public void RunTest()
{
// Example from
// http://www.cs.columbia.edu/4761/notes07/chapter4.3-HMM.pdf
int[][] observations =
{
new int[] { 0,0,0,1,0,0 },
new int[] { 1,0,0,1,0,0 },
new int[] { 0,0,1,0,0,0 },
new int[] { 0,0,0,0,1,0 },
new int[] { 1,0,0,0,1,0 },
new int[] { 0,0,0,1,1,0 },
new int[] { 1,0,0,0,0,0 },
new int[] { 1,0,1,0,0,0 },
};
int[][] paths =
{
new int[] { 0,0,1,0,1,0 },
new int[] { 1,0,1,0,1,0 },
new int[] { 1,0,0,1,1,0 },
new int[] { 1,0,1,1,1,0 },
new int[] { 1,0,0,1,0,1 },
new int[] { 0,0,1,0,0,1 },
new int[] { 0,0,1,1,0,1 },
new int[] { 0,1,1,1,0,0 },
};
HiddenMarkovModel model = new HiddenMarkovModel(states: 2, symbols: 2);
MaximumLikelihoodLearning target = new MaximumLikelihoodLearning(model);
target.UseLaplaceRule = false;
double logLikelihood = target.Run(observations, paths);
var pi = Matrix.Exp(model.Probabilities);
var A = Matrix.Exp(model.Transitions);
var B = Matrix.Exp(model.Emissions);
Assert.AreEqual(0.5, pi[0]);
Assert.AreEqual(0.5, pi[1]);
Assert.AreEqual(7 / 20.0, A[0, 0], 1e-5);
Assert.AreEqual(13 / 20.0, A[0, 1], 1e-5);
Assert.AreEqual(14 / 20.0, A[1, 0], 1e-5);
Assert.AreEqual(6 / 20.0, A[1, 1], 1e-5);
Assert.AreEqual(17 / 25.0, B[0, 0]);
Assert.AreEqual(8 / 25.0, B[0, 1]);
Assert.AreEqual(19 / 23.0, B[1, 0]);
Assert.AreEqual(4 / 23.0, B[1, 1]);
Assert.AreEqual(-1.1472359046136624, logLikelihood);
}
/// <summary>
/// Creates a new instance of the Viterbi learning algorithm.
/// </summary>
///
public ViterbiLearning(HiddenMarkovModel model)
{
this.mle = new MaximumLikelihoodLearning(model);
}
/// <summary>
/// Creates a new instance of the Viterbi learning algorithm.
/// </summary>
///
public ViterbiLearning(HiddenMarkovModel <TDistribution, TObservation> model)
{
this.mle = new MaximumLikelihoodLearning <TDistribution, TObservation>(model);
}
/// <summary>
/// Creates a new instance of the Viterbi learning algorithm.
/// </summary>
///
public ViterbiLearning(HiddenMarkovModel model)
{
this.convergence = new AbsoluteConvergence();
this.mle = new MaximumLikelihoodLearning(model);
}
/// <summary>
/// Creates a new instance of the Viterbi learning algorithm.
/// </summary>
///
public ViterbiLearning(HiddenMarkovModel model)
{
this.convergence = new AbsoluteConvergence();
this.mle = new MaximumLikelihoodLearning(model);
}
public void Train(CompositionCategory cat)
{
Accord.Math.Tools.SetupGenerator(42);
List<int[]> inputSequences = new List<int[]>();
List<int[]> outputSequences = new List<int[]>();
foreach(Composition comp in cat.Compositions)
{
if (comp.Tracks.Count < 2)
continue;
var melInput = comp.Tracks[0].GetMainSequence() as MelodySequence; melInput.Trim(100); melInput.NormalizeNotes(4);
var melOutput = comp.Tracks[1].GetMainSequence() as MelodySequence; melOutput.Trim(100); melOutput.NormalizeNotes(4);
if (melInput.Length > melOutput.Length)
melInput.Trim(melOutput.Length);
else if (melOutput.Length > melInput.Length)
melOutput.Trim(melInput.Length);
book.Add(melInput.Notes); book.Add(melOutput.Notes);
inputSequences.Add(book.ToCodes(melInput.ToArray()));
outputSequences.Add(book.ToCodes(melOutput.ToArray()));
}
if (outputSequences.Count != inputSequences.Count)
throw new Exception("MSP");
for(int i = 0; i < outputSequences.Count; i++)
{
if (outputSequences[i].Length != inputSequences[i].Length)
throw new Exception("MSP 2");
}
var topology = new Forward(states: 50);
hmm = new HiddenMarkovModel(20, book.TotalUniqueSymbols + 1);
var teacher = new Accord.Statistics.Models.Markov.Learning.MaximumLikelihoodLearning(hmm) {UseLaplaceRule=false /*Tolerance = 0.1, Iterations=0*/};
//var teacher = new ViterbiLearning(hmm);
double ll = teacher.Run(outputSequences.ToArray(), inputSequences.ToArray());
Console.WriteLine("Error: {0}", ll);
}
