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 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 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);
}