本文整理匯總了Java中cc.mallet.fst.PerClassAccuracyEvaluator類的典型用法代碼示例。如果您正苦於以下問題：Java PerClassAccuracyEvaluator類的具體用法？Java PerClassAccuracyEvaluator怎麽用？Java PerClassAccuracyEvaluator使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。
PerClassAccuracyEvaluator類屬於cc.mallet.fst包，在下文中一共展示了PerClassAccuracyEvaluator類的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: train
import cc.mallet.fst.PerClassAccuracyEvaluator; //導入依賴的package包/類
public CRF train(InstanceList trainingInstances, InstanceList testingInstances)
throws FileNotFoundException, IOException {
if (this.transducerTrainer == null) {
throw new IllegalStateException("crfTrainer needs to be set via one of the available methods");
}
// trainer.addEvaluator(new PerClassAccuracyEvaluator(trainingInstances,
// "training"));
this.transducerTrainer.addEvaluator(new PerClassAccuracyEvaluator(testingInstances, "testing"));
this.transducerTrainer.addEvaluator(new TokenAccuracyEvaluator(testingInstances, "testing"));
// this.transducerTrainer
// .addEvaluator(new FixedViterbiWriter(new
// File("/home/mkoerner/viterbi.txt"), testingInstances, "test"));
this.transducerTrainer.train(trainingInstances);
return this.crf;
}
示例2: evaluate
import cc.mallet.fst.PerClassAccuracyEvaluator; //導入依賴的package包/類
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: TrainCRF
import cc.mallet.fst.PerClassAccuracyEvaluator; //導入依賴的package包/類
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4: TrainWikiCRF
import cc.mallet.fst.PerClassAccuracyEvaluator; //導入依賴的package包/類
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);
}