本文整理汇总了Java中cc.mallet.fst.CRFTrainerByLabelLikelihood.setGaussianPriorVariance方法的典型用法代码示例。如果您正苦于以下问题：Java CRFTrainerByLabelLikelihood.setGaussianPriorVariance方法的具体用法？Java CRFTrainerByLabelLikelihood.setGaussianPriorVariance怎么用？Java CRFTrainerByLabelLikelihood.setGaussianPriorVariance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.fst.CRFTrainerByLabelLikelihood的用法示例。
在下文中一共展示了CRFTrainerByLabelLikelihood.setGaussianPriorVariance方法的5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.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
/**
*
* @param num_iterations
* @return
*/
public Boolean train(Integer num_iterations) {
this.model = new CRF(this.train_data.getPipe(), (Pipe) null);
for (int i = 0; i < this.model.numStates(); i++)
this.model.getState(i).setInitialWeight(Transducer.IMPOSSIBLE_WEIGHT);
String startName = this.model.addOrderNStates(this.train_data, new int[] { 1 }, null, DEFAULT_LABEL, Pattern.compile("\\s"), Pattern.compile(".*"), true);
this.model.getState(startName).setInitialWeight(0.0);
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(this.model);
crft.setGaussianPriorVariance(DEFAULT_PRIOR_VARIANCE);
crft.setUseSparseWeights(true);
crft.setUseSomeUnsupportedTrick(true);
for (int i = 0; i < num_iterations; i++)
if (crft.train(this.train_data, 1))
break;
return this.model != null;
}
示例2: makeNewTrainerSingleThreaded
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
private static CRFTrainerByLabelLikelihood makeNewTrainerSingleThreaded(CRF crf) {
CRFTrainerByLabelLikelihood trainer = new CRFTrainerByLabelLikelihood(crf);
trainer.setGaussianPriorVariance(2);
// trainer.setUseHyperbolicPrior(true);
trainer.setAddNoFactors(true);
trainer.setUseSomeUnsupportedTrick(false);
return trainer;
}
示例3: setCRFTrainerByLabelLikelihood
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
public void setCRFTrainerByLabelLikelihood(double gaussianPriorVariance) {
CRFTrainerByLabelLikelihood crfTrainerByLabelLikelihood = new CRFTrainerByLabelLikelihood(this.crf);
crfTrainerByLabelLikelihood.setGaussianPriorVariance(gaussianPriorVariance);
this.transducerTrainer = crfTrainerByLabelLikelihood;
}
示例4: TrainCRF
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
public TrainCRF(String trainingFilename, String testingFilename) throws IOException {
ArrayList<Pipe> pipes = new ArrayList<Pipe>();
int[][] conjunctions = new int[2][];
conjunctions[0] = new int[] { -1 };
conjunctions[1] = new int[] { 1 };
pipes.add(new SimpleTaggerSentence2TokenSequence());
pipes.add(new OffsetConjunctions(conjunctions));
//pipes.add(new FeaturesInWindow("PREV-", -1, 1));
pipes.add(new TokenTextCharSuffix("C1=", 1));
pipes.add(new TokenTextCharSuffix("C2=", 2));
pipes.add(new TokenTextCharSuffix("C3=", 3));
pipes.add(new RegexMatches("CAPITALIZED", Pattern.compile("^\\p{Lu}.*")));
pipes.add(new RegexMatches("STARTSNUMBER", Pattern.compile("^[0-9].*")));
pipes.add(new RegexMatches("HYPHENATED", Pattern.compile(".*\\-.*")));
pipes.add(new RegexMatches("DOLLARSIGN", Pattern.compile(".*\\$.*")));
pipes.add(new TokenFirstPosition("FIRSTTOKEN"));
pipes.add(new TokenSequence2FeatureVectorSequence());
Pipe pipe = new SerialPipes(pipes);
InstanceList trainingInstances = new InstanceList(pipe);
InstanceList testingInstances = new InstanceList(pipe);
trainingInstances.addThruPipe(new LineGroupIterator(new BufferedReader(new InputStreamReader(new GZIPInputStream(new FileInputStream(trainingFilename)))), Pattern.compile("^\\s*$"), true));
testingInstances.addThruPipe(new LineGroupIterator(new BufferedReader(new InputStreamReader(new GZIPInputStream(new FileInputStream(testingFilename)))), Pattern.compile("^\\s*$"), true));
CRF crf = new CRF(pipe, null);
//crf.addStatesForLabelsConnectedAsIn(trainingInstances);
crf.addStatesForThreeQuarterLabelsConnectedAsIn(trainingInstances);
crf.addStartState();
CRFTrainerByLabelLikelihood trainer =
new CRFTrainerByLabelLikelihood(crf);
trainer.setGaussianPriorVariance(10.0);
//CRFTrainerByStochasticGradient trainer =
//new CRFTrainerByStochasticGradient(crf, 1.0);
//CRFTrainerByL1LabelLikelihood trainer =
// new CRFTrainerByL1LabelLikelihood(crf, 0.75);
//trainer.addEvaluator(new PerClassAccuracyEvaluator(trainingInstances, "training"));
trainer.addEvaluator(new PerClassAccuracyEvaluator(testingInstances, "testing"));
trainer.addEvaluator(new TokenAccuracyEvaluator(testingInstances, "testing"));
trainer.train(trainingInstances);
}
示例5: TrainWikiCRF
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
public TrainWikiCRF(String trainingFilename, String testingFilename) throws IOException {
ArrayList<Pipe> pipes = new ArrayList<Pipe>();
int[][] conjunctions = new int[2][];
conjunctions[0] = new int[] { -1 };
conjunctions[1] = new int[] { 1 };
pipes.add(new SimpleTaggerSentence2TokenSequence());
pipes.add(new OffsetConjunctions(conjunctions));
//pipes.add(new FeaturesInWindow("PREV-", -1, 1));
pipes.add(new TokenTextCharSuffix("C1=", 1));
pipes.add(new TokenTextCharSuffix("C2=", 2));
pipes.add(new TokenTextCharSuffix("C3=", 3));
pipes.add(new RegexMatches("CAPITALIZED", Pattern.compile("^\\p{Lu}.*")));
pipes.add(new RegexMatches("STARTSNUMBER", Pattern.compile("^[0-9].*")));
pipes.add(new RegexMatches("HYPHENATED", Pattern.compile(".*\\-.*")));
pipes.add(new RegexMatches("DOLLARSIGN", Pattern.compile(".*\\$.*")));
pipes.add(new TokenFirstPosition("FIRSTTOKEN"));
pipes.add(new TokenSequence2FeatureVectorSequence());
Pipe pipe = new SerialPipes(pipes);
InstanceList trainingInstances = new InstanceList(pipe);
InstanceList testingInstances = new InstanceList(pipe);
trainingInstances.addThruPipe(new LineGroupIterator(new BufferedReader(new InputStreamReader(new GZIPInputStream(new FileInputStream(trainingFilename)))), Pattern.compile("^\\s*$"), true));
testingInstances.addThruPipe(new LineGroupIterator(new BufferedReader(new InputStreamReader(new GZIPInputStream(new FileInputStream(testingFilename)))), Pattern.compile("^\\s*$"), true));
CRF crf = new CRF(pipe, null);
//crf.addStatesForLabelsConnectedAsIn(trainingInstances);
crf.addStatesForThreeQuarterLabelsConnectedAsIn(trainingInstances);
crf.addStartState();
CRFTrainerByLabelLikelihood trainer =
new CRFTrainerByLabelLikelihood(crf);
trainer.setGaussianPriorVariance(10.0);
//CRFTrainerByStochasticGradient trainer =
//new CRFTrainerByStochasticGradient(crf, 1.0);
//CRFTrainerByL1LabelLikelihood trainer =
// new CRFTrainerByL1LabelLikelihood(crf, 0.75);
//trainer.addEvaluator(new PerClassAccuracyEvaluator(trainingInstances, "training"));
trainer.addEvaluator(new PerClassAccuracyEvaluator(testingInstances, "testing"));
trainer.addEvaluator(new TokenAccuracyEvaluator(testingInstances, "testing"));
trainer.train(trainingInstances);
}