controller.h

用C#写的一个神经网络结合遗传算法优化的程序

#ifndef _CONTROLLER_H_
#define _CONTROLLER_H_

#include "include.h"
#include "GA.h"
#include "NeuralNet.h"

class CController
{
private:
	
	vector<SGenome> m_vecThePopulation;
	CGA* m_pGA;
	int m_iNumWeightsInNN;
	
	vector<double> m_vecAverageFitness;
	vector<double> m_vecBestFitness;
public:
	int m_iGeneration;
	vector<CNeuralNet> m_vecNN;
	CController();
	~CController();
	void ComputeFitness(vector<vector<double>> &SetIn, vector<vector<double>> &SetOut);
};

CController::CController() : m_pGA(NULL),
                             m_iGeneration(0)
{
	for(int i=0; i<30; i++)
		m_vecNN.push_back(CNeuralNet());

	m_iNumWeightsInNN=m_vecNN[0].GetNumberOfWeights();

	m_pGA=new CGA(30,0.1,0.7,m_iNumWeightsInNN);
	
	m_vecThePopulation=m_pGA->GetChromos();

	
	for(int i=0; i<30; i++)
		m_vecNN[i].PutWeights(m_vecThePopulation[i].vecWeights);

}
CController::~CController()
{
	delete m_pGA;
}
void CController::ComputeFitness(vector<vector<double>> &SetIn, vector<vector<double>> &SetOut)
{
	vector<vector<double>> vecSetIn=SetIn;
	while(true)
	{
	    for(int i=0; i<30; i++)
	    {
		    m_vecNN[i].m_dErrorSum=0.0;
		    SetIn=vecSetIn;//
		    for(int vec=0; vec<(int)SetIn.size(); vec++)
		    {
			    vector<double> outputs=m_vecNN[i].Update(SetIn[vec]);
			    if(outputs.size()==0)
				    return;
			    double error=0;
			    for(int o=0; o<m_vecNN[i].GetNumOutputs(); o++)
			    {
				    error += (SetOut[vec][o]-outputs[o])*(SetOut[vec][o]-outputs[o]);
			    }
			    m_vecNN[i].m_dErrorSum += error;		
		    }
		    m_vecNN[i].m_dFitness = 100-m_vecNN[i].m_dErrorSum;//
		    m_vecThePopulation[i].dFitness = m_vecNN[i].m_dFitness;
	    }

	    m_vecThePopulation = m_pGA->Epoch(m_vecThePopulation);

		m_iGeneration++;

	    for(int i=0; i<30; i++)
	    {
		    m_vecNN[i].PutWeights(m_vecThePopulation[i].vecWeights);
		    m_vecNN[i].m_dFitness=0;
	    }

		if(m_vecNN[0].m_dErrorSum<0.01)
			break;
	}
	MessageBoxA(NULL,"Training complete!","GA",MB_OK);
}

#endif;