public void LearnTest1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a univariate sequence and the same sequence backwards.
double[][] sequences = new double[][]
{
new double[] { 0,1,2,3,4 }, // This is the first sequence with label = 0
new double[] { 4,3,2,1,0 }, // This is the second sequence with label = 1
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution density = new NormalDistribution();
var classifier = new HiddenMarkovClassifier<NormalDistribution, double>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<NormalDistribution, double>(classifier)
{
// Train each model until the log-likelihood changes less than 0.001
Learner = modelIndex => new BaumWelchLearning<NormalDistribution, double>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
};
// Train the sequence classifier using the algorithm
teacher.Learn(sequences, labels);
double logLikelihood = teacher.LogLikelihood;
// Calculate the probability that the given
// sequences originated from the model
double likelihood1, likelihood2;
// Try to classify the first sequence (output should be 0)
int c1 = classifier.Decide(sequences[0]);
likelihood1 = classifier.Probability(sequences[0]);
// Try to classify the second sequence (output should be 1)
int c2 = classifier.Decide(sequences[1]);
likelihood2 = classifier.Probability(sequences[1]);
Assert.AreEqual(0, c1);
Assert.AreEqual(1, c2);
Assert.AreEqual(-13.271981026832929, logLikelihood, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood1, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood2, 1e-10);
}