public AbstractModel trainModel(int iterations, DataIndexer di, int cutoff, boolean useAverage) { display("Incorporating indexed data for training... \n"); contexts = di.getContexts(); values = di.getValues(); numTimesEventsSeen = di.getNumTimesEventsSeen(); numEvents = di.getNumEvents(); numUniqueEvents = contexts.length; outcomeLabels = di.getOutcomeLabels(); outcomeList = di.getOutcomeList(); predLabels = di.getPredLabels(); numPreds = predLabels.length; numOutcomes = outcomeLabels.length; display("done.\n"); display("\tNumber of Event Tokens: " + numUniqueEvents + "\n"); display("\t Number of Outcomes: " + numOutcomes + "\n"); display("\t Number of Predicates: " + numPreds + "\n"); display("Computing model parameters...\n"); MutableContext[] finalParameters = findParameters(iterations, useAverage); display("...done.\n"); /*************** Create and return the model ******************/ return new PerceptronModel(finalParameters, predLabels, outcomeLabels); }
public AbstractModel trainModel(int iterations, DataIndexer di, int cutoff, boolean useAverage) { display("Incorporating indexed data for training... \n"); contexts = di.getContexts(); values = di.getValues(); numTimesEventsSeen = di.getNumTimesEventsSeen(); numEvents = di.getNumEvents(); numUniqueEvents = contexts.length; outcomeLabels = di.getOutcomeLabels(); outcomeList = di.getOutcomeList(); predLabels = di.getPredLabels(); numPreds = predLabels.length; numOutcomes = outcomeLabels.length; display("done.\n"); display("\tNumber of Event Tokens: " + numUniqueEvents + "\n"); display("\t Number of Outcomes: " + numOutcomes + "\n"); display("\t Number of Predicates: " + numPreds + "\n"); display("Computing model parameters...\n"); MutableContext[] finalParameters = findParameters(iterations, useAverage); display("...done.\n"); /*************** Create and return the model ******************/ return new PerceptronModel(finalParameters, predLabels, outcomeLabels); }
numEvents = di.getNumEvents();
numEvents = di.getNumEvents();