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/*********************************************************************
FLAME Implementation in MLDemos
Copyright (C) Pierre-Antoine Sondag (pasondag@gmail.com) 2012
Based on the standard implementation of FLAME data clustering algorithm.
Copyright (C) 2007, Fu Limin (phoolimin@gmail.com).
All rights reserved.
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public License,
version 3 as published by the Free Software Foundation.
This library 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
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#ifndef _CLUSTERER_FLAME_H_
#define _CLUSTERER_FLAME_H_
#include <boost/functional/hash.hpp>
#include <clusterer.h>
#include <flame.h>
#include <boost/unordered_map.hpp>
//#include <unordered_map>
#include <vector>
// generic hashing function
template <typename Container>
struct container_hash {
std::size_t operator()(Container const& c) const {
return boost::hash_range(c.begin(), c.end());
}
};
class ClustererFlame : public Clusterer {
private:
float **data;
Flame *flameStructure;
int knnParameter; // used by Flame_DefineSupports
int knnMetric; //passed to Flame_SetDataMatrix
int maxIterationsParameter; //used by Flame_MakeCluster
float epsilonParameter; //used by Flame_MakeCluster
bool isSeveralClasses; //used by Flame_MakeClusters
float thresholdParameter; //used by Flame_MakeClusters
boost::unordered_map<fvec, vector<int>, container_hash<fvec> > resultMap;
vector<fvec> supports;
void PrintDone();
public:
/** Constructor, instanciating everything that will be used */
ClustererFlame(){
data = NULL;
flameStructure = NULL;
knnParameter = 10;
maxIterationsParameter = 100;
epsilonParameter = 1e-6;
}
/** Deconstructor, deinstanciating everything that has been instanciated */
~ClustererFlame(){ }
/** The training function, called by the main program, all training is here. */
void Train(std::vector< fvec > samples);
/**
The testing function, returns a vector of size nbClusters,
with the contribution/weight of the point for each cluster */
fvec Test(const fvec &sample);
/**
Information string for the Algorithm Information and Statistics
panel in the main program interface. Here you probably will put
the number of parameters, the training time or anything else
*/
const char *GetInfoString();
/**
Function to set the algorithm hyper-parameters, called prior to the
training itself
*/
void SetParams(
int knnParameter,
int knnMetric,
int maxIterationsParameter,
bool isSeveralClasses,
float thresholdParameter);
/* Once the treining is done, returns the vectors choosen to be supports. */
vector<fvec> GetSupports();
};
#endif
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