/// <summary>
/// Raises the <see cref="E:GenerativeClassModelLearningStarted"/> event.
/// </summary>
///
/// <param name="args">The <see cref="BestCS.Statistics.Models.Markov.Learning.GenerativeLearningEventArgs"/> instance containing the event data.</param>
///
protected void OnGenerativeClassModelLearningStarted(GenerativeLearningEventArgs args)
{
if (ClassModelLearningStarted != null)
{
ClassModelLearningStarted(this, args);
}
}
/// <summary>
/// Trains each model to recognize each of the output labels.
/// </summary>
/// <returns>The sum log-likelihood for all models after training.</returns>
///
protected double Run <T>(T[] inputs, int[] outputs)
{
if (inputs == null)
{
throw new ArgumentNullException("inputs");
}
if (outputs == null)
{
throw new ArgumentNullException("outputs");
}
if (inputs.Length != outputs.Length)
{
throw new DimensionMismatchException("outputs",
"The number of inputs and outputs does not match.");
}
for (int i = 0; i < outputs.Length; i++)
{
if (outputs[i] < 0 || outputs[i] >= Classifier.Classes)
{
throw new ArgumentOutOfRangeException("outputs");
}
}
int classes = Classifier.Classes;
double[] logLikelihood = new double[classes];
int[] classCounts = new int[classes];
// For each model,
Parallel.For(0, classes, i =>
{
// We will start the class model learning problem
var args = new GenerativeLearningEventArgs(i, classes);
OnGenerativeClassModelLearningStarted(args);
// Select the input/output set corresponding
// to the model's specialization class
int[] inx = outputs.Find(y => y == i);
T[] observations = inputs.Submatrix(inx);
classCounts[i] = observations.Length;
if (observations.Length > 0)
{
// Create and configure the learning algorithm
IUnsupervisedLearning teacher = Algorithm(i);
// Train the current model in the input/output subset
logLikelihood[i] = teacher.Run(observations as Array[]);
}
// Update and report progress
OnGenerativeClassModelLearningFinished(args);
});
if (Empirical)
{
for (int i = 0; i < classes; i++)
{
Classifier.Priors[i] = (double)classCounts[i] / inputs.Length;
}
}
if (Rejection)
{
Classifier.Threshold = Threshold();
}
// Returns the sum log-likelihood for all models.
return(logLikelihood.Sum());
}
