/// <summary>
///
/// </summary>
/// <param name="A">Transition matrix</param>
/// <param name="B">Emision matrix</param>
/// <returns></returns>
public static HiddenMarkovModel FromMatrices(double[,] A, double[,] B, double[] pi, bool isLog = false)
{
HiddenMarkovModel model = new HiddenMarkovModel(A.GetLength(0), B.GetLength(1));
if (!isLog)
{
model.logTransition = MathE.elnify(A);
model.logEmission = MathE.elnify(B);
model.logPi = MathE.elnify(pi);
}
else
{
model.logTransition = A;
model.logEmission = B;
model.logPi = pi;
}
return(model);
}
public void UniformRandomPriors()
{
logPi = MathE.elnify(NormalMass(logPi.Length));
for (int i = 0; i < logTransition.GetLength(0); i++)
{
double[] trans = MathE.elnify(NormalMass(states));
for (int j = 0; j < logTransition.GetLength(0); j++)
{
logTransition[i, j] = trans[j];
}
}
for (int i = 0; i < logEmission.GetLength(0); i++)
{
double[] ems = MathE.elnify(NormalMass(observationSymbols));
for (int j = 0; j < logEmission.GetLength(1); j++)
{
logEmission[i, j] = ems[j];
}
}
}