onlinesvr.cpp

在线支持向量机C＋＋程序,程序中包含了应用的例子

/******************************************************************************
*                       ONLINE SUPPORT VECTOR REGRESSION                      *
*                      Copyright 2006 - Francesco Parrella                    *
*                                                                             *
*This program is distributed under the terms of the GNU General Public License*
******************************************************************************/


#ifndef ONLINE_SVR_CPP
#define ONLINE_SVR_CPP

#include "OnlineSVR.h"


namespace onlinesvr
{

	// Initialization
	OnlineSVR::OnlineSVR ()
	{
		this->C = 30;
		this->Epsilon = 0.1;
		this->KernelType = this->KERNEL_RBF;
		this->KernelParam = 30;
		this->KernelParam2 = 0;
		this->AutoErrorTollerance = true;
		this->ErrorTollerance = this->Epsilon/10;
		this->StabilizedLearning = true;
		this->SaveKernelMatrix = true;
		this->Verbosity = 1;
		this->X = new Matrix<double>();
		this->Y = new Vector<double>();	
		this->Weights = new Vector<double>();
		this->Bias = 0;
		this->SamplesTrainedNumber = 0;
		this->SupportSetIndexes = new Vector<int>();
		this->ErrorSetIndexes = new Vector<int>();
		this->RemainingSetIndexes = new Vector<int>();
		this->R = new Matrix<double>();
		this->KernelMatrix = new Matrix<double>();		
	}

	OnlineSVR::~OnlineSVR ()
	{		
		delete this->X;
		delete this->Y;
		delete this->Weights;
		delete this->SupportSetIndexes;
		delete this->ErrorSetIndexes;
		delete this->RemainingSetIndexes;
		delete this->R;
		delete this->KernelMatrix;
	}

	void OnlineSVR::Clear ()
	{
		this->SamplesTrainedNumber = 0;
		this-> Bias = 0;
		this->X->Clear();
		this->Y->Clear();
		this->Weights->Clear();
		this->Bias = 0;
		this->SupportSetIndexes->Clear();
		this->ErrorSetIndexes->Clear();
		this->RemainingSetIndexes->Clear();
		this->R->Clear();
		this->KernelMatrix->Clear();
	}

	OnlineSVR* OnlineSVR::Clone ()
	{
		OnlineSVR* SVR = new OnlineSVR();
		SVR->C = this->C;
		SVR->Epsilon = this->Epsilon;
		SVR->KernelType = this->KernelType;
		SVR->KernelParam = this->KernelParam;
		SVR->KernelParam2 = this->KernelParam2;
		SVR->AutoErrorTollerance = this->AutoErrorTollerance;
		SVR->ErrorTollerance = this->ErrorTollerance;
		SVR->StabilizedLearning = this->StabilizedLearning;
		SVR->SaveKernelMatrix = this->SaveKernelMatrix;
		SVR->Verbosity = this->Verbosity;
		SVR->X = this->X->Clone();
		SVR->Y = this->Y->Clone();
		SVR->Weights = this->Weights->Clone();
		SVR->Bias = this->Bias;
		SVR->SupportSetIndexes = this->SupportSetIndexes->Clone();
		SVR->ErrorSetIndexes = this->ErrorSetIndexes->Clone();
		SVR->RemainingSetIndexes = this->RemainingSetIndexes->Clone();
		SVR->R = this->R->Clone();
		SVR->KernelMatrix = this->KernelMatrix->Clone();
		SVR->SamplesTrainedNumber = this->SamplesTrainedNumber;
		return SVR;
	}
		

	// Attributes Operations
	double OnlineSVR::GetC ()
	{
		return this->C;
	}

	void OnlineSVR::SetC (double C)
	{
		this->C = C;
	}

	double OnlineSVR::GetEpsilon ()
	{
		return this->Epsilon;
	}

	void OnlineSVR::SetEpsilon (double Epsilon)
	{
		this->Epsilon = Epsilon;
	}

	int OnlineSVR::GetKernelType ()
	{
		return this->KernelType;
	}

	void OnlineSVR::SetKernelType (int KernelType)
	{
		this->KernelType = KernelType;
		if (this->SaveKernelMatrix) {
			this->BuildKernelMatrix();
		}
	}

	double OnlineSVR::GetKernelParam ()
	{
		return this->KernelParam;
	}

	void OnlineSVR::SetKernelParam (double KernelParam)
	{
		this->KernelParam = KernelParam;
		if (this->SaveKernelMatrix) {
			this->BuildKernelMatrix();
		}
	}

	double OnlineSVR::GetKernelParam2 ()
	{
		return this->KernelParam2;
	}

	void OnlineSVR::SetKernelParam2 (double KernelParam2)
	{
		this->KernelParam2 = KernelParam2;
		if (this->SaveKernelMatrix) {
			this->BuildKernelMatrix();
		}
	}

	bool OnlineSVR::GetAutoErrorTollerance ()
	{
		return this->AutoErrorTollerance;
	}

	void OnlineSVR::SetAutoErrorTollerance (bool AutoErrorTollerance)
	{
		this->AutoErrorTollerance = AutoErrorTollerance;
	}

	double OnlineSVR::GetErrorTollerance ()
	{
		return this->ErrorTollerance;
	}

	void OnlineSVR::SetErrorTollerance (double ErrorTollerance)
	{
		this->ErrorTollerance = ErrorTollerance;
	}

	int OnlineSVR::GetSamplesTrainedNumber ()
	{
		return this->SamplesTrainedNumber;
	}

	int OnlineSVR::GetSupportSetElementsNumber ()
	{
		return this->SupportSetIndexes->GetLength();
	}

	int OnlineSVR::GetErrorSetElementsNumber ()
	{
		return this->ErrorSetIndexes->GetLength();
	}

	int OnlineSVR::GetRemainingSetElementsNumber ()
	{
		return this->RemainingSetIndexes->GetLength();
	}

	int OnlineSVR::GetVerbosity ()
	{
		return this->Verbosity;
	}

	void OnlineSVR::SetVerbosity (int Verbosity)
	{
		this->Verbosity = Verbosity;
	}

	bool OnlineSVR::GetStabilizedLearning ()
	{
		return StabilizedLearning;
	}

	void OnlineSVR::SetStabilizedLearning (bool StabilizedLearning)
	{
		this->StabilizedLearning = StabilizedLearning;
	}
	bool OnlineSVR::GetSaveKernelMatrix ()
	{
		return SaveKernelMatrix;
	}

	void OnlineSVR::SetSaveKernelMatrix (bool SaveKernelMatrix)
	{
		if (!this->SaveKernelMatrix && SaveKernelMatrix) {
			this->BuildKernelMatrix();
		}
		else if (!SaveKernelMatrix) {
			this->KernelMatrix->Clear();
		}
		this->SaveKernelMatrix = SaveKernelMatrix;
	}

	Vector<int>* OnlineSVR::GetSupportSetIndexes()
	{
		return this->SupportSetIndexes;
	}

	Vector<int>* OnlineSVR::GetErrorSetIndexes()
	{
		return this->ErrorSetIndexes;
	}
	Vector<int>* OnlineSVR::GetRemainingSetIndexes()
	{
		return this->RemainingSetIndexes;
	}

		

	// Predict/Margin Operations
	double OnlineSVR::Predict (int Index)
	{		
		double PredictedValue = 0;
		for (int i=0; i<this->GetSamplesTrainedNumber(); i++) {		
			PredictedValue += this->Weights->GetValue(i) * this->KernelMatrix->GetValue(i,Index);
		}

		// Bias
		PredictedValue += this->Bias;
		return PredictedValue;
	}

	double OnlineSVR::Predict (Vector<double>* V)
	{	
		// Trained Elements
		double PredictedValue = 0;
		for (int i=0; i<this->GetSamplesTrainedNumber(); i++) {		
			PredictedValue += this->Weights->GetValue(i) * this->Kernel(this->X->GetRowRef(i),V);
		}

		// Bias
		PredictedValue += this->Bias;
		return PredictedValue;
	}

	Vector<double>* OnlineSVR::Predict (Matrix<double>* X)
	{
		if (this->X==X && this->SaveKernelMatrix) {
			Vector<double>* V = new Vector<double>(X->GetLengthRows());
			for (int i=0; i<X->GetLengthRows(); i++) {
				V->Add(this->Predict(i));
			}
			return V;
		}
		else {
			Vector<double>* V = new Vector<double>(X->GetLengthRows());
			for (int i=0; i<X->GetLengthRows(); i++) {
				V->Add(this->Predict(X->GetRowRef(i)));
			}
			return V;
		}
	}

	double OnlineSVR::Margin (Vector<double>* X, double Y)
	{
		return this->Predict(X)-Y;
	}

	Vector<double>* OnlineSVR::Margin (Matrix<double>* X, Vector<double>* Y)
	{
		Vector<double>* V = this->Predict(X);	
		V->SubtractVector(Y);
		return V;