/** * <!-- train(int,DoneWithRound) --> Trains {@link #learner} for the specified number of rounds. * This learning happens on top of any learning that {@link #learner} may have already done. * * @param rounds The number of passes to make over the training data. * @param dwr Performs post processing at the end of each round. **/ public void train(int rounds, DoneWithRound dwr) { train(1, rounds, dwr); }
/** * <!-- train(int) --> Trains {@link #learner} for the specified number of rounds. This learning * happens on top of any learning that {@link #learner} may have already done. * * @param rounds The number of passes to make over the training data. **/ public void train(int rounds) { train(1, rounds); }
/** * <!-- train(int,int) --> Trains {@link #learner} for the specified number of rounds. This * learning happens on top of any learning that {@link #learner} may have already done. * * @param start The 1-based number of the first training round. * @param rounds The total number of training rounds including those before <code>start</code>. **/ public void train(int start, int rounds) { train(start, rounds, new DoneWithRound() { public void doneWithRound(int r) {} }); }
public void trainOnAll() { QuantitiesClassifier classifier = new QuantitiesClassifier(modelName + ".lc", modelName + ".lex"); QuantitiesDataReader trainReader = new QuantitiesDataReader(dataDir + "/allData.txt", "train"); BatchTrainer trainer = new BatchTrainer(classifier, trainReader); trainer.train(45); classifier.save(); }
public void train() { QuantitiesClassifier classifier = new QuantitiesClassifier(modelName + ".lc", modelName + ".lex"); QuantitiesDataReader trainReader = new QuantitiesDataReader(dataDir + "/train.txt", "train"); BatchTrainer trainer = new BatchTrainer(classifier, trainReader); trainer.train(45); classifier.save(); }
public void trainOnAll() { QuantitiesClassifier classifier = new QuantitiesClassifier(modelName + ".lc", modelName + ".lex"); QuantitiesDataReader trainReader = new QuantitiesDataReader(dataDir + "/allData.txt", "train"); BatchTrainer trainer = new BatchTrainer(classifier, trainReader); trainer.train(45); classifier.save(); }
public void train() { QuantitiesClassifier classifier = new QuantitiesClassifier(modelName + ".lc", modelName + ".lex"); QuantitiesDataReader trainReader = new QuantitiesDataReader(dataDir + "/train.txt", "train"); BatchTrainer trainer = new BatchTrainer(classifier, trainReader); trainer.train(45); classifier.save(); }
trainer.train(lce.startingRound, trainingRounds); } else
messageIndent += " "; train(totalRounds, new DoneWithRound() { int r = 0;
messageIndent += " "; train(totalRounds, new DoneWithRound() { int r = 0;
bt1train.train(1); testParser1.reset(); TestDiscrete simpleTest = new TestDiscrete(); || (fixedNumIterations > 0 && i <= fixedNumIterations); ++i) { logger.info("Learning level 2 classifier; round " + i); bt2train.train(1); logger.info("Testing level 2 classifier; on prefetched data, round: " + i); testParser2.reset();
public void train() { if (!IOUtils.exists(modelsDir)) IOUtils.mkdir(modelsDir); Learner classifier = new PrepSRLClassifier(modelName + ".lc", modelName + ".lex"); Parser trainDataReader = new PrepSRLDataReader(dataDir, "train"); BatchTrainer trainer = new BatchTrainer(classifier, trainDataReader, 1000); trainer.train(20); classifier.save(); trainDataReader.close(); }
public void train() { if (!IOUtils.exists(modelsDir)) IOUtils.mkdir(modelsDir); Learner classifier = new PrepSRLClassifier(modelName + ".lc", modelName + ".lex"); Parser trainDataReader = new PrepSRLDataReader(dataDir, "train"); BatchTrainer trainer = new BatchTrainer(classifier, trainDataReader, 1000); trainer.train(20); classifier.save(); trainDataReader.close(); }
public static extent_classifier train_extent_classifier(ExtentReader train_parser, String prefix){ extent_classifier classifier = new extent_classifier(); String modelFileName = ""; if (prefix == null){ String postfix = train_parser.getId(); modelFileName = "tmp/extent_classifier_" + postfix; } else{ modelFileName = prefix; } classifier.setLexiconLocation(modelFileName + ".lex"); BatchTrainer trainer = new BatchTrainer(classifier, train_parser); Lexicon lexicon = trainer.preExtract(modelFileName + ".ex", true); classifier.setLexicon(lexicon); classifier.setModelLocation(modelFileName + ".lc"); trainer.train(1); classifier.saveModel(); return classifier; }
public static extent_classifier train_extent_classifier(ExtentReader train_parser, String prefix){ extent_classifier classifier = new extent_classifier(); String modelFileName = ""; if (prefix == null){ String postfix = train_parser.getId(); modelFileName = "tmp/extent_classifier_" + postfix; } else{ modelFileName = prefix; } classifier.setLexiconLocation(modelFileName + ".lex"); BatchTrainer trainer = new BatchTrainer(classifier, train_parser); Lexicon lexicon = trainer.preExtract(modelFileName + ".ex", true); classifier.setLexicon(lexicon); classifier.setModelLocation(modelFileName + ".lc"); trainer.train(1); classifier.saveModel(); return classifier; }
public static extent_classifier train_extent_classifier(ExtentReader train_parser, String prefix){ extent_classifier classifier = new extent_classifier(); String modelFileName = ""; if (prefix == null){ String postfix = train_parser.getId(); modelFileName = "tmp/extent_classifier_" + postfix; } else{ modelFileName = prefix; } classifier.setLexiconLocation(modelFileName + ".lex"); BatchTrainer trainer = new BatchTrainer(classifier, train_parser); Lexicon lexicon = trainer.preExtract(modelFileName + ".ex", true); classifier.setLexicon(lexicon); classifier.setModelLocation(modelFileName + ".lc"); trainer.train(1); classifier.saveModel(); return classifier; }
Lexicon lexicon = bt.preExtract(null); learner.setLexicon(lexicon); bt.train(250); learner.save(); foldParser.setFromPivot(true);
Lexicon lexicon = bt.preExtract(null); learner.setLexicon(lexicon); bt.train(250); learner.save(); foldParser.setFromPivot(true);
Lexicon lexicon = bt.preExtract(null); learner.setLexicon(lexicon); bt.train(learningRounds); if (!testOnTrain) foldParser.setFromPivot(true);
Lexicon lexicon = bt.preExtract(null); learner.setLexicon(lexicon); bt.train(learningRounds); if (!testOnTrain) foldParser.setFromPivot(true);