本文整理汇总了Java中cc.mallet.fst.MEMM类的典型用法代码示例。如果您正苦于以下问题：Java MEMM类的具体用法？Java MEMM怎么用？Java MEMM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。
MEMM类属于cc.mallet.fst包，在下文中一共展示了MEMM类的10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，您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: testGetSetParameters
import cc.mallet.fst.MEMM; //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();
MEMM memm = new MEMM (inputAlphabet, outputAlphabet);
String[] stateNames = new String[numStates];
for (int i = 0; i < numStates; i++)
stateNames[i] = "state" + i;
memm.addFullyConnectedStates(stateNames);
MEMMTrainer memmt = new MEMMTrainer (memm);
MEMMTrainer.MEMMOptimizableByLabelLikelihood omemm = memmt.getOptimizableMEMM (new InstanceList(null));
TestOptimizable.testGetSetParameters(omemm);
}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:18，代码来源:TestMEMM.java
示例2: testSpaceSerializable
import cc.mallet.fst.MEMM; //导入依赖的package包/类
public void testSpaceSerializable () throws IOException, ClassNotFoundException
{
Pipe p = makeSpacePredictionPipe ();
InstanceList training = new InstanceList (p);
training.addThruPipe (new ArrayIterator (data));
MEMM memm = new MEMM (p, null);
memm.addFullyConnectedStatesForLabels ();
memm.addStartState();
memm.setWeightsDimensionAsIn(training);
MEMMTrainer memmt = new MEMMTrainer (memm);
memmt.train (training, 10);
MEMM memm2 = (MEMM) TestSerializable.cloneViaSerialization (memm);
Optimizable.ByGradientValue mcrf1 = memmt.getOptimizableMEMM(training);
double val1 = mcrf1.getValue ();
Optimizable.ByGradientValue mcrf2 = memmt.getOptimizableMEMM(training);
double val2 = mcrf2.getValue ();
assertEquals (val1, val2, 1e-5);
}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:23，代码来源:TestMEMM.java
示例3: disabledtestPrint
import cc.mallet.fst.MEMM; //导入依赖的package包/类
public void disabledtestPrint ()
{
Pipe p = new SerialPipes (new Pipe[] {
new CharSequence2TokenSequence("."),
new TokenText(),
new TestMEMM.TestMEMMTokenSequenceRemoveSpaces(),
new TokenSequence2FeatureVectorSequence(),
new PrintInputAndTarget(),
});
InstanceList one = new InstanceList (p);
String[] data = new String[] { "ABCDE", };
one.addThruPipe (new ArrayIterator (data));
MEMM crf = new MEMM (p, null);
crf.addFullyConnectedStatesForLabels();
crf.setWeightsDimensionAsIn (one);
MEMMTrainer memmt = new MEMMTrainer (crf);
MEMMTrainer.MEMMOptimizableByLabelLikelihood mcrf = memmt.getOptimizableMEMM(one);
double[] params = new double[mcrf.getNumParameters()];
for (int i = 0; i < params.length; i++) {
params [i] = i;
}
mcrf.setParameters (params);
crf.print ();
}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:25，代码来源:TestMEMM.java
示例4: disabledtestPrint
import cc.mallet.fst.MEMM; //导入依赖的package包/类
public void disabledtestPrint ()
{
Pipe p = new SerialPipes (new Pipe[] {
new CharSequence2TokenSequence("."),
new TokenText(),
new TestMEMMTokenSequenceRemoveSpaces(),
new TokenSequence2FeatureVectorSequence(),
new PrintInputAndTarget(),
});
InstanceList one = new InstanceList (p);
String[] data = new String[] { "ABCDE", };
one.addThruPipe (new ArrayIterator (data));
MEMM crf = new MEMM (p, null);
crf.addFullyConnectedStatesForLabels();
crf.setWeightsDimensionAsIn (one);
MEMMTrainer memmt = new MEMMTrainer (crf);
MEMMTrainer.MEMMOptimizableByLabelLikelihood mcrf = memmt.getOptimizableMEMM(one);
double[] params = new double[mcrf.getNumParameters()];
for (int i = 0; i < params.length; i++) {
params [i] = i;
}
mcrf.setParameters (params);
crf.print ();
}
开发者ID:shalomeir，项目名称:tctm，代码行数:25，代码来源:TestMEMM.java
示例5: ignoretestSpaceSerializable
import cc.mallet.fst.MEMM; //导入依赖的package包/类
public void ignoretestSpaceSerializable () throws IOException, ClassNotFoundException
{
Pipe p = makeSpacePredictionPipe ();
InstanceList training = new InstanceList (p);
training.addThruPipe (new ArrayIterator (data));
MEMM memm = new MEMM (p, null);
memm.addFullyConnectedStatesForLabels ();
memm.addStartState();
memm.setWeightsDimensionAsIn(training);
MEMMTrainer memmt = new MEMMTrainer (memm);
memmt.train (training, 10);
MEMM memm2 = (MEMM) TestSerializable.cloneViaSerialization (memm);
Optimizable.ByGradientValue mcrf1 = memmt.getOptimizableMEMM(training);
double val1 = mcrf1.getValue ();
Optimizable.ByGradientValue mcrf2 = memmt.getOptimizableMEMM(training);
double val2 = mcrf2.getValue ();
assertEquals (val1, val2, 1e-5);
}
开发者ID:cmoen，项目名称:mallet，代码行数:23，代码来源:TestMEMM.java
示例6: testSpaceMaximizable
import cc.mallet.fst.MEMM; //导入依赖的package包/类
public void testSpaceMaximizable ()
{
Pipe p = makeSpacePredictionPipe ();
InstanceList training = new InstanceList (p);
// String[] data = { TestMEMM.data[0], }; // TestMEMM.data[1], TestMEMM.data[2], TestMEMM.data[3], };
// String[] data = { "ab" };
training.addThruPipe (new ArrayIterator (data));
// CRF4 memm = new CRF4 (p, null);
MEMM memm = new MEMM (p, null);
memm.addFullyConnectedStatesForLabels ();
memm.addStartState();
memm.setWeightsDimensionAsIn(training);
MEMMTrainer memmt = new MEMMTrainer (memm);
// memm.gatherTrainingSets (training); // ANNOYING: Need to set up per-instance training sets
memmt.train (training, 1); // Set weights dimension, gathers training sets, etc.
// memm.print();
// memm.printGradient = true;
// memm.printInstanceLists();
// memm.setGaussianPriorVariance (Double.POSITIVE_INFINITY);
Optimizable.ByGradientValue mcrf = memmt.getOptimizableMEMM(training);
TestOptimizable.setNumComponents (150);
TestOptimizable.testValueAndGradient (mcrf);
}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:28，代码来源:TestMEMM.java
示例7: ignoretestSpaceMaximizable
import cc.mallet.fst.MEMM; //导入依赖的package包/类
public void ignoretestSpaceMaximizable ()
{
Pipe p = makeSpacePredictionPipe ();
InstanceList training = new InstanceList (p);
// String[] data = { TestMEMM.data[0], }; // TestMEMM.data[1], TestMEMM.data[2], TestMEMM.data[3], };
// String[] data = { "ab" };
training.addThruPipe (new ArrayIterator (data));
// CRF4 memm = new CRF4 (p, null);
MEMM memm = new MEMM (p, null);
memm.addFullyConnectedStatesForLabels ();
memm.addStartState();
memm.setWeightsDimensionAsIn(training);
MEMMTrainer memmt = new MEMMTrainer (memm);
// memm.gatherTrainingSets (training); // ANNOYING: Need to set up per-instance training sets
memmt.train (training, 1); // Set weights dimension, gathers training sets, etc.
// memm.print();
// memm.printGradient = true;
// memm.printInstanceLists();
// memm.setGaussianPriorVariance (Double.POSITIVE_INFINITY);
Optimizable.ByGradientValue mcrf = memmt.getOptimizableMEMM(training);
TestOptimizable.setNumComponents (150);
TestOptimizable.testValueAndGradient (mcrf);
}
开发者ID:cmoen，项目名称:mallet，代码行数:28，代码来源:TestMEMM.java
示例8: disabledtestAddOrderNStates
import cc.mallet.fst.MEMM; //导入依赖的package包/类
public void disabledtestAddOrderNStates ()
{
Pipe p = makeSpacePredictionPipe ();
InstanceList instances = new InstanceList (p);
instances.addThruPipe (new ArrayIterator(data));
InstanceList[] lists = instances.split (new java.util.Random (678), new double[]{.5, .5});
// Compare 3 CRFs trained with addOrderNStates, and make sure
// that having more features leads to a higher likelihood
MEMM crf1 = new MEMM(p.getDataAlphabet(), p.getTargetAlphabet());
crf1.addOrderNStates (lists [0],
new int[] { 1, },
new boolean[] { false, },
"START",
null,
null,
false);
crf1.setWeightsDimensionAsIn(lists[0]);
MEMMTrainer memmt1 = new MEMMTrainer (crf1);
memmt1.train(lists [0]);
MEMM crf2 = new MEMM(p.getDataAlphabet(), p.getTargetAlphabet());
crf2.addOrderNStates (lists [0],
new int[] { 1, 2, },
new boolean[] { false, true },
"START",
null,
null,
false);
crf2.setWeightsDimensionAsIn(lists[0]);
MEMMTrainer memmt2 = new MEMMTrainer (crf2);
memmt2.train(lists [0]);
MEMM crf3 = new MEMM(p.getDataAlphabet(), p.getTargetAlphabet());
crf3.addOrderNStates (lists [0],
new int[] { 1, 2, },
new boolean[] { false, false },
"START",
null,
null,
false);
crf3.setWeightsDimensionAsIn(lists[0]);
MEMMTrainer memmt3 = new MEMMTrainer (crf3);
memmt3.train(lists [0]);
// Prevent cached values
double lik1 = getLikelihood (memmt1, lists[0]);
double lik2 = getLikelihood (memmt2, lists[0]);
double lik3 = getLikelihood (memmt3, lists[0]);
System.out.println("CRF1 likelihood "+lik1);
assertTrue ("Final zero-order likelihood <"+lik1+"> greater than first-order <"+lik2+">",
lik1 < lik2);
assertTrue ("Final defaults-only likelihood <"+lik2+"> greater than full first-order <"+lik3+">",
lik2 < lik3);
assertEquals (-167.335971702, lik1, 0.0001);
assertEquals (-166.212235389, lik2, 0.0001);
assertEquals ( -90.386005741, lik3, 0.0001);
}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:66，代码来源:TestMEMM.java
示例9: testDualSpaceViewer
import cc.mallet.fst.MEMM; //导入依赖的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10: ignoretestDualSpaceViewer
import cc.mallet.fst.MEMM; //导入依赖的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
注：本文中的cc.mallet.fst.MEMM类示例整理自Github/MSDocs等源码及文档管理平台，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。 |
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