public NaiveBayesClassifier(IDistribution[,] distribution, IDistribution classDistribution)
{
_classes = distribution.GetLength(0);
_distribution = distribution;
_classesProbablityDistribution = classDistribution;
}
/// <summary>
/// Constructs a new Hidden Markov Model.
/// </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 distributions for each state.
/// </param>
public ContinuousHiddenMarkovModel(ITopology topology, IDistribution[] emissions)
: base(topology)
{
if (emissions == null)
{
throw new ArgumentNullException("emissions");
}
if (emissions.GetLength(0) != States)
{
throw new ArgumentException(
"The emission matrix should have the same number of rows as the number of states in the model.",
"emissions");
}
B = emissions;
if (B[0] is IMultivariateDistribution)
dimension = ((IMultivariateDistribution) B[0]).Dimension;
else dimension = 1;
}