public void Ksi_ErgodicNotNormalized_EachEntryMatrixIsSummedToOne()
{
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 = false;
var baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), 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 = Helper.Convert(observations),
Model = model,
Normalized = model.Normalized
};
var estimator = new KsiEstimator <NormalDistribution>();
for (int t = 0; t < observations.Length - 1; t++)
{
Assert.AreEqual(1.0d, Math.Round(estimator.Estimate(@params)[t].Sum(), 5), string.Format("Failed Ksi [{1}] :{0}", new Matrix(estimator.Estimate(@params)[t]), t));
}
}
public void Gamma_RightLeftNotNormalized_EachEntryMatrixIsSummedToOne()
{
var delta = 3;
var numberOfStatesRightLeft = 4;
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 = 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 = Helper.Convert(observations), 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 = Helper.Convert(observations),
Model = model,
Normalized = model.Normalized
};
var estimator = new GammaEstimator <NormalDistribution>();
for (int i = 0; i < observations.Length; i++)
{
Assert.AreEqual(1.0d, Math.Round(estimator.Estimate(@params)[i].Sum(), 5), string.Format("Failed Gamma Component [{1}] : {0}", estimator.Estimate(@params)[i], i));
}
}
public double[][] Estimate(AdvancedEstimationParameters <TDistribution> parameters)
{
if (_gamma != null)
{
return(_gamma);
}
var denominator = new double[parameters.Observations.Count];
for (var t = 0; t < parameters.Observations.Count; t++)
{
denominator[t] = (parameters.Normalized) ? double.NaN : 0d;
for (var i = 0; i < parameters.Model.N; i++)
{
if (parameters.Normalized)
{
denominator[t] = LogExtention.eLnSum(denominator[t], LogExtention.eLnProduct(parameters.Alpha[t][i], parameters.Beta[t][i]));
}
else
{
denominator[t] += parameters.Alpha[t][i] * parameters.Beta[t][i];
}
}
}
try
{
_gamma = new double[parameters.Observations.Count][];
for (var t = 0; t < parameters.Observations.Count; t++)
{
_gamma[t] = new double[parameters.Model.N];
for (var i = 0; i < parameters.Model.N; i++)
{
if (parameters.Normalized)
{
_gamma[t][i] = LogExtention.eLnProduct(LogExtention.eLnProduct(parameters.Alpha[t][i], parameters.Beta[t][i]), -denominator[t]);
}
else
{
_gamma[t][i] = (parameters.Alpha[t][i] * parameters.Beta[t][i]) / denominator[t];
}
}
}
}
catch (Exception)
{
for (var t = 0; t < parameters.Observations.Count; t++)
{
for (var i = 0; i < parameters.Model.N; i++)
{
Debug.WriteLine("Gamma [{0}][{1}] : alpha : {2} , beta : {3} , denominator : {4} : gamma {5} ", t, i, parameters.Alpha[t][i], parameters.Beta[t][i], denominator[t], _gamma[t][i]);
}
}
throw;
}
return(_gamma);
}
public double[][,] Estimate(AdvancedEstimationParameters <TDistribution> parameters)
{
if (_ksi != null)
{
return(_ksi);
}
var denominator = new double[parameters.Observations.Count];
for (var t = 0; t < parameters.Observations.Count - 1; t++)
{
denominator[t] = (parameters.Normalized) ? double.NaN : 0d;
for (var i = 0; i < parameters.Model.N; i++)
{
for (var j = 0; j < parameters.Model.N; j++)
{
var o = EstimatorUtilities.GetProbability(parameters.Model.Emission[j], parameters.Observations, t + 1);
if (parameters.Normalized)
{
denominator[t] = LogExtention.eLnSum(denominator[t], LogExtention.eLnProduct(parameters.Alpha[t][i],
LogExtention.eLnProduct(LogExtention.eLn(parameters.Model.TransitionProbabilityMatrix[i][j]),
LogExtention.eLnProduct(parameters.Beta[t + 1][j], LogExtention.eLn(o)))));
}
else
{
denominator[t] += parameters.Alpha[t][i] * parameters.Model.TransitionProbabilityMatrix[i][j] * parameters.Beta[t + 1][j] * o;
}
}
}
}
_ksi = new double[parameters.Observations.Count][, ];
for (var t = 0; t < parameters.Observations.Count - 1; t++)
{
_ksi[t] = new double[parameters.Model.N, parameters.Model.N];
for (var i = 0; i < parameters.Model.N; i++)
{
for (var j = 0; j < parameters.Model.N; j++)
{
var o = EstimatorUtilities.GetProbability(parameters.Model.Emission[j], parameters.Observations, t + 1);
if (parameters.Normalized)
{
var nominator = LogExtention.eLnProduct(parameters.Alpha[t][i],
LogExtention.eLnProduct(LogExtention.eLn(parameters.Model.TransitionProbabilityMatrix[i][j]),
LogExtention.eLnProduct(parameters.Beta[t + 1][j], LogExtention.eLn(o))));
_ksi[t][i, j] = LogExtention.eLnProduct(nominator, -denominator[t]);
}
else
{
var nominator = parameters.Alpha[t][i] * parameters.Model.TransitionProbabilityMatrix[i][j] * parameters.Beta[t + 1][j] * o;
_ksi[t][i, j] = nominator / denominator[t];
}
}
}
}
return(_ksi);
}
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 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));
}
public IHiddenMarkovModel <DiscreteDistribution> Run(int maxIterations, double likelihoodTolerance)
{
// Initialize responce object
var forwardBackward = new ForwardBackward(Normalized);
do
{
maxIterations--;
if (!_estimatedModel.Likelihood.EqualsTo(0))
{
_currentModel = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <DiscreteDistribution> {
Pi = _estimatedPi, TransitionProbabilityMatrix = _estimatedTransitionProbabilityMatrix, Emissions = _estimatedEmissions
}); //new HiddenMarkovModelState<DiscreteDistribution>(_estimatedPi, _estimatedTransitionProbabilityMatrix, _estimatedEmissions);
_currentModel.Normalized = Normalized;
_currentModel.Likelihood = _estimatedModel.Likelihood;
}
// Run Forward-Backward procedure
forwardBackward.RunForward(_observations, _currentModel);
forwardBackward.RunBackward(_observations, _currentModel);
var @params = new AdvancedEstimationParameters <DiscreteDistribution>
{
Alpha = forwardBackward.Alpha,
Beta = forwardBackward.Beta,
Observations = _observations,
Model = _currentModel,
Normalized = _currentModel.Normalized
};
_gammaEstimator = new GammaEstimator <DiscreteDistribution>();
_ksiEstimator = new KsiEstimator <DiscreteDistribution>();
// Estimate transition probabilities and start distribution
EstimatePi(_gammaEstimator.Estimate(@params));
EstimateTransitionProbabilityMatrix(_gammaEstimator.Estimate(@params), _ksiEstimator.Estimate(@params), null, _observations.Count);
// Estimate Emmisions
for (var j = 0; j < _currentModel.N; j++)
{
_estimatedEmissions[j] = (DiscreteDistribution)_estimatedEmissions[j].Evaluate(_discreteObservations, _discreteSymbols, _gammaEstimator.Estimate(@params).GetColumn(j), Normalized);
}
_estimatedModel = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <DiscreteDistribution> {
Pi = _estimatedPi, TransitionProbabilityMatrix = _estimatedTransitionProbabilityMatrix, Emissions = _estimatedEmissions
});
_estimatedModel.Normalized = Normalized;
_estimatedModel.Likelihood = forwardBackward.RunForward(_observations, _estimatedModel);
_likelihoodDelta = Math.Abs(Math.Abs(_currentModel.Likelihood) - Math.Abs(_estimatedModel.Likelihood));
Debug.WriteLine("Iteration {3} , Current {0}, Estimate {1} Likelihood delta {2}", _currentModel.Likelihood, _estimatedModel.Likelihood, _likelihoodDelta, maxIterations);
}while (_currentModel != _estimatedModel && maxIterations > 0 && _likelihoodDelta > likelihoodTolerance);
return(_estimatedModel);
}
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]));
}
}
}
public void Ksi_RightLeftAndNotNormalized_KsiCalculated()
{
var delta = 3;
var numberOfStatesRightLeft = 4;
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 = numberOfStatesRightLeft, Delta = delta, Emissions = CreateEmissions(observations, numberOfStatesRightLeft)
}); //new HiddenMarkovModelState<NormalDistribution>(numberOfStatesRightLeft, delta, CreateEmissions(observations, numberOfStatesRightLeft)) { LogNormalized = true };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), 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 = Helper.Convert(observations),
Model = model,
Normalized = model.Normalized
};
var estimator = new KsiEstimator <NormalDistribution>();
Assert.IsNotNull(estimator);
for (int t = 0; t < observations.Length - 1; t++)
{
for (int i = 0; i < numberOfStatesRightLeft; i++)
{
for (int j = 0; j < numberOfStatesRightLeft; j++)
{
Assert.IsTrue(estimator.Estimate(@params)[t][i, j] >= 0 && estimator.Estimate(@params)[t][i, j] < 1, string.Format("Failed Ksi [{1}][{2},{3}]:{0}", estimator.Estimate(@params)[t][i, j], t, i, j));
}
}
}
}
public void Ksi_ErgodicAndLogNormalized_KsiCalculated()
{
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 baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), 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 = Helper.Convert(observations),
Model = model,
Normalized = model.Normalized
};
var estimator = new KsiEstimator <NormalDistribution>();
Assert.IsNotNull(estimator);
for (int t = 0; t < observations.Length - 1; t++)
{
for (int i = 0; i < NumberOfStates; i++)
{
for (int j = 0; j < NumberOfStates; j++)
{
Assert.IsTrue(estimator.Estimate(@params)[t][i, j] < 0, string.Format("Failed Ksi {0}", estimator.Estimate(@params)[t][i, j]));
}
}
}
}
public void Gamma_ErgodicAndNotNormalized_GammaCalculated()
{
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) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <NormalDistribution> {
Model = model, Observations = Helper.Convert(observations), 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 = Helper.Convert(observations),
Model = model,
Normalized = model.Normalized
};
var estimator = new GammaEstimator <NormalDistribution>();
Assert.IsNotNull(estimator);
for (int i = 0; i < observations.Length; i++)
{
for (int j = 0; j < NumberOfStates; j++)
{
Assert.IsTrue(estimator.Estimate(@params)[i][j] > 0 && estimator.Estimate(@params)[i][j] < 1, string.Format("Failed Gamma {0}, [{1}][{2}]", estimator.Estimate(@params)[i][j], i, j));
}
}
}
public double[][] Estimate(AdvancedEstimationParameters <TDistribution> parameters)
{
return(_gammaEstimator.Estimate(parameters));
}
public IHiddenMarkovModel <IMultivariateDistribution> Run(int maxIterations, double likelihoodTolerance)
{
// Initialize responce object
var forwardBackward = new ForwardBackward(Normalized);
do
{
maxIterations--;
if (!_estimatedModel.Likelihood.EqualsTo(0))
{
_currentModel = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <IMultivariateDistribution> {
Pi = _estimatedPi, TransitionProbabilityMatrix = _estimatedTransitionProbabilityMatrix, Emissions = _estimatedEmissions
}); //new HiddenMarkovModelState<IMultivariateDistribution>(_estimatedPi, _estimatedTransitionProbabilityMatrix, _estimatedEmissions) { LogNormalized = _estimatedModel.LogNormalized };
_currentModel.Normalized = Normalized;
_currentModel.Likelihood = _estimatedModel.Likelihood;
}
// Run Forward-Backward procedure
forwardBackward.RunForward(_observations, _currentModel);
forwardBackward.RunBackward(_observations, _currentModel);
var @params = new AdvancedEstimationParameters <IMultivariateDistribution>
{
Alpha = forwardBackward.Alpha,
Beta = forwardBackward.Beta,
Observations = _observations,
Model = _currentModel,
Normalized = _currentModel.Normalized
};
_gammaEstimator = new GammaEstimator <IMultivariateDistribution>();
_ksiEstimator = new KsiEstimator <IMultivariateDistribution>();
_muEstimator = new MuMultivariateEstimator <IMultivariateDistribution>();
_sigmaEstimator = new SigmaMultivariateEstimator <IMultivariateDistribution>();
EstimatePi(_gammaEstimator.Estimate(@params));
EstimateTransitionProbabilityMatrix(_gammaEstimator.Estimate(@params), _ksiEstimator.Estimate(@params), null, _observations.Count);
// Estimate observation probabilities
var muParams = new MuEstimationParameters <IMultivariateDistribution>
{
Gamma = _gammaEstimator.Estimate(@params),
Model = _currentModel,
Normalized = _currentModel.Normalized,
Observations = _observations
};
var muVector = _muEstimator.Estimate(muParams);
var sigmaVector = _sigmaEstimator.Estimate(new SigmaEstimationParameters <IMultivariateDistribution, double[][]>(muParams)
{
Mean = muVector
});
for (var n = 0; n < _currentModel.N; n++)
{
_estimatedEmissions[n] = new NormalDistribution(muVector[n], sigmaVector[n]);
}
_estimatedModel = HiddenMarkovModelStateFactory.GetState(new ModelCreationParameters <IMultivariateDistribution> {
Pi = _estimatedPi, TransitionProbabilityMatrix = _estimatedTransitionProbabilityMatrix, Emissions = _estimatedEmissions
});
_estimatedModel.Normalized = Normalized;
_estimatedModel.Likelihood = forwardBackward.RunForward(_observations, _estimatedModel);
_likelihoodDelta = Math.Abs(Math.Abs(_currentModel.Likelihood) - Math.Abs(_estimatedModel.Likelihood));
Debug.WriteLine("Iteration {3} , Current {0}, Estimate {1} Likelihood delta {2}", _currentModel.Likelihood, _estimatedModel.Likelihood, _likelihoodDelta, maxIterations);
}while (_currentModel != _estimatedModel && maxIterations > 0 && _likelihoodDelta > likelihoodTolerance);
return(_estimatedModel);
}
public void KsiEstimator_ABBAObservation_NotNormalizedTest()
{
var startDistribution = new[] { 0.85, 0.15 };
// s = 0, t = 1
var tpm = new double[2][];
tpm[0] = new[] { 0.3, 0.7 };
tpm[1] = new[] { 0.1, 0.9 };
var observations = new List <IObservation>
{
new Observation(new double[] { 0 }, "A"),
new Observation(new double[] { 1 }, "B"),
new Observation(new double[] { 1 }, "B"),
new Observation(new double[] { 0 }, "A")
};
var emissions = new DiscreteDistribution[2];
emissions[0] = new DiscreteDistribution(new double[] { 0, 1 }, new[] { 0.4, 0.6 });
emissions[1] = new DiscreteDistribution(new double[] { 0, 1 }, new[] { 0.5, 0.5 });
var model = HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <DiscreteDistribution>()
{
Pi = startDistribution, TransitionProbabilityMatrix = tpm, Emissions = emissions
}); //new HiddenMarkovModel(startDistribution, tpm, emissions) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <DiscreteDistribution> {
Model = model, Observations = observations, Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <DiscreteDistribution>();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <DiscreteDistribution>();
var beta = betaEstimator.Estimate(baseParameters);
var @params = new AdvancedEstimationParameters <DiscreteDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = observations,
Model = model,
Normalized = model.Normalized
};
var ksiEstimator = new KsiEstimator <DiscreteDistribution>();
var ksi = ksiEstimator.Estimate(@params);
Assert.AreEqual(0.28593281418422561, ksi[0][0, 0]);
Assert.AreEqual(0.53991543690975563, ksi[0][0, 1]);
Assert.AreEqual(0.021024471631193059, ksi[0][1, 0]);
Assert.AreEqual(0.15312727727482567, ksi[0][1, 1]);
Assert.AreEqual(1d, ksi[0].Sum());
Assert.AreEqual(0.10140018110107153, ksi[1][0, 0]);
Assert.AreEqual(0.20555710471434716, ksi[1][0, 1]);
Assert.AreEqual(0.0785838542018705, ksi[1][1, 0]);
Assert.AreEqual(0.61445885998271088, ksi[1][1, 1]);
Assert.AreEqual(1d, ksi[1].Sum());
Assert.AreEqual(0.045953370715644766, ksi[2][0, 0]);
Assert.AreEqual(0.13403066458729723, ksi[2][0, 1]);
Assert.AreEqual(0.06694007875078023, ksi[2][1, 0]);
Assert.AreEqual(0.75307588594627772, ksi[2][1, 1]);
Assert.AreEqual(1d, ksi[2].Sum());
}
public void GammaEstimator_ABBAObservations_NotNormalizedTest()
{
var startDistribution = new[] { 0.85, 0.15 };
// s = 0, t = 1
var tpm = new double[2][];
tpm[0] = new[] { 0.3, 0.7 };
tpm[1] = new[] { 0.1, 0.9 };
var observations = new List <IObservation>
{
new Observation(new double[] { 0 }, "A"),
new Observation(new double[] { 1 }, "B"),
new Observation(new double[] { 1 }, "B"),
new Observation(new double[] { 0 }, "A")
};
var emissions = new DiscreteDistribution[2];
emissions[0] = new DiscreteDistribution(new double[] { 0, 1 }, new[] { 0.4, 0.6 });
emissions[1] = new DiscreteDistribution(new double[] { 0, 1 }, new[] { 0.5, 0.5 });
var model = HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <DiscreteDistribution>()
{
Pi = startDistribution, TransitionProbabilityMatrix = tpm, Emissions = emissions
}); //new HiddenMarkovModel(startDistribution, tpm, emissions) { LogNormalized = false };
model.Normalized = false;
var baseParameters = new BasicEstimationParameters <DiscreteDistribution> {
Model = model, Observations = observations, Normalized = model.Normalized
};
var alphaEstimator = new AlphaEstimator <DiscreteDistribution>();
var alpha = alphaEstimator.Estimate(baseParameters);
var betaEstimator = new BetaEstimator <DiscreteDistribution>();
var beta = betaEstimator.Estimate(baseParameters);
var @params = new AdvancedEstimationParameters <DiscreteDistribution>
{
Alpha = alpha,
Beta = beta,
Observations = observations,
Model = model
};
var gammaEstimator = new GammaEstimator <DiscreteDistribution>();
var gamma = gammaEstimator.Estimate(@params);
Assert.AreEqual(0.8258482510939813, gamma[0][0]);
Assert.AreEqual(0.17415174890601867, gamma[0][1]);
Assert.AreEqual(1d, gamma[0].Sum());
Assert.AreEqual(0.3069572858154187, gamma[1][0]);
Assert.AreEqual(0.69304271418458141, gamma[1][1]);
Assert.AreEqual(1d, gamma[1].Sum());
Assert.AreEqual(0.17998403530294202, gamma[2][0]);
Assert.AreEqual(0.82001596469705806, gamma[2][1]);
Assert.AreEqual(1d, gamma[2].Sum());
Assert.AreEqual(0.112893449466425, gamma[3][0]);
Assert.AreEqual(0.887106550533575, gamma[3][1]);
Assert.AreEqual(1d, gamma[2].Sum());
}
