annbp.cpp

annbp_BP神经网络原代码,经过一些算法改动,希望大家多多支持.

void Annbp::SetCoverNodeNum()
{
	CoverNodeNum.SetSize(HideCoverNum+2);

	CoverNodeNum[0] = InputNum+1;
	for (int i=1;i<HideCoverNum+1;i++)
		CoverNodeNum[i] = HideNum[i-1]+1;
	CoverNodeNum[HideCoverNum+1] = OutputNum;
}

BOOL Annbp::ReadTest()
{
/*	CFile f;
	CFileException e;
	if( !f.Open( TestSet,CFile::modeRead,&e ) )
		{
		#ifdef _DEBUG
		afxDump << "File could not be opened " << e.m_cause << "\n";
		#endif
		return FALSE;
		}*/
	FILE *fp;
	if((fp = fopen("D:\\jlm\\ANN\\BPNN\\"+TestSet,"r"))==NULL)
	{
		MessageBox(NULL,"打开测试集样本文件错误","错误",MB_OK);
		return FALSE;
	}
	for (int n=0;n<TestSetNum;n++)
	{
		for (int i=1;i<InputNum+1;i++)
			fscanf(fp,"%lf",&t[n][i]);
		for (int j=0;j<OutputNum;j++)
			fscanf(fp,"%lf",&p[n][j]);
	}
	for (n=0;n<TestSetNum;n++)  //阈值
		t[n][0] = -1;
	fclose(fp);

	return TRUE;
}

double Annbp::Test()
{
	if (InputNum == 0 )
	{
		MessageBox(NULL,"请先进行神经网络设置！","提示",MB_ICONINFORMATION|MB_OK);
		return 9999;
	}
	if (!IsTrained)
	{
		MessageBox(NULL,"请先进行神经网络训练！","提示",MB_ICONINFORMATION|MB_OK);
		return 9999;
	}

	for (int i=0;i<t.GetSize();i++)
		t[i].RemoveAll();
	t.RemoveAll();
	t.SetSize(TestSetNum);
	for (i=0;i<TestSetNum;i++)
		t[i].SetSize(InputNum+1);
	for (i=0;i<p.GetSize();i++)
		p[i].RemoveAll();
	p.RemoveAll();
	p.SetSize(TestSetNum);
	for (i=0;i<TestSetNum;i++)
		p[i].SetSize(OutputNum);

	if(ReadTest()==FALSE) return 9999;

	CArray<CArray<double,double>,CArray<double,double>&> y;
	y.SetSize(HideCoverNum+2);
	SetCoverNodeNum();
	for (i=0;i<HideCoverNum+1;i++)
		y[i].SetSize(CoverNodeNum[i]+1);
	y[HideCoverNum+1].SetSize(OutputNum);
	double se;
///
	FILE *fp;
	if((fp = fopen("D:\\jlm\\ANN\\BPNN\\"+TestOutFile,"w"))==NULL)
	{
		MessageBox(NULL,"打开测试结果文件错误","错误",MB_OK);
		return 9999;
	}
///
	se = 0;
	for (int n=0;n<TestSetNum;n++)
	{
		for (int in=0;in<InputNum+1;in++)
			y[0][in] = t[n][in];

		for(int c=1;c<HideCoverNum+1;c++)//隐层
		{
			for(int j=1;j<CoverNodeNum[c];j++)
			{
				double v=0;
				for(int i=0;i<CoverNodeNum[c-1];i++)
					v += w[c][j][i]*y[c-1][i];
				y[c][j] = 1.0/(1+exp(-v));
			}
		}
		for(int k=0;k<OutputNum;k++)//输出层
		{
			double v=0;
			for(int i=0;i<CoverNodeNum[HideCoverNum];i++)
				v += w[HideCoverNum+1][k][i]*y[HideCoverNum][i];
			/*
			if(OutputFunctionType != 0)//线性
				y[HideCoverNum+1][k] = v;
			else//sigmoid
				y[HideCoverNum+1][k] = 1.0/(1+exp(-v));*/
			switch (OutputFunctionType)//0---sigmoid函数；1---线性阈值函数；2---线性多分段函数；3---线性01分段函数；
			{
				case 0:
					y[HideCoverNum+1][k] = 1.0/(1+exp(-v*FuncSlope));
					break;
				case 1:
					y[HideCoverNum+1][k] = v;
					break;
				case 2:
					break;
				case 3:
					if (v <= 0)
						y[HideCoverNum+1][k] = 0.0;
					else
						y[HideCoverNum+1][k] = 1.0;
					break;
			}
		}
		//误差
		CArray<double,double> e;
		e.SetSize(OutputNum);

		for(k=0;k<OutputNum;k++)
		{
			e[k] = p[n][k]-y[HideCoverNum+1][k];
			se = se + 0.5*pow(e[k],2);
		}

		for (in=1;in<InputNum+1;in++)
		{
			fprintf(fp,"%f ",y[0][in]);//
		}
		for(k=0;k<OutputNum;k++)
		{
			fprintf(fp,"：%f ",p[n][k]);//
			fprintf(fp," - %f ",y[HideCoverNum+1][k]);//
			fprintf(fp," = %f ",(p[n][k]-y[HideCoverNum+1][k]));//
		}
		fprintf(fp,"\n");

		e.RemoveAll();
	}
	se = se/TestSetNum;
	fprintf(fp,"均方误差：%f",se);//

	for (i=0;i<y.GetSize();i++)
		y[i].RemoveAll();
	y.RemoveAll();
	fclose(fp);//
	::ShellExecute(NULL,NULL,"测试结果.txt",NULL,"D:\\jlm\\ANN\\BPNN",SW_SHOW);//%SystemRoot%\system32\notepad.exe

	return se;
}

void Annbp::DrawStru()
{
	if (InputNum == 0 )
		return;
	CWnd* pWnd = AfxGetMainWnd();
    CDC* pDC = pWnd->GetDC();
	CRect* pRect = new CRect;
	pWnd->GetClientRect(pRect);
	ASSERT(pRect);

	int m_SumWidth = int(pRect->Width()*2/3.0);
	int m_Top = int(pRect->Height()*2/15.0);
	int m_Bot = int(pRect->Height()*13/15.0);
	int m_CellRad = int((m_Bot-m_Top)*1.0/MaxCovNod()*0.9/2.0);
	if (m_CellRad > 10)
		m_CellRad = 10;

	CBrush m_brush;
	CBrush* poldbrush;
	m_brush.CreateSolidBrush(pDC->GetPixel(2,m_Top));
	poldbrush = pDC->SelectObject(&m_brush);
	pDC->Rectangle(0,int(pRect->Height()/21.0),pRect->Width(),int(pRect->Height()*20.3/21.0));
	pDC->SelectObject(poldbrush);

	/*
	pDC->MoveTo(m_SumWidth,0);
	pDC->LineTo(m_SumWidth,pRect->Height());
	pDC->MoveTo(0,m_Bot);
	pDC->LineTo(m_SumWidth,m_Bot);
	*/
	pDC->SetBkMode(TRANSPARENT);
	//pDC->SetTextColor(RGB(255,0,255));
	LOGFONT logFont;
	logFont.lfHeight=20;
	logFont.lfWeight=0;
	logFont.lfEscapement=0;
	logFont.lfOrientation=0;
	logFont.lfWeight=FW_BOLD;
	logFont.lfItalic=0;
	logFont.lfUnderline=0;
	logFont.lfStrikeOut=0;
	logFont.lfCharSet=GB2312_CHARSET;
	logFont.lfQuality=PROOF_QUALITY;
	logFont.lfPitchAndFamily=VARIABLE_PITCH|FF_DONTCARE;
	strcpy(logFont.lfFaceName,"隶书");
	CFont font;
	font.CreateFontIndirect(&logFont);
	CFont* poldFont=pDC->SelectObject(&font);
	pDC->TextOut(int(m_SumWidth/3.0),int(pRect->Height()*1/15.0),"神经网络模型结构");
	pDC->SelectObject(poldFont);

	logFont.lfHeight=12;
	strcpy(logFont.lfFaceName,"宋体");
	CFont font1;
	font1.CreateFontIndirect(&logFont);
	poldFont=pDC->SelectObject(&font1);
	CString str,strH0,strH;
	str.Format("输入层 %d 个神经元，输出层 %d 个神经元",InputNum,OutputNum);
	strH0.Format("共有 %d 个隐含层:",HideCoverNum);
	//"，隐含层 %d 个神经元",HideNum[0],
	pDC->TextOut(int(pRect->Width()/9.0),int(pRect->Height()*13.3/15.0),str);
	pDC->TextOut(int(pRect->Width()/9.0),int(pRect->Height()*13.8/15.0),strH0);
	for (int n=0;n<HideCoverNum;n++)
	{
		strH.Format("第 %d 隐含层 %d 个神经元",n+1,HideNum[n]);
		pDC->TextOut(int(pRect->Width()/3.6),int(pRect->Height()*(13.8+n/3.0)/15.0),strH);
	}
	pDC->SelectObject(poldFont);

/*	CString str;
	str.Format("m_SumWidth：%10d",m_SumWidth);
	pWnd->MessageBox(str,NULL,MB_OK);
	str.Format("m_Top：%10d",m_Top);
	pWnd->MessageBox(str,NULL,MB_OK);
	str.Format("m_Bot：%10d",m_Bot);
	pWnd->MessageBox(str,NULL,MB_OK);
	str.Format("m_CellRad：%10d",m_CellRad);
	pWnd->MessageBox(str,NULL,MB_OK);
*/
	int x,y;
	for (int i=0;i<InputNum;i++)
	{
		x = int(m_SumWidth*1.0/2/(HideCoverNum+2));
		y = int(m_Top+(i+0.5)*(m_Bot-m_Top)*1.0/InputNum);
		DrawCell(x,y,m_CellRad,-1);
		DrawLine(int(x*1.0/3),y,x-m_CellRad,y,0);
	}
	for (i=0;i<HideCoverNum;i++)
	{
		for (int j=0;j<HideNum[i];j++)
		{
			x = int(m_SumWidth*1.0/(HideCoverNum+2)*(i+1.5));
			y = int(m_Top+(j+0.5)*(m_Bot-m_Top)*1.0/HideNum[i]);
			DrawCell(x,y,m_CellRad,0);

			if (i==0)
			{
				for (int k=0;k<InputNum;k++)
				{
					int x0 = int(m_SumWidth*1.0/2/(HideCoverNum+2));
					int y0 = int(m_Top+(k+0.5)*(m_Bot-m_Top)*1.0/InputNum);
					double a = tanh((y-y0)*1.0/(x-x0));
					DrawLine(int(x0+m_CellRad*cos(a)),int(y0+m_CellRad*sin(a)),int(x-m_CellRad*cos(a)),int(y-m_CellRad*sin(a)),1);
				}
			}
			else
			{
				for (int k=0;k<HideNum[i-1];k++)
				{
					int x0 = int(m_SumWidth*1.0/(HideCoverNum+2)*(i-1+1.5));
					int y0 = int(m_Top+(k+0.5)*(m_Bot-m_Top)*1.0/HideNum[i-1]);
					double a = tanh((y-y0)*1.0/(x-x0));
					DrawLine(int(x0+m_CellRad*cos(a)),int(y0+m_CellRad*sin(a)),int(x-m_CellRad*cos(a)),int(y-m_CellRad*sin(a)),1);
				}
			}
		}
	}
	for (i=0;i<OutputNum;i++)
	{
		x = int(m_SumWidth*1.0/(HideCoverNum+2)*(HideCoverNum+1.5));
		y = int(m_Top+(i+0.5)*(m_Bot-m_Top)*1.0/OutputNum);
		DrawCell(x,y,m_CellRad,OutputFunctionType);
		for (int j=0;j<HideNum[HideCoverNum-1];j++)
		{
			int x0 = int(m_SumWidth*1.0/(HideCoverNum+2)*(HideCoverNum-1+1.5));
			int y0 = int(m_Top+(j+0.5)*(m_Bot-m_Top)*1.0/HideNum[HideCoverNum-1]);
			double a = tanh((y-y0)*1.0/(x-x0));
			DrawLine(int(x0+m_CellRad*cos(a)),int(y0+m_CellRad*sin(a)),int(x-m_CellRad*cos(a)),int(y-m_CellRad*sin(a)),1);
		}
		DrawLine(x+m_CellRad,y,x+int(m_SumWidth*1.0/(HideCoverNum+2)/3),y,0);
	}

	delete pRect;
	ReleaseDC(pWnd->m_hWnd,pDC->m_hDC);//Release!!
}

int Annbp::MaxCovNod()
{
  int max;
  max = InputNum+1;
  for (int i=0;i<HideCoverNum;i++)
  {
	  if (HideNum[i]+1 > max)
		  max = HideNum[i]+1;
  }
  if (OutputNum > max)
	  max = OutputNum;
  return max;
}

void Annbp::DrawCell(int x,int y,int r,int type)
{
	CWnd* pWnd = AfxGetMainWnd();
    CDC* pDC = pWnd->GetDC();

	pDC->Ellipse(x-r,y-r,x+r,y+r);
	ReleaseDC(pWnd->m_hWnd,pDC->m_hDC);//Release!!
}

void Annbp::DrawLine(int x1,int y1,int x2,int y2,int type)
{
	CWnd* pWnd = AfxGetMainWnd();
    CDC* pDC = pWnd->GetDC();

	pDC->MoveTo(x1,y1);
	pDC->LineTo(x2,y2);

	double a,l,da;
	a = tanh((y2-y1)*1.0/(x2-x1));
	l = 8;
	da = 0.4;
	int xp,yp,xp0,yp0;
	if (type==1)
	{
		xp = int((x1+x2)/2.0);
		yp = int((y1+y2)/2.0);
	}
	else
	{
		xp = x2;
		yp = y2;
	}
	CPen m_pen;
	CPen* poldpen;
	m_pen.CreatePen(PS_SOLID,1,RGB(255,0,0));
	poldpen = pDC->SelectObject(&m_pen);
	xp0 = int(xp-l*cos(a+da));
	yp0 = int(yp-l*sin(a+da));
	pDC->MoveTo(xp0,yp0);
	pDC->LineTo(xp,yp);
	xp0 = int(xp-l*cos(a-da));
	yp0 = int(yp-l*sin(a-da));
	pDC->MoveTo(xp0,yp0);
	pDC->LineTo(xp,yp);
	pDC->SelectObject(poldpen);
	ReleaseDC(pWnd->m_hWnd,pDC->m_hDC);//Release!!
}

double Annbp::ExportDem()
{
	if (InputNum == 0 )
	{
		MessageBox(NULL,"请先进行神经网络设置！","提示",MB_ICONINFORMATION|MB_OK);
		return 9999;
	}
	if (!IsTrained)
	{
		MessageBox(NULL,"请先进行神经网络训练！","提示",MB_ICONINFORMATION|MB_OK);
		return 9999;
	}

	///dem
	FILE *fp0;
	if((fp0 = fopen("D:\\jlm\\ANN\\BPNN\\"+TrainingSet,"r"))==NULL)
	{
		MessageBox(NULL,"打开训练集样本文件错误","错误",MB_OK);
		return FALSE;
		exit(0);
	}
	double xtemp;
	CString stemp;
	int ntemp;
	for (int n=0;n<TrainingSetNum;n++)
	{
		for (int i=1;i<InputNum+1;i++)
			fscanf(fp0,"%lf",&xtemp);
		for (int j=0;j<OutputNum;j++)
			fscanf(fp0,"%lf",&xtemp);
	}
	fscanf(fp0,"%s",stemp);
	fscanf(fp0,"%d",&ntemp);//num
	fscanf(fp0,"%s",stemp);

	int nrows;
	fscanf(fp0,"%s",stemp);
	fscanf(fp0,"%d",&nrows);//rows

	int ncols;
	fscanf(fp0,"%s",stemp);
	fscanf(fp0,"%d",&ncols);//cols

	fclose(fp0);

	///
	//int nrows = 14;
	//int ncols = 17;
	int inputunitsnum = 2;

	/*for (int i=0;i<t.GetSize();i++)
		t[i].RemoveAll();
	t.RemoveAll();
	t.SetSize(ncols*nrows);
	for (i=0;i<TestSetNum;i++)
		t[i].SetSize(inputunitsnum+1);
	for (i=0;i<p.GetSize();i++)
		p[i].RemoveAll();
	p.RemoveAll();
	p.SetSize(TestSetNum);
	for (i=0;i<TestSetNum;i++)
		p[i].SetSize(OutputNum);

	if(ReadTest()==FALSE) return 9999;
	for (i=0;i<ncols*nrows;i++)
	{
		int row = int(i*1.0/ncols);
		int col = i%ncols;
		t[i][1] = row*0.8/nrows+0.1;
		t[i][2] = col*0.8/ncols+0.1;
	}
	for (int n=0;n<ncols*nrows;n++)  //阈值
		t[n][0] = -1;*/

	CArray<CArray<double,double>,CArray<double,double>&> y;
	y.SetSize(HideCoverNum+2);
	SetCoverNodeNum();
	for (int i=0;i<HideCoverNum+1;i++)
		y[i].SetSize(CoverNodeNum[i]+1);
	y[HideCoverNum+1].SetSize(OutputNum);
	//double se;
///
	FILE *fp;
	if((fp = fopen("D:\\jlm\\ANN\\BPNN\\gridresult.txt","w"))==NULL) //"D:\\jlm\\ANN\\BPNN\\"+TestOutFile
	{
		MessageBox(NULL,"打开gridresult.txt结果文件错误","错误",MB_OK);
		return 9999;
	}
///
	//se = 0;
	for (n=0;n<ncols*nrows;n++)
	{
		//for (int in=0;in<InputNum+1;in++)
		//	y[0][in] = t[n][in];
		int row = int(n*1.0/ncols);
		int col = n%ncols;
		y[0][0] = -1;
		y[0][1] = row*0.8/nrows+0.1;
		y[0][2] = col*0.8/ncols+0.1;

		for(int c=1;c<HideCoverNum+1;c++)//隐层
		{
			for(int j=1;j<CoverNodeNum[c];j++)
			{
				double v=0;
				for(int i=0;i<CoverNodeNum[c-1];i++)
					v += w[c][j][i]*y[c-1][i];
				y[c][j] = 1.0/(1+exp(-v));
			}
		}
		for(int k=0;k<OutputNum;k++)//输出层
		{
			double v=0;
			for(int i=0;i<CoverNodeNum[HideCoverNum];i++)
				v += w[HideCoverNum+1][k][i]*y[HideCoverNum][i];
			/*
			if(OutputFunctionType != 0)//线性
				y[HideCoverNum+1][k] = v;
			else//sigmoid
				y[HideCoverNum+1][k] = 1.0/(1+exp(-v));*/
			switch (OutputFunctionType)//0---sigmoid函数；1---线性阈值函数；2---线性多分段函数；3---线性01分段函数；
			{
				case 0:
					y[HideCoverNum+1][k] = 1.0/(1+exp(-v*FuncSlope));
					break;
				case 1:
					y[HideCoverNum+1][k] = v;
					break;
				case 2:
					break;
				case 3:
					if (v <= 0)
						y[HideCoverNum+1][k] = 0.0;
					else
						y[HideCoverNum+1][k] = 1.0;
					break;
			}
		}
		//误差
		/*CArray<double,double> e;
		e.SetSize(OutputNum);

		for(k=0;k<OutputNum;k++)
		{
			e[k] = p[n][k]-y[HideCoverNum+1][k];
			se = se + 0.5*pow(e[k],2);
		}
		for (in=1;in<InputNum+1;in++)
		{
			fprintf(fp,"%f ",y[0][in]);//
		}*/

		/*for(k=0;k<OutputNum;k++)
		{
			fprintf(fp,"：%f ",p[n][k]);//
			fprintf(fp," - %f ",y[HideCoverNum+1][k]);//
			fprintf(fp," = %f ",(p[n][k]-y[HideCoverNum+1][k]));//
		}*/

		fprintf(fp,"%lf ",y[HideCoverNum+1][0]);//
		if (((n+1)%ncols) == 0)
			fprintf(fp,"\n");

		//e.RemoveAll();
	}
	/*se = se/TestSetNum;
	fprintf(fp,"均方误差：%f",se);*/

	for (i=0;i<y.GetSize();i++)
		y[i].RemoveAll();
	y.RemoveAll();
	fclose(fp);//
	::ShellExecute(NULL,NULL,"gridresult.txt",NULL,"D:\\jlm\\ANN\\BPNN",SW_SHOW);//%SystemRoot%\system32\notepad.exe

	return 0.0;//se;
}