public LogLikelihoodFunction(DataIndexer indexer) { // get data from indexer. if (indexer instanceof OnePassRealValueDataIndexer) { this.values = indexer.getValues(); } else { this.values = null; } this.contexts = indexer.getContexts(); this.outcomeList = indexer.getOutcomeList(); this.numTimesEventsSeen = indexer.getNumTimesEventsSeen(); this.outcomeLabels = indexer.getOutcomeLabels(); this.predLabels = indexer.getPredLabels(); this.numOutcomes = indexer.getOutcomeLabels().length; this.numFeatures = indexer.getPredLabels().length; this.numContexts = this.contexts.length; this.domainDimension = numOutcomes * numFeatures; this.probModel = new double[numContexts][numOutcomes]; this.gradient = null; }
public LogLikelihoodFunction(DataIndexer indexer) { // get data from indexer. if (indexer instanceof OnePassRealValueDataIndexer) { this.values = indexer.getValues(); } else { this.values = null; } this.contexts = indexer.getContexts(); this.outcomeList = indexer.getOutcomeList(); this.numTimesEventsSeen = indexer.getNumTimesEventsSeen(); this.outcomeLabels = indexer.getOutcomeLabels(); this.predLabels = indexer.getPredLabels(); this.numOutcomes = indexer.getOutcomeLabels().length; this.numFeatures = indexer.getPredLabels().length; this.numContexts = this.contexts.length; this.domainDimension = numOutcomes * numFeatures; this.probModel = new double[numContexts][numOutcomes]; this.gradient = null; }
this.cutoff = cutoff; predicateCounts = di.getPredCounts(); numTimesEventsSeen = di.getNumTimesEventsSeen(); numUniqueEvents = trainingDataFeatNameIndices.length; this.prior = modelPrior;
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); }
this.cutoff = cutoff; predicateCounts = di.getPredCounts(); numTimesEventsSeen = di.getNumTimesEventsSeen(); numUniqueEvents = contexts.length; this.prior = modelPrior;
this.cutoff = cutoff; predicateCounts = di.getPredCounts(); numTimesEventsSeen = di.getNumTimesEventsSeen(); numUniqueEvents = contexts.length; this.prior = modelPrior;
this.cutoff = cutoff; predicateCounts = di.getPredCounts(); numTimesEventsSeen = di.getNumTimesEventsSeen(); numUniqueEvents = contexts.length; this.prior = modelPrior;