本文整理汇总了Java中cc.mallet.fst.TransducerEvaluator.evaluate方法的典型用法代码示例。如果您正苦于以下问题：Java TransducerEvaluator.evaluate方法的具体用法？Java TransducerEvaluator.evaluate怎么用？Java TransducerEvaluator.evaluate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.TransducerEvaluator的用法示例。
在下文中一共展示了TransducerEvaluator.evaluate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: run
import cc.mallet.fst.TransducerEvaluator; //导入方法依赖的package包/类
public static List<String> run(String trainingFilename, String testingFilename)
throws FileNotFoundException, IOException {
ArrayList<Pipe> pipes = new ArrayList<Pipe>();
pipes.add(new SimpleTaggerSentence2TokenSequence());
pipes.add(new TokenSequence2FeatureSequence());
Pipe pipe = new SerialPipes(pipes);
InstanceList trainingInstances = new InstanceList(pipe);
InstanceList testingInstances = new InstanceList(pipe);
trainingInstances.addThruPipe(new LineGroupIterator(
new BufferedReader(new InputStreamReader(new FileInputStream(trainingFilename))),
Pattern.compile("^\\s*$"), true));
testingInstances.addThruPipe(new LineGroupIterator(
new BufferedReader(new InputStreamReader(new FileInputStream(testingFilename))),
Pattern.compile("^\\s*$"), true));
HMM hmm = new HMM(pipe, null);
hmm.addStatesForLabelsConnectedAsIn(trainingInstances);
HMMTrainerByLikelihood trainer = new HMMTrainerByLikelihood(hmm);
TransducerEvaluator testingEvaluator = new SegmentationEvaluator(testingInstances, "testing");
trainer.train(trainingInstances, 100);
testingEvaluator.evaluate(trainer);
return testingInstances.stream().map(Instance::getData).map(Sequence.class::cast)
.map(hmm::transduce)
.flatMap(output -> IntStream.range(0, output.size()).mapToObj(output::get))
.map(String.class::cast).collect(toList());
// hmm.print();
}
示例2: evaluate
import cc.mallet.fst.TransducerEvaluator; //导入方法依赖的package包/类
public void evaluate(File file) throws FileNotFoundException {
// if (normalize) {
// normalizeSCLBlock(new InstanceList[] { instances }, limits);
// }
InstanceList instances = new InstanceList(pipe);
instances.addThruPipe(iteratorForFile(file));
TransducerEvaluator evaluator = new PerClassAccuracyEvaluator(
instances, "evaluation");
evaluator.evaluate(trainer);
}
示例3: testDualSpaceViewer
import cc.mallet.fst.TransducerEvaluator; //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);
}
示例4: ignoretestDualSpaceViewer
import cc.mallet.fst.TransducerEvaluator; //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);
}