main.cpp

BP神经网络解决异或问题

//main.cpp
//主程序

#include <iostream.h>
#include "bpnet.h"
#include "backprop.h"
#include "string.h"
//#include "dib.h"
//#include "Afx.h"
#define BPM_ITER	10000//迭代次数
//CDib m_Dib;
//HBITMAP m_hBitmap;
//void LoadBMP(CString strPath)
//{
//   	if(!m_Dib.LoadFromFile(strPath)) return;
//	m_hBitmap = (HBITMAP)::LoadImage(NULL,strPath,
//								     IMAGE_BITMAP,
//									 m_Dib.GetDibWidth(),
//									  m_Dib.GetDibHeight(),
//									  LR_LOADFROMFILE | LR_CREATEDIBSECTION);//| LR_DEFAULTSIZE
//	if(m_hBitmap){
//		::GetBitmapBits(m_hBitmap,m_Dib.GetDibHeight() * m_Dib.GetDibWidth(),m_Dib.pDIBData);
//		memcpy(m_Dib.pDIBDeal,m_Dib.pDIBData,sizeof(m_Dib.pDIBData));
//	//	Invalidate();
//	//	GetParent()->SetWindowText(strPath);
//	}
//	else
//		MessageBox("装载位图失败！","出错提示",MB_OK | MB_ICONWARNING);
//}


void main() {
	CBPNet bp;
	for (int i=0;i<BPM_ITER;i++) {
		bp.Train(0,0,0);
		bp.Train(0,1,1);
		bp.Train(1,0,1);
    	bp.Train(1,1,0);
	}

	cout << "0,0 = " << bp.Run(0,0) << endl;
	cout << "0,1 = " << bp.Run(0,1) << endl;
	cout << "1,0 = " << bp.Run(1,0) << endl;
	cout << "1,1 = " << bp.Run(1,1) << endl;
	
//  
//	//载入位图
//	//	m_StrPath = strPath;
////	int  index;
////	CString strPath;
//    double learnSpeed = 0.5;  //学习速率
//	double mc = 0.95;         //动量因子
//	double eo, eh;            //输出误差
//	double sample1[3] = {1.0, 0.0, 0.0}, sample2[3] = {1.0, 0.0, 1.0};
//	double a1[3] =      {1.0, 1.0, 1.0},      a2[3] = {1.0, 1.0, 0.0};
//	double result1[3] = {1.0, 1.0, 0.0}, result2[3] = {1.0, 0.0, 1.0};
// 	bpnn_initialize(1);
//	BPNN *pNet = bpnn_create(2, 10, 2);
//	//训练
//	for (int i=0; i<BPM_ITER; i++)
//	{
//        //随机产生0 - 9的随机数
//	//	index = int(drnd()*10);
//	//	strPath.Format("\\sample\%d.bmp", index);
//     //  LoadBMP(strPath);
//		memcpy(pNet->input_units, sample1, 3*sizeof(double));
//		memcpy(pNet->target, result1, 3*sizeof(double));
//		bpnn_train(pNet, learnSpeed, mc, &eo, &eh);
//		
//		memcpy(pNet->input_units, sample2, 3*sizeof(double));
//		memcpy(pNet->target, result2, 3*sizeof(double));
//		bpnn_train(pNet, learnSpeed, mc, &eo, &eh);
// 
//		memcpy(pNet->input_units, a1, 3*sizeof(double));
//		memcpy(pNet->target, result1, 3*sizeof(double));
//		bpnn_train(pNet, learnSpeed, mc, &eo, &eh);
//
//		memcpy(pNet->input_units, a2, 3*sizeof(double));
//		memcpy(pNet->target, result2, 3*sizeof(double));
//		bpnn_train(pNet, learnSpeed, mc, &eo, &eh);
//        
//	}
//	//识别
////	double a1[3] = {1.0, 1.0, 1.0}, a2[3] = {1.0, 1.0, 0.0};
////	
////	pNet->input_units = (1, 1);
//	memcpy(pNet->input_units, sample1, 3*sizeof(double));
//	bpnn_feedforward(pNet);
//	cout << "1,1 = " << pNet->output_units[1] << endl;
//    cout << "1,1 = " << pNet->output_units[2] << endl;
//	memcpy(pNet->input_units, a2, 3*sizeof(double));
//	bpnn_feedforward(pNet);
//	cout << "1,0 = " << pNet->output_units[1] << endl;
//	cout << "1,0 = " << pNet->output_units[2] << endl;
//	bpnn_free(pNet);
}