numEvents = di.getNumEvents();
Assert.assertEquals(3, di.getNumEvents()); Assert.assertEquals(2, di.getOutcomeLabels().length); Assert.assertEquals(6, di.getPredLabels().length);
Assert.assertEquals("opennlp.tools.ml.model.OnePassDataIndexer", di.getClass().getName()); di.index(eventStream); Assert.assertEquals(3, di.getNumEvents()); Assert.assertEquals(2, di.getOutcomeLabels().length); Assert.assertEquals(6, di.getPredLabels().length); Assert.assertEquals("opennlp.tools.ml.model.TwoPassDataIndexer", di.getClass().getName()); di.index(eventStream); Assert.assertEquals(3, di.getNumEvents()); Assert.assertEquals(2, di.getOutcomeLabels().length); Assert.assertEquals(6, di.getPredLabels().length);
public AbstractModel trainModel(DataIndexer di) { 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(); display("...done.\n"); /* Create and return the model ****/ return new NaiveBayesModel(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); }
Assert.assertArrayEquals(new int[]{0}, indexer.getContexts()[2]); Assert.assertNull(indexer.getValues()); Assert.assertEquals(5, indexer.getNumEvents()); Assert.assertArrayEquals(new int[]{0, 1, 2}, indexer.getOutcomeList()); Assert.assertArrayEquals(new int[]{3, 1, 1}, indexer.getNumTimesEventsSeen());
Assert.assertArrayEquals(new int[]{0}, indexer.getContexts()[2]); Assert.assertNull(indexer.getValues()); Assert.assertEquals(5, indexer.getNumEvents()); Assert.assertArrayEquals(new int[]{0, 1, 2}, indexer.getOutcomeList()); Assert.assertArrayEquals(new int[]{3, 1, 1}, indexer.getNumTimesEventsSeen());
Assert.assertArrayEquals(new float[]{0.1F, 0.2F, 0.1F, 0.1F, 0.1F, 0.1F, 0.1F, 0.1F, 0.1F}, indexer.getValues()[2], delta); Assert.assertEquals(5, indexer.getNumEvents()); Assert.assertArrayEquals(new int[]{0, 1, 2}, indexer.getOutcomeList()); Assert.assertArrayEquals(new int[]{3, 1, 1}, indexer.getNumTimesEventsSeen());
Assert.assertNull(indexer.getValues()[1]); Assert.assertNull(indexer.getValues()[2]); Assert.assertEquals(5, indexer.getNumEvents()); Assert.assertArrayEquals(new int[]{0, 1, 2}, indexer.getOutcomeList()); Assert.assertArrayEquals(new int[]{3, 1, 1}, indexer.getNumTimesEventsSeen());
numEvents = di.getNumEvents();
numEvents = di.getNumEvents();
public AbstractModel trainModel(DataIndexer di) { 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(); display("...done.\n"); /* Create and return the model ****/ return new NaiveBayesModel(finalParameters, predLabels, outcomeLabels); }
public AbstractModel trainModel(DataIndexer di) { 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(); display("...done.\n"); /* Create and return the model ****/ return new NaiveBayesModel(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); }
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); }