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 void Alpha_ErgodicAndNotNormalized_AlphaCalculated() { const int numberOfStates = 2; 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 alphaEstimator = new AlphaEstimator <NormalDistribution>(); var alpha = alphaEstimator.Estimate(new BasicEstimationParameters <NormalDistribution> { Model = model, Observations = Helper.Convert(observations), Normalized = model.Normalized }); Assert.IsNotNull(alpha); for (int i = 0; i < observations.Length; i++) { for (int j = 0; j < numberOfStates; j++) { Assert.IsTrue(alpha[i][j] > 0 && alpha[i][j] < 1, string.Format("Failed Alpha [{0}][{1}] : {2}", i, j, alpha[i][j])); } } }
public void GammaComponents_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 <Mixture <IMultivariateDistribution> >() { NumberOfStates = NumberOfStates, Emissions = CreateEmissions(observations, NumberOfStates, NumberOfComponents) }); //new HiddenMarkovModelState<Mixture<IMultivariateDistribution>>(NumberOfStates, CreateEmissions(observations, NumberOfStates, 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 estimator = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >(); var @params = new MixtureAdvancedEstimationParameters <Mixture <IMultivariateDistribution> > { Alpha = alpha, Beta = beta, L = model.Emission[0].Components.Length, Model = model, Normalized = model.Normalized, Observations = Helper.Convert(observations) }; var gammaComponents = estimator.Estimate(@params); for (int t = 0; t < observations.Length; t++) { Assert.AreEqual(1.0d, Math.Round(gammaComponents[t].Sum(), 5), string.Format("Failed Gamma Components {0} at time {1}", new Matrix(gammaComponents[t]), t)); } }
public void MixtureGammaEstimator_Parameters_MixtureGammaComponentsAndGammaInitialized() { 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 gamma = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >(); var @params = new MixtureAdvancedEstimationParameters <Mixture <IMultivariateDistribution> > { Alpha = alpha, Beta = beta, L = model.Emission[0].Components.Length, Model = model, Normalized = model.Normalized, Observations = Helper.Convert(observations) }; Assert.IsNotNull(gamma.Estimate(@params as AdvancedEstimationParameters <Mixture <IMultivariateDistribution> >)); Assert.IsNotNull(gamma.Estimate(@params)); }
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 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 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 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 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 void GammaComponents_RightLeftAndNotNormalized_GammaComponentsCalculated() { 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 estimator = new MixtureGammaEstimator <Mixture <IMultivariateDistribution> >(); Assert.IsNotNull(estimator); var @params = new MixtureAdvancedEstimationParameters <Mixture <IMultivariateDistribution> > { Alpha = alpha, Beta = beta, L = model.Emission[0].Components.Length, Model = model, Normalized = model.Normalized, Observations = Helper.Convert(observations) }; var gammaComponents = estimator.Estimate(@params); for (int t = 0; t < observations.Length; t++) { for (int i = 0; i < NumberOfStatesRightLeft; i++) { for (int l = 0; l < NumberOfComponents; l++) { Assert.IsTrue(gammaComponents[t][i, l] >= 0 && gammaComponents[t][i, l] < 1, string.Format("Failed Gamma Components {0}, [{1}][{2},{3}]", gammaComponents[t][i, l], t, i, l)); } } } }
public void AlphaEstimator_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 alphaEstimator = new AlphaEstimator <DiscreteDistribution>(); var alpha = alphaEstimator.Estimate(new BasicEstimationParameters <DiscreteDistribution> { Model = model, Observations = observations, Normalized = model.Normalized }); Assert.AreEqual(0.34, Math.Round(alpha[0][0], 9)); Assert.AreEqual(0.075, Math.Round(alpha[0][1], 9)); Assert.AreEqual(0.0657, Math.Round(alpha[1][0], 9)); Assert.AreEqual(0.15275, Math.Round(alpha[1][1], 9)); Assert.AreEqual(0.020991, Math.Round(alpha[2][0], 9)); Assert.AreEqual(0.0917325, Math.Round(alpha[2][1], 9)); Assert.AreEqual(0.00618822, Math.Round(alpha[3][0], 9)); Assert.AreEqual(0.048626475, Math.Round(alpha[3][1], 9)); }
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 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 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 Estimate_AlphaBetaParameters_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 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 weights = new double[observations.Length]; var estimator = new TransitionProbabilityEstimator <NormalDistribution>(); var parameters = new AlphaBetaTransitionProbabiltyMatrixParameters <NormalDistribution> { Alpha = alpha, Beta = beta, Model = model, Observations = observations, Normalized = model.Normalized, Weights = weights }; 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 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 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()); }
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()); }