/// <summary>
/// Constructs a new Hidden Markov Model with discrete state probabilities.
/// </summary>
/// <param name="topology">
/// A <see cref="Topology"/> object specifying the initial values of the matrix of transition
/// probabilities <c>A</c> and initial state probabilities <c>pi</c> to be used by this model.
/// </param>
/// <param name="emissions">
/// The initial emission probability distribution to be used by each of the states.
/// </param>
public ContinuousHiddenMarkovModel(ITopology topology, IDistribution emissions)
: base(topology)
{
if (emissions == null)
{
throw new ArgumentNullException("emissions");
}
// Initialize B using the initial distribution
B = new IDistribution[States];
for (int i = 0; i < B.Length; i++)
{
B[i] = (IDistribution)emissions.Clone();
}
if (B[0] is IMultivariateDistribution)
{
dimension = ((IMultivariateDistribution)B[0]).Dimension;
}
else
{
dimension = 1;
}
}
/// <summary>
/// Constructs a new Hidden Markov Model with discrete state probabilities.
/// </summary>
/// <param name="topology">
/// A <see cref="Topology"/> object specifying the initial values of the matrix of transition
/// probabilities <c>A</c> and initial state probabilities <c>pi</c> to be used by this model.
/// </param>
/// <param name="emissions">
/// The initial emission probability distribution to be used by each of the states.
/// </param>
public ContinuousHiddenMarkovModel(ITopology topology, IDistribution emissions)
: base(topology)
{
if (emissions == null)
{
throw new ArgumentNullException("emissions");
}
// Initialize B using the initial distribution
B = new IDistribution[States];
for (int i = 0; i < B.Length; i++)
B[i] = (IDistribution) emissions.Clone();
if (B[0] is IMultivariateDistribution)
dimension = ((IMultivariateDistribution) B[0]).Dimension;
else dimension = 1;
}