隐马尔科夫模型C#类库调用示例

本文通过C#代码示例介绍了隐马尔可夫模型(HMM)的实现与应用,包括前向算法、后向算法、维特比算法及Baum-Welch算法的具体使用方法。

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  1. using System;  
  2. using Splash;  
  3.   
  4. namespace SplashCheck  
  5. {  
  6.     class TestHMMCS  
  7.     {  
  8.         enum Weather { Sunny, Cloudy, Rainy };  // 隐藏状态(天气)   
  9.         enum Seaweed { Dry, Dryish, Damp, Soggy };  // 观察状态(海藻湿度)   
  10.   
  11.         static void Main(string[] args)  
  12.         {  
  13.             // 测试前向算法和后向算法   
  14.             CheckForwardAndBackward();  
  15.             Console.WriteLine();  
  16.   
  17.             // 测试维特比算法   
  18.             CheckViterbi();  
  19.             Console.WriteLine();  
  20.   
  21.             // 测试HMM学习算法   
  22.             CheckBaumWelch();  
  23.         }  
  24.   
  25.         // 测试前向算法和后向算法   
  26.         static void CheckForwardAndBackward()  
  27.         {  
  28.             // 状态转移矩阵   
  29.             Double[,] A = {  
  30.                 {0.500, 0.375, 0.125},  
  31.                 {0.250, 0.125, 0.625},  
  32.                 {0.250, 0.375, 0.375}  
  33.             };  
  34.   
  35.             // 混淆矩阵   
  36.             Double[,] B =   
  37.             {  
  38.                 {0.60, 0.20, 0.15, 0.05},  
  39.                 {0.25, 0.25, 0.25, 0.25},  
  40.                 {0.05, 0.10, 0.35, 0.50}  
  41.             };  
  42.   
  43.             // 初始概率向量   
  44.             Double[] PI = { 0.63, 0.17, 0.20 };  
  45.   
  46.             // 观察序列   
  47.             Int32[] OB = { (Int32)Seaweed.Dry, (Int32)Seaweed.Damp, (Int32)Seaweed.Soggy };  
  48.   
  49.             // 初始化HMM模型   
  50.             HMM hmm = new HMM(A.GetLength(0), B.GetLength(1));  
  51.             hmm.A = A;  
  52.             hmm.B = B;  
  53.             hmm.PI = PI;             
  54.   
  55.             // 观察序列的概率   
  56.             Console.WriteLine("------------前向算法:双精度运算-----------------");  
  57.             Double Probability = hmm.Forward(OB);  
  58.             Console.WriteLine("Probability =" + Probability.ToString("0.###E+0"));  
  59.             Console.WriteLine();  
  60.   
  61.             // 观察序列的概率   
  62.             Console.WriteLine("------------后向算法:双精度运算-----------------");  
  63.             Probability = hmm.Backward(OB);  
  64.             Console.WriteLine("Probability =" + Probability.ToString("0.###E+0"));  
  65.         }         
  66.   
  67.         // 测试维特比算法   
  68.         static void CheckViterbi()  
  69.         {  
  70.             // 状态转移矩阵   
  71.             Double[,] A = {  
  72.                 {0.500, 0.250, 0.250},  
  73.                 {0.375, 0.125, 0.375},  
  74.                 {0.125, 0.675, 0.375}  
  75.             };  
  76.   
  77.             // 混淆矩阵   
  78.             Double[,] B =   
  79.             {  
  80.                 {0.60, 0.20, 0.15, 0.05},  
  81.                 {0.25, 0.25, 0.25, 0.25},  
  82.                 {0.05, 0.10, 0.35, 0.50}  
  83.             };  
  84.   
  85.             // 初始概率向量   
  86.             Double[] PI = { 0.63, 0.17, 0.20 };  
  87.   
  88.             // 观察序列   
  89.             Int32[] OB = { (Int32)Seaweed.Dry, (Int32)Seaweed.Damp, (Int32)Seaweed.Soggy, (Int32)Seaweed.Dryish, (Int32)Seaweed.Dry };  
  90.   
  91.             // 初始化HMM模型   
  92.             HMM hmm = new HMM(A.GetLength(0), B.GetLength(1));  
  93.             hmm.A = A;  
  94.             hmm.B = B;  
  95.             hmm.PI = PI;             
  96.   
  97.             // 找出最有可能的隐藏状态序列   
  98.             Double Probability;  
  99.   
  100.             Console.WriteLine("------------维特比算法:双精度运算-----------------");  
  101.             Int32[] Q = hmm.Viterbi(OB, out Probability);  
  102.             Console.WriteLine("Probability =" + Probability.ToString("0.###E+0"));  
  103.             foreach (Int32 Value in Q)  
  104.             {  
  105.                 Console.WriteLine(((Weather)Value).ToString());  
  106.             }  
  107.             Console.WriteLine();  
  108.   
  109.             Console.WriteLine("------------维特比算法:对数运算-----------------");  
  110.             Q = hmm.ViterbiLog(OB, out Probability);  
  111.             Console.WriteLine("Probability =" + Probability.ToString("0.###E+0"));  
  112.             foreach (Int32 Value in Q)  
  113.             {  
  114.                 Console.WriteLine(((Weather)Value).ToString());  
  115.             }      
  116.         }  
  117.   
  118.         static void CheckBaumWelch()  
  119.         {  
  120.             // 状态转移矩阵   
  121.             Double[,] A = {  
  122.                 {0.500, 0.250, 0.250},  
  123.                 {0.375, 0.125, 0.375},  
  124.                 {0.125, 0.675, 0.375}  
  125.             };  
  126.   
  127.             // 混淆矩阵   
  128.             Double[,] B =   
  129.             {  
  130.                 {0.60, 0.20, 0.15, 0.05},  
  131.                 {0.25, 0.25, 0.25, 0.25},  
  132.                 {0.05, 0.10, 0.35, 0.50}  
  133.             };  
  134.   
  135.             // 初始概率向量   
  136.             Double[] PI = { 0.63, 0.17, 0.20 };  
  137.   
  138.             // 观察序列   
  139.             Int32[] OB = { (Int32)Seaweed.Dry, (Int32)Seaweed.Damp, (Int32)Seaweed.Soggy, (Int32)Seaweed.Dryish, (Int32)Seaweed.Dry };  
  140.   
  141.             // 初始化HMM模型   
  142.             HMM hmm = new HMM(A.GetLength(0), B.GetLength(1));  
  143.   
  144.             // 数组克隆,避免损坏原始数据   
  145.             hmm.A = (Double[,])A.Clone();  
  146.             hmm.B = (Double[,])B.Clone();  
  147.             hmm.PI = (Double[])PI.Clone();  
  148.   
  149.             // 前向-后向算法   
  150.             Console.WriteLine("------------Baum-Welch算法-----------------");  
  151.             Double LogProbInit, LogProbFinal;  
  152.             Int32 Iterations = hmm.BaumWelch(OB, out LogProbInit, out LogProbFinal);  
  153.             Console.WriteLine("迭代次数 = {0}", Iterations);  
  154.             Console.WriteLine("初始概率 = {0}", Math.Exp(LogProbInit));  
  155.             Console.WriteLine("最终概率 = {0}", Math.Exp(LogProbFinal));  
  156.             Console.WriteLine();  
  157.   
  158.             // 打印学习后的模型参数   
  159.             Console.WriteLine("新的模型参数:");  
  160.             Console.WriteLine("PI");  
  161.             for (Int32 i = 0; i < hmm.N; i++)  
  162.             {  
  163.                 if(i == 0)  
  164.                     Console.Write(hmm.PI[i].ToString("0.000"));  
  165.                 else  
  166.                     Console.Write(" " + hmm.PI[i].ToString("0.000"));  
  167.             }  
  168.             Console.WriteLine();  
  169.             Console.WriteLine();  
  170.   
  171.             Console.WriteLine("A");  
  172.             for (Int32 i = 0; i < hmm.N; i++)  
  173.             {  
  174.                 for (Int32 j = 0; j < hmm.N; j++)  
  175.                 {  
  176.                     if (j == 0)  
  177.                         Console.Write(hmm.A[i, j].ToString("0.000"));  
  178.                     else  
  179.                         Console.Write(" " + hmm.A[i, j].ToString("0.000"));  
  180.                 }  
  181.                 Console.WriteLine();  
  182.             }              
  183.             Console.WriteLine();  
  184.   
  185.             Console.WriteLine("B");  
  186.             for (Int32 i = 0; i < hmm.N; i++)  
  187.             {  
  188.                 for (Int32 j = 0; j < hmm.M; j++)  
  189.                 {  
  190.                     if (j == 0)  
  191.                         Console.Write(hmm.B[i, j].ToString("0.000"));  
  192.                     else  
  193.                         Console.Write(" " + hmm.B[i, j].ToString("0.000"));  
  194.                 }  
  195.                 Console.WriteLine();  
  196.             }  
  197.             Console.WriteLine();  
  198.         }  
  199.     }  
  200. }  


转载:http://blog.youkuaiyun.com/jhqin/article/details/6535013#
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