public double RunForward <TEmmisionType>(IList <IObservation> observations, IHiddenMarkovModel <TEmmisionType> model) where TEmmisionType : IDistribution
{
var alphaEstimator = new AlphaEstimator <TEmmisionType>();
_alpha = alphaEstimator.Estimate(new BasicEstimationParameters <TEmmisionType> {
Model = model, Observations = observations, Normalized = Normalized
});
var T = observations.Count;
var result = (Normalized) ? double.NaN : 0d;
// Calculate results
for (var i = 0; i < model.N; i++)
{
if (Normalized)
{
result = LogExtention.eLnSum(result, _alpha[T - 1][i]);
}
else
{
result += _alpha[T - 1][i];
}
}
return(result);
}
public double[][] Estimate(KsiGammaTransitionProbabilityMatrixParameters <TDistribution> parameters)
{
if (_estimatedTransitionProbabilityMatrix != null)
{
return(_estimatedTransitionProbabilityMatrix);
}
_estimatedTransitionProbabilityMatrix = new double[parameters.Model.N][];
for (var i = 0; i < parameters.Model.N; i++)
{
_estimatedTransitionProbabilityMatrix[i] = new double[parameters.Model.N];
for (var j = 0; j < parameters.Model.N; j++)
{
double denominator = (parameters.Model.Normalized) ? double.NaN : 0, nominator = (parameters.Model.Normalized) ? double.NaN : 0;
for (var t = 0; t < parameters.T - 1; t++)
{
if (parameters.Model.Normalized)
{
nominator = LogExtention.eLnSum(nominator, parameters.Ksi[t][i, j]);
denominator = LogExtention.eLnSum(denominator, parameters.Gamma[t][i]);
}
else
{
nominator += parameters.Ksi[t][i, j];
denominator += parameters.Gamma[t][i];
}
}
_estimatedTransitionProbabilityMatrix[i][j] = CalculateTransitionProbabilityMatrixEntry(nominator, denominator, parameters.Normalized);
}
}
return(_estimatedTransitionProbabilityMatrix);
}
public double[][] Estimate(AlphaBetaTransitionProbabiltyMatrixParameters <TDistribution> parameters)
{
if (_estimatedTransitionProbabilityMatrix != null)
{
return(_estimatedTransitionProbabilityMatrix);
}
var T = parameters.Observations.Length;
_estimatedTransitionProbabilityMatrix = new double[parameters.Model.N][];
for (var i = 0; i < parameters.Model.N; i++)
{
_estimatedTransitionProbabilityMatrix[i] = new double[parameters.Model.N];
for (var j = 0; j < parameters.Model.N; j++)
{
double nominator = (parameters.Normalized) ? double.NaN : 0.0d, denominator = (parameters.Normalized) ? double.NaN : 0.0d;
for (var t = 0; t < T - 1; t++)
{
var probability = parameters.Model.Emission[j].ProbabilityDensityFunction(parameters.Observations[t + 1]);
if (parameters.Normalized)
{
nominator = LogExtention.eLnSum(nominator, LogExtention.eLnProduct(parameters.Weights[t], LogExtention.eLnProduct(parameters.Alpha[t][i], LogExtention.eLnProduct(LogExtention.eLn(parameters.Model.TransitionProbabilityMatrix[i][j]), LogExtention.eLnProduct(LogExtention.eLn(probability), parameters.Beta[t + 1][j])))));
denominator = LogExtention.eLnSum(denominator, LogExtention.eLnProduct(parameters.Weights[t], LogExtention.eLnProduct(parameters.Alpha[t][i], parameters.Beta[t][j])));
}
else
{
nominator += parameters.Weights[t] * parameters.Alpha[t][i] * parameters.Model.TransitionProbabilityMatrix[i][j] * probability * parameters.Beta[t + 1][j];
denominator += parameters.Weights[t] * parameters.Alpha[t][i] * parameters.Beta[t][j];
}
}
_estimatedTransitionProbabilityMatrix[i][j] = CalculateTransitionProbabilityMatrixEntry(nominator, denominator, parameters.Normalized);
}
}
return(_estimatedTransitionProbabilityMatrix);
}
public double[] Estimate(MuEstimationParameters <TDistribution> parameters)
{
if (_muUnivariate != null)
{
return(_muUnivariate);
}
_muUnivariate = new double[parameters.Model.N];
for (var n = 0; n < parameters.Model.N; n++)
{
var T = parameters.Observations.Count;
var mean = 0d;
var nK = 0d;
for (var t = 0; t < T; t++)
{
if (parameters.Model.Normalized)
{
nK += LogExtention.eExp(parameters.Gamma[t][n]);
mean += parameters.Observations[t].Value[0] * LogExtention.eExp(parameters.Gamma[t][n]);
}
else
{
nK += parameters.Gamma[t][n];
mean += parameters.Observations[t].Value[0] * parameters.Gamma[t][n];
}
}
_muUnivariate[n] = mean / nK;
}
return(_muUnivariate);
}
public IDistribution Evaluate(double[] observations, double[] symbols, double[] gamma)
{
var M = symbols.Length;
var T = observations.Length;
var probabilities = new double[M];
for (var k = 0; k < M; k++)
{
double den = 0, num = 0;
for (var t = 0; t < T; t++)
{
if (observations[t] == symbols[k])
{
num = num + gamma[t];
}
den = den + gamma[t];
}
probabilities[k] = 1e-10;
if (num.EqualsToZero())
{
probabilities[k] = den.EqualsToZero() ? 0.0 : LogExtention.eExp(LogExtention.eLnProduct(num, -den));
}
}
return(new DiscreteDistributionNormalized(symbols, probabilities));
}
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 void eLn_XisZero_NaNReturned()
{
var x = 0;
var result = LogExtention.eLn(x);
Assert.AreEqual(double.NaN, result);
}
public void eExp_XisNumber_ePowerXReturned()
{
var x = 1d;
var result = LogExtention.eExp(x);
Assert.AreEqual(Math.E, result);
}
public void eLn_XisGreaterThanZero_LogXReturned()
{
var x = 1d;
var result = LogExtention.eLn(x);
Assert.AreEqual(0, result);
}
public void eExp_XisNaN_ZeroReturned()
{
var x = double.NaN;
var result = LogExtention.eExp(x);
Assert.AreEqual(0, result);
}
private double CalculateTransitionProbabilityMatrixEntry(double nominator, double denominator, bool normalized)
{
if (denominator.EqualsToZero())
{
return(0);
}
return((normalized) ? LogExtention.eExp(LogExtention.eLnProduct(nominator, -denominator)) : nominator / denominator);
}
public double[][] Estimate(BasicEstimationParameters <TDistribution> parameters)
{
if (_beta != null)
{
return(_beta);
}
var T = parameters.Observations.Count;
try
{
_beta = new double[T][];
_beta[T - 1] = new double[parameters.Model.N];
for (var i = 0; i < parameters.Model.N; i++)
{
_beta[T - 1][i] = (parameters.Normalized) ? 0d : 1d;
}
for (var t = T - 2; t >= 0; t--)
{
_beta[t] = new double[parameters.Model.N];
for (var i = 0; i < parameters.Model.N; i++)
{
_beta[t][i] = (parameters.Normalized) ? double.NaN : 0d;
for (var j = 0; j < parameters.Model.N; j++)
{
var o = EstimatorUtilities.GetProbability(parameters.Model.Emission[j], parameters.Observations, t + 1);
if (parameters.Normalized)
{
_beta[t][i] = LogExtention.eLnSum(_beta[t][i],
LogExtention.eLnProduct(LogExtention.eLn(parameters.Model.TransitionProbabilityMatrix[i][j]),
LogExtention.eLnProduct(LogExtention.eLn(o), _beta[t + 1][j])));
}
else
{
_beta[t][i] += parameters.Model.TransitionProbabilityMatrix[i][j] * o * _beta[t + 1][j];
}
}
}
}
}
catch (Exception)
{
for (var t = T - 2; t >= 0; t--)
{
for (var i = 0; i < parameters.Model.N; i++)
{
for (var j = 0; j < parameters.Model.N; j++)
{
Debug.WriteLine("[{0}][{1}] : beta : {2}", t, i, _beta[t][i]);
}
}
}
throw;
}
return(_beta);
}
public void eLnProduct_YandXnotEqualsNan_XPlusYReturned()
{
var x = 1d;
var y = 1d;
var result = LogExtention.eLnProduct(x, y);
Assert.AreEqual(x + y, result);
}
public void eLnSum_XGreaterThanY_eLnSumReturned()
{
var x = 2d;
var y = 1d;
var result = LogExtention.eLnSum(x, y);
Assert.AreEqual(x + LogExtention.eLn(1 + Math.Exp(y - x)), result);
}
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 eLnSum_YisNaN_XReturned()
{
var x = 1d;
var y = double.NaN;
var result = LogExtention.eLnSum(x, y);
Assert.AreEqual(x, result);
}
public void eLnSum_YGreaterThanX_eLnSumReturned()
{
var x = 1d;
var y = 2d;
var result = LogExtention.eLnSum(x, y);
Assert.AreEqual(y + LogExtention.eLn(1 + Math.Exp(x - y)), result);
}
public void eLnProduct_XisNaN_YReturned()
{
var x = double.NaN;
var y = 1d;
var result = LogExtention.eLnProduct(x, y);
Assert.AreEqual(double.NaN, result);
}
public double[][,] Estimate(SigmaEstimationParameters <TDistribution, double[][]> parameters)
{
if (_sigmaMultivariate != null)
{
return(_sigmaMultivariate);
}
try
{
_sigmaMultivariate = new double[parameters.Model.N][, ];
var K = parameters.Observations[0].Dimention;
var T = parameters.Observations.Count;
for (var n = 0; n < parameters.Model.N; n++)
{
var covariance = new double[K, K];
var nK = 0d;
for (var t = 0; t < T; t++)
{
var x = parameters.Observations[t].Value;
var z = x.Substruct(parameters.Mean[n]);
var m = z.OuterProduct(z);
if (parameters.Model.Normalized)
{
nK += LogExtention.eExp(parameters.Gamma[t][n]);
m = m.Product(LogExtention.eExp(parameters.Gamma[t][n]));
}
else
{
nK += parameters.Gamma[t][n];
m = m.Product(parameters.Gamma[t][n]);
}
covariance = covariance.Add(m);
}
_sigmaMultivariate[n] = covariance.Product(1 / nK);
var matrix = new Matrix(_sigmaMultivariate[n]);
if (!matrix.PositiviDefinite)
{
_sigmaMultivariate[n] = matrix.ConvertToPositiveDefinite();
Debug.WriteLine("HMM State {0} Sigma is not Positive Definite. Converting.", n);
Debug.WriteLine("{0}", matrix);
}
Debug.WriteLine("HMM State {0} Sigma : {1}", n, new Matrix(_sigmaMultivariate[n]));
}
}
catch (Exception)
{
for (var n = 0; n < parameters.Model.N; n++)
{
Debug.WriteLine("HMM State {0} Sigma : {1}", n, new Matrix(_sigmaMultivariate[n]));
}
throw;
}
return(_sigmaMultivariate);
}
protected void EstimatePi(double[][] gamma)
{
var checksum = 0d;
for (var i = 0; i < _model.N; i++)
{
_estimatedPi[i] = (_model.Normalized) ? LogExtention.eExp(gamma[0][i]) : gamma[0][i];
checksum += _estimatedPi[i];
}
CheckPi(checksum);
}
public double[, ][,] Estimate(MixtureSigmaEstimationParameters <TDistribution> parameters)
{
if (_sigma != null)
{
return(_sigma);
}
try
{
_sigma = 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, parameters.Observations[0].Dimention];
for (var t = 0; t < parameters.Observations.Count; t++)
{
// TODO : weights here
var weight = GetWeightValue(t, parameters.ObservationWeights);
var gammaComponents = (parameters.Model.Normalized) ? LogExtention.eExp(parameters.GammaComponents[t][i, l]) : parameters.GammaComponents[t][i, l];
var x = parameters.Observations[t].Value;
var z = x.Substruct(parameters.Mu[i, l]);
var m = z.OuterProduct(z);
m = m.Product(weight * gammaComponents);
denominator += weight * gammaComponents;
nominator = nominator.Add(m);
}
_sigma[i, l] = nominator.Product(1 / denominator);
var matrix = new Matrix(_sigma[i, l]);
if (!matrix.PositiviDefinite)
{
_sigma[i, l] = matrix.ConvertToPositiveDefinite();
Debug.WriteLine("HMM State [{0},{1}] Sigma is not Positive Definite. Converting.", i, l);
Debug.WriteLine("{0}", matrix);
}
}
}
}
catch (Exception)
{
for (var i = 0; i < parameters.Model.N; i++)
{
for (var l = 0; l < parameters.L; l++)
{
Debug.WriteLine("Mixture Sigma [{0},{1}] : {2}", i, l, new Matrix(_sigma[i, l]));
}
}
throw;
}
return(_sigma);
}
public double[][,] Estimate(MixtureAdvancedEstimationParameters <TDistribution> parameters)
{
if (_gammaComponents != null)
{
return(_gammaComponents);
}
try
{
_gammaComponents = new double[parameters.Observations.Count][, ];
for (var t = 0; t < parameters.Observations.Count; t++)
{
_gammaComponents[t] = new double[parameters.Model.N, parameters.L];
for (var i = 0; i < parameters.Model.N; i++)
{
var d = parameters.Model.Emission[i] as Mixture <IMultivariateDistribution>;
if (d != null)
{
for (var l = 0; l < parameters.L; l++)
{
//Emmision in our case are Mixture<T>
var p = d.ProbabilityDensityFunction(l, parameters.Observations[t].Value);
if (parameters.Normalized)
{
_gammaComponents[t][i, l] = LogExtention.eLnProduct(_gammaEstimator.Estimate(parameters)[t][i], LogExtention.eLn(p));
}
else
{
_gammaComponents[t][i, l] = _gammaEstimator.Estimate(parameters)[t][i] * p;
}
}
}
}
}
}
catch (Exception)
{
for (var t = 0; t < parameters.Observations.Count; t++)
{
if (Math.Round(_gammaComponents[t].Sum(), 5) > 1)
{
Debug.WriteLine("Mixture Gamma Components [{0}] : {1}", t, new Matrix(_gammaComponents[t]));
throw new ApplicationException(string.Format("Mixture Sigma is greater than 1. [{0}] : {1} : {2}", t, new Matrix(_gammaComponents[t]), Math.Round(_gammaComponents[t].Sum(), 5)));
}
}
throw;
}
return(_gammaComponents);
}
private double GetWeightValue(int t, decimal[] weights)
{
var weight = 0.0;
if (weights != null)
{
weight = (_parameters.Model.Normalized) ? LogExtention.eLn((double)weights[t]) : (double)weights[t];
}
else
{
weight = (_parameters.Model.Normalized) ? 0 : 1;
}
return(weight);
}
public void Denormalize()
{
if (!_denormalized)
{
for (var i = 0; i < _parameters.Model.N; i++)
{
for (var l = 0; l < _parameters.L; l++)
{
_coefficients[i][l] = LogExtention.eExp(_coefficients[i][l]);
}
}
_denormalized = true;
}
}
public double[] Estimate(PiParameters parameters)
{
if (_estimatedPi != null)
{
return(_estimatedPi);
}
_estimatedPi = new double[parameters.N];
for (var i = 0; i < parameters.N; i++)
{
_estimatedPi[i] = (parameters.Normalized) ? LogExtention.eExp(parameters.Gamma[0][i]) : parameters.Gamma[0][i];
}
return(_estimatedPi);
}
public double[][] Estimate(BasicEstimationParameters <TDistribution> parameters)
{
if (_alpha != null)
{
return(_alpha);
}
_alpha = new double[parameters.Observations.Count][];
_alpha[0] = new double[parameters.Model.N];
// Initialize
for (var i = 0; i < parameters.Model.N; i++)
{
var o = EstimatorUtilities.GetProbability(parameters.Model.Emission[i], parameters.Observations, 0);
_alpha[0][i] = (parameters.Normalized) ? LogExtention.eLnProduct(LogExtention.eLn(parameters.Model.Pi[i]), LogExtention.eLn(o)) : parameters.Model.Pi[i] * o;
}
// Induction
for (var t = 1; t < parameters.Observations.Count; t++)
{
_alpha[t] = new double[parameters.Model.N];
for (var j = 0; j < parameters.Model.N; j++)
{
var sum = (parameters.Normalized) ? double.NaN : 0d;
for (var i = 0; i < parameters.Model.N; i++)
{
if (parameters.Normalized)
{
sum = LogExtention.eLnSum(sum, LogExtention.eLnProduct(_alpha[t - 1][i], LogExtention.eLn(parameters.Model.TransitionProbabilityMatrix[i][j])));
}
else
{
sum += _alpha[t - 1][i] * parameters.Model.TransitionProbabilityMatrix[i][j];
}
}
var o = EstimatorUtilities.GetProbability(parameters.Model.Emission[j], parameters.Observations, t);
if (parameters.Normalized)
{
_alpha[t][j] = LogExtention.eLnProduct(sum, LogExtention.eLn(o));
}
else
{
_alpha[t][j] = sum * o;
}
}
}
return(_alpha);
}
public IDistribution Evaluate(double[] observations, double[] symbols, double[] gamma, bool logNormalized)
{
var M = symbols.Length;
var T = observations.Length;
var probabilities = new double[M];
for (var k = 0; k < M; k++)
{
double den = (logNormalized) ? double.NaN : 0, num = (logNormalized) ? double.NaN : 0;
for (var t = 0; t < T; t++)
{
if (observations[t] == symbols[k])
{
if (logNormalized)
{
num = LogExtention.eLnSum(num, gamma[t]);
}
else
{
num = num + gamma[t];
}
}
if (logNormalized)
{
den = LogExtention.eLnSum(den, gamma[t]);
}
else
{
den = den + gamma[t];
}
}
probabilities[k] = 1e-10;
if (!num.EqualsToZero())
{
if (logNormalized)
{
probabilities[k] = den.EqualsToZero() ? 0.0 : LogExtention.eExp(LogExtention.eLnProduct(num, -den));
}
else
{
probabilities[k] = den.EqualsToZero() ? 0.0 : num / den;
}
}
}
return(new DiscreteDistribution(symbols, probabilities));
}
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 double[][] Estimate(MuEstimationParameters <TDistribution> parameters)
{
if (_muMultivariate != null)
{
return(_muMultivariate);
}
try
{
_muMultivariate = new double[parameters.Model.N][];
var K = parameters.Observations[0].Dimention; // Number of dimentions
var T = parameters.Observations.Count;
for (var n = 0; n < parameters.Model.N; n++)
{
var mean = new double[K];
var nK = 0d;
for (var t = 0; t < T; t++)
{
if (parameters.Model.Normalized)
{
nK += LogExtention.eExp(parameters.Gamma[t][n]);
mean = mean.Add(parameters.Observations[t].Value.Product(LogExtention.eExp(parameters.Gamma[t][n])));
}
else
{
nK += parameters.Gamma[t][n];
mean = mean.Add(parameters.Observations[t].Value.Product(parameters.Gamma[t][n]));
}
}
_muMultivariate[n] = mean.Product(1 / nK);
Debug.WriteLine(string.Format("HMM State {0} : Mu {1}", n, new Vector(_muMultivariate[n])));
}
}
catch (Exception)
{
for (var n = 0; n < parameters.Model.N; n++)
{
Debug.WriteLine(string.Format("HMM State {0} : Mu {1}", n, new Vector(_muMultivariate[n])));
}
throw;
}
return(_muMultivariate);
}
protected void EstimateTransitionProbabilityMatrix(double[][] gamma, double[][,] ksi, decimal[] observationWeights, int sequenceLength)
{
var checksum = new double[_model.N];
for (var i = 0; i < _model.N; i++)
{
_estimatedTransitionProbabilityMatrix[i] = new double[_model.N];
for (var j = 0; j < _model.N; j++)
{
double den = (_model.Normalized) ? double.NaN : 0, num = (_model.Normalized) ? double.NaN : 0;
for (var t = 0; t < sequenceLength - 1; t++)
{
var weight = GetWeightValue(t, observationWeights);
if (_model.Normalized)
{
num = LogExtention.eLnSum(num, LogExtention.eLnProduct(weight, ksi[t][i, j]));
den = LogExtention.eLnSum(den, LogExtention.eLnProduct(weight, gamma[t][i]));
}
else
{
num += weight * ksi[t][i, j];
den += weight * gamma[t][i];
}
}
if (_model.Normalized)
{
_estimatedTransitionProbabilityMatrix[i][j] = den.EqualsToZero() ? 0.0 : LogExtention.eExp(LogExtention.eLnProduct(num, -den));
}
else
{
_estimatedTransitionProbabilityMatrix[i][j] = den.EqualsToZero() ? 0.0 : num / den;
}
checksum[i] = checksum[i] + _estimatedTransitionProbabilityMatrix[i][j];
}
}
CheckTransitionProbabilityMatrix(checksum);
}
