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 Mu_ErgodicAndLogNormalized_MuCalculated()
{
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 mu = new MixtureMuEstimator <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++)
{
for (int d = 0; d < observations[0].Length; d++)
{
Assert.IsTrue(mu.Estimate(@params)[i, l][d] > 0, string.Format("Failed Mu {0}", mu.Estimate(@params)[i, l][d]));
}
}
}
}
public double[, ][] Estimate(MixtureCoefficientEstimationParameters <TDistribution> parameters)
{
if (_mu != null)
{
return(_mu);
}
try
{
_mu = new double[parameters.Model.N, parameters.L][];
for (var i = 0; i < parameters.Model.N; i++)
{
for (var l = 0; l < parameters.L; l++)
{
var denominator = 0.0d;
var nominator = new double[parameters.Observations[0].Dimention];
for (var t = 0; t < parameters.Observations.Count; t++)
{
// TODO : weights here
var weight = GetWeightValue(t, parameters.ObservationWeights);
var x = parameters.Observations[t].Value;
var gamma = (parameters.Model.Normalized) ? LogExtention.eExp(parameters.GammaComponents[t][i, l])
: parameters.GammaComponents[t][i, l];
denominator += weight * gamma;
x = x.Product(gamma * weight);
nominator = nominator.Add(x);
}
_mu[i, l] = nominator.Product(1 / denominator);
}
}
}
catch (Exception)
{
for (var i = 0; i < parameters.Model.N; i++)
{
for (var l = 0; l < parameters.L; l++)
{
Debug.WriteLine("Mixture Mu [{0},{1}] : {2}", i, l, new Vector(_mu[i, l]));
}
}
throw;
}
return(_mu);
}
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));
}
}
public double[][] Estimate(MixtureCoefficientEstimationParameters <TDistribution> parameters)
{
_parameters = parameters;
if (_coefficients != null)
{
return(_coefficients);
}
try
{
_coefficients = new double[parameters.Model.N][];
_denominator = new double[parameters.Model.N];
for (var i = 0; i < parameters.Model.N; i++)
{
_denominator[i] = (parameters.Model.Normalized) ? double.NaN : 0d;
for (var t = 0; t < parameters.Observations.Count; t++)
{
var weight = GetWeightValue(t, parameters.ObservationWeights);
if (parameters.Normalized)
{
_denominator[i] = LogExtention.eLnSum(LogExtention.eLnProduct(weight, parameters.Gamma[t][i]), _denominator[i]);
}
else
{
_denominator[i] += weight * parameters.Gamma[t][i];
}
}
}
for (var i = 0; i < parameters.Model.N; i++)
{
_coefficients[i] = new double[parameters.L];
for (var l = 0; l < parameters.L; l++)
{
var nominator = (parameters.Model.Normalized) ? double.NaN : 0d;
for (var t = 0; t < parameters.Observations.Count; t++)
{
var weight = GetWeightValue(t, parameters.ObservationWeights);
if (parameters.Normalized)
{
nominator = LogExtention.eLnSum(LogExtention.eLnProduct(weight, parameters.GammaComponents[t][i, l]), nominator);
}
else
{
nominator += weight * parameters.GammaComponents[t][i, l];
}
}
if (parameters.Normalized)
{
_coefficients[i][l] = LogExtention.eLnProduct(nominator, -_denominator[i]);
}
else
{
_coefficients[i][l] = nominator / _denominator[i];
}
if (Math.Round(_coefficients[i].Sum(), 5) > 1)
{
Debug.WriteLine("Mixture Coeffiecients [{0},{1}] : {2}", i, l, new Vector(_coefficients[i]));
throw new ApplicationException(string.Format("Mixture Coeffiecients is greater than 1. [{0},{1}] : {2} : {3}", i, l, new Vector(_coefficients[i]), Math.Round(_coefficients[i].Sum(), 5)));
}
}
}
}
catch (Exception)
{
for (var i = 0; i < parameters.Model.N; i++)
{
for (var l = 0; l < parameters.L; l++)
{
Debug.WriteLine("Coeffiecients[{0}][{1}] : {2}", i, l, _coefficients[i][l]);
if (Math.Round(_coefficients[i].Sum(), 5) > 1)
{
Debug.WriteLine("Mixture Coeffiecients [{0},{1}] : {2}", i, l, new Vector(_coefficients[i]));
throw new ApplicationException(string.Format("Mixture Coeffiecients is greater than 1. [{0},{1}] : {2} : {3}", i, l, new Vector(_coefficients[i]), Math.Round(_coefficients[i].Sum(), 5)));
}
}
}
throw;
}
return(_coefficients);
}