Package | Description |
---|---|
de.jstacs.classifiers |
This package provides the framework for any classifier.
|
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.classifiers.differentiableSequenceScoreBased.sampling |
Provides the classes for
AbstractScoreBasedClassifier s that are based on
SamplingDifferentiableStatisticalModel s
and that sample parameters using the Metropolis-Hastings algorithm. |
de.jstacs.sequenceScores.statisticalModels |
Provides all
StatisticalModel s, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.StatisticalModel s can be further differentiated into TrainableStatisticalModel s,
which can be learned from a single input DataSet , and DifferentiableStatisticalModel s,
which define a proper likelihood but can also compute gradients like DifferentiableSequenceScore s. |
de.jstacs.sequenceScores.statisticalModels.differentiable |
Provides all
DifferentiableStatisticalModel s, which can compute the gradient with
respect to their parameters for a given input Sequence . |
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels |
Provides
DifferentiableStatisticalModel s that are directed graphical models. |
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture |
Provides
DifferentiableSequenceScore s that are mixtures of other DifferentiableSequenceScore s. |
de.jstacs.sequenceScores.statisticalModels.trainable |
Provides all
TrainableStatisticalModel s, which can
be learned from a single DataSet . |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete | |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous | |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous |
This package contains various inhomogeneous models.
|
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared | |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
This package is the super package for any mixture model.
|
de.jstacs.utils |
This package contains a bundle of useful classes and interfaces like ...
|
Modifier and Type | Method and Description |
---|---|
protected void |
AbstractScoreBasedClassifier.check(DataSet s)
This method checks if the given
DataSet can be used. |
protected void |
AbstractScoreBasedClassifier.check(Sequence seq)
This method checks if the given
Sequence can be used. |
protected double |
MappingClassifier.getScore(Sequence seq,
int i,
boolean check) |
protected abstract double |
AbstractScoreBasedClassifier.getScore(Sequence seq,
int i,
boolean check)
This method returns the score for a given
Sequence and a given
class. |
Modifier and Type | Method and Description |
---|---|
protected double |
ScoreClassifier.getScore(Sequence seq,
int i,
boolean check) |
Modifier and Type | Method and Description |
---|---|
protected double |
SamplingScoreBasedClassifier.getScore(Sequence seq,
int cls,
boolean check) |
Modifier and Type | Method and Description |
---|---|
DataSet |
StatisticalModel.emitDataSet(int numberOfSequences,
int... seqLength)
This method returns a
DataSet object containing artificial
sequence(s). |
Modifier and Type | Method and Description |
---|---|
DataSet |
UniformDiffSM.emitDataSet(int numberOfSequences,
int... seqLength) |
DataSet |
MarkovRandomFieldDiffSM.emitDataSet(int numberOfSequences,
int... seqLength) |
DataSet |
IndependentProductDiffSM.emitDataSet(int numberOfSequences,
int... seqLength) |
DataSet |
AbstractDifferentiableStatisticalModel.emitDataSet(int numberOfSequences,
int... seqLength) |
Modifier and Type | Method and Description |
---|---|
DataSet |
BayesianNetworkDiffSM.emitDataSet(int numberOfSequences,
int... seqLength) |
Modifier and Type | Method and Description |
---|---|
DataSet |
MixtureDiffSM.emitDataSet(int numberOfSequences,
int... seqLength) |
Modifier and Type | Method and Description |
---|---|
protected void |
AbstractTrainableStatisticalModel.check(Sequence sequence,
int startpos,
int endpos)
This method checks all parameters before a probability can be computed for a sequence.
|
DataSet |
AbstractTrainableStatisticalModel.emitDataSet(int numberOfSequences,
int... seqLength) |
double |
VariableLengthWrapperTrainSM.getLogProbFor(Sequence sequence,
int startpos,
int endpos) |
double |
DifferentiableStatisticalModelWrapperTrainSM.getLogProbFor(Sequence sequence,
int startpos,
int endpos) |
double |
CompositeTrainSM.getLogProbFor(Sequence sequence,
int startpos,
int endpos) |
Modifier and Type | Method and Description |
---|---|
protected void |
DiscreteGraphicalTrainSM.check(Sequence sequence,
int startpos,
int endpos)
Checks some conditions on a
Sequence . |
Modifier and Type | Method and Description |
---|---|
protected void |
HomogeneousTrainSM.check(Sequence sequence,
int startpos,
int endpos)
Checks some constraints, these are in general conditions on the
AlphabetContainer of a (sub)Sequence
between startpos und endpos . |
DataSet |
HomogeneousTrainSM.emitDataSet(int no,
int... length)
|
double |
HomogeneousTrainSM.getLogProbFor(Sequence sequence,
int startpos,
int endpos) |
Modifier and Type | Method and Description |
---|---|
protected void |
InhomogeneousDGTrainSM.check(Sequence sequence,
int startpos,
int endpos) |
DataSet |
MEManager.emitDataSet(int n,
int... lengths) |
DataSet |
DAGTrainSM.emitDataSet(int n,
int... lengths) |
double |
MEManager.getLogProbFor(Sequence sequence,
int startpos,
int endpos) |
double |
DAGTrainSM.getLogProbFor(Sequence sequence,
int startpos,
int endpos) |
String |
MEManager.getStructure() |
abstract String |
InhomogeneousDGTrainSM.getStructure()
Returns a
String representation of the underlying graph. |
String |
DAGTrainSM.getStructure() |
Modifier and Type | Method and Description |
---|---|
String |
SharedStructureMixture.getStructure()
Returns a
String representation of the structure of the used
models. |
Modifier and Type | Method and Description |
---|---|
protected Sequence[] |
StrandTrainSM.emitDataSetUsingCurrentParameterSet(int n,
int... lengths) |
double |
AbstractMixtureTrainSM.getScoreForBestRun()
Returns the value of the optimized function from the best run of the last
training.
|
Modifier and Type | Method and Description |
---|---|
static DataSet |
DiscreteInhomogenousDataSetEmitter.emitDataSet(StatisticalModel m,
int n)
This method emits a data set with
n sequences from the discrete inhomogeneous model m
. |