public void LearnTest6()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 2, 3 },
new double[] { 3, 4 },
new double[] { 4, 5 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4, 3 },
new double[] { 3, 2 },
new double[] { 2, 1 },
new double[] { 1, 0 },
new double[] { 0, -1 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
var density = new MultivariateNormalDistribution(2);
try
{
new HiddenMarkovClassifier<MultivariateNormalDistribution>(
2, new Custom(new double[2, 2], new double[2]), density);
Assert.Fail();
}
catch (ArgumentException)
{
}
var topology = new Custom(
new[,] { { 1 / 2.0, 1 / 2.0 }, { 1 / 2.0, 1 / 2.0 } },
new[] { 1.0, 0.0 });
Array.Clear(topology.Initial, 0, topology.Initial.Length);
Array.Clear(topology.Transitions, 0, topology.Transitions.Length);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<MultivariateNormalDistribution, double[]>(
2, topology, density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution, double[]>(classifier)
{
// Train each model until the log-likelihood changes less than 0.0001
Learner = modelIndex => new BaumWelchLearning<MultivariateNormalDistribution, double[]>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
FittingOptions = new NormalOptions() { Diagonal = true }
}
};
// 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 response1, response2;
// Try to classify the first sequence (output should be 0)
int c1 = classifier.Decide(sequences[0]);
response1 = classifier.Probability(sequences[0]);
// Try to classify the second sequence (output should be 1)
int c2 = classifier.Decide(sequences[1]);
response2 = classifier.Probability(sequences[1]);
Assert.AreEqual(double.NegativeInfinity, logLikelihood);
Assert.AreEqual(0, response1);
Assert.AreEqual(0, response2);
}