| public Run ( int observations, int paths ) : double | ||
| observations | int | An array of observation sequences to be used to train the model. |
| paths | int | An array of state labels associated to each observation sequence. |
| Результат | double | |
/// <summary>
/// Runs the learning algorithm.
/// </summary>
///
/// <remarks>
/// Learning problem. Given some training observation sequences O = {o1, o2, ..., oK}
/// and general structure of HMM (numbers of hidden and visible states), determine
/// HMM parameters M = (A, B, pi) that best fit training data.
/// </remarks>
///
public double Run(params int[][] observations)
{
var model = mle.Model;
convergence.Clear();
double logLikelihood = Double.NegativeInfinity;
for (int i = 0; i < observations.Length; i++)
logLikelihood = Special.LogSum(logLikelihood, model.Evaluate(observations[i]));
double newLogLikelihood = Double.NegativeInfinity;
do // Until convergence or max iterations is reached
{
logLikelihood = newLogLikelihood;
// Compute the Viterbi path for all sequences
int[][] paths = new int[observations.Length][];
for (int i = 0; i < observations.Length; i++)
paths[i] = model.Decode(observations[i]);
// Compute Maximum Likelihood Estimation
mle.Run(observations, paths);
// Compute log-likelihood
newLogLikelihood = Double.NegativeInfinity;
for (int i = 0; i < observations.Length; i++)
newLogLikelihood = Special.LogSum(newLogLikelihood, model.Evaluate(observations[i]));
// Check convergence
convergence.NewValue = newLogLikelihood;
} while (convergence.HasConverged);
return newLogLikelihood;
}
/// <summary>
/// Runs one single epoch (iteration) of the learning algorithm.
/// </summary>
///
/// <param name="inputs">The observation sequences.</param>
/// <param name="outputs">A vector to be populated with the decoded Viterbi sequences.</param>
///
protected override void RunEpoch(int[][] inputs, int[][] outputs)
{
var model = mle.Model;
// Compute the Viterbi path for all sequences
for (int i = 0; i < inputs.Length; i++)
{
outputs[i] = model.Decode(inputs[i]);
}
// Compute Maximum Likelihood Estimation
mle.Run(inputs, outputs);
}
/// <summary>
/// Runs the learning algorithm.
/// </summary>
///
/// <remarks>
/// Learning problem. Given some training observation sequences O = {o1, o2, ..., oK}
/// and general structure of HMM (numbers of hidden and visible states), determine
/// HMM parameters M = (A, B, pi) that best fit training data.
/// </remarks>
///
public double Run(params Array[] observations)
{
var model = mle.Model;
convergence.Clear();
// Convert the generic representation to a vector of multivariate sequences
double[][][] vectorObservations = new double[observations.Length][][];
for (int i = 0; i < observations.Length; i++)
{
vectorObservations[i] = convert(observations[i], model.Dimension);
}
double logLikelihood = Double.NegativeInfinity;
for (int i = 0; i < observations.Length; i++)
{
logLikelihood = Special.LogSum(logLikelihood, model.Evaluate(observations[i]));
}
double newLogLikelihood = Double.NegativeInfinity;
do // Until convergence or max iterations is reached
{
logLikelihood = newLogLikelihood;
// Compute the Viterbi path for all sequences
int[][] paths = new int[observations.Length][];
for (int i = 0; i < observations.Length; i++)
{
paths[i] = model.Decode(vectorObservations[i]);
}
// Compute Maximum Likelihood Estimation
mle.Run(vectorObservations, paths);
// Compute log-likelihood
newLogLikelihood = Double.NegativeInfinity;
for (int i = 0; i < observations.Length; i++)
{
newLogLikelihood = Special.LogSum(newLogLikelihood, model.Evaluate(observations[i]));
}
// Check convergence
convergence.NewValue = newLogLikelihood;
} while (!convergence.HasConverged);
return(newLogLikelihood);
}
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);
}
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);
}
