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/*********************************************************************
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_EXAMPLE_H_
#define _CLUSTERER_EXAMPLE_H_
#include <vector>
#include <clusterer.h>
/**
Clusterer example implementing all the necessary functions from the interface
*/
class ClustererExample : public Clusterer
{
public:
/**
Constructor, instanciating everything that will be used
*/
ClustererExample(){}
/**
Deconstructor, deinstanciating everything that has been instanciated
*/
~ClustererExample(){}
/**
The training function, called by the main program, all training should go 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(float param1, int param2, bool param3){}
};
#endif // _CLUSTERER_EXAMPLE_H_
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