private static ACRFTrainer createTrainer () { if (usePiecewiseTraining.value) { return new PiecewiseACRFTrainer(); } else if (usePwplTraining.value) { return new PwplACRFTrainer(); } else if (usePlTraining.value) { return new PseudolikelihoodACRFTrainer (); } else { return new DefaultAcrfTrainer (); } }
public boolean train (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter) { return train (acrf, training, validation, testing, new LogEvaluator (), numIter); }
public void test (ACRF acrf, InstanceList testing, ACRFEvaluator[] evals) { List pred = acrf.getBestLabels (testing); for (int i = 0; i < evals.length; i++) { evals[i].setOutputPrefix (outputPrefix); evals[i].test (testing, pred, "Testing"); } }
public boolean incrementalTrain (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter) { return incrementalTrain (acrf, training, validation, testing, new LogEvaluator (), numIter); }
public void test (ACRF acrf, InstanceList data, String description) { List ret = acrf.getBestLabels (data); test (data, ret, description); }
private static ACRFTrainer createTrainer () { if (usePiecewiseTraining.value) { return new PiecewiseACRFTrainer(); } else if (usePwplTraining.value) { return new PwplACRFTrainer(); } else if (usePlTraining.value) { return new PseudolikelihoodACRFTrainer (); } else { return new DefaultAcrfTrainer (); } }
public boolean train (ACRF acrf, InstanceList training) { return train (acrf, training, null, null, new LogEvaluator (), 1); }
public void test (ACRF acrf, InstanceList testing, ACRFEvaluator[] evals) { List pred = acrf.getBestLabels (testing); for (int i = 0; i < evals.length; i++) { evals[i].setOutputPrefix (outputPrefix); evals[i].test (testing, pred, "Testing"); } }
public boolean incrementalTrain (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter) { return incrementalTrain (acrf, training, validation, testing, new LogEvaluator (), numIter); }
public void test (ACRF acrf, InstanceList data, String description) { List ret = acrf.getBestLabels (data); test (data, ret, description); }
private static ACRFTrainer createTrainer () { if (usePiecewiseTraining.value) { return new PiecewiseACRFTrainer(); } else if (usePwplTraining.value) { return new PwplACRFTrainer(); } else if (usePlTraining.value) { return new PseudolikelihoodACRFTrainer (); } else { return new DefaultAcrfTrainer (); } }
public boolean train (ACRF acrf, InstanceList training, int numIter) { return train (acrf, training, null, null, new LogEvaluator (), numIter); }
public void test (ACRF acrf, InstanceList testing, ACRFEvaluator[] evals) { List pred = acrf.getBestLabels (testing); for (int i = 0; i < evals.length; i++) { evals[i].setOutputPrefix (outputPrefix); evals[i].test (testing, pred, "Testing"); } }
public boolean incrementalTrain (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter) { return incrementalTrain (acrf, training, validation, testing, new LogEvaluator (), numIter); }
public boolean train (ACRF acrf, InstanceList training, int numIter) { return train (acrf, training, null, null, new LogEvaluator (), numIter); }
public boolean train (ACRF acrf, InstanceList training, int numIter) { return train (acrf, training, null, null, new LogEvaluator (), numIter); }
public boolean train (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter) { return train (acrf, training, validation, testing, new LogEvaluator (), numIter); }
public boolean train (ACRF acrf, InstanceList training) { return train (acrf, training, null, null, new LogEvaluator (), 1); }
public boolean train (ACRF acrf, InstanceList training) { return train (acrf, training, null, null, new LogEvaluator (), 1); }
public boolean train (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter) { return train (acrf, training, validation, testing, new LogEvaluator (), numIter); }