Uses of Interface
de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModel

Packages that use TrainableStatisticalModel

de.jstacs.classifiers	This package provides the framework for any classifier.
de.jstacs.classifiers.assessment	This package allows to assess classifiers. It contains the class ClassifierAssessment that is used as a super-class of all implemented methodologies of an assessment to assess classifiers.
de.jstacs.classifiers.trainSMBased	Provides the classes for Classifiers that are based on TrainableStatisticalModels.
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, 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.hmm	The package provides all interfaces and classes for a hidden Markov model (HMM).
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.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Uses of TrainableStatisticalModel in de.jstacs.classifiers

Methods in de.jstacs.classifiers with parameters of type TrainableStatisticalModel

static AbstractClassifier

ClassifierFactory.createGenerativeClassifier(TrainableStatisticalModel... models) Creates a classifier that is based on at least two TrainableStatisticalModels.

Uses of TrainableStatisticalModel in de.jstacs.classifiers.assessment

Fields in de.jstacs.classifiers.assessment declared as TrainableStatisticalModel

protected TrainableStatisticalModel[][]

ClassifierAssessment.myModel This array contains for each class the internal used models.

Constructors in de.jstacs.classifiers.assessment with parameters of type TrainableStatisticalModel

ClassifierAssessment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and, in addition, classifiers that will be constructed using the given TrainableStatisticalModels.

ClassifierAssessment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new ClassifierAssessment from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers.

ClassifierAssessment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
Creates a new ClassifierAssessment from a set of TrainableStatisticalModels.

KFoldCrossValidation(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a KFoldCrossValidation .

KFoldCrossValidation(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new KFoldCrossValidation from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers.

KFoldCrossValidation(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
Creates a new KFoldCrossValidation from a set of TrainableStatisticalModels.

RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a RepeatedHoldOutExperiment.

RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new RepeatedHoldOutExperiment from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers.

RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
Creates a new RepeatedHoldOutExperiment from a set of TrainableStatisticalModels.

RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a RepeatedSubSamplingExperiment.

RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new RepeatedSubSamplingExperiment from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers.

RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
Creates a new RepeatedSubSamplingExperiment from a set of TrainableStatisticalModels.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a Sampled_RepeatedHoldOutExperiment.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new Sampled_RepeatedHoldOutExperiment from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers.

Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
Creates a new Sampled_RepeatedHoldOutExperiment from a set of TrainableStatisticalModels.

Uses of TrainableStatisticalModel in de.jstacs.classifiers.trainSMBased

Fields in de.jstacs.classifiers.trainSMBased declared as TrainableStatisticalModel

protected TrainableStatisticalModel[]

TrainSMBasedClassifier.models The internal TrainableStatisticalModels.

Methods in de.jstacs.classifiers.trainSMBased that return TrainableStatisticalModel

TrainableStatisticalModel

TrainSMBasedClassifier.getModel(int classIndex) Returns a clone of the TrainableStatisticalModel for a specified class.

Methods in de.jstacs.classifiers.trainSMBased with parameters of type TrainableStatisticalModel

static int

TrainSMBasedClassifier.getPossibleLength(TrainableStatisticalModel... models) This method returns the possible length of a classifier that would use the given TrainableStatisticalModels.

Constructors in de.jstacs.classifiers.trainSMBased with parameters of type TrainableStatisticalModel

TrainSMBasedClassifier(boolean cloneModels, TrainableStatisticalModel... models)
This constructor creates a new instance with the given TrainableStatisticalModels and clones these if necessary.

TrainSMBasedClassifier(TrainableStatisticalModel... models)
The default constructor that creates a new instance with the given TrainableStatisticalModels.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable

Classes in de.jstacs.sequenceScores.statisticalModels.trainable that implement TrainableStatisticalModel

class	AbstractTrainableStatisticalModel Abstract class for a model for pattern recognition.
class	CompositeTrainSM This class is for modelling sequences by modelling the different positions of the each sequence by different models.
class	DifferentiableStatisticalModelWrapperTrainSM This model can be used to use a DifferentiableStatisticalModel as model.
class	UniformTrainSM This class represents a uniform model.
class	VariableLengthWrapperTrainSM This class allows to train any TrainableStatisticalModel on DataSets of Sequences with variable length if each individual length is at least SequenceScore.getLength().

Fields in de.jstacs.sequenceScores.statisticalModels.trainable declared as TrainableStatisticalModel

protected TrainableStatisticalModel[]

CompositeTrainSM.models The models for the components

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that return TrainableStatisticalModel

TrainableStatisticalModel	TrainableStatisticalModel.clone() Creates a clone (deep copy) of the current TrainableStatisticalModel instance.
TrainableStatisticalModel[]	CompositeTrainSM.getModels() Returns the a deep copy of the models.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type TrainableStatisticalModel

static MixtureTrainSM	TrainableStatisticalModelFactory.createMixtureModel(double[] hyper, TrainableStatisticalModel[] model) This method allows to create a MixtureTrainSM that allows to model a DataSet as a mixture of individual components.
static StrandTrainSM	TrainableStatisticalModelFactory.createStrandModel(TrainableStatisticalModel model) This method allows to create a StrandTrainSM that allows to score binding sites on both strand of DNA.
static ZOOPSTrainSM	TrainableStatisticalModelFactory.createZOOPS(TrainableStatisticalModel motif, TrainableStatisticalModel bg, double[] hyper, boolean trainOnlyMotifModel) This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a DataSet.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type TrainableStatisticalModel

CompositeTrainSM(AlphabetContainer alphabets, int[] assignment, TrainableStatisticalModel... models)
Creates a new CompositeTrainSM.

VariableLengthWrapperTrainSM(TrainableStatisticalModel m)
This is the main constructor that creates an instance from any TrainableStatisticalModel.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that implement TrainableStatisticalModel

class

DiscreteGraphicalTrainSM This is the main class for all discrete graphical models (DGM).

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that implement TrainableStatisticalModel

class	HomogeneousMM This class implements homogeneous Markov models (hMM) of arbitrary order.
class	HomogeneousTrainSM This class implements homogeneous models of arbitrary order.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that implement TrainableStatisticalModel

class	BayesianNetworkTrainSM The class implements a Bayesian network ( StructureLearner.ModelType.BN ) of fixed order.
class	DAGTrainSM The abstract class for directed acyclic graphical models (DAGTrainSM).
class	FSDAGModelForGibbsSampling This is the class for a fixed structure directed acyclic graphical model (see FSDAGTrainSM) that can be used in a Gibbs sampling.
class	FSDAGTrainSM This class can be used for any discrete fixed structure directed acyclic graphical model ( FSDAGTrainSM).
class	FSMEManager This class can be used for any discrete fixed structure maximum entropy model (FSMEM).
class	InhomogeneousDGTrainSM This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGTrainSM).
class	MEManager This class is the super class for all maximum entropy models

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous with parameters of type TrainableStatisticalModel

static void

FSDAGTrainSM.train(TrainableStatisticalModel[] models, int[][] graph, double[][] weights, DataSet... data) Computes the models with structure graph.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared that implement TrainableStatisticalModel

class

SharedStructureMixture This class handles a mixture of models with the same structure that is learned via EM.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that implement TrainableStatisticalModel

class

AbstractHMM This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement TrainableStatisticalModel

class	DifferentiableHigherOrderHMM This class combines an HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods.
class	HigherOrderHMM This class implements a higher order hidden Markov model.
class	SamplingHigherOrderHMM
class	SamplingPhyloHMM This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that implement TrainableStatisticalModel

class	AbstractMixtureTrainSM This is the abstract class for all kinds of mixture models.
class	MixtureTrainSM The class for a mixture model of any TrainableStatisticalModels.
class	StrandTrainSM This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.mixture declared as TrainableStatisticalModel

protected TrainableStatisticalModel[]	AbstractMixtureTrainSM.alternativeModel The alternative models for the EM.
protected TrainableStatisticalModel[]	AbstractMixtureTrainSM.model The model for the sequences.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that return TrainableStatisticalModel

TrainableStatisticalModel	AbstractMixtureTrainSM.getModel(int i) Returns a deep copy of the i-th model.
TrainableStatisticalModel[]	AbstractMixtureTrainSM.getModels() Returns a deep copy of the models.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type TrainableStatisticalModel

AbstractMixtureTrainSM(int length, TrainableStatisticalModel[] models, boolean[] optimizeModel, int dimension, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates a new AbstractMixtureTrainSM.

MixtureTrainSM(int length, TrainableStatisticalModel[] models, double[] weights, int starts, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and fixed component probabilities.

MixtureTrainSM(int length, TrainableStatisticalModel[] models, double[] weights, int starts, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and fixed component probabilities.

MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates a new MixtureTrainSM.

MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and estimating the component probabilities.

MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and sampling the component probabilities.

StrandTrainSM(TrainableStatisticalModel model, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double forwardStrandProb, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates a new StrandTrainSM.

StrandTrainSM(TrainableStatisticalModel model, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and estimating the component probabilities.

StrandTrainSM(TrainableStatisticalModel model, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and sampling the component probabilities.

StrandTrainSM(TrainableStatisticalModel model, int starts, double forwardStrandProb, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and fixed component probabilities.

StrandTrainSM(TrainableStatisticalModel model, int starts, double forwardStrandProb, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and fixed component probabilities.

Uses of TrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif that implement TrainableStatisticalModel

class	HiddenMotifMixture This is the main class that every generative motif discoverer should implement.
class	ZOOPSTrainSM This class enables the user to search for a single motif in a sequence.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type TrainableStatisticalModel

HiddenMotifMixture(TrainableStatisticalModel[] models, boolean[] optimzeArray, int components, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates a new HiddenMotifMixture.

ZOOPSTrainSM(TrainableStatisticalModel motif, TrainableStatisticalModel bg, boolean trainOnlyMotifModel, int starts, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)
Creates a new ZOOPSTrainSM.

ZOOPSTrainSM(TrainableStatisticalModel motif, TrainableStatisticalModel bg, boolean trainOnlyMotifModel, int starts, double[] componentHyperParams, PositionPrior posPrior, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)
Creates a new ZOOPSTrainSM using EM and estimating the probability for finding a motif.

ZOOPSTrainSM(TrainableStatisticalModel motif, TrainableStatisticalModel bg, boolean trainOnlyMotifModel, int starts, double motifProb, PositionPrior posPrior, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)
Creates a new ZOOPSTrainSM using EM and fixed probability for finding a motif.