/// <summary>
/// Execute the training operation.
/// </summary>
/// <param name="indexer">The data indexer.</param>
/// <returns>The trained <see cref="IMaxentModel"/> model.</returns>
protected override IMaxentModel DoTrain(IDataIndexer indexer)
{
Display("Incorporating indexed data for training...");
indexer.Execute();
contexts = indexer.GetContexts();
values = indexer.Values;
numTimesEventsSeen = indexer.GetNumTimesEventsSeen();
numEvents = indexer.GetNumEvents();
numUniqueEvents = contexts.Length;
outcomeLabels = indexer.GetOutcomeLabels();
outcomeList = indexer.GetOutcomeList();
predLabels = indexer.GetPredLabels();
numPreds = predLabels.Length;
numOutcomes = outcomeLabels.Length;
Display("done.");
Display("\tNumber of Event Tokens: " + numUniqueEvents);
Display("\t Number of Outcomes: " + numOutcomes);
Display("\t Number of Predicates: " + numPreds);
Display("Computing model parameters...");
// ReSharper disable once CoVariantArrayConversion - we read the parameters ;)
Context[] finalParameters = FindParameters();
Display("...done.\n");
return(new NaiveBayesModel(finalParameters, predLabels, outcomeLabels));
}
/// <summary>
/// Train a model using the Perceptron algorithm.
/// </summary>
/// <param name="iterations">The number of Perceptron iterations to perform.</param>
/// <param name="indexer">The object which will be used for event compilation.</param>
/// <param name="cutoff">The number of times a predicate must occur to be used in a model.</param>
/// <param name="useAverage"></param>
/// <returns>The newly trained model, which can be used immediately or saved to disk using a <see cref="IO.PerceptronModelWriter"/> object.</returns>
public AbstractModel TrainModel(int iterations, IDataIndexer indexer, int cutoff, bool useAverage)
{
Display("Incorporating indexed data for training...");
info.Append("Trained using Perceptron algorithm.\n\n");
// Executes the data indexer
indexer.Execute();
contexts = indexer.GetContexts();
values = indexer.Values;
numTimesEventsSeen = indexer.GetNumTimesEventsSeen();
numEvents = indexer.GetNumEvents();
numUniqueEvents = contexts.Length;
outcomeLabels = indexer.GetOutcomeLabels();
outcomeList = indexer.GetOutcomeList();
predLabels = indexer.GetPredLabels();
numPreds = predLabels.Length;
numOutcomes = outcomeLabels.Length;
Display("\ndone.\n");
info.Append("Number of Event Tokens: {0}\n" +
" Number of Outcomes: {1}\n" +
" Number of Predicates: {2}\n", numEvents, numOutcomes, numPreds);
Display("\tNumber of Event Tokens: " + numUniqueEvents);
Display("\t Number of Outcomes: " + numOutcomes);
Display("\t Number of Predicates: " + numPreds);
Display("Computing model parameters.");
var finalParameters = FindParameters(iterations, useAverage);
Display("\ndone.\n");
// ReSharper disable once CoVariantArrayConversion
return(new PerceptronModel(finalParameters, predLabels, outcomeLabels)
{
info = info
});
}
/// <summary>
/// Train a model using the Perceptron algorithm.
/// </summary>
/// <param name="iterations">The number of Perceptron iterations to perform.</param>
/// <param name="indexer">The object which will be used for event compilation.</param>
/// <param name="cutoff">The number of times a predicate must occur to be used in a model.</param>
/// <param name="useAverage"></param>
/// <returns>The newly trained model, which can be used immediately or saved to disk using a <see cref="IO.PerceptronModelWriter"/> object.</returns>
public AbstractModel TrainModel(int iterations, IDataIndexer indexer, int cutoff, bool useAverage) {
Display("Incorporating indexed data for training...");
info.Append("Trained using Perceptron algorithm.\n\n");
// Executes the data indexer
indexer.Execute();
contexts = indexer.GetContexts();
values = indexer.Values;
numTimesEventsSeen = indexer.GetNumTimesEventsSeen();
numEvents = indexer.GetNumEvents();
numUniqueEvents = contexts.Length;
outcomeLabels = indexer.GetOutcomeLabels();
outcomeList = indexer.GetOutcomeList();
predLabels = indexer.GetPredLabels();
numPreds = predLabels.Length;
numOutcomes = outcomeLabels.Length;
Display("\ndone.\n");
info.Append("Number of Event Tokens: {0}\n" +
" Number of Outcomes: {1}\n" +
" Number of Predicates: {2}\n", numEvents, numOutcomes, numPreds);
Display("\tNumber of Event Tokens: " + numUniqueEvents);
Display("\t Number of Outcomes: " + numOutcomes);
Display("\t Number of Predicates: " + numPreds);
Display("Computing model parameters.");
var finalParameters = FindParameters(iterations, useAverage);
Display("\ndone.\n");
// ReSharper disable once CoVariantArrayConversion
return new PerceptronModel(finalParameters, predLabels, outcomeLabels) {
info = info
};
}
