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.classifiers.performanceMeasures |
This package provides the implementations of performance measures that can be used to assess any classifier.
|
de.jstacs.classifiers.trainSMBased |
Provides the classes for
Classifier s that are based on TrainableStatisticalModel s. |
de.jstacs.results |
This package provides classes for results and sets of results.
|
de.jstacs.sequenceScores |
Provides all
SequenceScore s, which can be used to score a Sequence , typically using some model assumptions. |
de.jstacs.sequenceScores.differentiable | |
de.jstacs.sequenceScores.statisticalModels.trainable |
Provides all
TrainableStatisticalModel s, which can
be learned from a single DataSet . |
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.hmm.models |
The package provides different implementations of hidden Markov models based on
AbstractHMM . |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
This package is the super package for any mixture model.
|
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
MappingClassifier.getNumericalCharacteristics() |
abstract NumericalResultSet |
AbstractClassifier.getNumericalCharacteristics()
Returns the subset of numerical values that are also returned by
AbstractClassifier.getCharacteristics() . |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
ScoreClassifier.getNumericalCharacteristics() |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
SamplingScoreBasedClassifier.getNumericalCharacteristics() |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
NumericalPerformanceMeasure.compute(double[][][] classSpecificScores)
This method allows to compute the performance measure of given class specific scores.
|
NumericalResultSet |
ClassificationRate.compute(double[][][] classSpecificScores) |
NumericalResultSet |
AucROC.compute(double[][][] classSpecificScores) |
NumericalResultSet |
AucPR.compute(double[][][] classSpecificScores) |
NumericalResultSet |
AbstractNumericalTwoClassPerformanceMeasure.compute(double[][][] classSpecificScores) |
NumericalResultSet |
NumericalPerformanceMeasure.compute(double[][][] classSpecificScores,
double[][] weights)
This method allows to compute the performance measure of given class specific scores.
|
NumericalResultSet |
ClassificationRate.compute(double[][][] classSpecificScores,
double[][] weights) |
NumericalResultSet |
AucROC.compute(double[][][] classSpecificScores,
double[][] weights) |
NumericalResultSet |
AucPR.compute(double[][][] classSpecificScores,
double[][] weights) |
NumericalResultSet |
AbstractNumericalTwoClassPerformanceMeasure.compute(double[][][] classSpecificScores,
double[][] weights) |
NumericalResultSet |
NumericalPerformanceMeasure.compute(double[] sortedScoresClass0,
double[] sortedScoresClass1)
This method allows to compute the performance measure of given sorted score ratios.
|
NumericalResultSet |
ClassificationRate.compute(double[] sortedScoresClass0,
double[] sortedScoresClass1) |
NumericalResultSet |
AucROC.compute(double[] sortedScoresClass0,
double[] sortedScoresClass1) |
NumericalResultSet |
AucPR.compute(double[] sortedScoresClass0,
double[] sortedScoresClass1) |
NumericalResultSet |
AbstractNumericalTwoClassPerformanceMeasure.compute(double[] sortedScoresClass0,
double[] sortedScoresClass1) |
NumericalResultSet |
SensitivityForFixedSpecificity.compute(double[] sortedScoresClass0,
double[] weightClass0,
double[] sortedScoresClass1,
double[] weightClass1) |
NumericalResultSet |
PositivePredictiveValueForFixedSensitivity.compute(double[] sortedScoresClass0,
double[] weightsClass0,
double[] sortedScoresClass1,
double[] weightsClass1) |
NumericalResultSet |
NumericalPerformanceMeasure.compute(double[] sortedScoresClass0,
double[] weightsClass0,
double[] sortedScoresClass1,
double[] weightsClass1)
This method allows to compute the performance measure of given sorted score ratios.
|
NumericalResultSet |
MaximumNumericalTwoClassMeasure.compute(double[] sortedScoresClass0,
double[] weightsClass0,
double[] sortedScoresClass1,
double[] weightsClass1) |
NumericalResultSet |
FalsePositiveRateForFixedSensitivity.compute(double[] sortedScoresClass0,
double[] weightsClass0,
double[] sortedScoresClass1,
double[] weightsClass1) |
NumericalResultSet |
CorrelationCoefficient.compute(double[] sortedScoresClass0,
double[] weightsClass0,
double[] sortedScoresClass1,
double[] weightsClass1) |
NumericalResultSet |
ClassificationRate.compute(double[] sortedScoresClass0,
double[] weightsClass0,
double[] sortedScoresClass1,
double[] weightsClass1) |
NumericalResultSet |
AucROC.compute(double[] sortedScoresClass0,
double[] weightsClass0,
double[] sortedScoresClass1,
double[] weightsClass1) |
NumericalResultSet |
AucPR.compute(double[] sortedScoresClass0,
double[] weightsClass0,
double[] sortedScoresClass1,
double[] weightsClass1) |
abstract NumericalResultSet |
AbstractNumericalTwoClassPerformanceMeasure.compute(double[] sortedScoresClass0,
double[] weightClass0,
double[] sortedScoresClass1,
double[] weightsClass1) |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
TrainSMBasedClassifier.getNumericalCharacteristics() |
Modifier and Type | Class and Description |
---|---|
class |
MeanResultSet
Class that computes the mean and the standard error of a series of
NumericalResultSet s. |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
MeanResultSet.getStatistics()
Returns the means and (if possible the) standard errors of the results in
this
MeanResultSet as a new NumericalResultSet . |
Modifier and Type | Method and Description |
---|---|
void |
MeanResultSet.addResults(NumericalResultSet... rs)
Adds
NumericalResultSet s to this MeanResultSet . |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
SequenceScore.getNumericalCharacteristics()
Returns the subset of numerical values that are also returned by
SequenceScore.getCharacteristics() . |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
AbstractDifferentiableSequenceScore.getNumericalCharacteristics() |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
VariableLengthWrapperTrainSM.getNumericalCharacteristics() |
NumericalResultSet |
UniformTrainSM.getNumericalCharacteristics() |
NumericalResultSet |
PFMWrapperTrainSM.getNumericalCharacteristics() |
NumericalResultSet |
DifferentiableStatisticalModelWrapperTrainSM.getNumericalCharacteristics() |
NumericalResultSet |
CompositeTrainSM.getNumericalCharacteristics() |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
HomogeneousTrainSM.getNumericalCharacteristics() |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
MEManager.getNumericalCharacteristics() |
NumericalResultSet |
DAGTrainSM.getNumericalCharacteristics() |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
HigherOrderHMM.getNumericalCharacteristics() |
Modifier and Type | Method and Description |
---|---|
NumericalResultSet |
AbstractMixtureTrainSM.getNumericalCharacteristics() |