gpctree.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 87 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 __GPCTREE_H__
#define __GPCTREE_H__

#include "GLearner.h"

class GPCTreeNode;


// A PCTree is similar a DecisionTree, except it divides as follows:
// It finds the mean and principle component of the output vectors.
// It divides all the vectors into two groups, one that has a
// positive dot-product with the principle component (after subtracting
// the mean) and one that has a negative dot-product with the
// principle component (after subtracting the mean). Next it finds the
// average input vector for each of the two groups. Then it finds
// the mean and principle component of those two vectors. The dividing
// criteria for this node is to subtract the mean and then see whether
// the dot-product with the principle component is positive or negative
class GPCTree : public GSupervisedLearner
{
protected:
	GPCTreeNode* m_pRoot;
	int m_nInputVectorSize;
	int m_nOutputVectorSize;
	double* m_pEvalVector;

public:
	GPCTree(GArffRelation* pRelation);
	virtual ~GPCTree();

	// Discard any training (but not any settings) so it can be trained again
	virtual void Reset();

	// Inductively build the tree
	virtual void Train(GArffData* pData);

	// Deduce the output values from the input values
	virtual void Eval(double* pVector);

protected:
	GPCTreeNode* BuildNode(GArffData* pData, double* pBuf);
};




// This is like a PC-tree, but the division hyper-surface is
// selected randomly
class GArbitraryTree : public GSupervisedLearner
{
protected:
	GPCTreeNode* m_pRoot;
	int m_nInputVectorSize;
	int m_nOutputVectorSize;
	double* m_pEvalVector;
	bool m_bAxisAligned;

public:
	GArbitraryTree(GArffRelation* pRelation, bool bAxisAligned);
	virtual ~GArbitraryTree();

	// Discard any training (but not any settings) so it can be trained again
	virtual void Reset();

	// Inductively build the tree
	virtual void Train(GArffData* pData);

	// Deduce the output values from the input values
	virtual void Eval(double* pVector);

protected:
	GPCTreeNode* BuildNode(GArffData* pData);
};

#endif // __GPCTREE_H__