本文整理汇总了Java中cc.mallet.fst.CRFTrainerByLabelLikelihood类的典型用法代码示例。如果您正苦于以下问题：Java CRFTrainerByLabelLikelihood类的具体用法？Java CRFTrainerByLabelLikelihood怎么用？Java CRFTrainerByLabelLikelihood使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
CRFTrainerByLabelLikelihood类属于cc.mallet.fst包，在下文中一共展示了CRFTrainerByLabelLikelihood类的11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: testGetSetParameters
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的package包/类
public void testGetSetParameters() {
int inputVocabSize = 100;
int numStates = 5;
Alphabet inputAlphabet = new Alphabet();
for (int i = 0; i < inputVocabSize; i++)
inputAlphabet.lookupIndex("feature" + i);
Alphabet outputAlphabet = new Alphabet();
CRF crf = new CRF(inputAlphabet, outputAlphabet);
String[] stateNames = new String[numStates];
for (int i = 0; i < numStates; i++)
stateNames[i] = "state" + i;
crf.addFullyConnectedStates(stateNames);
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
Optimizable.ByGradientValue mcrf = crft
.getOptimizableCRF(new InstanceList(null));
TestOptimizable.testGetSetParameters(mcrf);
}
示例2: testTokenAccuracy
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的package包/类
public void testTokenAccuracy() {
Pipe p = makeSpacePredictionPipe();
InstanceList instances = new InstanceList(p);
instances.addThruPipe(new ArrayIterator(data));
InstanceList[] lists = instances.split(new Random(777), new double[] {
.5, .5 });
CRF crf = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());
crf.addFullyConnectedStatesForLabels();
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
crft.setUseSparseWeights(true);
crft.trainIncremental(lists[0]);
TokenAccuracyEvaluator eval = new TokenAccuracyEvaluator(lists,
new String[] { "Train", "Test" });
eval.evaluateInstanceList(crft, lists[1], "Test");
assertEquals(0.9409, eval.getAccuracy("Test"), 0.001);
}
示例3: testPrint
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的package包/类
public void testPrint() {
Pipe p = new SerialPipes(new Pipe[] {
new CharSequence2TokenSequence("."), new TokenText(),
new TestCRFTokenSequenceRemoveSpaces(),
new TokenSequence2FeatureVectorSequence(),
new PrintInputAndTarget(), });
InstanceList one = new InstanceList(p);
String[] data = new String[] { "ABCDE", };
one.addThruPipe(new ArrayIterator(data));
CRF crf = new CRF(p, null);
crf.addFullyConnectedStatesForThreeQuarterLabels(one);
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
crf.setWeightsDimensionAsIn(one, false);
Optimizable mcrf = crft.getOptimizableCRF(one);
double[] params = new double[mcrf.getNumParameters()];
for (int i = 0; i < params.length; i++) {
params[i] = i;
}
mcrf.setParameters(params);
crf.print();
}
示例4: testSpaceViewer
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的package包/类
public void testSpaceViewer () throws IOException
{
Pipe pipe = TestMEMM.makeSpacePredictionPipe ();
String[] data0 = { TestCRF.data[0] };
String[] data1 = { TestCRF.data[1] };
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);
crft.trainIncremental (training);
CRFExtractor extor = TestLatticeViewer.hackCrfExtor (crf);
Extraction extraction = extor.extract (new ArrayIterator (data1));
if (!outputDir.exists ()) outputDir.mkdir ();
DocumentViewer.writeExtraction (outputDir, extraction);
}
示例5: train
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的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6: ignoretestTokenAccuracy
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的package包/类
public void ignoretestTokenAccuracy() {
Pipe p = makeSpacePredictionPipe();
InstanceList instances = new InstanceList(p);
instances.addThruPipe(new ArrayIterator(data));
InstanceList[] lists = instances.split(new Random(777), new double[] {
.5, .5 });
CRF crf = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());
crf.addFullyConnectedStatesForLabels();
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
crft.setUseSparseWeights(true);
crft.trainIncremental(lists[0]);
TokenAccuracyEvaluator eval = new TokenAccuracyEvaluator(lists,
new String[] { "Train", "Test" });
eval.evaluateInstanceList(crft, lists[1], "Test");
assertEquals(0.9409, eval.getAccuracy("Test"), 0.001);
}
示例7: ignoretestSpaceViewer
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的package包/类
public void ignoretestSpaceViewer () throws IOException
{
Pipe pipe = TestMEMM.makeSpacePredictionPipe ();
String[] data0 = { TestCRF.data[0] };
String[] data1 = { TestCRF.data[1] };
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);
crft.trainIncremental (training);
CRFExtractor extor = TestLatticeViewer.hackCrfExtor (crf);
Extraction extraction = extor.extract (new ArrayIterator (data1));
if (!outputDir.exists ()) outputDir.mkdir ();
DocumentViewer.writeExtraction (outputDir, extraction);
}
示例8: getLikelihood
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的package包/类
double getLikelihood(CRF crf, InstanceList data) {
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
Optimizable.ByGradientValue mcrf = crft.getOptimizableCRF(data);
// Do this elaborate thing so that crf.cachedValueStale is forced true
double[] params = new double[mcrf.getNumParameters()];
mcrf.getParameters(params);
mcrf.setParameters(params);
return mcrf.getValue();
}
示例9: testDenseFeatureSelection
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的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);
}
示例10: testXis
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的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);
}
}
}
示例11: testSpaceViewer
import cc.mallet.fst.CRFTrainerByLabelLikelihood; //导入依赖的package包/类
public void testSpaceViewer () throws FileNotFoundException
{
Pipe pipe = TestMEMM.makeSpacePredictionPipe ();
String[] data0 = { TestCRF.data[0] };
String[] data1 = { TestCRF.data[1] };
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);
crft.trainIncremental (training);
CRFExtractor extor = hackCrfExtor (crf);
Extraction extration = extor.extract (new ArrayIterator (data1));
PrintStream out = new PrintStream (new FileOutputStream (htmlFile));
LatticeViewer.extraction2html (extration, extor, out);
out.close();
out = new PrintStream (new FileOutputStream (latticeFile));
LatticeViewer.extraction2html (extration, extor, out, true);
out.close();
}