| Package | Description |
|---|---|
| de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix |
Provides an implementation of a classifier that allows to train the parameters of a set of
DifferentiableStatisticalModels by
a unified generative-discriminative learning principle. |
| de.jstacs.classifiers.differentiableSequenceScoreBased.msp |
Provides an implementation of a classifier that allows to train the parameters of a set of
DifferentiableStatisticalModels either
by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL). |
| de.jstacs.classifiers.differentiableSequenceScoreBased.sampling |
Provides the classes for
AbstractScoreBasedClassifiers that are based on
SamplingDifferentiableStatisticalModels
and that sample parameters using the Metropolis-Hastings algorithm. |
| Modifier and Type | Method and Description |
|---|---|
static GenDisMixClassifier[] |
GenDisMixClassifier.create(GenDisMixClassifierParameterSet params,
LogPrior prior,
double[] weights,
DifferentiableStatisticalModel[]... functions)
This method creates an array of GenDisMixClassifiers by using the cross-product of the given
DifferentiableStatisticalModels. |
| Constructor and Description |
|---|
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double[] beta,
DifferentiableStatisticalModel... score)
The main constructor.
|
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double lastScore,
double[] beta,
DifferentiableSequenceScore... score)
This constructor creates an instance and sets the value of the last (external) optimization.
|
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double lastScore,
double[] beta,
DifferentiableStatisticalModel... score)
This constructor creates an instance and sets the value of the last (external) optimization.
|
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double genBeta,
double disBeta,
double priorBeta,
DifferentiableStatisticalModel... score)
This convenience constructor agglomerates the
genBeta, disBeta, and priorBeta into an array and calls the main constructor. |
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
LearningPrinciple key,
DifferentiableStatisticalModel... score)
This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.
|
| Constructor and Description |
|---|
MSPClassifier(GenDisMixClassifierParameterSet params,
DifferentiableSequenceScore... score)
This convenience constructor creates an
MSPClassifier that used MCL principle for training. |
MSPClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
DifferentiableSequenceScore... score)
The default constructor that creates a new
MSPClassifier from a
given parameter set, a prior and DifferentiableSequenceScores for the
classes. |
MSPClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double lastScore,
DifferentiableSequenceScore... score)
This constructor that creates a new
MSPClassifier from a
given parameter set, a prior and DifferentiableSequenceScores for the
classes. |
| Modifier and Type | Method and Description |
|---|---|
GenDisMixClassifier |
SamplingGenDisMixClassifier.getClassifierForBestParameters(GenDisMixClassifierParameterSet params)
Returns a standard, i.e., non-sampling,
GenDisMixClassifier, where the parameters
are set to those that yielded the maximum value of the objective functions among all sampled
parameter values. |
GenDisMixClassifier |
SamplingGenDisMixClassifier.getClassifierForMeanParameters(GenDisMixClassifierParameterSet params,
boolean testBurnIn,
int minBurnInSteps)
Returns a standard, i.e., non-sampling,
GenDisMixClassifier, where the parameters
are set to the mean values over all sampled
parameter values in the stationary phase. |