public void RunTest()
{
// Example from
// http://www.cs.columbia.edu/4761/notes07/chapter4.3-HMM.pdf
int[][] observations =
{
new int[] { 0,0,0,1,0,0 },
new int[] { 1,0,0,1,0,0 },
new int[] { 0,0,1,0,0,0 },
new int[] { 0,0,0,0,1,0 },
new int[] { 1,0,0,0,1,0 },
new int[] { 0,0,0,1,1,0 },
new int[] { 1,0,0,0,0,0 },
new int[] { 1,0,1,0,0,0 },
};
int[][] paths =
{
new int[] { 0,0,1,0,1,0 },
new int[] { 1,0,1,0,1,0 },
new int[] { 1,0,0,1,1,0 },
new int[] { 1,0,1,1,1,0 },
new int[] { 1,0,0,1,0,1 },
new int[] { 0,0,1,0,0,1 },
new int[] { 0,0,1,1,0,1 },
new int[] { 0,1,1,1,0,0 },
};
HiddenMarkovModel model = new HiddenMarkovModel(states: 2, symbols: 2);
MaximumLikelihoodLearning target = new MaximumLikelihoodLearning(model);
target.UseLaplaceRule = false;
double logLikelihood = target.Run(observations, paths);
var pi = Matrix.Exp(model.Probabilities);
var A = Matrix.Exp(model.Transitions);
var B = Matrix.Exp(model.Emissions);
Assert.AreEqual(0.5, pi[0]);
Assert.AreEqual(0.5, pi[1]);
Assert.AreEqual(7 / 20.0, A[0, 0], 1e-5);
Assert.AreEqual(13 / 20.0, A[0, 1], 1e-5);
Assert.AreEqual(14 / 20.0, A[1, 0], 1e-5);
Assert.AreEqual(6 / 20.0, A[1, 1], 1e-5);
Assert.AreEqual(17 / 25.0, B[0, 0]);
Assert.AreEqual(8 / 25.0, B[0, 1]);
Assert.AreEqual(19 / 23.0, B[1, 0]);
Assert.AreEqual(4 / 23.0, B[1, 1]);
Assert.AreEqual(-1.1472359046136624, logLikelihood);
}
}
