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, InstanceList validation, InstanceList testing, int numIter) { return train (acrf, training, validation, testing, 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 void train (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, ACRFEvaluator eval, double[] proportions, int iterPerProportion) { for (int i = 0; i < proportions.length; i++) { double proportion = proportions[i]; InstanceList[] lists = training.split (r, new double[]{proportion, 1.0}); logger.info ("ACRF trainer: Round " + i + ", training proportion = " + proportion); train (acrf, lists[0], validation, testing, eval, iterPerProportion); } logger.info ("ACRF trainer: Training on full data"); train (acrf, training, validation, testing, eval, 99999); }
public void train (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, ACRFEvaluator eval, double[] proportions, int iterPerProportion) { for (int i = 0; i < proportions.length; i++) { double proportion = proportions[i]; InstanceList[] lists = training.split (r, new double[]{proportion, 1.0}); logger.info ("ACRF trainer: Round " + i + ", training proportion = " + proportion); train (acrf, lists[0], validation, testing, eval, iterPerProportion); } logger.info ("ACRF trainer: Training on full data"); train (acrf, training, validation, testing, eval, 99999); }
public void train (ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, ACRFEvaluator eval, double[] proportions, int iterPerProportion) { for (int i = 0; i < proportions.length; i++) { double proportion = proportions[i]; InstanceList[] lists = training.split (r, new double[]{proportion, 1.0}); logger.info ("ACRF trainer: Round " + i + ", training proportion = " + proportion); train (acrf, lists[0], validation, testing, eval, iterPerProportion); } logger.info ("ACRF trainer: Training on full data"); train (acrf, training, validation, testing, eval, 99999); }
public boolean train (ACRF acrf, InstanceList training) { return train (acrf, training, null, null, new LogEvaluator (), 1); }
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, int numIter) { return train (acrf, training, null, null, new LogEvaluator (), numIter); }
public boolean train (ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter) { Optimizable.ByGradientValue macrf = createOptimizable (acrf, trainingList); return train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); }
public boolean train (ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter) { Optimizable.ByGradientValue macrf = createOptimizable (acrf, trainingList); return train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); }
public boolean train (ACRF acrf, InstanceList training) { return train (acrf, training, null, null, new LogEvaluator (), 1); }
public boolean train (ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter) { Optimizable.ByGradientValue macrf = createOptimizable (acrf, trainingList); return train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); }
public boolean train (ACRF acrf, InstanceList training) { return train (acrf, training, null, null, new LogEvaluator (), 1); }
public boolean train (ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter, Optimizable.ByGradientValue macrf) { if (wrongWrongType == NO_WRONG_WRONG) { return super.train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); } else { Maxable bipwMaxable = (Maxable) macrf; // add wrong wrongs after 5 iterations logger.info ("BiconditionalPiecewiseACRFTrainer: Initial training"); super.train (acrf, trainingList, validationList, testSet, eval, wrongWrongIter, macrf); FileUtils.writeGzippedObject (new File (outputPrefix, "initial-acrf.ser.gz"), acrf); logger.info ("BiconditionalPiecewiseACRFTrainer: Adding wrong-wrongs"); bipwMaxable.addWrongWrong (trainingList); logger.info ("BiconditionalPiecewiseACRFTrainer: Adding wrong-wrongs"); boolean converged = super.train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); reportTrainingLikelihood (acrf, trainingList); return converged; } }
public boolean train (ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter, Optimizable.ByGradientValue macrf) { if (wrongWrongType == NO_WRONG_WRONG) { return super.train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); } else { Maxable bipwMaxable = (Maxable) macrf; // add wrong wrongs after 5 iterations logger.info ("BiconditionalPiecewiseACRFTrainer: Initial training"); super.train (acrf, trainingList, validationList, testSet, eval, wrongWrongIter, macrf); FileUtils.writeGzippedObject (new File (outputPrefix, "initial-acrf.ser.gz"), acrf); logger.info ("BiconditionalPiecewiseACRFTrainer: Adding wrong-wrongs"); bipwMaxable.addWrongWrong (trainingList); logger.info ("BiconditionalPiecewiseACRFTrainer: Adding wrong-wrongs"); boolean converged = super.train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); reportTrainingLikelihood (acrf, trainingList); return converged; } }
public boolean someUnsupportedTrain (ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter) { Optimizable.ByGradientValue macrf = createOptimizable (acrf, trainingList); train (acrf, trainingList, validationList, testSet, eval, 5, macrf); ACRF.Template[] tmpls = acrf.getTemplates (); for (int ti = 0; ti < tmpls.length; ti++) tmpls[ti].addSomeUnsupportedWeights (trainingList); logger.info ("Some unsupporetd weights initialized. Training..."); return train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); }
public boolean someUnsupportedTrain (ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter) { Optimizable.ByGradientValue macrf = createOptimizable (acrf, trainingList); train (acrf, trainingList, validationList, testSet, eval, 5, macrf); ACRF.Template[] tmpls = acrf.getTemplates (); for (int ti = 0; ti < tmpls.length; ti++) tmpls[ti].addSomeUnsupportedWeights (trainingList); logger.info ("Some unsupporetd weights initialized. Training..."); return train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); }
public boolean someUnsupportedTrain (ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter) { Optimizable.ByGradientValue macrf = createOptimizable (acrf, trainingList); train (acrf, trainingList, validationList, testSet, eval, 5, macrf); ACRF.Template[] tmpls = acrf.getTemplates (); for (int ti = 0; ti < tmpls.length; ti++) tmpls[ti].addSomeUnsupportedWeights (trainingList); logger.info ("Some unsupporetd weights initialized. Training..."); return train (acrf, trainingList, validationList, testSet, eval, numIter, macrf); }