public double[] Estimate(SigmaEstimationParameters <TDistribution, double[]> parameters)
{
if (_sigmaUnivariate != null)
{
return(_sigmaUnivariate);
}
_sigmaUnivariate = new double[parameters.Model.N];
for (var n = 0; n < parameters.Model.N; n++)
{
var T = parameters.Observations.Count;
var variance = 0d;
var nK = 0d;
for (var t = 0; t < T; t++)
{
nK += parameters.Gamma[t][n];
var x = parameters.Observations[t].Value[0];
var z = x - parameters.Mean[n];
var m = z * z;
m = m * parameters.Gamma[t][n];
variance = variance + m;
}
_sigmaUnivariate[n] = variance / nK;
}
return(_sigmaUnivariate);
}
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);
}
