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Java CRFTrainerByLabelLikelihood.train方法代码示例

本文整理汇总了Java中cc.mallet.fst.CRFTrainerByLabelLikelihood.train方法的典型用法代码示例。如果您正苦于以下问题：Java CRFTrainerByLabelLikelihood.train方法的具体用法？Java CRFTrainerByLabelLikelihood.train怎么用？Java CRFTrainerByLabelLikelihood.train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.train方法的7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;
}
开发者ID:hakchul77，项目名称:irnlp_toolkit，代码行数:24，代码来源:MalletWrapper.java

示例2: testDenseFeatureSelection

import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
public void testDenseFeatureSelection() {
	Pipe p = makeSpacePredictionPipe();

	InstanceList instances = new InstanceList(p);
	instances.addThruPipe(new ArrayIterator(data));

	// Test that dense observations wights aren't added for
	// "default-feature" edges.
	CRF crf1 = new CRF(p, null);
	crf1.addOrderNStates(instances, new int[] { 0 }, null, "start", null,
			null, true);
	CRFTrainerByLabelLikelihood crft1 = new CRFTrainerByLabelLikelihood(
			crf1);
	crft1.setUseSparseWeights(false);
	crft1.train(instances, 1); // Set weights dimension
	int nParams1 = crft1.getOptimizableCRF(instances).getNumParameters();

	CRF crf2 = new CRF(p, null);
	crf2.addOrderNStates(instances, new int[] { 0, 1 }, new boolean[] {
			false, true }, "start", null, null, true);
	CRFTrainerByLabelLikelihood crft2 = new CRFTrainerByLabelLikelihood(
			crf2);
	crft2.setUseSparseWeights(false);
	crft2.train(instances, 1); // Set weights dimension
	int nParams2 = crft2.getOptimizableCRF(instances).getNumParameters();

	assertEquals(nParams2, nParams1 + 4);

}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:30，代码来源:TestCRF.java

示例3: testXis

import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
public void testXis() {
	Pipe p = makeSpacePredictionPipe();

	InstanceList instances = new InstanceList(p);
	instances.addThruPipe(new ArrayIterator(data));

	CRF crf1 = new CRF(p, null);
	crf1.addFullyConnectedStatesForLabels();
	CRFTrainerByLabelLikelihood crft1 = new CRFTrainerByLabelLikelihood(
			crf1);
	crft1.train(instances, 10); // Let's get some parameters

	Instance inst = instances.get(0);
	Sequence input = (Sequence) inst.getData();
	SumLatticeDefault lattice = new SumLatticeDefault(crf1, input,
			(Sequence) inst.getTarget(), null, true);
	for (int ip = 0; ip < lattice.length() - 1; ip++) {
		for (int i = 0; i < crf1.numStates(); i++) {
			Transducer.State state = crf1.getState(i);
			Transducer.TransitionIterator it = state.transitionIterator(
					input, ip);
			double gamma = lattice.getGammaProbability(ip, state);
			double xiSum = 0;
			while (it.hasNext()) {
				Transducer.State dest = it.nextState();
				double xi = lattice.getXiProbability(ip, state, dest);
				xiSum += xi;
			}
			assertEquals(gamma, xiSum, 1e-5);
		}
	}
}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:33，代码来源:TestCRF.java

示例4: testDualSpaceViewer

import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
public void testDualSpaceViewer () throws IOException
{
  Pipe pipe = TestMEMM.makeSpacePredictionPipe ();
  String[] data0 = { TestCRF.data[0] };
  String[] data1 = TestCRF.data;

  InstanceList training = new InstanceList (pipe);
  training.addThruPipe (new ArrayIterator (data0));
  InstanceList testing = new InstanceList (pipe);
  testing.addThruPipe (new ArrayIterator (data1));

  CRF crf = new CRF (pipe, null);
  crf.addFullyConnectedStatesForLabels ();
  CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf);
  TokenAccuracyEvaluator eval = new TokenAccuracyEvaluator (new InstanceList[] {training, testing}, new String[] {"Training", "Testing"});
  for (int i = 0; i < 5; i++) {
  	crft.train (training, 1);
  	eval.evaluate(crft);
  }

  CRFExtractor extor = hackCrfExtor (crf);
  Extraction e1 = extor.extract (new ArrayIterator (data1));

  Pipe pipe2 = TestMEMM.makeSpacePredictionPipe ();
  InstanceList training2 = new InstanceList (pipe2);
  training2.addThruPipe (new ArrayIterator (data0));
  InstanceList testing2 = new InstanceList (pipe2);
  testing2.addThruPipe (new ArrayIterator (data1));

  MEMM memm = new MEMM (pipe2, null);
  memm.addFullyConnectedStatesForLabels ();
  MEMMTrainer memmt = new MEMMTrainer (memm);
  TransducerEvaluator memmeval = new TokenAccuracyEvaluator (new InstanceList[] {training2, testing2}, new String[] {"Training2", "Testing2"});
  memmt.train (training2, 5);
  memmeval.evaluate(memmt);

  CRFExtractor extor2 = hackCrfExtor (memm);
  Extraction e2 = extor2.extract (new ArrayIterator (data1));

  if (!htmlDir.exists ()) htmlDir.mkdir ();
  LatticeViewer.viewDualResults (htmlDir, e1, extor, e2, extor2);

}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:44，代码来源:TestLatticeViewer.java

示例5: ignoretestDualSpaceViewer

import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入方法依赖的package包/类
public void ignoretestDualSpaceViewer () throws IOException
{
  Pipe pipe = TestMEMM.makeSpacePredictionPipe ();
  String[] data0 = { TestCRF.data[0] };
  String[] data1 = TestCRF.data;

  InstanceList training = new InstanceList (pipe);
  training.addThruPipe (new ArrayIterator (data0));
  InstanceList testing = new InstanceList (pipe);
  testing.addThruPipe (new ArrayIterator (data1));

  CRF crf = new CRF (pipe, null);
  crf.addFullyConnectedStatesForLabels ();
  CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf);
  TokenAccuracyEvaluator eval = new TokenAccuracyEvaluator (new InstanceList[] {training, testing}, new String[] {"Training", "Testing"});
  for (int i = 0; i < 5; i++) {
  	crft.train (training, 1);
  	eval.evaluate(crft);
  }

  CRFExtractor extor = hackCrfExtor (crf);
  Extraction e1 = extor.extract (new ArrayIterator (data1));

  Pipe pipe2 = TestMEMM.makeSpacePredictionPipe ();
  InstanceList training2 = new InstanceList (pipe2);
  training2.addThruPipe (new ArrayIterator (data0));
  InstanceList testing2 = new InstanceList (pipe2);
  testing2.addThruPipe (new ArrayIterator (data1));

  MEMM memm = new MEMM (pipe2, null);
  memm.addFullyConnectedStatesForLabels ();
  MEMMTrainer memmt = new MEMMTrainer (memm);
  TransducerEvaluator memmeval = new TokenAccuracyEvaluator (new InstanceList[] {training2, testing2}, new String[] {"Training2", "Testing2"});
  memmt.train (training2, 5);
  memmeval.evaluate(memmt);

  CRFExtractor extor2 = hackCrfExtor (memm);
  Extraction e2 = extor2.extract (new ArrayIterator (data1));

  if (!htmlDir.exists ()) htmlDir.mkdir ();
  LatticeViewer.viewDualResults (htmlDir, e1, extor, e2, extor2);

}
开发者ID:cmoen，项目名称:mallet，代码行数:44，代码来源:TestLatticeViewer.java

示例6: 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);

    }
开发者ID:karahindiba，项目名称:WikiInfoboxExtractor，代码行数:51，代码来源:TrainCRF.java

示例7: 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);
	
}
开发者ID:karahindiba，项目名称:WikiInfoboxExtractor，代码行数:51，代码来源:TrainWikiCRF.java


注：本文中的cc.mallet.fst.CRFTrainerByLabelLikelihood.train方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。