转自http://blog.csdn.net/qw_study/article/details/1504147
using System;
using System.IO;
using System.Text;
namespace BpANNet
{
/// <summary>
/// BpNet 的摘要说明。
/// </summary>
public class BpNet
{
public int inNum;//输入节点数
int hideNum;//隐层节点数
public int outNum;//输出层节点数
public int sampleNum;//样本总数
Random R;
double [] x;//输入节点的输入数据
double [] x1;//隐层节点的输出
double [] x2;//输出节点的输出
double [] o1;//隐层的输入
double [] o2;//输出层的输入
public double [,] w;//权值矩阵w
public double [,] v;//权值矩阵V
public double [,] dw;//权值矩阵w
public double [,] dv;//权值矩阵V
public double rate;//学习率
public double [] b1;//隐层阈值矩阵
public double [] b2;//输出层阈值矩阵
public double [] db1;//隐层阈值矩阵
public double [] db2;//输出层阈值矩阵
double [] pp;//输出层的误差
double [] qq;//隐层的误差
double [] yd;//输出层的教师数据
public double e;//均方误差
double in_rate;//归一化比例系数
public int computeHideNum(int m,int n)
{
double s=Math.Sqrt(0.43*m*n+0.12*n*n+2.54*m+0.77*n+0.35)+0.51;
int ss=Convert.ToInt32(s);
return ((s-(double)ss)>0.5) ? ss+1:ss;
}
public BpNet(double [,] p,double [,] t)
{
// 构造函数逻辑
R=new Random();
this.inNum=p.GetLength(1);
this.outNum=t.GetLength(1);
this.hideNum=computeHideNum(inNum,outNum);
// this.hideNum=18;
this.sampleNum=p.GetLength(0);
Console.WriteLine("输入节点数目： "+inNum);
Console.WriteLine("隐层节点数目："+hideNum);
Console.WriteLine("输出层节点数目："+outNum);
Console.ReadLine();
x=new double[inNum];
x1=new double[hideNum];
x2=new double[outNum];
o1=new double[hideNum];
o2=new double[outNum];
w=new double[inNum,hideNum];
v=new double[hideNum,outNum];
dw=new double[inNum,hideNum];
dv=new double[hideNum,outNum];
b1=new double[hideNum];
b2=new double[outNum];
db1=new double[hideNum];
db2=new double[outNum];
pp=new double[hideNum];
qq=new double[outNum];
yd=new double[outNum];
//初始化w
for(int i=0;i<inNum;i++)
{
for(int j=0;j<hideNum;j++)
{
w[i,j]=(R.NextDouble()*2-1.0)/2;
}
}
//初始化v
for(int i=0;i<hideNum;i++)
{
for(int j=0;j<outNum;j++)
{
v[i,j]=(R.NextDouble()*2-1.0)/2;
}
}
rate=0.8;
e=0.0;
in_rate=1.0;
}
//训练函数
public void train(double [,] p,double [,] t)
{
e=0.0;
//求p，t中的最大值
double pMax=0.0;
for(int isamp=0;isamp<sampleNum;isamp++)
{
for(int i=0;i<inNum;i++)
{
if(Math.Abs(p[isamp,i])>pMax)
{
pMax=Math.Abs(p[isamp,i]);
}
}
for(int j=0;j<outNum;j++)
{
if(Math.Abs(t[isamp,j])>pMax)
{
pMax=Math.Abs(t[isamp,j]);
}
}
in_rate=pMax;
}//end isamp
for(int isamp=0;isamp<sampleNum;isamp++)
{
//数据归一化
for(int i=0;i<inNum;i++)
{
x[i]=p[isamp,i]/in_rate;
}
for(int i=0;i<outNum;i++)
{
yd[i]=t[isamp,i]/in_rate;
}
//计算隐层的输入和输出
for(int j=0;j<hideNum;j++)
{
o1[j]=0.0;
for(int i=0;i<inNum;i++)
{
o1[j]+=w[i,j]*x[i];
}
x1[j]=1.0/(1.0+Math.Exp(-o1[j]-b1[j]));
}
//计算输出层的输入和输出
for(int k=0;k<outNum;k++)
{
o2[k]=0.0;
for(int j=0;j<hideNum;j++)
{
o2[k]+=v[j,k]*x1[j];
}
x2[k]=1.0/(1.0+Math.Exp(-o2[k]-b2[k]));
}
//计算输出层误差和均方差
for(int k=0;k<outNum;k++)
{
qq[k]=(yd[k]-x2[k])*x2[k]*(1.0-x2[k]);
e+=(yd[k]-x2[k])*(yd[k]-x2[k]);
//更新V
for(int j=0;j<hideNum;j++)
{
v[j,k]+=rate*qq[k]*x1[j];
}
}
//计算隐层误差
for(int j=0;j<hideNum;j++)
{
pp[j]=0.0;
for(int k=0;k<outNum;k++)
{
pp[j]+=qq[k]*v[j,k];
}
pp[j]=pp[j]*x1[j]*(1-x1[j]);
//更新W
for(int i=0;i<inNum;i++)
{
w[i,j]+=rate*pp[j]*x[i];
}
}
//更新b2
for(int k=0;k<outNum;k++)
{
b2[k]+=rate*qq[k];
}
//更新b1
for(int j=0;j<hideNum;j++)
{
b1[j]+=rate*pp[j];
}
}//end isamp
e=Math.Sqrt(e);
// adjustWV(w,dw);
// adjustWV(v,dv);
}//end train
public void adjustWV(double [,] w,double[,] dw)
{
for(int i=0;i<w.GetLength(0);i++)
{
for(int j=0;j<w.GetLength(1);j++)
{
w[i,j]+=dw[i,j];
}
}
}
public void adjustWV(double [] w,double[] dw)
{
for(int i=0;i<w.Length;i++)
{
w[i]+=dw[i];
}
}
//数据仿真函数
public double[] sim(double [] psim)
{
for(int i=0;i<inNum;i++)
x[i]=psim[i]/in_rate;
for(int j=0;j<hideNum;j++)
{
o1[j]=0.0;
for(int i=0;i<inNum;i++)
o1[j]=o1[j]+w[i,j]*x[i];
x1[j]=1.0/(1.0+Math.Exp(-o1[j]-b1[j]));
}
for(int k=0;k<outNum;k++)
{
o2[k]=0.0;
for(int j=0;j<hideNum;j++)
o2[k]=o2[k]+v[j,k]*x1[j];
x2[k]=1.0/(1.0+Math.Exp(-o2[k]-b2[k]));
x2[k]=in_rate*x2[k];
}
return x2;
} //end sim
//保存矩阵w,v
public void saveMatrix(double [,] w,string filename)
{
StreamWriter sw=File.CreateText(filename);
for(int i=0;i<w.GetLength(0);i++)
{
for(int j=0;j<w.GetLength(1);j++)
{
sw.Write(w[i,j]+" ");
}
sw.WriteLine();
}
sw.Close();
}
//保存矩阵b1,b2
public void saveMatrix(double [] b,string filename)
{
StreamWriter sw=File.CreateText(filename);
for(int i=0;i<b.Length;i++)
{
sw.Write(b[i]+" ");
}
sw.Close();
}
//读取矩阵W,V
public void readMatrixW(double [,] w,string filename)
{
StreamReader sr;
try
{
sr = new StreamReader(filename,Encoding.GetEncoding("gb2312"));
String line;
int i=0;
while ((line = sr.ReadLine()) != null)
{
string[] s1=line.Trim().Split(' ');
for(int j=0;j<s1.Length;j++)
{
w[i,j]=Convert.ToDouble(s1[j]);
}
i++;
}
sr.Close();
}
catch (Exception e)
{
// Let the user know what went wrong.
Console.WriteLine("The file could not be read:");
Console.WriteLine(e.Message);
}
}
//读取矩阵b1,b2
public void readMatrixB(double [] b,string filename)
{
StreamReader sr;
try
{
sr = new StreamReader(filename,Encoding.GetEncoding("gb2312"));
String line;
int i=0;
while ((line = sr.ReadLine()) != null)
{
b[i]=Convert.ToDouble(line);
i++;
}
sr.Close();
}
catch (Exception e)
{
// Let the user know what went wrong.
Console.WriteLine("The file could not be read:");
Console.WriteLine(e.Message);
}
}
}//end bpnet
} //end namespace
namespace BpANNet
{
/// <summary>
/// Class1 的摘要说明。
/// </summary>
class Class1
{
/// <summary>
/// 应用程序的主入口点。
/// </summary>
[STAThread]
static void Main(string[] args)
{
//0.1399,0.1467,0.1567,0.1595,0.1588,0.1622,0.1611,0.1615,0.1685,0.1789,0.1790
// double [,] p1=new double[,]{{0.05,0.02},{0.09,0.11},{0.12,0.20},{0.15,0.22},{0.20,0.25},{0.75,0.75},{0.80,0.83},{0.82,0.80},{0.90,0.89},{0.95,0.89},{0.09,0.04},{0.1,0.1},{0.14,0.21},{0.18,0.24},{0.22,0.28},{0.77,0.78},{0.79,0.81},{0.84,0.82},{0.94,0.93},{0.98,0.99}};
// double [,] t1=new double[,]{{1,0},{1,0},{1,0},{1,0},{1,0},{0,1},{0,1},{0,1},{0,1},{0,1},{1,0},{1,0},{1,0},{1,0},{1,0},{0,1},{0,1},{0,1},{0,1},{0,1}};
double [,] p1=new double[,]{{0.1399,0.1467,0.1567,0.1595,0.1588,0.1622},{0.1467,0.1567,0.1595,0.1588,0.1622,0.1611},{0.1567,0.1595,0.1588,0.1622,0.1611,0.1615},{0.1595,0.1588,0.1622,0.1611,0.1615,0.1685},{0.1588,0.1622,0.1611,0.1615,0.1685,0.1789}};
double [,] t1=new double[,]{{0.1622},{0.1611},{0.1615},{0.1685},{0.1789},{0.1790}};
BpNet bp=new BpNet(p1,t1);
int study=0;
do
{
study++;
bp.train(p1,t1);
// bp.rate=0.95-(0.95-0.3)*study/50000;
// Console.Write("第 "+ study+"次学习： ");
// Console.WriteLine(" 均方差为 "+bp.e);
}while(bp.e>0.001 && study <50000);
Console.Write("第 "+ study+"次学习： ");
Console.WriteLine(" 均方差为 "+bp.e);
bp.saveMatrix(bp.w,"w.txt");
bp.saveMatrix(bp.v,"v.txt");
bp.saveMatrix(bp.b1,"b1.txt");
bp.saveMatrix(bp.b2,"b2.txt");
// double [,] p2=new double[,]{{0.05,0.02},{0.09,0.11},{0.12,0.20},{0.15,0.22},{0.20,0.25},{0.75,0.75},{0.80,0.83},{0.82,0.80},{0.90,0.89},{0.95,0.89},{0.09,0.04},{0.1,0.1},{0.14,0.21},{0.18,0.24},{0.22,0.28},{0.77,0.78},{0.79,0.81},{0.84,0.82},{0.94,0.93},{0.98,0.99}};
double [,] p2=new double[,]{{0.1399,0.1467,0.1567,0.1595,0.1588,0.1622},{0.1622,0.1611,0.1615,0.1685,0.1789,0.1790}};
int aa=bp.inNum;
int bb=bp.outNum;
int cc=p2.GetLength(0);
double [] p21=new double[aa];
double [] t2=new double[bb];
for(int n=0;n<cc;n++)
{
for(int i=0;i<aa;i++)
{
p21[i]=p2[n,i];
}
t2=bp.sim(p21);
for(int i=0;i<t2.Length;i++)
{
Console.WriteLine(t2[i]+" ");
}
}
Console.ReadLine();
}
}
}