gbag.h

来自「一个由Mike Gashler完成的机器学习方面的includes neural」· C头文件 代码 · 共 78 行

/*
	Copyright (C) 2006, Mike Gashler

	This library is free software; you can redistribute it and/or
	modify it under the terms of the GNU Lesser General Public
	License as published by the Free Software Foundation; either
	version 2.1 of the License, or (at your option) any later version.

	see http://www.gnu.org/copyleft/lesser.html
*/

#ifndef __GBAG_H__
#define __GBAG_H__

#include "GLearner.h"

class GPointerArray;
class GArffRelation;


// BAG stans for bootstrap aggregator. This class basically holds a bunch
// of supervised learning algorithms and combines their results to produce
// final results with lower variance.
class GBag : public GSupervisedLearner
{
protected:
	GPointerArray* m_pLearners;
	int m_nVectorModeOutputSize;
	double* m_pVectorModeOutputs;
	double* m_pAccumulator;

public:
	GBag(GArffRelation* pRelation, int nInitialSize);
	virtual ~GBag();

	// Discard any training (but not any learners that were added) so it can be trained again
	virtual void Reset();

	// Removes and deletes all the learners
	void Flush();

	// Adds a learner to the bag. This takes ownership of pLearner (so
	// it will delete it when it's done with it)
	void AddLearner(GSupervisedLearner* pLearner);


	// Trains all the learning algorithms with the complete data set. Note
	// that it's common to train each algorithm with a different portion
	// of the data set. That's not what this method does. todo: write one
	// that does that.
	virtual void Train(GArffData* pData);

	// Evaluates with each of the learning algorithms and
	// combines the results
	virtual void Eval(double* pVector);
};


// When you call Train, GSmartForest performs a meta-test to determine
// which algorithm will do the best, then it adds them all to the bag
// and trains them
class GSmartForest : public GBag
{
protected:
	int m_nSize;

public:
	GSmartForest(GArffRelation* pRelation, int nSize);
	virtual ~GSmartForest();

	virtual void Train(GArffData* pData);

protected:
	void DoMetaLearning(GArffData* pData);
};

#endif // __GBAG_H__