/// <summary>
/// Learns the parameters for the given system.
/// </summary>
/// <param name="initial">The initial.</param>
/// <param name="transition">The transition.</param>
/// <param name="emission">The emission.</param>
/// <param name="trainingSet">The training set.</param>
public void Learn([NotNull] InitialStateMatrix initial, [NotNull] TransitionMatrix transition,
[NotNull] EmissionMatrix emission,
[NotNull] IList <IList <LabeledObservation> > trainingSet)
{
Learn(initial, trainingSet);
Learn(transition, trainingSet);
Learn(emission, trainingSet);
}
/// <summary>
/// Initializes a new instance of the <see cref="HiddenMarkovModel" /> class.
/// </summary>
/// <param name="states">The states.</param>
/// <param name="inital">The inital state matrix.</param>
/// <param name="transition">The transmission matrix.</param>
/// <param name="emission">The emission matrix.</param>
public HiddenMarkovModel(
[NotNull] ReadOnlyCollection <IState> states,
[NotNull] InitialStateMatrix inital,
[NotNull] TransitionMatrix transition,
[NotNull] EmissionMatrix emission)
{
_states = states;
_inital = inital;
_transition = transition;
_emission = emission;
}
/// <summary>
/// Learns the initial probabilities from the specified training set.
/// </summary>
/// <param name="initial">The initial state matrix.</param>
/// <param name="trainingSet">The training set.</param>
private void Learn([NotNull] InitialStateMatrix initial, [NotNull] IList <IList <LabeledObservation> > trainingSet)
{
// group all traning sentences beginning with the same state
var groups = trainingSet.GroupBy(set => set.First().State).ToList();
var totalCount = trainingSet.Count;
// get the probability by dividing the count of each state's occurrence
// by the total number of training sets
foreach (var group in groups)
{
var state = group.Key;
var occurrences = group.Count();
var probability = (double)occurrences / totalCount;
initial.SetProbability(state, probability);
}
}