隐马尔科夫模型C#类库调用示例

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using System;
using Splash;
namespace SplashCheck
{
class TestHMMCS
{
enum Weather { Sunny, Cloudy, Rainy }; // 隐藏状态（天气）
enum Seaweed { Dry, Dryish, Damp, Soggy }; // 观察状态（海藻湿度）
static void Main(string[] args)
{
// 测试前向算法和后向算法
CheckForwardAndBackward();
Console.WriteLine();
// 测试维特比算法
CheckViterbi();
Console.WriteLine();
// 测试HMM学习算法
CheckBaumWelch();
}
// 测试前向算法和后向算法
static void CheckForwardAndBackward()
{
// 状态转移矩阵
Double[,] A = {
{0.500, 0.375, 0.125},
{0.250, 0.125, 0.625},
{0.250, 0.375, 0.375}
};
// 混淆矩阵
Double[,] B =
{
{0.60, 0.20, 0.15, 0.05},
{0.25, 0.25, 0.25, 0.25},
{0.05, 0.10, 0.35, 0.50}
};
// 初始概率向量
Double[] PI = { 0.63, 0.17, 0.20 };
// 观察序列
Int32[] OB = { (Int32)Seaweed.Dry, (Int32)Seaweed.Damp, (Int32)Seaweed.Soggy };
// 初始化HMM模型
HMM hmm = new HMM(A.GetLength(0), B.GetLength(1));
hmm.A = A;
hmm.B = B;
hmm.PI = PI;
// 观察序列的概率
Console.WriteLine("------------前向算法：双精度运算-----------------");
Double Probability = hmm.Forward(OB);
Console.WriteLine("Probability =" + Probability.ToString("0.###E+0"));
Console.WriteLine();
// 观察序列的概率
Console.WriteLine("------------后向算法：双精度运算-----------------");
Probability = hmm.Backward(OB);
Console.WriteLine("Probability =" + Probability.ToString("0.###E+0"));
}
// 测试维特比算法
static void CheckViterbi()
{
// 状态转移矩阵
Double[,] A = {
{0.500, 0.250, 0.250},
{0.375, 0.125, 0.375},
{0.125, 0.675, 0.375}
};
// 混淆矩阵
Double[,] B =
{
{0.60, 0.20, 0.15, 0.05},
{0.25, 0.25, 0.25, 0.25},
{0.05, 0.10, 0.35, 0.50}
};
// 初始概率向量
Double[] PI = { 0.63, 0.17, 0.20 };
// 观察序列
Int32[] OB = { (Int32)Seaweed.Dry, (Int32)Seaweed.Damp, (Int32)Seaweed.Soggy, (Int32)Seaweed.Dryish, (Int32)Seaweed.Dry };
// 初始化HMM模型
HMM hmm = new HMM(A.GetLength(0), B.GetLength(1));
hmm.A = A;
hmm.B = B;
hmm.PI = PI;
// 找出最有可能的隐藏状态序列
Double Probability;
Console.WriteLine("------------维特比算法：双精度运算-----------------");
Int32[] Q = hmm.Viterbi(OB, out Probability);
Console.WriteLine("Probability =" + Probability.ToString("0.###E+0"));
foreach (Int32 Value in Q)
{
Console.WriteLine(((Weather)Value).ToString());
}
Console.WriteLine();
Console.WriteLine("------------维特比算法：对数运算-----------------");
Q = hmm.ViterbiLog(OB, out Probability);
Console.WriteLine("Probability =" + Probability.ToString("0.###E+0"));
foreach (Int32 Value in Q)
{
Console.WriteLine(((Weather)Value).ToString());
}
}
static void CheckBaumWelch()
{
// 状态转移矩阵
Double[,] A = {
{0.500, 0.250, 0.250},
{0.375, 0.125, 0.375},
{0.125, 0.675, 0.375}
};
// 混淆矩阵
Double[,] B =
{
{0.60, 0.20, 0.15, 0.05},
{0.25, 0.25, 0.25, 0.25},
{0.05, 0.10, 0.35, 0.50}
};
// 初始概率向量
Double[] PI = { 0.63, 0.17, 0.20 };
// 观察序列
Int32[] OB = { (Int32)Seaweed.Dry, (Int32)Seaweed.Damp, (Int32)Seaweed.Soggy, (Int32)Seaweed.Dryish, (Int32)Seaweed.Dry };
// 初始化HMM模型
HMM hmm = new HMM(A.GetLength(0), B.GetLength(1));
// 数组克隆，避免损坏原始数据
hmm.A = (Double[,])A.Clone();
hmm.B = (Double[,])B.Clone();
hmm.PI = (Double[])PI.Clone();
// 前向-后向算法
Console.WriteLine("------------Baum-Welch算法-----------------");
Double LogProbInit, LogProbFinal;
Int32 Iterations = hmm.BaumWelch(OB, out LogProbInit, out LogProbFinal);
Console.WriteLine("迭代次数 = {0}", Iterations);
Console.WriteLine("初始概率 = {0}", Math.Exp(LogProbInit));
Console.WriteLine("最终概率 = {0}", Math.Exp(LogProbFinal));
Console.WriteLine();
// 打印学习后的模型参数
Console.WriteLine("新的模型参数：");
Console.WriteLine("PI");
for (Int32 i = 0; i < hmm.N; i++)
{
if(i == 0)
Console.Write(hmm.PI[i].ToString("0.000"));
else
Console.Write(" " + hmm.PI[i].ToString("0.000"));
}
Console.WriteLine();
Console.WriteLine();
Console.WriteLine("A");
for (Int32 i = 0; i < hmm.N; i++)
{
for (Int32 j = 0; j < hmm.N; j++)
{
if (j == 0)
Console.Write(hmm.A[i, j].ToString("0.000"));
else
Console.Write(" " + hmm.A[i, j].ToString("0.000"));
}
Console.WriteLine();
}
Console.WriteLine();
Console.WriteLine("B");
for (Int32 i = 0; i < hmm.N; i++)
{
for (Int32 j = 0; j < hmm.M; j++)
{
if (j == 0)
Console.Write(hmm.B[i, j].ToString("0.000"));
else
Console.Write(" " + hmm.B[i, j].ToString("0.000"));
}
Console.WriteLine();
}
Console.WriteLine();
}
}
}