/// <summary>
/// Optimizes the defined function.
/// </summary>
///
/// <param name="values">The initial guess values for the parameters.</param>
///
public double Minimize(double[] values)
{
solution = values;
convergence.Clear();
do
{
runEpoch();
}while (!convergence.HasConverged);
return(convergence.NewValue);
}
/// <summary>
/// Runs the learning algorithm.
/// </summary>
///
/// <remarks>
/// Learning problem. Given some training observation sequences O = {o1, o2, ..., oK}
/// and general structure of HMM (numbers of hidden and visible states), determine
/// HMM parameters M = (A, B, pi) that best fit training data.
/// </remarks>
///
public double Run(params int[][] observations)
{
var model = mle.Model;
convergence.Clear();
double logLikelihood = Double.NegativeInfinity;
for (int i = 0; i < observations.Length; i++)
logLikelihood = Special.LogSum(logLikelihood, model.Evaluate(observations[i]));
double newLogLikelihood = Double.NegativeInfinity;
do // Until convergence or max iterations is reached
{
logLikelihood = newLogLikelihood;
// Compute the Viterbi path for all sequences
int[][] paths = new int[observations.Length][];
for (int i = 0; i < observations.Length; i++)
paths[i] = model.Decode(observations[i]);
// Compute Maximum Likelihood Estimation
mle.Run(observations, paths);
// Compute log-likelihood
newLogLikelihood = Double.NegativeInfinity;
for (int i = 0; i < observations.Length; i++)
newLogLikelihood = Special.LogSum(newLogLikelihood, model.Evaluate(observations[i]));
// Check convergence
convergence.NewValue = newLogLikelihood;
} while (convergence.HasConverged);
return newLogLikelihood;
}
/// <summary>
/// Runs the learning algorithm.
/// </summary>
///
/// <remarks>
/// Learning problem. Given some training observation sequences O = {o1, o2, ..., oK}
/// and general structure of HMM (numbers of hidden and visible states), determine
/// HMM parameters M = (A, B, pi) that best fit training data.
/// </remarks>
///
public double Run(params Array[] observations)
{
var model = mle.Model;
convergence.Clear();
// Convert the generic representation to a vector of multivariate sequences
double[][][] vectorObservations = new double[observations.Length][][];
for (int i = 0; i < observations.Length; i++)
{
vectorObservations[i] = convert(observations[i], model.Dimension);
}
double logLikelihood = Double.NegativeInfinity;
for (int i = 0; i < observations.Length; i++)
{
logLikelihood = Special.LogSum(logLikelihood, model.Evaluate(observations[i]));
}
double newLogLikelihood = Double.NegativeInfinity;
do // Until convergence or max iterations is reached
{
logLikelihood = newLogLikelihood;
// Compute the Viterbi path for all sequences
int[][] paths = new int[observations.Length][];
for (int i = 0; i < observations.Length; i++)
{
paths[i] = model.Decode(vectorObservations[i]);
}
// Compute Maximum Likelihood Estimation
mle.Run(vectorObservations, paths);
// Compute log-likelihood
newLogLikelihood = Double.NegativeInfinity;
for (int i = 0; i < observations.Length; i++)
{
newLogLikelihood = Special.LogSum(newLogLikelihood, model.Evaluate(observations[i]));
}
// Check convergence
convergence.NewValue = newLogLikelihood;
} while (!convergence.HasConverged);
return(newLogLikelihood);
}
