本文整理汇总了Java中cc.mallet.fst.MEMMTrainer类的典型用法代码示例。如果您正苦于以下问题：Java MEMMTrainer类的具体用法？Java MEMMTrainer怎么用？Java MEMMTrainer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。
MEMMTrainer类属于cc.mallet.fst包，在下文中一共展示了MEMMTrainer类的8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.MEMMTrainer; //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.MEMMTrainer; //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.MEMMTrainer; //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.MEMMTrainer; //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.MEMMTrainer; //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.MEMMTrainer; //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: getLikelihood
import cc.mallet.fst.MEMMTrainer; //导入依赖的package包/类
double getLikelihood (MEMMTrainer memmt, InstanceList data) {
Optimizable.ByGradientValue mcrf = memmt.getOptimizableMEMM(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 ();
}
开发者ID:kostagiolasn，项目名称:NucleosomePatternClassifier，代码行数:9，代码来源:TestMEMM.java
示例8: ignoretestSpaceMaximizable
import cc.mallet.fst.MEMMTrainer; //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
注：本文中的cc.mallet.fst.MEMMTrainer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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