graph& getGraph() const {return *m_pGraph;}
/**
* \brief compute some kind of clustering on the graph m_pGraph
*
* This is the algorithm call that has to be implemented by derived classes
*
* @param sl is the resulting list of clusters
*/
virtual void computeClustering(SList<SimpleCluster*> &sl) = 0;
//! translate computed clustering into cluster hierarchy in cluster graph C
virtual void createClusterGraph(ClusterGraph &C) = 0;
//! compute a clustering index for each vertex
virtual double computeCIndex(const Graph & G, node v) = 0;
//! compute a clustering index for each vertex
virtual double computeCIndex(node v) = 0;
//! compute the average clustering index for the given graph
virtual double averageCIndex()
{
return averageCIndex(*m_pGraph);
}
virtual double averageCIndex(const Graph &G)
{
node v;
double ciSum = 0.0;
forall_nodes(v, G)
{
ciSum += computeCIndex(G, v);
}
return ciSum / (G.numberOfNodes());
}
protected:
const Graph* m_pGraph; //the graph to be clustered
OGDF_MALLOC_NEW_DELETE
};//class ClustererModule
} //end namespace ogdf
#endif /*CLUSTERERMODULE_H_*/
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