public void TestBaumWelchGaussian()
{
var algo = new BaumWelchGaussianDistribution(_observations, HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <NormalDistribution>()
{
Pi = _startDistribution, TransitionProbabilityMatrix = _tpm, Emissions = _distributions
})); //new HiddenMarkovModelGaussianDistribution(_startDistribution, _tpm, _distributions));
var res = algo.Run(100, LikelihoodTolerance);
Assert.AreEqual(1d, Math.Round(res.Pi.Sum(), 5));
Assert.AreEqual(1d, Math.Round(res.TransitionProbabilityMatrix[0].Sum(), 5));
Assert.AreEqual(1d, Math.Round(res.TransitionProbabilityMatrix[1].Sum(), 5));
Assert.AreEqual(1d, Math.Round(res.TransitionProbabilityMatrix[2].Sum(), 5));
var emissionValue = res.Emission[0].ProbabilityDensityFunction(new double[] { _observations[0].Value[0] });
Assert.IsTrue(emissionValue > 0 && emissionValue < 1);
}
public void Train(double[] observations)
{
if (_pi == null || _transitionProbabilityMatrix == null || _emission == null)
{
throw new ApplicationException("Initialize the model with initial valuesss");
}
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution> {
Pi = _pi, TransitionProbabilityMatrix = _transitionProbabilityMatrix, Emissions = _emission
}); //new HiddenMarkovModelState<NormalDistribution>(_pi, _transitionProbabilityMatrix, _emission);
model.Normalized = Normalized;
var alg = new BaumWelchGaussianDistribution(Helper.Convert(observations), model);
var estimatedParameters = alg.Run(100, 20);
_pi = estimatedParameters.Pi;
_transitionProbabilityMatrix = estimatedParameters.TransitionProbabilityMatrix;
_emission = estimatedParameters.Emission as NormalDistribution[];
Likelihood = estimatedParameters.Likelihood;
}
