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Uses of Storable in de.jstacs |
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Classes in de.jstacs that implement Storable | |
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class |
AnnotatedEntity
Superclass for all Jstacs entities that have a name, a comment, and a data type as annotations. |
Uses of Storable in de.jstacs.algorithms.optimization.termination |
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Subinterfaces of Storable in de.jstacs.algorithms.optimization.termination | |
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interface |
TerminationCondition
This interface can be used in any iterative algorithm for determining the end of the algorithm. |
Classes in de.jstacs.algorithms.optimization.termination that implement Storable | |
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class |
AbsoluteValueCondition
Deprecated. use of the absolute value condition is not recommended and it may be removed in future releases |
static class |
AbsoluteValueCondition.AbsoluteValueConditionParameterSet
Deprecated. This class implements the parameter set for a AbsoluteValueCondition . |
class |
AbstractTerminationCondition
This class is the abstract super class of many TerminationCondition s. |
static class |
AbstractTerminationCondition.AbstractTerminationConditionParameterSet
This class implements the super class of all parameter sets of instances from AbstractTerminationCondition . |
class |
CombinedCondition
This class allows to use many TerminationCondition s at once. |
static class |
CombinedCondition.CombinedConditionParameterSet
This class implements the parameter set for a CombinedCondition . |
class |
IterationCondition
This class will stop an optimization if the number of iteration reaches a given number. |
static class |
IterationCondition.IterationConditionParameterSet
This class implements the parameter set for a IterationCondition . |
class |
SmallDifferenceOfFunctionEvaluationsCondition
This class implements a TerminationCondition that stops an optimization
if the difference of the current and the last function evaluations will be small, i.e.,
![]() |
static class |
SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
This class implements the parameter set for a SmallDifferenceOfFunctionEvaluationsCondition . |
class |
SmallGradientConditon
This class implements a TerminationCondition that allows no further iteration in an optimization if the
the gradient becomes small, i.e.,
![]() |
static class |
SmallGradientConditon.SmallGradientConditonParameterSet
This class implements the parameter set for a SmallStepCondition . |
class |
SmallStepCondition
This class implements a TerminationCondition that allows no further iteration in an optimization if the
scalar product of the current and the last values of x will be small, i.e.,
![]() |
static class |
SmallStepCondition.SmallStepConditionParameterSet
This class implements the parameter set for a SmallStepCondition . |
class |
TimeCondition
This class implements a TerminationCondition that stops the optimization if the elapsed time in seconds is
greater than a given value. |
static class |
TimeCondition.TimeConditionParameterSet
This class implements the parameter set for a TimeCondition . |
Uses of Storable in de.jstacs.classifiers |
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Classes in de.jstacs.classifiers that implement Storable | |
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class |
AbstractClassifier
The super class for any classifier. |
class |
AbstractScoreBasedClassifier
This class is the main class for all score based classifiers. |
static class |
AbstractScoreBasedClassifier.DoubleTableResult
This class is for Result s given as a table of double
s. |
class |
MappingClassifier
This class allows the user to train the classifier on a given number of classes and to evaluate the classifier on a smaller number of classes by mapping classes together. |
Uses of Storable in de.jstacs.classifiers.assessment |
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Classes in de.jstacs.classifiers.assessment that implement Storable | |
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class |
ClassifierAssessmentAssessParameterSet
This class is the superclass used by all ClassifierAssessmentAssessParameterSet s. |
class |
KFoldCrossValidationAssessParameterSet
This class implements a ClassifierAssessmentAssessParameterSet that
must be used to call method assess( ... ) of a
KFoldCrossValidation . |
class |
RepeatedHoldOutAssessParameterSet
This class implements a ClassifierAssessmentAssessParameterSet that
must be used to call method assess( ... ) of a
RepeatedHoldOutExperiment . |
class |
RepeatedSubSamplingAssessParameterSet
This class implements a ClassifierAssessmentAssessParameterSet that
must be used to call method assess( ... ) of a
RepeatedSubSamplingExperiment . |
class |
Sampled_RepeatedHoldOutAssessParameterSet
This class implements a ClassifierAssessmentAssessParameterSet that
must be used to call the method assess( ... ) of a
Sampled_RepeatedHoldOutExperiment . |
Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased |
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Classes in de.jstacs.classifiers.differentiableSequenceScoreBased that implement Storable | |
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class |
ScoreClassifier
This abstract class implements the main functionality of a DifferentiableSequenceScore based classifier. |
class |
ScoreClassifierParameterSet
A set of Parameter s for any
ScoreClassifier . |
Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix |
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Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that implement Storable | |
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class |
GenDisMixClassifier
This class implements a classifier the optimizes the following function ![]() |
class |
GenDisMixClassifierParameterSet
This class contains the parameters for the GenDisMixClassifier . |
Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior |
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Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior that implement Storable | |
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class |
CompositeLogPrior
This class implements a composite prior that can be used for DifferentiableStatisticalModel. |
class |
DoesNothingLogPrior
This class defines a LogPrior that does not penalize any parameter. |
class |
LogPrior
The abstract class for any log-prior used e.g. for maximum supervised posterior optimization. |
class |
SeparateGaussianLogPrior
Class for a LogPrior that defines a Gaussian prior on the parameters
of a set of DifferentiableStatisticalModel s
and a set of class parameters. |
class |
SeparateLaplaceLogPrior
Class for a LogPrior that defines a Laplace prior on the parameters
of a set of DifferentiableStatisticalModel s
and a set of class parameters. |
class |
SeparateLogPrior
Abstract class for priors that penalize each parameter value independently and have some variances (and possible means) as hyperparameters. |
class |
SimpleGaussianSumLogPrior
This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter. |
Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased.msp |
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Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.msp that implement Storable | |
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class |
MSPClassifier
This class implements a classifier that allows the training via MCL or MSP principle. |
Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling |
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Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that implement Storable | |
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class |
SamplingGenDisMixClassifier
A classifier that samples its parameters from a LogGenDisMixFunction using the
Metropolis-Hastings algorithm. |
class |
SamplingGenDisMixClassifierParameterSet
ParameterSet to instantiate a SamplingGenDisMixClassifier . |
class |
SamplingScoreBasedClassifier
A classifier that samples the parameters of SamplingDifferentiableStatisticalModel s by the Metropolis-Hastings algorithm. |
class |
SamplingScoreBasedClassifierParameterSet
ParameterSet to instantiate a SamplingScoreBasedClassifier . |
Uses of Storable in de.jstacs.classifiers.performanceMeasures |
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Classes in de.jstacs.classifiers.performanceMeasures that implement Storable | |
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class |
AbstractPerformanceMeasure
This class is the abstract super class of any performance measure used to evaluate an AbstractClassifier . |
class |
AucPR
This class implements the area under curve of the precision-recall curve. |
class |
AucROC
This class implements the area under curve of the Receiver Operating Characteristics curve. |
class |
ClassificationRate
This class implements the classification rate, i.e. |
class |
ConfusionMatrix
This class implements the performance measure confusion matrix. |
class |
FalsePositiveRateForFixedSensitivity
This class implements the false positive rate for a fixed sensitivity. |
class |
MaximumCorrelationCoefficient
This class implements the maximum of the correlation coefficient ![]() |
class |
MaximumFMeasure
Computes the maximum of the general F-measure given a positive real parameter ![]() |
class |
MaximumNumericalTwoClassMeasure
This class prepares everything for an easy implementation of a maximum of any numerical performance measure. |
class |
NumericalPerformanceMeasureParameterSet
This class implements a container for NumericalPerformanceMeasure s that can be used, for instance, in an repeated assessment,
(cf. |
class |
PerformanceMeasureParameterSet
This class implements a container of AbstractPerformanceMeasure s that can be used
in AbstractClassifier.evaluate(PerformanceMeasureParameterSet, boolean, de.jstacs.data.DataSet...) . |
class |
PositivePredictiveValueForFixedSensitivity
This class implements the positive predictive value for a fixed sensitivity. |
class |
PRCurve
This class implements the precision-recall curve and its area under the curve. |
class |
ROCCurve
This class implements the Receiver Operating Characteristics curve and the area under the curve. |
class |
SensitivityForFixedSpecificity
This class implements the sensitivity for a fixed specificity. |
class |
TwoClassAbstractPerformanceMeasure
This class is the abstract super class of any performance measure that can only be computed for binary classifiers. |
Uses of Storable in de.jstacs.classifiers.trainSMBased |
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Classes in de.jstacs.classifiers.trainSMBased that implement Storable | |
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class |
TrainSMBasedClassifier
Classifier that works on TrainableStatisticalModel s for each of the different classes. |
Uses of Storable in de.jstacs.data |
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Classes in de.jstacs.data that implement Storable | |
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class |
AlphabetContainer
The container for Alphabet s used in a Sequence ,
DataSet , AbstractTrainableStatisticalModel or ... . |
static class |
AlphabetContainer.AbstractAlphabetContainerParameterSet<T extends AlphabetContainer>
This class is the super class of any InstanceParameterSet for AlphabetContainer . |
class |
AlphabetContainerParameterSet
Class for the AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet of an AlphabetContainer . |
static class |
AlphabetContainerParameterSet.AlphabetArrayParameterSet
Class for the parameters of an array of Alphabet s of defined
length. |
static class |
AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
Class for the parameter set of an array of Alphabet s where each
Alphabet may be used for one or more sections of positions. |
Uses of Storable in de.jstacs.data.alphabets |
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Classes in de.jstacs.data.alphabets that implement Storable | |
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class |
Alphabet
Class for a set of symbols, i.e. an Alphabet . |
static class |
Alphabet.AlphabetParameterSet<T extends Alphabet>
The super class for the InstanceParameterSet of any
Alphabet . |
class |
ComplementableDiscreteAlphabet
This abstract class indicates that an alphabet can be used to compute the complement. |
class |
ContinuousAlphabet
Class for a continuous alphabet. |
static class |
ContinuousAlphabet.ContinuousAlphabetParameterSet
Class for the ParameterSet of a
ContinuousAlphabet . |
class |
DiscreteAlphabet
Class for an alphabet that consists of arbitrary String s. |
static class |
DiscreteAlphabet.DiscreteAlphabetParameterSet
Class for the ParameterSet of a
DiscreteAlphabet . |
class |
DiscreteAlphabetMapping
This class implements the transformation of discrete values to other discrete values which define a DiscreteAlphabet . |
class |
DNAAlphabet
This class implements the discrete alphabet that is used for DNA. |
static class |
DNAAlphabet.DNAAlphabetParameterSet
The parameter set for a DNAAlphabet . |
class |
DNAAlphabetContainer
This class implements a singleton for an AlphabetContainer that can be used for DNA. |
static class |
DNAAlphabetContainer.DNAAlphabetContainerParameterSet
This class implements a singleton for the ParameterSet of a DNAAlphabetContainer . |
class |
GenericComplementableDiscreteAlphabet
This class implements an generic complementable discrete alphabet. |
static class |
GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet
This class is used as container for the parameters of a GenericComplementableDiscreteAlphabet . |
class |
ProteinAlphabet
This class implements the discrete alphabet that is used for proteins (one letter code). |
static class |
ProteinAlphabet.ProteinAlphabetParameterSet
The parameter set for a ProteinAlphabet . |
Uses of Storable in de.jstacs.data.sequences.annotation |
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Classes in de.jstacs.data.sequences.annotation that implement Storable | |
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class |
CisRegulatoryModuleAnnotation
Annotation for a cis-regulatory module as defined by a set of MotifAnnotation s of the motifs in the module. |
class |
IntronAnnotation
Annotation class for an intron as defined by a donor and an acceptor splice site. |
class |
LocatedSequenceAnnotation
Class for a SequenceAnnotation that has a position on the sequence,
e.g for transcription start sites or intron-exon junctions. |
class |
LocatedSequenceAnnotationWithLength
Class for a SequenceAnnotation that has a position on the sequence
and a length, e.g. for donor splice sites, exons or genes. |
class |
MotifAnnotation
Class for a StrandedLocatedSequenceAnnotationWithLength that is a
motif. |
class |
ReferenceSequenceAnnotation
This class implements a SequenceAnnotation that contains a reference
sequence. |
class |
SequenceAnnotation
Class for a general annotation of a Sequence . |
class |
SinglePositionSequenceAnnotation
Class for some annotations that consist mainly of one position on a sequence. |
class |
StrandedLocatedSequenceAnnotationWithLength
Class for a SequenceAnnotation that has a position, a length and an
orientation on the strand of a Sequence . |
Uses of Storable in de.jstacs.motifDiscovery |
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Subinterfaces of Storable in de.jstacs.motifDiscovery | |
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interface |
MotifDiscoverer
This is the interface that any tool for de-novo motif discovery should implement. |
interface |
MutableMotifDiscoverer
This is the interface that any tool for de-novo motif discovery should implement that allows any modify-operations like shift, shrink and expand. |
Uses of Storable in de.jstacs.motifDiscovery.history |
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Subinterfaces of Storable in de.jstacs.motifDiscovery.history | |
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interface |
History
This interface is used to manage the history of some process. |
Classes in de.jstacs.motifDiscovery.history that implement Storable | |
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class |
CappedHistory
This class combines a threshold on the number of steps which can be performed with any other History . |
class |
NoRevertHistory
This class implements a history that allows operations, that are not a priorily forbidden and do not create a configuration that has already be considered. |
class |
RestrictedRepeatHistory
This class implements a history that allows operations (i.e. a pair of int ), that are not a priorily forbidden and that are done before
less than a specified threshold. |
class |
SimpleHistory
This class implements a simple history that has a limited memory that will be used cyclicly. |
Uses of Storable in de.jstacs.parameters |
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Classes in de.jstacs.parameters that implement Storable | |
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class |
AbstractSelectionParameter
Class for a collection parameter, i.e. a parameter that provides some collection of possible values the user can choose from. |
class |
ArrayParameterSet
Class for a ParameterSet that consists of a length-Parameter
that defines the length of the array and an array of
ParameterSetContainer s of this length. |
class |
EnumParameter
This class implements a SelectionParameter based on an Enum . |
class |
ExpandableParameterSet
A class for a ParameterSet that can be expanded by additional
Parameter s at runtime. |
class |
FileParameter
Class for a Parameter that represents a local file. |
static class |
FileParameter.FileRepresentation
Class that represents a file. |
class |
InstanceParameterSet<T extends InstantiableFromParameterSet>
Container class for a set of Parameter s that can be used to
instantiate another class. |
class |
MultiSelectionParameter
Class for a Parameter that provides a collection of possible values. |
class |
Parameter
Abstract class for a parameter that shall be used as the parameter of some method, constructor, etc. |
class |
ParameterSet
(Container) class for a set of Parameter s. |
class |
ParameterSetContainer
Class for a ParameterSetContainer that contains a
ParameterSet as value. |
class |
RangeParameter
Class for a parameter wrapper that allows SimpleParameter s to be set
to a set of values. |
class |
SelectionParameter
Class for a collection parameter, i.e. a parameter that provides some collection of possible values the user can choose from. |
class |
SequenceScoringParameterSet<T extends InstantiableFromParameterSet>
Abstract class for a ParameterSet containing all parameters necessary
to construct an Object that implements
InstantiableFromParameterSet . |
class |
SimpleParameter
Class for a "simple" parameter. |
class |
SimpleParameterSet
Class for a ParameterSet that is constructed from an array of Parameter s. |
Uses of Storable in de.jstacs.parameters.validation |
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Subinterfaces of Storable in de.jstacs.parameters.validation | |
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interface |
Constraint
Interface for a constraint that must be fulfilled in a ConstraintValidator . |
interface |
ParameterValidator
Interface for a parameter validator, i.e. a class that can validate some possible parameter values. |
Classes in de.jstacs.parameters.validation that implement Storable | |
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class |
ConstraintValidator
Class for a ParameterValidator that is based on Constraint s. |
class |
NumberValidator<E extends Comparable<? extends Number>>
Class that validates all subclasses of Number that implement
Comparable (e.g. |
class |
SimpleStaticConstraint
Class for a Constraint that checks values against static values using
the comparison operators defined in the interface Constraint . |
class |
StorableValidator
Class for a validator that validates instances and XML representations for the correct class types (e.g. |
Constructor parameters in de.jstacs.parameters.validation with type arguments of type Storable | |
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StorableValidator(Class<? extends Storable> clazz)
Creates a new StorableValidator for a subclass of
Storable . |
|
StorableValidator(Class<? extends Storable> clazz,
boolean trained)
Creates a new StorableValidator for a subclass of
AbstractTrainableStatisticalModel or AbstractClassifier . |
Uses of Storable in de.jstacs.results |
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Classes in de.jstacs.results that implement Storable | |
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class |
CategoricalResult
A class for categorical results (i.e. non-numerical results) for primitives and String s. |
class |
DataSetResult
Result that contains a DataSet . |
class |
ImageResult
A class for results that are images of the PNG format. |
class |
ListResult
Class for a Result that contains a list or a matrix, respectively, of
ResultSet s. |
class |
MeanResultSet
Class that computes the mean and the standard error of a series of NumericalResultSet s. |
class |
NumericalResult
Class for numerical Result values. |
class |
NumericalResultSet
Class for a set of numerical result values, which are all of the type NumericalResult . |
class |
Result
The abstract class for any result. |
class |
ResultSet
Class for a set of Result s which provides methods to access single
Result s in the set, to retrieve the number of Result s in the
set, to get a String representation or an XML representation of all
the Result s in the set. |
class |
SimpleResult
Abstract class for a Result with a value of a primitive data type or
String . |
class |
StorableResult
Class for Result s that are Storable s. |
Methods in de.jstacs.results that return Storable | |
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Storable |
StorableResult.getResultInstance()
Returns the instance of the Storable that is the result of this
StorableResult . |
Constructors in de.jstacs.results with parameters of type Storable | |
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StorableResult(String name,
String comment,
Storable object)
Creates a result for an XML representation of an object. |
Uses of Storable in de.jstacs.sampling |
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Subinterfaces of Storable in de.jstacs.sampling | |
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interface |
BurnInTest
This is the abstract super class for any test of the length of the burn-in phase. |
Classes in de.jstacs.sampling that implement Storable | |
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class |
AbstractBurnInTest
This abstract class implements some of the methods of BurnInTest to
alleviate the implementation of efficient and new burn-in tests. |
class |
AbstractBurnInTestParameterSet
Class for the parameters of a AbstractBurnInTest . |
class |
SimpleBurnInTest
Deprecated. since this burn test ignore the data coming from the sampling, it might be problematic to use this test |
class |
VarianceRatioBurnInTest
In this class the Variance-Ratio method of Gelman and Rubin is implemented to test the length of the burn-in phase of a multi-chain Gibbs Sampling (number of chains >2). |
class |
VarianceRatioBurnInTestParameterSet
Class for the parameters of a VarianceRatioBurnInTest . |
Uses of Storable in de.jstacs.sequenceScores |
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Subinterfaces of Storable in de.jstacs.sequenceScores | |
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interface |
SequenceScore
This interface defines a scoring function that assigns a score to each input sequence. |
Uses of Storable in de.jstacs.sequenceScores.differentiable |
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Subinterfaces of Storable in de.jstacs.sequenceScores.differentiable | |
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interface |
DifferentiableSequenceScore
This interface is the main part of any ScoreClassifier . |
Classes in de.jstacs.sequenceScores.differentiable that implement Storable | |
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class |
AbstractDifferentiableSequenceScore
This class is the main part of any ScoreClassifier . |
class |
IndependentProductDiffSS
This class enables the user to model parts of a sequence independent of each other. |
class |
UniformDiffSS
This DifferentiableSequenceScore does nothing. |
Uses of Storable in de.jstacs.sequenceScores.differentiable.logistic |
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Subinterfaces of Storable in de.jstacs.sequenceScores.differentiable.logistic | |
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interface |
LogisticConstraint
This interface defines the function ![]() ![]() LogisticDiffSS . |
Classes in de.jstacs.sequenceScores.differentiable.logistic that implement Storable | |
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class |
LogisticDiffSS
This class implements a logistic function. |
class |
ProductConstraint
This class implements product constraints, i.e., the method ProductConstraint.getValue(Sequence,int)
returns the product of the values for the positions defined in the constructor. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels |
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Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels | |
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interface |
StatisticalModel
This interface declares methods of a statistical model, i.e., a SequenceScore that defines a proper likelihood
over the input Sequence s. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable |
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Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable | |
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interface |
DifferentiableStatisticalModel
The interface for normalizable DifferentiableSequenceScore s. |
interface |
SamplingDifferentiableStatisticalModel
Interface for DifferentiableStatisticalModel s that can be used for
Metropolis-Hastings sampling in a SamplingScoreBasedClassifier . |
interface |
VariableLengthDiffSM
This is an interface for all DifferentiableStatisticalModel s that allow to score
subsequences of arbitrary length. |
Classes in de.jstacs.sequenceScores.statisticalModels.differentiable that implement Storable | |
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class |
AbstractDifferentiableStatisticalModel
This class is the main part of any ScoreClassifier . |
class |
AbstractVariableLengthDiffSM
This abstract class implements some methods declared in DifferentiableStatisticalModel based on the declaration
of methods in VariableLengthDiffSM . |
class |
CyclicMarkovModelDiffSM
This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length. |
class |
IndependentProductDiffSM
This class enables the user to model parts of a sequence independent of each other. |
class |
MappingDiffSM
This class implements a DifferentiableStatisticalModel that works on
mapped Sequence s. |
class |
MarkovRandomFieldDiffSM
This class implements the scoring function for any MRF (Markov Random Field). |
class |
MultiDimensionalSequenceWrapperDiffSM
This class implements a simple wrapper for multidimensional sequences. |
class |
NormalizedDiffSM
This class makes an unnormalized DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel . |
class |
UniformDiffSM
This DifferentiableStatisticalModel does nothing. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement Storable | |
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class |
BayesianNetworkDiffSM
This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network. |
class |
BayesianNetworkDiffSMParameterSet
Class for the parameters of a BayesianNetworkDiffSM . |
class |
BNDiffSMParameter
Class for the parameters of a BayesianNetworkDiffSM . |
class |
BNDiffSMParameterTree
Class for the tree that represents the context of a BNDiffSMParameter in a
BayesianNetworkDiffSM . |
class |
BNDiffSMParameterTree.TreeElement
Class for the nodes of a BNDiffSMParameterTree |
class |
MarkovModelDiffSM
This class implements a AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures that implement Storable | |
---|---|
class |
InhomogeneousMarkov
Class for a network structure of a BayesianNetworkDiffSM
that is an inhomogeneous Markov model. |
static class |
InhomogeneousMarkov.InhomogeneousMarkovParameterSet
Class for an InstanceParameterSet that defines the parameters of
an InhomogeneousMarkov structure Measure . |
class |
Measure
Class for structure measures that derive an optimal structure with respect to some criterion within a class of possible structures from data. |
static class |
Measure.MeasureParameterSet
This class is the super class of any ParameterSet that can be used to instantiate a Measure . |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures that implement Storable | |
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class |
BTExplainingAwayResidual
Structure learning Measure that computes a maximum spanning tree
based on the explaining away residual and uses the resulting tree structure
as structure of a Bayesian tree (special case of a Bayesian network) in a
BayesianNetworkDiffSM
. |
static class |
BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet
Class for the parameters of a BTExplainingAwayResidual structure
Measure . |
class |
BTMutualInformation
Structure learning Measure that computes a maximum spanning tree
based on mutual information and uses the resulting tree structure as
structure of a Bayesian tree (special case of a Bayesian network) in a
BayesianNetworkDiffSM
. |
static class |
BTMutualInformation.BTMutualInformationParameterSet
Class for the parameters of a BTMutualInformation structure
Measure . |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures that implement Storable | |
---|---|
class |
PMMExplainingAwayResidual
Class for the network structure of a BayesianNetworkDiffSM
that is a permuted Markov model based on the explaining away residual. |
static class |
PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet
Class for the parameters of a PMMExplainingAwayResidual structure
Measure . |
class |
PMMMutualInformation
Class for the network structure of a BayesianNetworkDiffSM
that is a permuted Markov model based on mutual information. |
static class |
PMMMutualInformation.PMMMutualInformationParameterSet
Class for the parameters of a PMMMutualInformation structure
Measure . |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement Storable | |
---|---|
class |
HomogeneousDiffSM
This is the main class for all homogeneous DifferentiableSequenceScore s. |
class |
HomogeneousMM0DiffSM
This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length. |
class |
HomogeneousMMDiffSM
This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length. |
class |
UniformHomogeneousDiffSM
This scoring function does nothing. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that implement Storable | |
---|---|
class |
AbstractMixtureDiffSM
This main abstract class for any mixture scoring function (e.g. |
class |
MixtureDiffSM
This class implements a real mixture model. |
class |
StrandDiffSM
This class enables the user to search on both strand. |
class |
VariableLengthMixtureDiffSM
This class implements a mixture of VariableLengthDiffSM by extending MixtureDiffSM and implementing the methods of VariableLengthDiffSM . |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement Storable | |
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class |
DurationDiffSM
This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models. |
class |
ExtendedZOOPSDiffSM
This class handles mixtures with at least one hidden motif. |
class |
MixtureDurationDiffSM
This class implements a mixture of DurationDiffSM s. |
class |
PositionDiffSM
This class implements a position scoring function that enables the user to get a score without using a Sequence object. |
class |
SkewNormalLikeDurationDiffSM
This class implements a skew normal like discrete truncated distribution. |
class |
UniformDurationDiffSM
This scoring function implements a uniform distribution for positions. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable |
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Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels.trainable | |
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interface |
TrainableStatisticalModel
This interface defines all methods for a probabilistic model. |
Classes in de.jstacs.sequenceScores.statisticalModels.trainable that implement Storable | |
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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 DataSet s of Sequence s with
variable length if each individual length is at least SequenceScore.getLength() . |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that implement Storable | |
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class |
Constraint
The main class for all constraints on models. |
class |
DGTrainSMParameterSet<T extends DiscreteGraphicalTrainSM>
The super ParameterSet for any parameter set of
a DiscreteGraphicalTrainSM . |
class |
DiscreteGraphicalTrainSM
This is the main class for all discrete graphical models (DGM). |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that implement Storable | |
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class |
HomogeneousMM
This class implements homogeneous Markov models (hMM) of arbitrary order. |
class |
HomogeneousTrainSM
This class implements homogeneous models of arbitrary order. |
protected class |
HomogeneousTrainSM.HomCondProb
This class handles the (conditional) probabilities of a homogeneous model in a fast way. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters that implement Storable | |
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class |
HomMMParameterSet
This class implements a container for all parameters of a homogeneous Markov model. |
class |
HomogeneousTrainSMParameterSet
This class implements a container for all parameters of any homogeneous model. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that implement Storable | |
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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 |
InhCondProb
This class handles (conditional) probabilities of sequences for inhomogeneous models. |
class |
InhConstraint
This class is the superclass for all inhomogeneous constraints. |
class |
InhomogeneousDGTrainSM
This class is the main class for all inhomogeneous discrete graphical models ( InhomogeneousDGTrainSM ). |
class |
MEMConstraint
This constraint can be used for any maximum entropy model (MEM) application. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters that implement Storable | |
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class |
BayesianNetworkTrainSMParameterSet
The ParameterSet for the class
BayesianNetworkTrainSM . |
class |
FSDAGModelForGibbsSamplingParameterSet
The class for the parameters of a FSDAGModelForGibbsSampling . |
class |
FSDAGTrainSMParameterSet
The class for the parameters of a FSDAGTrainSM (fixed
structure directed acyclic graphical
model). |
class |
IDGTrainSMParameterSet
This is the abstract container of parameters that is a root container for all inhomogeneous discrete graphical model parameter containers. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared that implement Storable | |
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class |
SharedStructureClassifier
This class enables you to learn the structure on all classes of the classifier together. |
class |
SharedStructureMixture
This class handles a mixture of models with the same structure that is learned via EM. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that implement Storable | |
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class |
AbstractHMM
This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement Storable | |
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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 Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions |
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Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions | |
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interface |
DifferentiableEmission
This interface declares all methods needed in an emission during a numerical optimization of HMM. |
interface |
Emission
This interface declares all method for an emission of a state. |
interface |
SamplingEmission
|
Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions that implement Storable | |
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class |
MixtureEmission
This class implements a mixture of Emission s. |
class |
SilentEmission
This class implements a silent emission which is used to create silent states. |
class |
UniformEmission
This class implements a simple uniform emission. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous that implement Storable | |
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class |
GaussianEmission
Emission for continuous values following a Gaussian distribution. |
class |
PluginGaussianEmission
Basic Gaussian emission distribution without random initialization of parameters. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete that implement Storable | |
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class |
AbstractConditionalDiscreteEmission
The abstract super class of discrete emissions. |
class |
DiscreteEmission
This class implements a simple discrete emission without any condition. |
class |
PhyloDiscreteEmission
Phylogenetic discrete emission This class uses a phylogenetic tree to describe multidimensional data It implements Felsensteins model for nucleotide substitution (F81) |
class |
ReferenceSequenceDiscreteEmission
This class implements a discrete emission that depends on some ReferenceSequenceAnnotation
at a certain reference position. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training that implement Storable | |
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class |
BaumWelchParameterSet
This class implements an HMMTrainingParameterSet for the Baum-Welch training of an AbstractHMM . |
class |
HMMTrainingParameterSet
This class implements an abstract ParameterSet that is used for the training of an AbstractHMM . |
class |
MaxHMMTrainingParameterSet
This class is the super class for any HMMTrainingParameterSet that
is used for a maximizing training algorithm of a hidden Markov model. |
class |
MultiThreadedTrainingParameterSet
This class is the super class for any MaxHMMTrainingParameterSet that
is used for a multi-threaded maximizing training algorithm of a hidden Markov model. |
class |
NumericalHMMTrainingParameterSet
This class implements an ParameterSet for numerical training of an AbstractHMM . |
class |
SamplingHMMTrainingParameterSet
This class contains the parameters for training training an AbstractHMM using a sampling strategy. |
class |
ViterbiParameterSet
This class implements an ParameterSet for the viterbi training of an AbstractHMM . |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions |
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Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions | |
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interface |
DifferentiableTransition
This class declares methods that allow for optimizing the parameters numerically using the Optimizer . |
interface |
SamplingTransition
This interface declares all method used during a sampling. |
interface |
TrainableAndDifferentiableTransition
This interface unifies the interfaces TrainableTransition and DifferentiableTransition . |
interface |
TrainableTransition
This class declares methods that allow for estimating the parameters from a sufficient statistic, as for instance done in the (modified) Baum-Welch algorithm, viterbi training, or Gibbs sampling. |
interface |
Transition
This interface declares the methods of the transition used in a hidden Markov model. |
interface |
TransitionWithSufficientStatistic
This interface defines method for reseting and filling an internal sufficient statistic. |
Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions that implement Storable | |
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class |
BasicHigherOrderTransition
This class implements the basic transition that allows to be trained using the viterbi or the Baum-Welch algorithm. |
static class |
BasicHigherOrderTransition.AbstractTransitionElement
This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states. |
class |
HigherOrderTransition
This class can be used in any AbstractHMM allowing to use gradient based or sampling training algorithm. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements that implement Storable | |
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class |
BasicPluginTransitionElement
Basic transition element without random initialization of parameters. |
class |
BasicTransitionElement
This class implements the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set of previously visited states. |
class |
DistanceBasedScaledTransitionElement
Distance-based scaled transition element for an HMM with distance-scaled transition matrices (DSHMM). |
class |
ReferenceBasedTransitionElement
This class implements transition elements that utilize a reference sequence to determine the transition probability. |
class |
ScaledTransitionElement
Scaled transition element for an HMM with scaled transition matrices (SHMM). |
class |
TransitionElement
This class implements an transition element implements method used for training via sampling or gradient based optimization approach. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that implement Storable | |
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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. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif that implement Storable | |
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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. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior that implement Storable | |
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class |
GaussianLikePositionPrior
This class implements a gaussian like discrete truncated prior. |
class |
PositionPrior
This is the main class for any position prior that can be used in a motif discovery. |
class |
UniformPositionPrior
This prior implements a uniform distribution for the start position. |
Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.phylo |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.phylo that implement Storable | |
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class |
PhyloNode
This class implements a node in a PhyloTree
A PhyloNode contains some basic informations of itself and the incoming edge
Furthermore it contains a list of PhyloNode s that represent the children nodes |
class |
PhyloTree
This class implements a simple (phylogenetic) tree. |
Uses of Storable in de.jstacs.utils |
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Classes in de.jstacs.utils that implement Storable | |
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class |
DoubleList
A simple list of primitive type double . |
Uses of Storable in de.jstacs.utils.galaxy |
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Classes in de.jstacs.utils.galaxy that implement Storable | |
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static class |
GalaxyAdaptor.FileResult
Result for files that are results of some computation. |
static class |
GalaxyAdaptor.LinkedImageResult
Class for an ImageResult that is linked to a file that can be downloaded. |
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