private void trainingStats(MutableContext[] params) throws IOException { int numCorrect = 0; int oei = 0; sequenceStream.reset(); Sequence sequence; while ((sequence = sequenceStream.read()) != null) { Event[] taggerEvents = sequenceStream.updateContext(sequence, new PerceptronModel(params,predLabels,outcomeLabels)); for (int ei = 0; ei < taggerEvents.length; ei++, oei++) { int max = omap.get(taggerEvents[ei].getOutcome()); if (max == outcomeList[oei]) { numCorrect ++; } } } display(". (" + numCorrect + "/" + numEvents + ") " + ((double) numCorrect / numEvents) + "\n"); } }
featureCounts.add(new HashMap<>()); PerceptronModel model = new PerceptronModel(params,predLabels,outcomeLabels); model = new PerceptronModel(params,predLabels,outcomeLabels);
/** * Retrieve a model from disk. It assumes that models are saved in the * following sequence: * * <br>Perceptron (model type identifier) * <br>1. # of parameters (int) * <br>2. # of outcomes (int) * <br> * list of outcome names (String) * <br>3. # of different types of outcome patterns (int) * <br> * list of (int int[]) * <br> [# of predicates for which outcome pattern is true] [outcome pattern] * <br>4. # of predicates (int) * <br> * list of predicate names (String) * * <p>If you are creating a reader for a format which won't work with this * (perhaps a database or xml file), override this method and ignore the * other methods provided in this abstract class. * * @return The PerceptronModel stored in the format and location specified to * this PerceptronModelReader (usually via its the constructor). */ public AbstractModel constructModel() throws IOException { String[] outcomeLabels = getOutcomes(); int[][] outcomePatterns = getOutcomePatterns(); String[] predLabels = getPredicates(); Context[] params = getParameters(outcomePatterns); return new PerceptronModel(params, predLabels, outcomeLabels); }
return new PerceptronModel(averageParams, updatedPredLabels, outcomeLabels); return new PerceptronModel(params, updatedPredLabels, 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); }
private void trainingStats(MutableContext[] params) throws IOException { int numCorrect = 0; int oei = 0; sequenceStream.reset(); Sequence sequence; while ((sequence = sequenceStream.read()) != null) { Event[] taggerEvents = sequenceStream.updateContext(sequence, new PerceptronModel(params,predLabels,outcomeLabels)); for (int ei = 0; ei < taggerEvents.length; ei++, oei++) { int max = omap.get(taggerEvents[ei].getOutcome()); if (max == outcomeList[oei]) { numCorrect ++; } } } display(". (" + numCorrect + "/" + numEvents + ") " + ((double) numCorrect / numEvents) + "\n"); } }
private void trainingStats(MutableContext[] params) throws IOException { int numCorrect = 0; int oei = 0; sequenceStream.reset(); Sequence sequence; while ((sequence = sequenceStream.read()) != null) { Event[] taggerEvents = sequenceStream.updateContext(sequence, new PerceptronModel(params,predLabels,outcomeLabels)); for (int ei = 0; ei < taggerEvents.length; ei++, oei++) { int max = omap.get(taggerEvents[ei].getOutcome()); if (max == outcomeList[oei]) { numCorrect ++; } } } display(". (" + numCorrect + "/" + numEvents + ") " + ((double) numCorrect / numEvents) + "\n"); } }
featureCounts.add(new HashMap<>()); PerceptronModel model = new PerceptronModel(params,predLabels,outcomeLabels); model = new PerceptronModel(params,predLabels,outcomeLabels);
featureCounts.add(new HashMap<>()); PerceptronModel model = new PerceptronModel(params,predLabels,outcomeLabels); model = new PerceptronModel(params,predLabels,outcomeLabels);
/** * Retrieve a model from disk. It assumes that models are saved in the * following sequence: * * <br>Perceptron (model type identifier) * <br>1. # of parameters (int) * <br>2. # of outcomes (int) * <br> * list of outcome names (String) * <br>3. # of different types of outcome patterns (int) * <br> * list of (int int[]) * <br> [# of predicates for which outcome pattern is true] [outcome pattern] * <br>4. # of predicates (int) * <br> * list of predicate names (String) * * <p>If you are creating a reader for a format which won't work with this * (perhaps a database or xml file), override this method and ignore the * other methods provided in this abstract class. * * @return The PerceptronModel stored in the format and location specified to * this PerceptronModelReader (usually via its the constructor). */ public AbstractModel constructModel() throws IOException { String[] outcomeLabels = getOutcomes(); int[][] outcomePatterns = getOutcomePatterns(); String[] predLabels = getPredicates(); Context[] params = getParameters(outcomePatterns); return new PerceptronModel(params, predLabels, outcomeLabels); }
/** * Retrieve a model from disk. It assumes that models are saved in the * following sequence: * * <br>Perceptron (model type identifier) * <br>1. # of parameters (int) * <br>2. # of outcomes (int) * <br> * list of outcome names (String) * <br>3. # of different types of outcome patterns (int) * <br> * list of (int int[]) * <br> [# of predicates for which outcome pattern is true] [outcome pattern] * <br>4. # of predicates (int) * <br> * list of predicate names (String) * * <p>If you are creating a reader for a format which won't work with this * (perhaps a database or xml file), override this method and ignore the * other methods provided in this abstract class. * * @return The PerceptronModel stored in the format and location specified to * this PerceptronModelReader (usually via its the constructor). */ public AbstractModel constructModel() throws IOException { String[] outcomeLabels = getOutcomes(); int[][] outcomePatterns = getOutcomePatterns(); String[] predLabels = getPredicates(); Context[] params = getParameters(outcomePatterns); return new PerceptronModel(params, predLabels, outcomeLabels); }
return new PerceptronModel(averageParams, updatedPredLabels, outcomeLabels); return new PerceptronModel(params, updatedPredLabels, outcomeLabels);
return new PerceptronModel(averageParams, updatedPredLabels, outcomeLabels); return new PerceptronModel(params, updatedPredLabels, 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); }