本文整理汇总了Java中cc.mallet.util.FileUtils.writeGzippedObject方法的典型用法代码示例。如果您正苦于以下问题：Java FileUtils.writeGzippedObject方法的具体用法？Java FileUtils.writeGzippedObject怎么用？Java FileUtils.writeGzippedObject使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cc.mallet.util.FileUtils的用法示例。
在下文中一共展示了FileUtils.writeGzippedObject方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: train
import cc.mallet.util.FileUtils; //导入方法依赖的package包/类
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;
}
}
示例2: main
import cc.mallet.util.FileUtils; //导入方法依赖的package包/类
public static void main (String[] args) throws FileNotFoundException
{
File trainFile = new File (args[0]);
File testFile = new File (args[1]);
File crfFile = new File (args[2]);
Pipe pipe = new SerialPipes (new Pipe[] {
new GenericAcrfData2TokenSequence (2),
new TokenSequence2FeatureVectorSequence (true, true),
});
InstanceList training = new InstanceList (pipe);
training.addThruPipe (new LineGroupIterator (new FileReader (trainFile),
Pattern.compile ("\\s*"),
true));
InstanceList testing = new InstanceList (pipe);
testing.addThruPipe (new LineGroupIterator (new FileReader (testFile),
Pattern.compile ("\\s*"),
true));
ACRF.Template[] tmpls = new ACRF.Template[] {
new ACRF.BigramTemplate (0),
new ACRF.BigramTemplate (1),
new ACRF.PairwiseFactorTemplate (0,1),
new CrossTemplate1 (0,1)
};
ACRF acrf = new ACRF (pipe, tmpls);
ACRFTrainer trainer = new DefaultAcrfTrainer ();
trainer.train (acrf, training, null, testing, 99999);
FileUtils.writeGzippedObject (crfFile, acrf);
}
示例3: evaluate
import cc.mallet.util.FileUtils; //导入方法依赖的package包/类
public boolean evaluate (ACRF acrf, int iter, InstanceList training, InstanceList validation, InstanceList testing)
{
if (iter > 0 && iter % interval == 0) {
ACRFExtractor extor = new ACRFExtractor (acrf, tokPipe, featurePipe);
FileUtils.writeGzippedObject (new File (directory, "extor."+iter+".ser.gz"), extor);
}
return true;
}
示例4: main
import cc.mallet.util.FileUtils; //导入方法依赖的package包/类
public static void main (String[] args) throws FileNotFoundException
{
File trainFile = new File (args[0]);
File testFile = new File (args[1]);
File crfFile = new File (args[2]);
Pipe pipe = new SerialPipes (new Pipe[] {
new GenericAcrfData2TokenSequence (2),
new TokenSequence2FeatureVectorSequence (true, true),
});
InstanceList training = new InstanceList (pipe);
training.addThruPipe (new LineGroupIterator (new FileReader (trainFile),
Pattern.compile ("\\s*"),
true));
InstanceList testing = new InstanceList (pipe);
training.addThruPipe (new LineGroupIterator (new FileReader (testFile),
Pattern.compile ("\\s*"),
true));
ACRF.Template[] tmpls = new ACRF.Template[] {
new ACRF.BigramTemplate (0),
new ACRF.BigramTemplate (1),
new ACRF.PairwiseFactorTemplate (0,1),
new CrossTemplate1 (0,1)
};
ACRF acrf = new ACRF (pipe, tmpls);
ACRFTrainer trainer = new DefaultAcrfTrainer ();
trainer.train (acrf, training, null, testing, 99999);
FileUtils.writeGzippedObject (crfFile, acrf);
}