public void Sigma_RightLeftAndParametersAnnNotNormalized_SigmaCalculated()
{
var delta = 3;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfStates = NumberOfStatesRightLeft, Delta = delta, Emissions = CreateEmissions(observations, NumberOfStatesRightLeft, NumberOfComponents)
}); //new HiddenMarkovModelState<Mixture<IMultivariateDistribution>>(NumberOfStatesRightLeft, delta, CreateEmissions(observations, NumberOfStatesRightLeft, NumberOfComponents)) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <Mixture <IMultivariateDistribution> > {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <Mixture <IMultivariateDistribution> >();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <Mixture <IMultivariateDistribution> >();
var beta = betaEstimator.Estimate(baseParameters);
var parameters = new ParameterEstimations <Mixture <IMultivariateDistribution> >(model, Helper.Convert(observations), alpha, beta);
var sigma = new MixtureSigmaEstimator <Mixture <IMultivariateDistribution> >();
var mixtureGammaEstimator = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >();
var mixtureMuEstimator = new MixtureMuEstimator <Mixture <IMultivariateDistribution> >();
var @params = new MixtureSigmaEstimationParameters <Mixture <IMultivariateDistribution> >
{
Model = model,
Normalized = model.Normalized,
Alpha = alpha,
Beta = beta,
Observations = Helper.Convert(observations),
L = model.Emission[0].Components.Length
};
var gamma = mixtureGammaEstimator.Estimate(@params as AdvancedEstimationParameters <Mixture <IMultivariateDistribution> >);
var gammaComponens = mixtureGammaEstimator.Estimate(@params);
@params.Gamma = gamma;
@params.GammaComponents = gammaComponens;
@params.Mu = mixtureMuEstimator.Estimate(@params);
for (int i = 0; i < NumberOfStatesRightLeft; i++)
{
for (int l = 0; l < NumberOfComponents; l++)
{
for (int rows = 0; rows < parameters.Observation[0].Dimention; rows++)
{
for (int cols = 0; cols < parameters.Observation[0].Dimention; cols++)
{
Assert.IsTrue(sigma.Estimate(@params)[i, l][rows, cols] > 0, string.Format("Failed Sigma {0}", sigma.Estimate(@params)[i, l][rows, cols]));
}
}
}
}
}
public void Denormalized_NormalizedEstimator_SigmaDenormalized()
{
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates, NumberOfComponents)
}); //new HiddenMarkovModelState<Mixture<IMultivariateDistribution>>(NumberOfStates, CreateEmissions(observations, NumberOfStates, NumberOfComponents)) { LogNormalized = true };
model.Normalized = true;
var baseParameters = new BasicEstimationParameters <Mixture <IMultivariateDistribution> > {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <Mixture <IMultivariateDistribution> >();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <Mixture <IMultivariateDistribution> >();
var beta = betaEstimator.Estimate(baseParameters);
var parameters = new ParameterEstimations <Mixture <IMultivariateDistribution> >(model, Helper.Convert(observations), alpha, beta);
var coefficients = new MixtureCoefficientsEstimator <Mixture <IMultivariateDistribution> >();
var mixtureGammaEstimator = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >();
var @params = new MixtureCoefficientEstimationParameters <Mixture <IMultivariateDistribution> >
{
Model = model,
Normalized = model.Normalized,
Alpha = alpha,
Beta = beta,
Observations = Helper.Convert(observations),
L = model.Emission[0].Components.Length
};
var gamma = mixtureGammaEstimator.Estimate(@params as AdvancedEstimationParameters <Mixture <IMultivariateDistribution> >);
var gammaComponens = mixtureGammaEstimator.Estimate(@params);
@params.Gamma = gamma;
@params.GammaComponents = gammaComponens;
for (int i = 0; i < NumberOfStates; i++)
{
for (int l = 0; l < NumberOfComponents; l++)
{
Assert.IsTrue(coefficients.Estimate(@params)[i][l] < 0, string.Format("Failed Coefficients {0}", coefficients.Estimate(@params)[i][l]));
}
}
coefficients.Denormalize();
for (int i = 0; i < NumberOfStates; i++)
{
for (int l = 0; l < NumberOfComponents; l++)
{
Assert.IsTrue(coefficients.Estimate(@params)[i][l] > 0 && coefficients.Estimate(@params)[i][l] < 1, string.Format("Failed Coefficients {0}", coefficients.Estimate(@params)[i][l]));
}
}
}
public void Coefficients_RightLeftAndNotNormilized_EachEntryMatrixIsSummedToOne()
{
var delta = 3;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfStates = NumberOfStatesRightLeft, Delta = delta, Emissions = CreateEmissions(observations, NumberOfStatesRightLeft, NumberOfComponents)
}); //new HiddenMarkovModelState<Mixture<IMultivariateDistribution>>(NumberOfStatesRightLeft, delta, CreateEmissions(observations, NumberOfStatesRightLeft, NumberOfComponents)) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <Mixture <IMultivariateDistribution> > {
Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <Mixture <IMultivariateDistribution> >();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <Mixture <IMultivariateDistribution> >();
var beta = betaEstimator.Estimate(baseParameters);
var parameters = new ParameterEstimations <Mixture <IMultivariateDistribution> >(model, Helper.Convert(observations), alpha, beta);
var coefficients = new MixtureCoefficientsEstimator <Mixture <IMultivariateDistribution> >();
var mixtureGammaEstimator = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >();
var @params = new MixtureCoefficientEstimationParameters <Mixture <IMultivariateDistribution> >
{
Model = model,
Normalized = model.Normalized,
Alpha = alpha,
Beta = beta,
Observations = Helper.Convert(observations),
L = model.Emission[0].Components.Length
};
var gamma = mixtureGammaEstimator.Estimate(@params as AdvancedEstimationParameters <Mixture <IMultivariateDistribution> >);
var gammaComponens = mixtureGammaEstimator.Estimate(@params);
@params.Gamma = gamma;
@params.GammaComponents = gammaComponens;
for (int i = 0; i < NumberOfStates; i++)
{
Assert.AreEqual(1.0d, Math.Round(coefficients.Estimate(@params)[i].Sum(), 5), string.Format("Failed Coefficients {0} at component {1}", new Vector(coefficients.Estimate(@params)[i]), i));
}
}
