public double[][] Estimate(KsiGammaTransitionProbabilityMatrixParameters <TDistribution> parameters)
{
if (_estimatedTransitionProbabilityMatrix != null)
{
return(_estimatedTransitionProbabilityMatrix);
}
_estimatedTransitionProbabilityMatrix = new double[parameters.Model.N][];
for (var i = 0; i < parameters.Model.N; i++)
{
_estimatedTransitionProbabilityMatrix[i] = new double[parameters.Model.N];
for (var j = 0; j < parameters.Model.N; j++)
{
double denominator = (parameters.Model.Normalized) ? double.NaN : 0, nominator = (parameters.Model.Normalized) ? double.NaN : 0;
for (var t = 0; t < parameters.T - 1; t++)
{
if (parameters.Model.Normalized)
{
nominator = LogExtention.eLnSum(nominator, parameters.Ksi[t][i, j]);
denominator = LogExtention.eLnSum(denominator, parameters.Gamma[t][i]);
}
else
{
nominator += parameters.Ksi[t][i, j];
denominator += parameters.Gamma[t][i];
}
}
_estimatedTransitionProbabilityMatrix[i][j] = CalculateTransitionProbabilityMatrixEntry(nominator, denominator, parameters.Normalized);
}
}
return(_estimatedTransitionProbabilityMatrix);
}
public void Estimate_KsiGammaParameters_TransitionProbabilityMatrixCalculatedAndReturned()
{
const int numberOfStates = 2;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = numberOfStates, Emissions = CreateEmissions(observations, numberOfStates)
});
model.Normalized = true;
var observationsList = new List <IObservation>();
for (var i = 0; i < observations.Length; i++)
{
observationsList.Add(new Observation(observations[i], i.ToString()));
}
var baseEstimator = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <NormalDistribution>();
var alpha = alphaEstimator.Estimate(baseEstimator);
var betaEstimator = new BetaEstimator <NormalDistribution>();
var beta = betaEstimator.Estimate(baseEstimator);
var @params = new AdvancedEstimationParameters <NormalDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = observationsList,
Model = model,
Normalized = model.Normalized
};
var gammaEstimator = new GammaEstimator <NormalDistribution>();
var ksiEstimator = new KsiEstimator <NormalDistribution>();
var gamma = gammaEstimator.Estimate(@params);
var ksi = ksiEstimator.Estimate(@params);
var estimator = new TransitionProbabilityEstimator <NormalDistribution>();
var parameters = new KsiGammaTransitionProbabilityMatrixParameters <NormalDistribution>
{
Model = model,
Ksi = ksi,
Gamma = gamma,
T = observations.Length,
Normalized = model.Normalized
};
var estimatedTransitionProbabilityMatrix = estimator.Estimate(parameters);
Assert.AreEqual(1d, Math.Round(estimatedTransitionProbabilityMatrix[0][0] + estimatedTransitionProbabilityMatrix[0][1], 5));
Assert.AreEqual(1d, Math.Round(estimatedTransitionProbabilityMatrix[1][0] + estimatedTransitionProbabilityMatrix[1][1], 5));
}