Package | Description |
---|---|
de.jstacs.classifiers.differentiableSequenceScoreBased |
Provides the classes for
Classifier s that are based on SequenceScore s.It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior. |
de.jstacs.data |
Provides classes for the representation of data.
The base classes to represent data are Alphabet and AlphabetContainer for representing alphabets,
Sequence and its sub-classes to represent continuous and discrete sequences, and
DataSet to represent data sets comprising a set of sequences. |
de.jstacs.io |
Provides classes for reading data from and writing to a file and storing a number of datatypes, including all primitives, arrays of primitives, and
Storable s to an XML-representation. |
de.jstacs.parameters |
This package provides classes for parameters that establish a general convention for the description of parameters
as defined in the
Parameter -interface. |
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels |
Provides
DifferentiableStatisticalModel s that are directed graphical models. |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training |
The package provides all classes used to determine the training algorithm of a hidden Markov model.
|
Modifier and Type | Method and Description |
---|---|
AbstractTerminationCondition |
ScoreClassifierParameterSet.getTerminantionCondition()
This method returns the
AbstractTerminationCondition for stopping the training, e.g., if the
difference of the scores between two iterations is smaller than a given
threshold the training is stopped. |
Constructor and Description |
---|
AlphabetContainer(AlphabetContainerParameterSet parameters)
Creates a new
AlphabetContainer from an
AlphabetContainerParameterSet that contains all necessary
parameters. |
Modifier and Type | Method and Description |
---|---|
static <T extends InstantiableFromParameterSet> |
ParameterSetParser.getInstanceFromParameterSet(InstanceParameterSet<T> pars)
Returns an instance of a subclass of
InstantiableFromParameterSet
that can be instantiated by the InstanceParameterSet
pars . |
static <T extends InstantiableFromParameterSet> |
ParameterSetParser.getInstanceFromParameterSet(ParameterSet pars,
Class<T> instanceClass)
Returns an instance of a subclass of
InstantiableFromParameterSet
that can be instantiated by the ParameterSet pars . |
Modifier and Type | Method and Description |
---|---|
T |
InstanceParameterSet.getInstance()
Returns a new instance of the class of
InstanceParameterSet.getInstanceClass() that
was created using this ParameterSet . |
Modifier and Type | Method and Description |
---|---|
Measure |
BayesianNetworkDiffSMParameterSet.getMeasure()
Returns the structure
Measure defined by this set of parameters. |
Constructor and Description |
---|
BayesianNetworkDiffSM(BayesianNetworkDiffSMParameterSet parameters)
Creates a new
BayesianNetworkDiffSM that has neither
been initialized nor trained from a
BayesianNetworkDiffSMParameterSet . |
Modifier and Type | Method and Description |
---|---|
AbstractBurnInTest |
SamplingHMMTrainingParameterSet.getBurnInTest()
This method return the burn in test to be used during sampling.
|
AbstractTerminationCondition |
MaxHMMTrainingParameterSet.getTerminationCondition()
This method returns the
AbstractTerminationCondition for stopping the training, e.g., if the
difference of the scores between two iterations is smaller than a given
threshold the training is stopped. |