private static HiddenMarkovClassifier<NormalDistribution> createClassifier(
out double[][] sequences, bool rejection = false)
{
sequences = new double[][]
{
new double[] { 0,1,2,3,4 },
new double[] { 4,3,2,1,0 },
};
int[] labels = { 0, 1 };
NormalDistribution density = new NormalDistribution();
HiddenMarkovClassifier<NormalDistribution> classifier =
new HiddenMarkovClassifier<NormalDistribution>(2, new Ergodic(2), density);
var teacher = new HiddenMarkovClassifierLearning<NormalDistribution>(classifier,
modelIndex => new BaumWelchLearning<NormalDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
teacher.Rejection = rejection;
teacher.Run(sequences, labels);
return classifier;
}