public 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();
}