C#BP神经网络 类与实例

1、转载：在<<C# BP神经网络 类与实例>>这篇文章中，笔者转载了网友提供的一个类。非常感谢这位网友的无私奉献，使笔者得以快速的完成了软件的一个功能。貌似我找到的是网友对原作改动过的版本，使得该类存在着一些不足，笔者对其进行了相应改进。1. 未保存训练样本的结果，使得每次使用时都要重新训练数据，耗费时间：虽然类中已经提供了保存w、v、b1、b2数组的方法，但其构造函数却有问题，同时没有保存in_rate inNum HideNum outNum 这些下面会用到的数据。对此笔者进行了以下改进：新建了一个无参的构造函数用来预测新样本时实例化对象；提供sa

2、veParas、readParas方法用于保存和读取这些参数；提供initial方法用来建立预测过程中用到的动态数组2. 类中用伪随机数来初始化w、v数组，使得每次建立新对象时w、v会不同。笔者对该算法没有深入研究，不清楚为什么这样设计。笔者把R = new Random(); 这行代码进行了修改R = new Random(32);  /加了一个参数，使产生的伪随机序列相同下面是我修改后的类和示例：using System;using System.Collections.Generic;using System.Linq;using System.Text;using

3、System.IO;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 pu

4、blic 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

5、 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(32); /加了一个参

6、数，使产生的伪随机序列相同 this.inNum = p.GetLength(1); /数组第二维大小为 输入节点数 this.outNum = t.GetLength(1); /输出节点数 this.hideNum = computeHideNum(inNum, outNum); /隐藏节点数，不知其原理 this.sampleNum = p.GetLength(0); /数组第一维大小为 Console.WriteLine("输入节点数目： " + inNum); Console.WriteLine("隐层节点数目：" + hideNum); Con

7、sole.WriteLine("输出层节点数目：" + outNum); Console.ReadLine(); x = new doubleinNum; x1 = new doublehideNum; x2 = new doubleoutNum; o1 = new doublehideNum; o2 = new doubleoutNum; w = new doubleinNum, hideNum; v = new doublehideNum, outNum; dw = new doubleinNum, hideNum; dv = new doublehideNum, ou

8、tNum; b1 = new doublehideNum; b2 = new doubleoutNum; db1 = new doublehideNum; db2 = new doubleoutNum; pp = new doublehideNum; qq = new doubleoutNum; yd = new doubleoutNum; /初始化w for (int i = 0; i < inNum; i+) for (int j = 0; j < hideNum; j+) wi, j = (R.NextDouble() * 2 - 1.0) / 2; /初始化v for (i

9、nt i = 0; i < hideNum; i+) for (int j = 0; j < outNum; j+) vi, 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 < i

10、nNum; i+) if (Math.Abs(pisamp, i) > pMax) pMax = Math.Abs(pisamp, i); for (int j = 0; j < outNum; j+) if (Math.Abs(tisamp, j) > pMax) pMax = Math.Abs(tisamp, 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+) xi = pisamp

11、, i / in_rate; for (int i = 0; i < outNum; i+) ydi = tisamp, i / in_rate; /计算隐层的输入和输出 for (int j = 0; j < hideNum; j+) o1j = 0.0; for (int i = 0; i < inNum; i+) o1j += wi, j * xi; x1j = 1.0 / (1.0 + Math.Exp(-o1j - b1j); /计算输出层的输入和输出 for (int k = 0; k < outNum; k+) o2k = 0.0; for (int j

12、= 0; j < hideNum; j+) o2k += vj, k * x1j; x2k = 1.0 / (1.0 + Math.Exp(-o2k - b2k); /计算输出层误差和均方差 for (int k = 0; k < outNum; k+) qqk = (ydk - x2k) * x2k * (1.0 - x2k); e += (ydk - x2k) * (ydk - x2k); /更新V for (int j = 0; j < hideNum; j+) vj, k += rate * qqk * x1j; /计算隐层误差 for (int j = 0; j &

13、lt; hideNum; j+) ppj = 0.0; for (int k = 0; k < outNum; k+) ppj += qqk * vj, k; ppj = ppj * x1j * (1 - x1j); /更新W for (int i = 0; i < inNum; i+) wi, j += rate * ppj * xi; /更新b2 for (int k = 0; k < outNum; k+) b2k += rate * qqk; /更新b1 for (int j = 0; j < hideNum; j+) b1j += rate * ppj; /e

14、nd 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+) wi, j += dwi, j; public void adjustWV(double w, double dw) for (int i = 0; i < w.Length; i+)

15、wi += dwi; public BpNet() /仿真函数 用的构造函数 /数据仿真函数 public double sim(double psim) /in_rate inNum HideNum outNum for (int i = 0; i < inNum; i+) xi = psimi / in_rate; for (int j = 0; j < hideNum; j+) o1j = 0.0; for (int i = 0; i < inNum; i+) o1j = o1j + wi, j * xi; x1j = 1.0 / (1.0 + Math.Exp(-o1

16、j - b1j); for (int k = 0; k < outNum; k+) o2k = 0.0; for (int j = 0; j < hideNum; j+) o2k = o2k + vj, k * x1j; x2k = 1.0 / (1.0 + Math.Exp(-o2k - b2k); x2k = in_rate * x2k; return x2; /end sim /保存矩阵w,v public void saveMatrix(double, w, string filename) StreamWriter sw = new StreamWriter(filena

17、me); for (int i = 0; i < w.GetLength(0); i+) for (int j = 0; j < w.GetLength(1); j+) sw.Write(wi, j + " "); sw.WriteLine(); sw.Close(); /保存矩阵b1,b2 public void saveMatrix(double b, string filename) StreamWriter sw = new StreamWriter(filename); for (int i = 0; i < b.Length; i+) sw.W

18、rite(bi + " "); sw.Close(); /保存参数 in_rate inNum HideNum outNum public void saveParas(string filename) try StreamWriter sw = new StreamWriter(filename); string str = inNum.ToString() + " " + hideNum.ToString() + " " + outNum.ToString() + " " + in_rate.ToString(

19、); sw.WriteLine(str); sw.Close(); catch (Exception e) / Let the user know what went wrong. Console.WriteLine("The file could not be read:"); Console.WriteLine(e.Message); /读回参数 in_rate inNum HideNum outNum, tjt 预测新数据 public void readParas(string filename) StreamReader sr; try sr = new Stre

20、amReader(filename); String line; if(line = sr.ReadLine() != null) string strArr = line.Split(' '); this.inNum = Convert.ToInt32(strArr0); this.hideNum = Convert.ToInt32(strArr1); this.outNum = Convert.ToInt32(strArr2); this.in_rate = Convert.ToDouble(strArr3); sr.Close(); catch (Exception e)

21、 / Let the user know what went wrong. Console.WriteLine("The file could not be read:"); Console.WriteLine(e.Message); public void initial() / 建立一些中间数组 tjt 预测新数据 x = new doubleinNum; x1 = new doublehideNum; x2 = new doubleoutNum; o1 = new doublehideNum; o2 = new doubleoutNum; w = new double

22、inNum, hideNum; v = new doublehideNum, outNum; dw = new doubleinNum, hideNum; dv = new doublehideNum, outNum; b1 = new doublehideNum; b2 = new doubleoutNum; db1 = new doublehideNum; db2 = new doubleoutNum; pp = new doublehideNum; qq = new doubleoutNum; yd = new doubleoutNum; /读取矩阵W,V public void rea

23、dMatrixW(double, w, string filename) StreamReader sr; try sr = new StreamReader(filename); String line; int i = 0; while (line = sr.ReadLine() != null) string s1 = line.Trim().Split(' '); for (int j = 0; j < s1.Length; j+) wi, j = Convert.ToDouble(s1j); i+; sr.Close(); catch (Exception e)

24、 / 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); String line; while (line = sr.ReadLine() != null) int i

25、= 0; string s1 = line.Trim().Split(' '); for (int j = 0; j < s1.Length; j+) bi = Convert.ToDouble(s1j); 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 namesp

26、aceusing System;using System.Collections.Generic;using System.Linq;using System.Text;namespace BpANNet / <summary> / Class1 的摘要说明。 / </summary> class Class1 static void Main(string args) double, p1 = new double, 0.1399, 0.1467, 0.1567, 0.1595, 0.1588, 0.1622 , 0.1467, 0.1567, 0.1595, 0.1

27、588, 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); 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.s