Uses of Interface
de.jstacs.models.Model

Packages that use Model

de.jstacs.classifier.assessment	This package allows to assess classifiers.
de.jstacs.classifier.modelBased	Provides the classes for Classifiers that are based on Models
de.jstacs.models	Provides the interface Model and its abstract implementation AbstractModel, which is the super class of all other models.
de.jstacs.models.discrete
de.jstacs.models.discrete.homogeneous
de.jstacs.models.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.shared
de.jstacs.models.hmm	The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.models.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM
de.jstacs.models.mixture	This package is the super package for any mixture model.
de.jstacs.models.mixture.motif
de.jstacs.models.utils

Uses of Model in de.jstacs.classifier.assessment

Fields in de.jstacs.classifier.assessment declared as Model

protected Model[][]

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

Constructors in de.jstacs.classifier.assessment with parameters of type Model

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Uses of Model in de.jstacs.classifier.modelBased

Fields in de.jstacs.classifier.modelBased declared as Model

protected Model[]

ModelBasedClassifier.models The internal Models.

Methods in de.jstacs.classifier.modelBased that return Model

Model

ModelBasedClassifier.getModel(int classIndex) Returns a clone of the Model for a specified class.

Methods in de.jstacs.classifier.modelBased with parameters of type Model

static int

ModelBasedClassifier.getPossibleLength(Model... models) This method returns the possible length of a classifier that would use the given Models.

Constructors in de.jstacs.classifier.modelBased with parameters of type Model

ModelBasedClassifier(boolean cloneModels, Model... models)
This constructor creates a new instance with the given Models and clones these if necessary.

ModelBasedClassifier(Model... models)
The default constructor that creates a new instance with the given Models.

Uses of Model in de.jstacs.models

Classes in de.jstacs.models that implement Model

class	AbstractModel Abstract class for a model for pattern recognition.
class	CompositeModel This class is for modelling sequences by modelling the different positions of the each sequence by different models.
class	NormalizableScoringFunctionModel This model can be used to use a NormalizableScoringFunction as model.
class	UniformModel This class represents a uniform model.
class	VariableLengthWrapperModel This class allows to train any Model on Samples of Sequences with variable length if each individual length is at least getLength().

Fields in de.jstacs.models declared as Model

protected Model[]

CompositeModel.models The models for the components

Methods in de.jstacs.models that return Model

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

Methods in de.jstacs.models with parameters of type Model

static MixtureModel	ModelFactory.createMixtureModel(double[] hyper, Model[] model) This method allows to create a MixtureModel that allows to model a Sample as a mixture of individual components.
static StrandModel	ModelFactory.createStrandModel(Model model) This method allows to create a StrandModel that allows to score binding sites on both strand of DNA.
static SingleHiddenMotifMixture	ModelFactory.createZOOPS(Model motif, Model bg, double[] hyper, boolean trainOnlyMotifModel) This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discovers binding sites in a Sample.

Constructors in de.jstacs.models with parameters of type Model

CompositeModel(AlphabetContainer alphabets, int[] assignment, Model... models)
Creates a new CompositeModel.

VariableLengthWrapperModel(Model m)
This is the main constructor that creates an instance from any Model.

Uses of Model in de.jstacs.models.discrete

Classes in de.jstacs.models.discrete that implement Model

class

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

Uses of Model in de.jstacs.models.discrete.homogeneous

Classes in de.jstacs.models.discrete.homogeneous that implement Model

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

Uses of Model in de.jstacs.models.discrete.inhomogeneous

Classes in de.jstacs.models.discrete.inhomogeneous that implement Model

class	BayesianNetworkModel The class implements a Bayesian network ( StructureLearner.ModelType.BN ) of fixed order.
class	DAGModel The abstract class for directed acyclic graphical models (DAGModel).
class	FSDAGModel This class can be used for any discrete fixed structure directed acyclic graphical model ( FSDAGModel).
class	FSDAGModelForGibbsSampling This is the class for a fixed structure directed acyclic graphical model (see FSDAGModel) that can be used in a Gibbs sampling.
class	InhomogeneousDGM This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGM).

Methods in de.jstacs.models.discrete.inhomogeneous with parameters of type Model

static void

FSDAGModel.train(Model[] models, int[][] graph, double[][] weights, Sample... data) Computes the models with structure graph.

Uses of Model in de.jstacs.models.discrete.inhomogeneous.shared

Classes in de.jstacs.models.discrete.inhomogeneous.shared that implement Model

class

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

Uses of Model in de.jstacs.models.hmm

Classes in de.jstacs.models.hmm that implement Model

class

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

Uses of Model in de.jstacs.models.hmm.models

Classes in de.jstacs.models.hmm.models that implement Model

class	DifferentiableHigherOrderHMM This class combines an HigherOrderHMM and a NormalizableScoringFunction 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 Model in de.jstacs.models.mixture

Classes in de.jstacs.models.mixture that implement Model

class	AbstractMixtureModel This is the abstract class for all kinds of mixture models.
class	MixtureModel The class for a mixture model of any Models.
class	StrandModel This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Fields in de.jstacs.models.mixture declared as Model

protected Model[]	AbstractMixtureModel.alternativeModel The alternative models for the EM.
protected Model[]	AbstractMixtureModel.model The model for the sequences.

Methods in de.jstacs.models.mixture that return Model

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

Constructors in de.jstacs.models.mixture with parameters of type Model

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

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

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

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

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

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

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

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

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

StrandModel(Model model, int starts, double forwardStrandProb, double alpha, TerminationCondition tc, AbstractMixtureModel.Parameterization parametrization)
Creates an instance using EM and fixed component probabilities.

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

Uses of Model in de.jstacs.models.mixture.motif

Classes in de.jstacs.models.mixture.motif that implement Model

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

Constructors in de.jstacs.models.mixture.motif with parameters of type Model

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

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

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

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

Uses of Model in de.jstacs.models.utils

Methods in de.jstacs.models.utils with parameters of type Model

static Sample	DiscreteInhomogenousSampleEmitter.emitSample(Model m, int n) This method emits a sample with n sequences from the discrete inhomogeneous model m .
static double	ModelTester.getKLDivergence(Model m1, Model m2, int length) Returns the Kullback-Leibler-divergence D(p_m1||p_m2).
static double	ModelTester.getLogLikelihood(Model m, Sample data) Returns the log-likelihood of a Sample data for a given model m.
static double	ModelTester.getLogLikelihood(Model m, Sample data, double[] weights) Returns the log-likelihood of a Sample data for a given model m.
static double	ModelTester.getMarginalDistribution(Model m, int[] constraint) This method computes the marginal distribution for any discrete model m and all sequences that fulfill the constraint , if possible.
static double	ModelTester.getMaxOfDeviation(Model m1, Model m2, int length) This method computes the maximum deviation between the probabilities for all sequences of length for discrete models m1 and m2.
static Sequence	ModelTester.getMostProbableSequence(Model m, int length) Returns one most probable sequence for the discrete model m.
static double	ModelTester.getShannonEntropy(Model m, int length) This method computes the Shannon entropy for any discrete model m and all sequences of length, if possible.
static double	ModelTester.getShannonEntropyInBits(Model m, int length) This method computes the Shannon entropy in bits for any discrete model m and all sequences of length, if possible.
static double	ModelTester.getSumOfDeviation(Model m1, Model m2, int length) This method computes the sum of deviations between the probabilities for all sequences of length for discrete models m1 and m2.
static double	ModelTester.getSumOfDistribution(Model m, int length) This method computes the marginal distribution for any discrete model m and all sequences of length, if possible.
static double	ModelTester.getSymKLDivergence(Model m1, Model m2, int length) Returns the difference of the Kullback-Leibler-divergences, i.e.
static double	ModelTester.getValueOfAIC(Model m, Sample s, int k) This method computes the value of Akaikes Information Criterion (AIC).
static double	ModelTester.getValueOfBIC(Model m, Sample s, int k) This method computes the value of the Bayesian Information Criterion (BIC).