public void TestBaumWelchMultivariate()
{
var model = HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <IMultivariateDistribution>()
{
Pi = _startDistribution, TransitionProbabilityMatrix = _tpm, Emissions = _emissions
}); //new HiddenMarkovModelMultivariateGaussianDistribution(_startDistribution, _tpm, _emissions)
model.Normalized = false;
var algo = new BaumWelchMultivariateDistribution(_observations, model)
{
Normalized = false
};
var res = algo.Run(100, LikelihoodTolerance);
Assert.AreEqual(1d, res.Pi.Sum());
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));
}
public void Train(double[][] observations, int numberOfIterations, double likelihoodTolerance)
{
if (_initialize)
{
Initialize(observations);
}
if (_pi == null || _transitionProbabilityMatrix == null || _emission == null)
{
throw new ApplicationException("Initialize the model with initial valuesss");
}
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <IMultivariateDistribution> {
Pi = _pi, TransitionProbabilityMatrix = _transitionProbabilityMatrix, Emissions = _emission
}); //new HiddenMarkovModelState<IMultivariateDistribution>(_pi, _transitionProbabilityMatrix, _emission);
model.Normalized = Normalized;
var alg = new BaumWelchMultivariateDistribution(Helper.Convert(observations), model);
var estimatedParameters = alg.Run(numberOfIterations, likelihoodTolerance);
_pi = estimatedParameters.Pi;
_transitionProbabilityMatrix = estimatedParameters.TransitionProbabilityMatrix;
_emission = estimatedParameters.Emission;
Likelihood = estimatedParameters.Likelihood;
}
