public void Sigma_ErgodicAndObservationAndLogNormalized_SigmaCalculated()
{
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var sequence = Helper.Convert(observations);
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates)
}); //new HiddenMarkovModelState<NormalDistribution>(NumberOfStates, CreateEmissions(observations, NumberOfStates)) { LogNormalized = true };
model.Normalized = true;
var baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = sequence, Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <NormalDistribution>();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <NormalDistribution>();
var beta = betaEstimator.Estimate(baseParameters);
var @params = new AdvancedEstimationParameters <NormalDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = sequence,
Model = model
};
var gammaEstimator = new GammaEstimator <NormalDistribution>();
var muEstimator = new MuMultivariateEstimator <NormalDistribution>();
var estimator = new SigmaMultivariateEstimator <NormalDistribution>();
var muParams = new MuEstimationParameters <NormalDistribution>
{
Gamma = gammaEstimator.Estimate(@params),
Model = model,
Normalized = model.Normalized,
Observations = Helper.Convert(observations)
};
Assert.IsNotNull(estimator);
var sigma = estimator.Estimate(new SigmaEstimationParameters <NormalDistribution, double[][]>(muParams)
{
Mean = muEstimator.Estimate(muParams)
});
for (int n = 0; n < NumberOfStates; n++)
{
for (int i = 0; i < sequence[0].Dimention; i++)
{
for (int j = 0; j < sequence[0].Dimention; j++)
{
Assert.IsTrue(sigma[n][i, j] > 0, string.Format("Failed Sigma {0}", sigma[n][i, j]));
}
}
}
}
public void MuEstimator_ModelAndObservations_MuEstimatorCreated()
{
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 <NormalDistribution>()
{
NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates)
}); //new HiddenMarkovModelState<NormalDistribution>(NumberOfStates, CreateEmissions(observations, NumberOfStates)) { LogNormalized = true };
model.Normalized = true;
var estimator = new MuMultivariateEstimator <NormalDistribution>();
Assert.IsNotNull(estimator);
}
public void Mu_MultivariateAndRightLeftAndNotNormalized_MuCalculated()
{
var delta = 3;
var util = new TestDataUtils();
var observations = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 11, 18), new DateTime(2011, 12, 18));
var sequence = Helper.Convert(observations);
var model = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <NormalDistribution>()
{
NumberOfStates = NumberOfStatesRightLeft, Delta = delta, Emissions = CreateEmissions(observations, NumberOfStatesRightLeft)
}); //new HiddenMarkovModelState<NormalDistribution>(NumberOfStatesRightLeft, delta, CreateEmissions(observations, NumberOfStatesRightLeft)) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = sequence, Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <NormalDistribution>();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <NormalDistribution>();
var beta = betaEstimator.Estimate(baseParameters);
var @params = new AdvancedEstimationParameters <NormalDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = sequence,
Model = model
};
var gammaEstimator = new GammaEstimator <NormalDistribution>();
var estimator = new MuMultivariateEstimator <NormalDistribution>();
var muParams = new MuEstimationParameters <NormalDistribution>
{
Gamma = gammaEstimator.Estimate(@params),
Model = model,
Normalized = model.Normalized,
Observations = sequence
};
Assert.IsNotNull(estimator);
var mu = estimator.Estimate(muParams);
for (int i = 0; i < NumberOfStatesRightLeft; i++)
{
for (int j = 0; j < sequence[0].Dimention; j++)
{
Assert.IsTrue(mu[i][j] > 0, string.Format("Failed Mu {0}", mu[i][j]));
}
}
}
