/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
///
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
///
public override object Clone()
{
double[][] A = LogTransitions.Copy();
double[][] B = LogEmissions.Copy();
double[] pi = LogInitial.Copy();
return(new HiddenMarkovModel(A, B, pi, logarithm: true));
}
/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
///
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
///
public virtual object Clone()
{
double[][] A = LogTransitions.Copy();
double[] pi = LogInitial.Copy();
TDistribution[] B = Emissions.MemberwiseClone();
return(new HiddenMarkovModel <TDistribution, TObservation>(A, B, pi, logarithm: true));
}
/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
///
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
///
public override object Clone()
{
double[,] A = (double[, ])LogTransitions.Clone();
double[,] B = (double[, ])Emissions.Clone();
double[] pi = (double[])LogInitial.Clone();
return(new HiddenMarkovModel(A, B, pi, logarithm: true));
}
public HiddenMarkovModel <GeneralDiscreteDistribution> ToContinuousModel()
{
var transitions = (double[, ])LogTransitions.Clone();
var probabilities = (double[])LogInitial.Clone();
var emissions = new GeneralDiscreteDistribution[States];
for (int i = 0; i < emissions.Length; i++)
{
emissions[i] = new GeneralDiscreteDistribution(Accord.Math.Matrix.GetRow(Emissions, i));
}
return(new HiddenMarkovModel <GeneralDiscreteDistribution>(transitions, emissions, probabilities));
}
