public static HiddenMarkovModel<GeneralDiscreteDistribution, int> CreateDiscrete(double[,] transitions,
double[,] emissions, double[] probabilities, bool logarithm = false)
{
ITopology topology = new Custom(transitions, probabilities, logarithm);
if (emissions == null)
{
throw new ArgumentNullException("emissions");
}
if (emissions.GetLength(0) != topology.States)
{
throw new ArgumentException(
"The emission matrix should have the same number of rows as the number of states in the model.",
"emissions");
}
// Initialize B using a discrete distribution
var B = new GeneralDiscreteDistribution[topology.States];
for (int i = 0; i < B.Length; i++)
B[i] = new GeneralDiscreteDistribution(Accord.Math.Matrix.GetRow(emissions, i));
return new HiddenMarkovModel<GeneralDiscreteDistribution, int>(topology, B);
}
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>(
2, topology, density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning<MultivariateNormalDistribution>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
FittingOptions = new NormalOptions() { Diagonal = true }
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
// Calculate the probability that the given
// sequences originated from the model
double response1, response2;
// Try to classify the 1st sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out response1);
// Try to classify the 2nd sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out response2);
Assert.AreEqual(double.NegativeInfinity, logLikelihood);
Assert.AreEqual(0, response1);
Assert.AreEqual(0, response2);
Assert.IsFalse(double.IsNaN(logLikelihood));
Assert.IsFalse(double.IsNaN(response1));
Assert.IsFalse(double.IsNaN(response2));
}
