Modifier and Type | Method and Description |
---|---|
static AbstractClassifier |
ClassifierFactory.createGenerativeClassifier(TrainableStatisticalModel... models)
Creates a classifier that is based on at least two
TrainableStatisticalModel s. |
Modifier and Type | Field and Description |
---|---|
protected TrainableStatisticalModel[][] |
ClassifierAssessment.myModel
This array contains for each class the internal used models.
|
Constructor and Description |
---|
ClassifierAssessment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given
AbstractClassifier s and, in addition, classifiers that will be
constructed using the given TrainableStatisticalModel s. |
ClassifierAssessment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new
ClassifierAssessment from an array of
AbstractClassifier s 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 TrainableStatisticalModel s. |
KFoldCrossValidation(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given
AbstractClassifier s and those constructed using the given
TrainableStatisticalModel s by a KFoldCrossValidation
. |
KFoldCrossValidation(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new
KFoldCrossValidation from an array of
AbstractClassifier s 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 TrainableStatisticalModel s. |
RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given
AbstractClassifier s and those constructed using the given
TrainableStatisticalModel s by a
RepeatedHoldOutExperiment . |
RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new
RepeatedHoldOutExperiment from an array of
AbstractClassifier s 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
TrainableStatisticalModel s. |
RepeatedSubSamplingExperiment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given
AbstractClassifier s and those constructed using the given
TrainableStatisticalModel s by a
RepeatedSubSamplingExperiment . |
RepeatedSubSamplingExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new
RepeatedSubSamplingExperiment from an array of
AbstractClassifier s 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
TrainableStatisticalModel s. |
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given
AbstractClassifier s and those constructed using the given
TrainableStatisticalModel s by a
Sampled_RepeatedHoldOutExperiment . |
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new
Sampled_RepeatedHoldOutExperiment from an array of
AbstractClassifier s 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
TrainableStatisticalModel s. |
Modifier and Type | Field and Description |
---|---|
protected TrainableStatisticalModel[] |
TrainSMBasedClassifier.models
The internal
TrainableStatisticalModel s. |
Modifier and Type | Method and Description |
---|---|
TrainableStatisticalModel |
TrainSMBasedClassifier.getModel(int classIndex)
Returns a clone of the
TrainableStatisticalModel for a specified class. |
Modifier and Type | Method and Description |
---|---|
static int |
TrainSMBasedClassifier.getPossibleLength(TrainableStatisticalModel... models)
This method returns the possible length of a classifier that would use
the given
TrainableStatisticalModel s. |
Constructor and Description |
---|
TrainSMBasedClassifier(boolean cloneModels,
TrainableStatisticalModel... models)
This constructor creates a new instance with the given
TrainableStatisticalModel s and
clones these if necessary. |
TrainSMBasedClassifier(TrainableStatisticalModel... models)
The default constructor that creates a new instance with the given
TrainableStatisticalModel s. |
Modifier and Type | Class and Description |
---|---|
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 |
PFMWrapperTrainSM
A wrapper class for representing position weight matrices or position frequency matrices
from databases as
TrainableStatisticalModel s. |
class |
UniformTrainSM
This class represents a uniform model.
|
class |
VariableLengthWrapperTrainSM
This class allows to train any
TrainableStatisticalModel on DataSet s of Sequence s with
variable length if each individual length is at least SequenceScore.getLength() . |
Modifier and Type | Field and Description |
---|---|
protected TrainableStatisticalModel[] |
CompositeTrainSM.models
The models for the components
|
Modifier and Type | Method and Description |
---|---|
TrainableStatisticalModel |
TrainableStatisticalModel.clone()
Creates a clone (deep copy) of the current
TrainableStatisticalModel instance. |
TrainableStatisticalModel[] |
CompositeTrainSM.getModels()
Returns the a deep copy of the models.
|
Modifier and Type | Method and Description |
---|---|
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 . |
Constructor and Description |
---|
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 . |
Modifier and Type | Class and Description |
---|---|
class |
DiscreteGraphicalTrainSM
This is the main class for all discrete graphical models
(DGM).
|
Modifier and Type | Class and Description |
---|---|
class |
HomogeneousMM
This class implements homogeneous Markov models (hMM) of arbitrary order.
|
class |
HomogeneousTrainSM
This class implements homogeneous models of arbitrary order.
|
Modifier and Type | Class and Description |
---|---|
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
|
Modifier and Type | Method and Description |
---|---|
static void |
FSDAGTrainSM.train(TrainableStatisticalModel[] models,
int[][] graph,
double[][] weights,
DataSet... data)
Computes the models with structure
graph . |
Modifier and Type | Class and Description |
---|---|
class |
SharedStructureMixture
This class handles a mixture of models with the same structure that is
learned via EM.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractHMM
This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.
|
Modifier and Type | Class and Description |
---|---|
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.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMixtureTrainSM
This is the abstract class for all kinds of mixture models.
|
class |
MixtureTrainSM
The class for a mixture model of any
TrainableStatisticalModel s. |
class |
StrandTrainSM
This model handles sequences that can either lie on the forward strand or on
the reverse complementary strand.
|
Modifier and Type | Field and Description |
---|---|
protected TrainableStatisticalModel[] |
AbstractMixtureTrainSM.alternativeModel
The alternative models for the EM.
|
protected TrainableStatisticalModel[] |
AbstractMixtureTrainSM.model
The model for the sequences.
|
Modifier and Type | Method and Description |
---|---|
TrainableStatisticalModel |
AbstractMixtureTrainSM.getModel(int i)
Returns a deep copy of the
i -th model. |
TrainableStatisticalModel[] |
AbstractMixtureTrainSM.getModels()
Returns a deep copy of the models.
|
Constructor and Description |
---|
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.
|
Modifier and Type | Class and Description |
---|---|
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.
|
Constructor and Description |
---|
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. |