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if for test and train data set the sequences of
the non-reference classes have the same length as the corresponding
sequence of the reference class.
SamplingScoreBasedClassifier.burnInTest
and then samples the number of
stationary steps as set in SamplingScoreBasedClassifier.params
.
ClassifierAssessmentAssessParameterSet
that
must be used to call the method assess( ...- Sampled_RepeatedHoldOutAssessParameterSet() -
Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
- Constructs a new
Sampled_RepeatedHoldOutAssessParameterSet
with
empty parameter values.
- Sampled_RepeatedHoldOutAssessParameterSet(StringBuffer) -
Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
- The standard constructor for the interface
Storable
.
- Sampled_RepeatedHoldOutAssessParameterSet(DataSet.PartitionMethod, int, boolean, int, int, double, boolean) -
Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
- Constructs a new
Sampled_RepeatedHoldOutAssessParameterSet
with
given parameter values.
- Sampled_RepeatedHoldOutExperiment - Class in de.jstacs.classifiers.assessment
- This class is a special
ClassifierAssessment
that partitions the data
of a user-specified reference class (typically the smallest class) and
data sets non-overlapping for all other classes, so that one gets the same
number of sequences (and the same lengths of the sequences) in each train and
test data set. - Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], TrainableStatisticalModel[][], boolean, boolean) -
Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
- 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(AbstractClassifier...) -
Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
- Creates a new
Sampled_RepeatedHoldOutExperiment
from a set of
AbstractClassifier
s.
- Sampled_RepeatedHoldOutExperiment(boolean, TrainableStatisticalModel[]...) -
Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
- Creates a new
Sampled_RepeatedHoldOutExperiment
from a set of
TrainableStatisticalModel
s.
- Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], boolean, TrainableStatisticalModel[]...) -
Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
- This constructor allows to assess a collection of given
AbstractClassifier
s and those constructed using the given
TrainableStatisticalModel
s by a
Sampled_RepeatedHoldOutExperiment
.
- sampleNSteps(DiffSSBasedOptimizableFunction, SamplingScoreBasedClassifier.DiffSMSamplingComponent, BurnInTest, int, SamplingScoreBasedClassifier.SamplingScheme) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- Samples a predefined number of steps appended to the current sampling
- samplePath(IntList, int, int, Sequence) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
- This method samples a valid path for the given sequence
seq
using the internal parameters.
- SamplingComponent - Interface in de.jstacs.sampling
- This interface defines methods that are used during a sampling.
- SamplingDifferentiableStatisticalModel - Interface in de.jstacs.sequenceScores.statisticalModels.differentiable
- Interface for
DifferentiableStatisticalModel
s that can be used for
Metropolis-Hastings sampling in a SamplingScoreBasedClassifier
. - SamplingEmission - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
-
- SamplingFromStatistic - Interface in de.jstacs.sampling
- This is the interface for sampling based on a sufficient statistic.
- SamplingGenDisMixClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
- A classifier that samples its parameters from a
LogGenDisMixFunction
using the
Metropolis-Hastings algorithm. - SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet, BurnInTest, double[], LogPrior, double[], SamplingDifferentiableStatisticalModel...) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
- Creates a new
SamplingGenDisMixClassifier
using the external parameters
params
, a burn-in test, a set of sampling variances for the different classes,
a prior on the parameters, weights beta
for the three components of the
LogGenDisMixFunction
, i.e., likelihood, conditional likelihood, and prior,
and scoring functions that model the distribution for each of the classes.
- SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet, BurnInTest, double[], LogPrior, LearningPrinciple, SamplingDifferentiableStatisticalModel...) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
- Creates a new
SamplingGenDisMixClassifier
using the external parameters
params
, a burn-in test, a set of sampling variances for the different classes,
a prior on the parameters, a learning principle,
and scoring functions that model the distribution for each of the classes.
- SamplingGenDisMixClassifier(StringBuffer) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
- Creates a new
SamplingGenDisMixClassifier
from its XML-representation
- SamplingGenDisMixClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
ParameterSet
to instantiate a SamplingGenDisMixClassifier
.- SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String, int) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
- Create a new
SamplingGenDisMixClassifierParameterSet
.
- SamplingGenDisMixClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String, int) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
- Create a new
SamplingGenDisMixClassifierParameterSet
.
- SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, int, int, String, int) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
- Create a new
SamplingGenDisMixClassifierParameterSet
with a grouped sampling scheme, sampling all parameters
(and not only the free ones), and adaption of the variance.
- SamplingHigherOrderHMM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
-
- SamplingHigherOrderHMM(SamplingHMMTrainingParameterSet, String[], int[], boolean[], SamplingEmission[], TransitionElement...) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
- This is the main constructor.
- SamplingHigherOrderHMM(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM
- The standard constructor for the interface
Storable
.
- SamplingHigherOrderHMM.ViterbiComputation - Enum in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
- Emumeration of all possible Viterbi-Path methods
- SamplingHMMTrainingParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training
- This class contains the parameters for training training an
AbstractHMM
using a sampling strategy. - SamplingHMMTrainingParameterSet() -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
- This is the empty constructor that can be used to fill the parameters after creation.
- SamplingHMMTrainingParameterSet(int, int, int, AbstractBurnInTestParameterSet) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
- This is the main constructor creating an already filled parameter set for training an
AbstractHMM
using a sampling strategy.
- SamplingHMMTrainingParameterSet(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.SamplingHMMTrainingParameterSet
- The standard constructor for the interface
Storable
.
- samplingIndex -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
- The index of the current sampling.
- samplingIndex -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
- The index of the current sampling.
- samplingIndex -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
- The index of the current sampling.
- samplingIndex -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- The current index of the sampling.
- SamplingPhyloHMM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
- This class implements an (higher order) HMM that contains multi-dimensional emissions described
by a phylogenetic tree.
- SamplingPhyloHMM(SamplingHMMTrainingParameterSet, String[], int[], boolean[], PhyloDiscreteEmission[], TransitionElement...) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingPhyloHMM
- This is the main constructor for a hidden markov model with phylogenetic emission(s)
This model can be trained using a metropolis hastings algorithm
- SamplingPhyloHMM(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingPhyloHMM
- The standard constructor for the interface
Storable
.
- SamplingScoreBasedClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
- A classifier that samples the parameters of
SamplingDifferentiableStatisticalModel
s by the Metropolis-Hastings algorithm. - SamplingScoreBasedClassifier(StringBuffer) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- This is the constructor for
Storable
.
- SamplingScoreBasedClassifier(SamplingScoreBasedClassifierParameterSet, BurnInTest, double[], SamplingDifferentiableStatisticalModel...) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- Creates a new
SamplingScoreBasedClassifier
using the parameters in params
,
a specified BurnInTest
(or null
for no burn-in test), a set of sampling variances,
which may be different for each of the classes (in analogy to equivalent sample size for the Dirichlet distribution),
and set set of SamplingDifferentiableStatisticalModel
s for each of the classes.
- SamplingScoreBasedClassifier.DiffSMSamplingComponent - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
- The
SamplingComponent
that handles storing and loading sampled parameters values
to and from files. - SamplingScoreBasedClassifier.DiffSMSamplingComponent(String) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
- Creates a new
SamplingScoreBasedClassifier.DiffSMSamplingComponent
that uses temporary files
with name prefix outfilePrefix
to store sampled parameters.
- SamplingScoreBasedClassifier.SamplingScheme - Enum in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
- Sampling scheme for sampling the parameters of the scoring functions.
- SamplingScoreBasedClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
ParameterSet
to instantiate a SamplingScoreBasedClassifier
.- SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
- Create a new
SamplingScoreBasedClassifierParameterSet
.
- SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, int, int, String) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
- Create a new
SamplingScoreBasedClassifierParameterSet
with a grouped sampling scheme, sampling all parameters
(and not only the free ones), and adaption of the variance.
- SamplingState - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
-
- samplingStopped() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
-
- samplingStopped() -
Method in interface de.jstacs.sampling.SamplingComponent
- This method is the opposite of the method
SamplingComponent.extendSampling(int, boolean)
.
- samplingStopped() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
-
- samplingStopped() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- samplingStopped() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- samplingStopped() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
-
- samplingStopped() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
-
- samplingStopped() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method is the opposite of the method
AbstractMixtureTrainSM.initModelForSampling(int)
.
- SamplingTransition - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions
- This interface declares all method used during a sampling.
- satisfiesSpecificConstraint(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint
- This method returns the index of the specific constraint that is
fulfilled by the
Sequence
seq
beginning at position
start
.
- satisfiesSpecificConstraint(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb
-
- satisfiesSpecificConstraint(Sequence, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint
-
- satisfiesSpecificConstraint(SequenceIterator) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
- Returns the index of the constraint that is satisfied by
sequence
.
- save(File) -
Method in class de.jstacs.data.DataSet
- This method writes the
DataSet
to a file f
.
- save(OutputStream, char, SequenceAnnotationParser) -
Method in class de.jstacs.data.DataSet
- This method allows to write all
Sequence
s including their
SequenceAnnotation
s into a OutputStream
.
- saveParameters() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent
- Saves the parameter values of all parameter files to
a
StringBuffer
representing these as XML.
- ScaledTransitionElement - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements
- Scaled transition element for an HMM with scaled transition matrices (SHMM).
- ScaledTransitionElement(int[], int[], double[], double, double[], String) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
- Creates an object representing the transition probabilities of a Hidden Markov TrainableStatisticalModel with scaled transition matrices (SHMM) for the given context.
- ScaledTransitionElement(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
- The standard constructor for the interface
Storable
.
- scalingFactor -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
- The maximal scaling factor.
- scalingFactor -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement
- The scaling factors of the individual transition classes.
- score -
Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
- These
DifferentiableSequenceScore
s are used during the parallel computation.
- score -
Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
- The internally used scoring functions.
- score -
Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- The internally used
DifferentiableSequenceScore
s.
- score -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
- The type of the score that is evaluated
- ScoreClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased
- This abstract class implements the main functionality of a
DifferentiableSequenceScore
based classifier. - ScoreClassifier(ScoreClassifierParameterSet, double, DifferentiableSequenceScore...) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
- Creates a new
ScoreClassifier
from a given
ScoreClassifierParameterSet
and DifferentiableSequenceScore
s .
- ScoreClassifier(StringBuffer) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
- The standard constructor for the interface
Storable
.
- ScoreClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased
- A set of
Parameter
s for any
ScoreClassifier
. - ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, boolean, AlphabetContainer.AlphabetContainerType, boolean) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
- Creates a new
ScoreClassifierParameterSet
with empty parameter
values.
- ScoreClassifierParameterSet(StringBuffer) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
- The standard constructor for the interface
Storable
.
- ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, AlphabetContainer, int, byte, double, double, double, boolean, OptimizableFunction.KindOfParameter) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
- The constructor for a simple, instantiated parameter set.
- ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, AlphabetContainer, int, byte, AbstractTerminationCondition, double, double, boolean, OptimizableFunction.KindOfParameter) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
- The constructor for a simple, instantiated parameter set.
- scoringFunctions -
Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
SamplingDifferentiableStatisticalModel
s
- sd(int, int) -
Method in class de.jstacs.utils.DoubleList
- This method computes the standard deviation of a part of the list.
- sd(int, int, double[]) -
Static method in class de.jstacs.utils.ToolBox
- This method returns the standard deviation of the elements of an
array
between start
and end
.
- SelectionParameter - Class in de.jstacs.parameters
- Class for a collection parameter, i.e.
- SelectionParameter(DataType, String[], Object[], String, String, boolean) -
Constructor for class de.jstacs.parameters.SelectionParameter
- Constructor for a
SelectionParameter
.
- SelectionParameter(DataType, String[], Object[], String[], String, String, boolean) -
Constructor for class de.jstacs.parameters.SelectionParameter
- Constructor for a
SelectionParameter
.
- SelectionParameter(String, String, boolean, ParameterSet...) -
Constructor for class de.jstacs.parameters.SelectionParameter
- Constructor for a
SelectionParameter
from an array of
ParameterSet
s.
- SelectionParameter(String, String, boolean, Class<? extends ParameterSet>...) -
Constructor for class de.jstacs.parameters.SelectionParameter
- Constructor for a
SelectionParameter
from an array of
Class
es of ParameterSet
s.
- SelectionParameter(StringBuffer) -
Constructor for class de.jstacs.parameters.SelectionParameter
- The standard constructor for the interface
Storable
.
- SensitivityForFixedSpecificity - Class in de.jstacs.classifiers.performanceMeasures
- This class implements the sensitivity for a fixed specificity.
- SensitivityForFixedSpecificity() -
Constructor for class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
- Constructs a new instance of the performance measure
SensitivityForFixedSpecificity
with empty parameter values.
- SensitivityForFixedSpecificity(double) -
Constructor for class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
- Constructs a new instance of the performance measure
SensitivityForFixedSpecificity
with given specificity
.
- SensitivityForFixedSpecificity(StringBuffer) -
Constructor for class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
- The standard constructor for the interface
Storable
.
- SeparateGaussianLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
- Class for a
LogPrior
that defines a Gaussian prior on the parameters
of a set of DifferentiableStatisticalModel
s
and a set of class parameters. - SeparateGaussianLogPrior(double[], double[], double[]) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateGaussianLogPrior
- Creates a new
SeparateGaussianLogPrior
from a set of base
variances vars
, a set of class variances
classVars
and a set of class means classMus
.
- SeparateGaussianLogPrior(StringBuffer) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateGaussianLogPrior
- The standard constructor for the interface
Storable
.
- SeparateLaplaceLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
- Class for a
LogPrior
that defines a Laplace prior on the parameters
of a set of DifferentiableStatisticalModel
s
and a set of class parameters. - SeparateLaplaceLogPrior(double[], double[], double[]) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLaplaceLogPrior
- Creates a new
SeparateLaplaceLogPrior
from a set of base
variances vars
, a set of class variances
classVars
and a set of class means classMus
.
- SeparateLaplaceLogPrior(StringBuffer) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLaplaceLogPrior
- The standard constructor for the interface
Storable
.
- SeparateLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
- Abstract class for priors that penalize each parameter value independently
and have some variances (and possible means) as hyperparameters.
- SeparateLogPrior(double[], double[], double[]) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
- Creates a new
SeparateLogPrior
using the class-specific base
variances vars
, the variances classVars
and the
means classMus
for the class parameters.
- SeparateLogPrior(StringBuffer) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
- The standard constructor for the interface
Storable
.
- SeqLogoPlotter - Class in de.jstacs.utils
- Class with static methods for plotting sequence logos of DNA motifs, i.e., position weight matrices defined over a
DNAAlphabet
. - SeqLogoPlotter() -
Constructor for class de.jstacs.utils.SeqLogoPlotter
-
- Sequence<T> - Class in de.jstacs.data.sequences
- This is the main class for all sequences.
- Sequence(AlphabetContainer, SequenceAnnotation[]) -
Constructor for class de.jstacs.data.sequences.Sequence
- Creates a new
Sequence
with the given AlphabetContainer
and the given annotation, but without the content.
- Sequence.CompositeSequence<T> - Class in de.jstacs.data.sequences
- The class handles composite
Sequence
s. - Sequence.CompositeSequence(Sequence, int[], int[]) -
Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
- This is a very efficient way to create a
Sequence.CompositeSequence
for Sequence
s with a simple AlphabetContainer
.
- Sequence.CompositeSequence(AlphabetContainer, Sequence<T>, int[], int[]) -
Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
- This constructor should be used if one wants to create a
DataSet
of Sequence.CompositeSequence
s.
- Sequence.RecursiveSequence<T> - Class in de.jstacs.data.sequences
- This is the main class for subsequences, composite sequences, ...
- Sequence.RecursiveSequence(AlphabetContainer, SequenceAnnotation[], Sequence<T>) -
Constructor for class de.jstacs.data.sequences.Sequence.RecursiveSequence
- Creates a new
Sequence.RecursiveSequence
on the Sequence
seq
with the AlphabetContainer
alphabet
and the annotation annotation
.
- Sequence.RecursiveSequence(AlphabetContainer, Sequence<T>) -
Constructor for class de.jstacs.data.sequences.Sequence.RecursiveSequence
- Creates a new
Sequence.RecursiveSequence
on the Sequence
seq
with the AlphabetContainer
alphabet
using the annotation of the given Sequence
.
- Sequence.SubSequence<T> - Class in de.jstacs.data.sequences
- This class handles subsequences.
- Sequence.SubSequence(AlphabetContainer, Sequence, int, int) -
Constructor for class de.jstacs.data.sequences.Sequence.SubSequence
- This constructor should be used if one wants to create a
DataSet
of Sequence.SubSequence
s of defined length.
- Sequence.SubSequence(Sequence, int, int) -
Constructor for class de.jstacs.data.sequences.Sequence.SubSequence
- This is a very efficient way to create a
Sequence.SubSequence
of
defined length for Sequence
s with a simple
AlphabetContainer
.
- SequenceAnnotation - Class in de.jstacs.data.sequences.annotation
- Class for a general annotation of a
Sequence
. - SequenceAnnotation(String, String, Result) -
Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
- Creates a new
SequenceAnnotation
of type type
with
identifier identifier
and additional annotation (that does
not fit the SequenceAnnotation
definitions) given as a
Result
result
.
- SequenceAnnotation(String, String, Result[]...) -
Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
- Creates a new
SequenceAnnotation
of type type
with
identifier identifier
and additional annotation (that does
not fit the SequenceAnnotation
definitions) given as an array of
Result
s results
.
- SequenceAnnotation(String, String, SequenceAnnotation[], Result...) -
Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
- Creates a new
SequenceAnnotation
of type type
with
identifier identifier
and additional annotation (that does
not fit the SequenceAnnotation
definitions) given as an array of
Result
s additionalAnnotation
.
- SequenceAnnotation(String, String, Collection<? extends Result>) -
Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
- Creates a new
SequenceAnnotation
of type type
with
identifier identifier
and additional annotation (that does
not fit the SequenceAnnotation
definitions) given as a
Collection
of Result
s results
.
- SequenceAnnotation(StringBuffer) -
Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
- The standard constructor for the interface
Storable
.
- SequenceAnnotationParser - Interface in de.jstacs.data.sequences.annotation
- This interface declares the methods which are used by
AbstractStringExtractor
to annotate a String
which will be parsed to a Sequence
. - SequenceEnumeration - Class in de.jstacs.data
- This class implements a
RecyclableSequenceEnumerator
on user-specified Sequence
s. - SequenceEnumeration(Sequence...) -
Constructor for class de.jstacs.data.SequenceEnumeration
- This constructor creates an instance based on the user-specified
Sequence
s sequences
.
- SequenceEnumeration(Collection<Sequence>) -
Constructor for class de.jstacs.data.SequenceEnumeration
- This constructor creates an instance based on the user-specified
Collection
of Sequence
s sequences
.
- SequenceIterator - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
- This class is used to iterate over a discrete sequence.
- SequenceIterator(int) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
- Creates a new
SequenceIterator
with maximal length
.
- sequenceIteratorToDataSet(SequenceIterator, FeatureFilter) -
Static method in class de.jstacs.data.bioJava.BioJavaAdapter
- This method creates a new
DataSet
from a SequenceIterator
.
- SequenceScore - Interface in de.jstacs.sequenceScores
- This interface defines a scoring function that assigns a score to each input sequence.
- SequenceScoringParameterSet<T extends InstantiableFromParameterSet> - Class in de.jstacs.parameters
- Abstract class for a
ParameterSet
containing all parameters necessary
to construct an Object
that implements
InstantiableFromParameterSet
. - SequenceScoringParameterSet(Class<T>, AlphabetContainer.AlphabetContainerType, boolean) -
Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
- Constructs an
InstanceParameterSet
having empty parameter values.
- SequenceScoringParameterSet(Class<T>, AlphabetContainer.AlphabetContainerType, boolean, boolean) -
Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
- Constructs a
SequenceScoringParameterSet
having empty parameter
values.
- SequenceScoringParameterSet(StringBuffer) -
Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
- The standard constructor for the interface
Storable
.
- SequenceScoringParameterSet(Class<T>, AlphabetContainer, int, boolean) -
Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
- Constructs a
SequenceScoringParameterSet
from an
AlphabetContainer
and the length of a sequence.
- SequenceScoringParameterSet(Class<T>, AlphabetContainer) -
Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
- Constructs a
SequenceScoringParameterSet
for an object that can
handle sequences of variable length and with the
AlphabetContainer
alphabet
.
- seqWeights -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- The weights of the (sub-)sequence used to train the components (internal models).
- set() -
Method in class de.jstacs.algorithms.optimization.termination.AbsoluteValueCondition
- Deprecated.
- set() -
Method in class de.jstacs.algorithms.optimization.termination.AbstractTerminationCondition
- This method sets internal member variables from
AbstractTerminationCondition.parameter
.
- set() -
Method in class de.jstacs.algorithms.optimization.termination.CombinedCondition
-
- set() -
Method in class de.jstacs.algorithms.optimization.termination.IterationCondition
-
- set() -
Method in class de.jstacs.algorithms.optimization.termination.MultipleIterationsCondition
-
- set() -
Method in class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
-
- set() -
Method in class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
-
- set() -
Method in class de.jstacs.algorithms.optimization.termination.SmallStepCondition
-
- set() -
Method in class de.jstacs.algorithms.optimization.termination.TimeCondition
-
- set(int, T) -
Method in class de.jstacs.AnnotatedEntityList
- Replaces the
AnnotatedEntity
at index idx
with
the AnnotatedEntity
entity/code>
- set(boolean, DifferentiableSequenceScore...) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
-
- set(boolean, DifferentiableSequenceScore...) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior
- Resets all pre-computed values to their initial values using the
DifferentiableSequenceScore
s funs
.
- set(boolean, DifferentiableSequenceScore...) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
-
- set(int, Parameter) -
Method in class de.jstacs.parameters.ParameterSet.ParameterList
-
- set(DGTrainSMParameterSet, boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
- Sets the parameters as internal parameters and does some essential
computations.
- set(DGTrainSMParameterSet, boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
-
- set(DGTrainSMParameterSet, boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM
-
- set(DGTrainSMParameterSet, boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM
-
- set(DGTrainSMParameterSet, boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM
-
- set(DGTrainSMParameterSet, boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSMEManager
-
- set(DGTrainSMParameterSet, boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
-
- setAlpha(double) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Sets the parameter of the Dirichlet distribution which is used when you
invoke
train
to init the gammas.
- setBounds(int[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
- This method sets the bounds for each position.
- setClassWeights(boolean, double...) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
- Sets new class weights.
- setClassWeights(boolean, double[], int) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
- Sets new class weights.
- setCurrentLength(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.CombinationIterator
- This method sets the current used number of selected elements.
- setCurrentSamplingIndex(int) -
Method in class de.jstacs.sampling.AbstractBurnInTest
-
- setCurrentSamplingIndex(int) -
Method in interface de.jstacs.sampling.BurnInTest
- This method sets the value of the current sampling.
- setCurrentSamplingIndex(int) -
Method in class de.jstacs.sampling.SimpleBurnInTest
- Deprecated.
- setDataAndWeights(DataSet[], double[][]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
-
- setDataAndWeights(DataSet[], double[][]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
-
- setDataAndWeights(DataSet[], double[][]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction
-
- setDataAndWeights(DataSet[], double[][]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction
- This method sets the data set and the sequence weights to be used.
- setDefault(Object) -
Method in class de.jstacs.parameters.AbstractSelectionParameter
- Sets the default value of this
AbstractSelectionParameter
to
defaultValue
.
- setDefault(Object) -
Method in class de.jstacs.parameters.EnumParameter
-
- setDefault(Object) -
Method in class de.jstacs.parameters.FileParameter
-
- setDefault(Object) -
Method in class de.jstacs.parameters.MultiSelectionParameter
-
- setDefault(Object) -
Method in class de.jstacs.parameters.Parameter
- Sets the default value of the
Parameter
to
defaultValue
.
- setDefault(Object) -
Method in class de.jstacs.parameters.ParameterSetContainer
-
- setDefault(Object) -
Method in class de.jstacs.parameters.RangeParameter
-
- setDefault(Object) -
Method in class de.jstacs.parameters.SelectionParameter
-
- setDefault(Object) -
Method in class de.jstacs.parameters.SimpleParameter
-
- setDefaultSelected(int[]) -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Sets the default selection of this
MultiSelectionParameter
to
defaultSelection
.
- setDeleteOnExit(boolean) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- If set to
true
(which is the default), the temporary files for storing sampled parameter
values are deleted on exit of the program.
- setEss(double) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DGTrainSMParameterSet
- This method can be used to set the ess (equivalent sample
size) of this parameter set.
- setESS(double) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
- This method sets the ess (equivalent sample size) of
the
StructureLearner
.
- setExpLambda(int, double) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
- Sets the exponential value of .
(Additionally it sets the value of to the logarithmic value of val
:
.)
- setExtension(String) -
Method in class de.jstacs.parameters.FileParameter.FileRepresentation
- Sets the extension of this
FileParameter
.
- setExtension(String) -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
- Sets the filename extension
- setFilename(String) -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
- Sets the file
- setForwardProb(double) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
- This method can be used to set the forward strand probability.
- setFrameParameterOptimization(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
- This method enables the user to choose whether the frame parameters should be optimized or not.
- setFreqs(String[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb
- This method is used to restore the values of a Gibbs Sampling run.
- setFurtherInformation(StringBuffer) -
Method in class de.jstacs.sampling.AbstractBurnInTest
- This method sets further information for the
AbstractBurnInTest
.
- setFurtherInformation(StringBuffer) -
Method in class de.jstacs.sampling.VarianceRatioBurnInTest
-
- setFurtherModelInfos(StringBuffer) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
- This method replaces the internal model information with those from a
StringBuffer
.
- setFurtherModelInfos(StringBuffer) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM
-
- setFurtherModelInfos(StringBuffer) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM
-
- setFurtherModelInfos(StringBuffer) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling
-
- setFurtherModelInfos(StringBuffer) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager
-
- setHelp(String) -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor
- Sets the help, i.e., a more detailed description of the program
to
help
.
- setHelp(File) -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor
- Sets the help, i.e., a more detailed description of the program
to the contents of
helpfile
.
- setHiddenParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
- This method sets the hidden parameters of the model.
- setId(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
- This method set the ID of the current PhyloNode
The ID should be unique in the PhyloTree
- setIndexOfDescendantTransitionElement(int, int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- This method sets the index of the descendant transition element for the child with index
index
.
- setInitParameters(double[]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- Sets the initial parameters of the sampling to
parameters
.
- setLambda(int, double) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
- Sets the value of .
(Additionally it sets the exponential value of
to the exponential value of val
:
.
- setLastDistance(double) -
Method in class de.jstacs.algorithms.optimization.ConstantStartDistance
-
- setLastDistance(double) -
Method in class de.jstacs.algorithms.optimization.LimitedMedianStartDistance
-
- setLastDistance(double) -
Method in interface de.jstacs.algorithms.optimization.StartDistanceForecaster
- Sets the last used distance.
- setLinear(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
- If set to true, the probabilities are mapped to colors by directly, otherwise
a logistic mapping is used to emphasize deviations from the uniform distribution.
- setMax(int) -
Method in class de.jstacs.utils.DefaultProgressUpdater
-
- setMax(int) -
Method in class de.jstacs.utils.GUIProgressUpdater
-
- setMax(int) -
Method in class de.jstacs.utils.NullProgressUpdater
-
- setMax(int) -
Method in interface de.jstacs.utils.ProgressUpdater
- Sets the maximal value that will be set by
ProgressUpdater.setValue(int)
, so a
value of max indicates the end of the supervised method call.
- setMeasure(T) -
Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasureParameterSet
- Sets the given measure as content of the internally last
ParameterSetContainer
.
- setModelType(String) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.BayesianNetworkTrainSMParameterSet
- This method allows a simple change of the model type.
- setMotifLength(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior
-
- setMotifLength(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
- Sets the length of the current motif.
- setName(String) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
- This method set a name for the current instance
- setNumberOfStarts(int) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
- Sets the number of starts to
i
- setNumberOfThreads(int) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
- This method allows to set the number of threads used while optimization.
- setNumberOfThreads(int) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifierParameterSet
- This method set the number of threads used during optimization.
- setOffset() -
Method in class de.jstacs.utils.NullProgressUpdater
- After
NullProgressUpdater.setOffset()
is called the current value
will be added to every value set by
NullProgressUpdater.setValue(int)
.
- setOutputStream(OutputStream) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
- Sets the
OutputStream
that is used e.g.
- setOutputStream(OutputStream) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.DifferentiableStatisticalModelWrapperTrainSM
- Sets the OutputStream that is used e.g.
- setOutputStream(OutputStream) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
- Sets the
OutputStream
for the model.
- setOutputStream(OutputStream) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- Sets the
OutputStream
that is used e.g.
- setOutputStream(OutputStream) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Sets the
OutputStream
that is used e.g.
- setParameter(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- setParameter(double[], int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
- This method sets the internal parameters using the given global parameter array, the global offset of the HMM and the internal offset.
- setParameter(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- setParameter(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- setParameter(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- setParameterFor(int, int[][], BNDiffSMParameter) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree
- Sets the instance of the
BNDiffSMParameter
for symbol symbol
and
context context
to BNDiffSMParameter
par
.
- setParameterOffset(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- setParameterOffset(int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.DifferentiableEmission
- This method sets the internal parameter offset and returns the new parameter offset for further use.
- setParameterOffset(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- setParameterOffset(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- setParameterOffset(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- setParameterOffset(int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.DifferentiableTransition
- This method sets the internal offset of the parameter index.
- setParameterOffset(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
- This method sets the internal
TransitionElement.offset
used for several methods (cf.
- setParameterOffset(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
-
- setParameterOffset() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
- This method allows to set the parameter offset in each internally used
TransitionElement
.
- setParameterOptimization(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
- This method enables the user to choose whether the parameters should be optimized or not.
- setParameterOptimization(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
- This method allows the user to specify whether the parameters should be
optimized or not.
- setParameters(double[], int) -
Method in interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
- This method sets the internal parameters to the values of
params
between start
and
start + DifferentiableSequenceScore.getNumberOfParameters()
- 1
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.differentiable.logistic.LogisticDiffSS
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.differentiable.MultiDimensionalSequenceWrapperDiffSS
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
-
- setParameters(double, double, double) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
- this method can be used to set the parameters even if the parameters are not allowed to be optimized.
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
-
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM
-
- setParameters(Emission) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission
-
- setParameters(Emission) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.MultivariateGaussianEmission
-
- setParameters(Emission) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
-
- setParameters(Emission) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission
- Set values of parameters of the instance to the value of the parameters of the given instance.
- setParameters(Emission) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
-
- setParameters(Emission) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
-
- setParameters(Emission) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission
-
- setParameters(BasicHigherOrderTransition.AbstractTransitionElement) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- Set values of parameters of the instance to the value of the parameters of the given instance.
- setParameters(Transition) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
-
- setParameters(double[], int) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.DifferentiableTransition
- This method allows to set the parameters of the transition.
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.TransitionElement
- This method sets the internal parameters to the values of
params
beginning at index start
.
- setParameters(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
-
- setParameters(Transition) -
Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition
- Set values of parameters of the instance to the value of the parameters of the given instance.
- setParametersForFunction(int, double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM
- This method allows to set the parameters for specific functions.
- setParametersToValue(MEMConstraint[], double) -
Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools
- This method is a convenience method that sets the same value for all parameter of the constraints
- setParams(double[]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
-
- setParams(int) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
- This method sets the parameters for thread
index
- setParams(int) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
-
- setParams(double[]) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction
- Sets the current values as parameters.
- setParams(double[], int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition
- This method allows to set the new parameters using a specific offset.
- setParamsStarts() -
Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- This method set the value of the array
IndependentProductDiffSS.startIndexOfParams
.
- setParent(ParameterSet) -
Method in class de.jstacs.parameters.Parameter
- Sets the reference of the enclosing
ParameterSet
of this
Parameter
to parent
.
- setParent(ParameterSetContainer) -
Method in class de.jstacs.parameters.ParameterSet
- Sets the enclosing
ParameterSetContainer
of this
ParameterSet
to parent
.
- setParser(SequenceAnnotationParser) -
Method in class de.jstacs.results.DataSetResult
- Sets the
SequenceAnnotationParser
that can be used to
write this DataSetResult
including annotations on the contained Sequence
s
to a file
- setPath(String) -
Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult
- Sets the path of the directory containing the file to
path
- setPlugInParameters(int, boolean, DataSet[], double[][]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
- Computes and sets the plug-in parameters (MAP estimated parameters) from
data
using weights
.
- setPrior(LogPrior) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
- This method set a new prior that should be used for optimization.
- setRangeable(boolean) -
Method in class de.jstacs.parameters.AbstractSelectionParameter
- Sets the value returned by
AbstractSelectionParameter.isRangeable()
to
rangeable
.
- setRangeable(boolean) -
Method in class de.jstacs.parameters.SimpleParameter
- Sets the value returned by
SimpleParameter.isRangeable()
to
rangeable
.
- setRootValue(int, double) -
Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
-
- setRootValue(int, double) -
Method in class de.jstacs.algorithms.graphs.tensor.SubTensor
-
- setRootValue(int, double) -
Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
-
- setRootValue(int, double) -
Method in class de.jstacs.algorithms.graphs.tensor.Tensor
- Sets the value
val
for the root node child
.
- setSeed(long) -
Method in class de.jstacs.utils.random.RandomNumberGenerator
-
- setSelected(String, boolean) -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Sets the selection of the option with key
key
to the value
of selected
.
- setSelected(int, boolean) -
Method in class de.jstacs.parameters.MultiSelectionParameter
- Sets the selection of option with no.
- setShape(String) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
- Sets the graphviz shape of the node that uses this emission to some non-standard value
(standard is "house").
- setShiftCorrection(boolean) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
- Enables or disables the phase shift correction.
- setStartParamsToConditionalStationaryDistributions() -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
- Sets the start parameters of this homogeneous Markov model to
the corresponding stationary distributions of the transition probabilities.
- setStatisticForHyperparameters(int[], double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
-
- setStatisticForHyperparameters(int[], double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM
-
- setStatisticForHyperparameters(int[], double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM
-
- setStatisticForHyperparameters(int[], double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
-
- setStatisticForHyperparameters(int[], double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.VariableLengthMixtureDiffSM
-
- setStatisticForHyperparameters(int[], double[]) -
Method in interface de.jstacs.sequenceScores.statisticalModels.differentiable.VariableLengthDiffSM
- This method sets the hyperparameters for the model parameters by
evaluating the given statistic.
- setStoreAll(boolean) -
Method in class de.jstacs.classifiers.assessment.ClassifierAssessmentAssessParameterSet
- This method allows to set the switch for storing all individual performance measure values of each iteration of the
ClassifierAssessment
.
- setStringToBeParsed(String) -
Method in class de.jstacs.io.SymbolExtractor
- Sets a new
String
to be parsed.
- setTempDir(File) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
- Sets the directory for parameter files set in this
SamplingScoreBasedClassifier
.
- setThreadIndependentParameters() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
- This method allows to set thread independent parameters.
- setThreadIndependentParameters() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
-
- setThresholdClassWeights(boolean, double) -
Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier
- Sets a new threshold for 2-class-classifiers.
Only available if this AbstractScoreBasedClassifier
distinguishes
between 2 classes 0 and 1.
- setTrainData(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method is invoked by the
train
-method and sets for a
given data set the data set that should be used for train
.
- setTrainData(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
-
- setTrainData(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
-
- setTrainData(DataSet) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
-
- setValidator(ParameterValidator) -
Method in class de.jstacs.parameters.SimpleParameter
- Sets a new
ParameterValidator
for this SimpleParameter
.
- setValue(byte, double, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.AsymmetricTensor
-
- setValue(byte, double, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.SubTensor
-
- setValue(byte, double, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
- Sets the value if it is bigger than the current value and keeps the
parents information.
- setValue(byte, double, int, int...) -
Method in class de.jstacs.algorithms.graphs.tensor.Tensor
- Sets the value for the edge
parents[0],...,parents[k-1] -> child
.
- setValue(Object) -
Method in class de.jstacs.parameters.EnumParameter
-
- setValue(Object) -
Method in class de.jstacs.parameters.FileParameter
-
- setValue(Object) -
Method in class de.jstacs.parameters.MultiSelectionParameter
-
- setValue(Object) -
Method in class de.jstacs.parameters.Parameter
- Sets the value of this
Parameter
to value
.
- setValue(Object) -
Method in class de.jstacs.parameters.ParameterSetContainer
-
- setValue(Object) -
Method in class de.jstacs.parameters.RangeParameter
-
- setValue(Object) -
Method in class de.jstacs.parameters.SelectionParameter
- Sets the selected value to the one that is specified by the key
value
.
- setValue(Object) -
Method in class de.jstacs.parameters.SimpleParameter
-
- setValue(double) -
Method in class de.jstacs.sampling.AbstractBurnInTest
-
- setValue(double) -
Method in interface de.jstacs.sampling.BurnInTest
- This method can be used to fill the internal memory with the values that
will be used to determine the length of the burn-in phase.
- setValue(double) -
Method in class de.jstacs.sampling.SimpleBurnInTest
- Deprecated.
- setValue(double) -
Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
- Sets the current value of this parameter.
- setValue(int) -
Method in class de.jstacs.utils.DefaultProgressUpdater
-
- setValue(int) -
Method in class de.jstacs.utils.GUIProgressUpdater
-
- setValue(int) -
Method in class de.jstacs.utils.NullProgressUpdater
-
- setValue(int) -
Method in interface de.jstacs.utils.ProgressUpdater
- Sets the current value the supervised process has reached.
- setValue(int) -
Method in class de.jstacs.utils.TimeLimitedProgressUpdater
-
- setValueFromTag(String, Object) -
Method in class de.jstacs.parameters.ParameterSetTagger
- This method allows to easily set the value of a parameter defined by the tag.
- setValues(String) -
Method in class de.jstacs.parameters.RangeParameter
- Sets a list of values from a
String
containing a space separated
list of values.
- setValues(Object, int, Object, RangeParameter.Scale) -
Method in class de.jstacs.parameters.RangeParameter
- Sets the values of this
RangeParameter
as a range of values,
specified by a start value, a last value, a number of steps between these
values (without the last value) and a scale in that the values between
the first and the last value are chosen.
- setValues(double[]) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools.DualFunction
- This method set the values of the Lagrange multiplicators of the constraints
- setValuesInLogScale(boolean, double, Object, int, Object) -
Method in class de.jstacs.parameters.RangeParameter
- This method enables you to set a list of values in an easy manner.
- setWeight(double) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.phylo.PhyloNode
- This method set the weight (length, rate ...) for the incoming edge
- setWeights(double...) -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier
- This method set the weights for the summand of the function.
- setWeights(double...) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- Sets the weights of each component.
- SGIS -
Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools
- This constant can be used to specify that the model should use the iterative scaling for
training.
- SGIS_P -
Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools
- This constant can be used to specify that the model should use the iterative scaling for
training.
- shallBeRanged() -
Method in class de.jstacs.parameters.RangeParameter
- Returns one of
LIST, RANGE
or NO
depending on
the input used to specify this RangeParameter
.
- SharedStructureClassifier - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
- This class enables you to learn the structure on all classes of the
classifier together.
- SharedStructureClassifier(int, StructureLearner.ModelType, byte, StructureLearner.LearningType, FSDAGTrainSM...) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
- Creates a new
SharedStructureClassifier
from given
FSDAGTrainSM
s.
- SharedStructureClassifier(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier
- The standard constructor for the interface
Storable
.
- SharedStructureMixture - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
- This class handles a mixture of models with the same structure that is
learned via EM.
- SharedStructureMixture(FSDAGTrainSM[], StructureLearner.ModelType, byte, int, double, TerminationCondition) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
- Creates a new
SharedStructureMixture
instance which estimates the
component probabilities/weights.
- SharedStructureMixture(FSDAGTrainSM[], StructureLearner.ModelType, byte, int, double[], double, TerminationCondition) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
- Creates a new
SharedStructureMixture
instance with fixed
component weights.
- SharedStructureMixture(FSDAGTrainSM[], StructureLearner.ModelType, byte, int, boolean, double[], double, TerminationCondition) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
- Creates a new
SharedStructureMixture
instance with all relevant
values.
- SharedStructureMixture(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
- The standard constructor for the interface
Storable
.
- shortcut -
Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.DiffSSBasedOptimizableFunction
- These shortcuts indicate the beginning of a new part in the parameter vector.
- ShortSequence - Class in de.jstacs.data.sequences
- This class is for sequences with the alphabet symbols encoded as
shorts
s and can therefore be used for discrete
AlphabetContainer
s with alphabets that use many different symbols. - ShortSequence(AlphabetContainer, short[]) -
Constructor for class de.jstacs.data.sequences.ShortSequence
- Creates a new
ShortSequence
from an array of short
-
encoded alphabet symbols.
- ShortSequence(AlphabetContainer, String) -
Constructor for class de.jstacs.data.sequences.ShortSequence
- Creates a new
ShortSequence
from a String
representation
using the default delimiter.
- ShortSequence(AlphabetContainer, SequenceAnnotation[], String, String) -
Constructor for class de.jstacs.data.sequences.ShortSequence
- Creates a new
ShortSequence
from a String
representation
using the delimiter delim
.
- ShortSequence(AlphabetContainer, SequenceAnnotation[], SymbolExtractor) -
Constructor for class de.jstacs.data.sequences.ShortSequence
- Creates a new
ShortSequence
from a SymbolExtractor
.
- shouldBeNormalized() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifierParameterSet
- This method indicates if a normalization shall be used while
optimization.
- showImage(String, BufferedImage) -
Static method in class de.jstacs.utils.REnvironment
- Enables you to show an image.
- showImage(String, BufferedImage, int) -
Static method in class de.jstacs.utils.REnvironment
- Enables you to show an image.
- SignificantMotifOccurrencesFinder - Class in de.jstacs.motifDiscovery
- This class enables the user to predict motif occurrences given a specific significance level.
- SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.RandomSeqType, boolean, int, double) -
Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- This constructor creates an instance of
SignificantMotifOccurrencesFinder
that uses the given SignificantMotifOccurrencesFinder.RandomSeqType
to determine the siginificance level.
- SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.RandomSeqType, SignificantMotifOccurrencesFinder.JoinMethod, boolean, int, double) -
Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- This constructor creates an instance of
SignificantMotifOccurrencesFinder
that uses the given SignificantMotifOccurrencesFinder.RandomSeqType
to determine the siginificance level.
- SignificantMotifOccurrencesFinder(MotifDiscoverer, DataSet, double[], double) -
Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- This constructor creates an instance of
SignificantMotifOccurrencesFinder
that uses a DataSet
to determine the siginificance level.
- SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.JoinMethod, DataSet, double[], double) -
Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
- This constructor creates an instance of
SignificantMotifOccurrencesFinder
that uses a DataSet
to determine the siginificance level.
- SignificantMotifOccurrencesFinder.JoinMethod - Interface in de.jstacs.motifDiscovery
- Interface for methods that combine several profiles over the same sequence
into one common profile
- SignificantMotifOccurrencesFinder.RandomSeqType - Enum in de.jstacs.motifDiscovery
-
- SignificantMotifOccurrencesFinder.SumOfProbabilities - Class in de.jstacs.motifDiscovery
- Joins several profiles containing log-probabilities into one profile containing
the logarithm of the sum of the probabilities of the single profiles.
- SignificantMotifOccurrencesFinder.SumOfProbabilities() -
Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder.SumOfProbabilities
-
- SilentEmission - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
- This class implements a silent emission which is used to create silent states.
- SilentEmission() -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
- The main constructor.
- SilentEmission(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission
- The standard constructor for the interface
Storable
.
- SimpleBurnInTest - Class in de.jstacs.sampling
- Deprecated. since this burn test ignore the data coming from the sampling, it might be problematic to use this test
- SimpleBurnInTest(int) -
Constructor for class de.jstacs.sampling.SimpleBurnInTest
- Deprecated. This is the main constructor that creates an instance of
SimpleBurnInTest
with fixed burn-in length.
- SimpleBurnInTest(StringBuffer) -
Constructor for class de.jstacs.sampling.SimpleBurnInTest
- Deprecated. The standard constructor for the interface
Storable
.
- SimpleCosts - Class in de.jstacs.algorithms.alignment.cost
- Class for simple costs with costs
match
for a match,
mismatch
for a mismatch, and
gap
for a gap (of length 1).
- SimpleCosts(double, double, double) -
Constructor for class de.jstacs.algorithms.alignment.cost.SimpleCosts
- Creates a new instance of simple costs with costs
match
for a match,
mismatch
for a mismatch, and
gap
for a gap (of length 1).
- SimpleDifferentiableState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
- This class implements a
State
based on Emission
that allows to reuse Emission
s for different State
s. - SimpleDifferentiableState(DifferentiableEmission, String, boolean) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleDifferentiableState
- This is the constructor of a
SimpleState
.
- SimpleDiscreteSequence - Class in de.jstacs.data.sequences
- This is the main class for any discrete sequence.
- SimpleDiscreteSequence(AlphabetContainer, SequenceAnnotation[]) -
Constructor for class de.jstacs.data.sequences.SimpleDiscreteSequence
- This constructor creates a new
SimpleDiscreteSequence
with the
AlphabetContainer
container
and the annotation
annotation
but without the content.
- SimpleGaussianSumLogPrior - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
- This class implements a prior that is a product of Gaussian distributions
with mean 0 and equal variance for each parameter.
- SimpleGaussianSumLogPrior(double) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SimpleGaussianSumLogPrior
- Creates a new
SimpleGaussianSumLogPrior
with mean 0 and variance
sigma
for all parameters, including the class parameters.
- SimpleGaussianSumLogPrior(StringBuffer) -
Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SimpleGaussianSumLogPrior
- The standard constructor for the interface
Storable
.
- SimpleHistory - Class in de.jstacs.motifDiscovery.history
- This class implements a simple history that has a limited memory that will be
used cyclicly.
- SimpleHistory(int) -
Constructor for class de.jstacs.motifDiscovery.history.SimpleHistory
- This constructor creates a simple history with limited memory.
- SimpleHistory(int, boolean, boolean, boolean) -
Constructor for class de.jstacs.motifDiscovery.history.SimpleHistory
- This constructor creates a simple history with limited memory.
- SimpleHistory(StringBuffer) -
Constructor for class de.jstacs.motifDiscovery.history.SimpleHistory
- This is the constructor for the interface
Storable
.
- SimpleParameter - Class in de.jstacs.parameters
- Class for a "simple" parameter.
- SimpleParameter(StringBuffer) -
Constructor for class de.jstacs.parameters.SimpleParameter
- The standard constructor for the interface
Storable
.
- SimpleParameter(DataType, String, String, boolean) -
Constructor for class de.jstacs.parameters.SimpleParameter
- Constructor for a
SimpleParameter
without
ParameterValidator
.
- SimpleParameter(DataType, String, String, boolean, Object) -
Constructor for class de.jstacs.parameters.SimpleParameter
- Constructor for a
SimpleParameter
without
ParameterValidator
but with a default value.
- SimpleParameter(DataType, String, String, boolean, ParameterValidator) -
Constructor for class de.jstacs.parameters.SimpleParameter
- Constructor for a
SimpleParameter
with a
ParameterValidator
.
- SimpleParameter(DataType, String, String, boolean, ParameterValidator, Object) -
Constructor for class de.jstacs.parameters.SimpleParameter
- Constructor for a
SimpleParameter
with validator and default
value.
- SimpleParameter.DatatypeNotValidException - Exception in de.jstacs.parameters
- Class for an
Exception
that can be thrown if the provided
int
-value that represents a data type is not one of the
values defined in DataType
. - SimpleParameter.DatatypeNotValidException(String) -
Constructor for exception de.jstacs.parameters.SimpleParameter.DatatypeNotValidException
- Creates a new
SimpleParameter.DatatypeNotValidException
with an error
message.
- SimpleParameter.IllegalValueException - Exception in de.jstacs.parameters
- This exception is thrown if a parameter is not valid.
- SimpleParameter.IllegalValueException(String) -
Constructor for exception de.jstacs.parameters.SimpleParameter.IllegalValueException
- Creates a new
SimpleParameter.IllegalValueException
with the reason of the
exception reason
as error message.
- SimpleParameterSet - Class in de.jstacs.parameters
- Class for a
ParameterSet
that is constructed from an array of Parameter
s. - SimpleParameterSet(Parameter...) -
Constructor for class de.jstacs.parameters.SimpleParameterSet
- Creates a new
SimpleParameterSet
from an array of Parameter
s.
- SimpleParameterSet(StringBuffer) -
Constructor for class de.jstacs.parameters.SimpleParameterSet
- The standard constructor for the interface
Storable
.
- SimpleResult - Class in de.jstacs.results
- Abstract class for a
Result
with a value of a primitive data type or
String
. - SimpleResult(String, String, DataType) -
Constructor for class de.jstacs.results.SimpleResult
- The main constructor which takes the main information of a result.
- SimpleResult(StringBuffer) -
Constructor for class de.jstacs.results.SimpleResult
- This is the constructor for
Storable
.
- SimpleSamplingState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
- This class implements a state that can be used for a HMM that obtains its parameters from sampling.
- SimpleSamplingState(SamplingEmission, String, boolean) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState
- This constructor creates a state that can be used in a HMM that obtains its parameters from sampling.
- SimpleSequenceAnnotationParser - Class in de.jstacs.data.sequences.annotation
- This class implements a naive
SequenceAnnotationParser
which simply paste the comments into SequenceAnnotation
. - SimpleSequenceAnnotationParser() -
Constructor for class de.jstacs.data.sequences.annotation.SimpleSequenceAnnotationParser
- The constructor of a
SimpleSequenceAnnotationParser
which simply paste the comments into SequenceAnnotation
.
- SimpleSequenceIterator - Class in de.jstacs.data.bioJava
- Class that implements the
SequenceIterator
interface of BioJava in a
simple way, backed by an array of Sequence
s. - SimpleSequenceIterator(Sequence...) -
Constructor for class de.jstacs.data.bioJava.SimpleSequenceIterator
- Creates a new
SimpleSequenceIterator
from an array of
Sequence
s.
- SimpleState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
- This class implements a
State
based on Emission
that allows to reuse Emission
s for different State
s. - SimpleState(Emission, String, boolean) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState
- This is the constructor of a
SimpleState
.
- SimpleStaticConstraint - Class in de.jstacs.parameters.validation
- Class for a
Constraint
that checks values against static values using
the comparison operators defined in the interface Constraint
. - SimpleStaticConstraint(Number, int) -
Constructor for class de.jstacs.parameters.validation.SimpleStaticConstraint
- Creates a new
SimpleStaticConstraint
from a Number
-reference and a comparison operator as defined in Constraint
.
- SimpleStaticConstraint(String, int) -
Constructor for class de.jstacs.parameters.validation.SimpleStaticConstraint
- Creates a new
SimpleStaticConstraint
from a String
-reference and a comparison operator as defined in Constraint
.
- SimpleStaticConstraint(StringBuffer) -
Constructor for class de.jstacs.parameters.validation.SimpleStaticConstraint
- The standard constructor for the interface
Storable
.
- SimpleStringExtractor - Class in de.jstacs.io
- This is a simple class that extracts
String
s. - SimpleStringExtractor(String...) -
Constructor for class de.jstacs.io.SimpleStringExtractor
- This constructor packs the
String
s in an instance of
SimpleStringExtractor
.
- simpleWeights(double[]) -
Static method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasure
- Returns true if all weights in
weight
are 1.
- SinglePositionSequenceAnnotation - Class in de.jstacs.data.sequences.annotation
- Class for some annotations that consist mainly of one position on a sequence.
- SinglePositionSequenceAnnotation(SinglePositionSequenceAnnotation.Type, String, int) -
Constructor for class de.jstacs.data.sequences.annotation.SinglePositionSequenceAnnotation
- Creates a new
SinglePositionSequenceAnnotation
of type
type
with identifier identifier
and position
position
.
- SinglePositionSequenceAnnotation(SinglePositionSequenceAnnotation.Type, String, int, Result...) -
Constructor for class de.jstacs.data.sequences.annotation.SinglePositionSequenceAnnotation
- Creates a new
SinglePositionSequenceAnnotation
of type
type
with identifier identifier
, position
position
and additional annotations
additionalAnnotation
.
- SinglePositionSequenceAnnotation(StringBuffer) -
Constructor for class de.jstacs.data.sequences.annotation.SinglePositionSequenceAnnotation
- The standard constructor for the interface
Storable
.
- SinglePositionSequenceAnnotation.Type - Enum in de.jstacs.data.sequences.annotation
- This
enum
defines possible types of a
SinglePositionSequenceAnnotation
. - SINGLETON -
Static variable in class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
- The only instance of this class.
- SINGLETON -
Static variable in class de.jstacs.data.alphabets.DNAAlphabet
- The only instance of this class.
- SINGLETON -
Static variable in class de.jstacs.data.alphabets.DNAAlphabetContainer.DNAAlphabetContainerParameterSet
- The only instance of this class.
- SINGLETON -
Static variable in class de.jstacs.data.alphabets.DNAAlphabetContainer
- The only instance of this class.
- SINGLETON -
Static variable in class de.jstacs.data.alphabets.ProteinAlphabet.ProteinAlphabetParameterSet
- The only instance of this class.
- SINGLETON -
Static variable in class de.jstacs.data.alphabets.ProteinAlphabet
- The only instance of this class.
- Singleton - Interface in de.jstacs
- This interface states that the implementing class has only one immutable instance.
- Singleton.SingletonHandler - Class in de.jstacs
- This handler helps to retrieve the single instance of a
Singleton
. - Singleton.SingletonHandler() -
Constructor for class de.jstacs.Singleton.SingletonHandler
-
- size() -
Method in class de.jstacs.AnnotatedEntityList
- Returns the number of
AnnotatedEntity
s (not the capacity)
in the AnnotatedEntityList
.
- SkewNormalLikeDurationDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
- This class implements a skew normal like discrete truncated distribution.
- SkewNormalLikeDurationDiffSM(int, int, double, double, double) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
- This is the main constructor if the parameters are fixed.
- SkewNormalLikeDurationDiffSM(int, int, boolean, double, double, boolean, double, double, boolean, double, double, int) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
- This is the constructor that allows the most flexible handling of the parameters.
- SkewNormalLikeDurationDiffSM(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
- This is the constructor for
Storable
.
- skip(int) -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.SequenceIterator
- This method skips some position.
- skipInit -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
- Indicates if the model should be initialized (randomly) before optimization
- skipLastClassifiersDuringClassifierTraining -
Variable in class de.jstacs.classifiers.assessment.ClassifierAssessment
- Skip last classifier.
- SmallDifferenceOfFunctionEvaluationsCondition - Class in de.jstacs.algorithms.optimization.termination
- 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.,
. - SmallDifferenceOfFunctionEvaluationsCondition(double) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
- This constructor creates an instance that stops the optimization if the difference of the
current and the last function evaluations is smaller than
epsilon
.
- SmallDifferenceOfFunctionEvaluationsCondition(SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
- This is the main constructor creating an instance from a given parameter set.
- SmallDifferenceOfFunctionEvaluationsCondition(StringBuffer) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition
- The standard constructor for the interface
Storable
.
- SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination
- This class implements the parameter set for a
SmallDifferenceOfFunctionEvaluationsCondition
. - SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet() -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
- This constructor creates an empty parameter set.
- SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet(StringBuffer) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
- The standard constructor for the interface
Storable
.
- SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet(double) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet
- This constructor creates a filled instance of a parameters set.
- SmallGradientConditon - Class in de.jstacs.algorithms.optimization.termination
- This class implements a
TerminationCondition
that allows no further iteration in an optimization if the
the gradient becomes small, i.e.,
. - SmallGradientConditon(double) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
- This constructor creates an instance that stops the optimization if the sum of the absolute
values of gradient components is smaller than
epsilon
.
- SmallGradientConditon(SmallGradientConditon.SmallGradientConditonParameterSet) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
- This is the main constructor creating an instance from a given parameter set.
- SmallGradientConditon(StringBuffer) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon
- The standard constructor for the interface
Storable
.
- SmallGradientConditon.SmallGradientConditonParameterSet - Class in de.jstacs.algorithms.optimization.termination
- This class implements the parameter set for a
SmallStepCondition
. - SmallGradientConditon.SmallGradientConditonParameterSet() -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet
- This constructor creates an empty parameter set.
- SmallGradientConditon.SmallGradientConditonParameterSet(StringBuffer) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet
- The standard constructor for the interface
Storable
.
- SmallGradientConditon.SmallGradientConditonParameterSet(double) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallGradientConditon.SmallGradientConditonParameterSet
- This constructor creates a filled instance of a parameters set.
- SmallStepCondition - Class in de.jstacs.algorithms.optimization.termination
- 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.,
. - SmallStepCondition(double) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition
- This constructor creates an instance that allows no further iteration in an optimization if the
scalar product of the current and the last values of
x
is smaller than epsilon
.
- SmallStepCondition(SmallStepCondition.SmallStepConditionParameterSet) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition
- This is the main constructor creating an instance from a given parameter set.
- SmallStepCondition(StringBuffer) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition
- The standard constructor for the interface
Storable
.
- SmallStepCondition.SmallStepConditionParameterSet - Class in de.jstacs.algorithms.optimization.termination
- This class implements the parameter set for a
SmallStepCondition
. - SmallStepCondition.SmallStepConditionParameterSet() -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet
- This constructor creates an empty parameter set.
- SmallStepCondition.SmallStepConditionParameterSet(StringBuffer) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet
- The standard constructor for the interface
Storable
.
- SmallStepCondition.SmallStepConditionParameterSet(double) -
Constructor for class de.jstacs.algorithms.optimization.termination.SmallStepCondition.SmallStepConditionParameterSet
- This constructor creates a filled instance of a parameters set.
- smooth(double[]) -
Method in class de.jstacs.data.DinucleotideProperty.MeanSmoothing
-
- smooth(double[]) -
Method in class de.jstacs.data.DinucleotideProperty.MedianSmoothing
-
- smooth(double[]) -
Method in class de.jstacs.data.DinucleotideProperty.NoSmoothing
-
- smooth(double[]) -
Method in class de.jstacs.data.DinucleotideProperty.Smoothing
- Returns the smoothed version of
original
.
- SoftOneOfN - Class in de.jstacs.utils.random
- This random generator returns
1-epsilon
for one and equal parts
for the rest of a random vector. - SoftOneOfN(double) -
Constructor for class de.jstacs.utils.random.SoftOneOfN
- This constructor can be used for (soft) sampling one of n.
- SoftOneOfN() -
Constructor for class de.jstacs.utils.random.SoftOneOfN
- This constructor can be used for (hard) sampling one of n.
- sort(String) -
Method in class de.jstacs.results.ListResult
- This method enables you to sort the entries of this container by a
specified column.
- sort() -
Method in class de.jstacs.utils.IntList
- This method sorts the elements of the list.
- sortAlongWith(double[], double[]...) -
Static method in class de.jstacs.utils.ToolBox
- This method implements a sort algorithm on the array
arrayToBeSorted
.
- sostream -
Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
- This stream is used for comments, e.g.
- sostream -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
- This stream is used for comments, computation steps/results or any other
kind of output during the training, ...
- sostream -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- This is the stream for writing information while training.
- sostream -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This is the stream for writing information while training.
- source -
Variable in class de.jstacs.algorithms.graphs.Edge
- The source node.
- SparseSequence - Class in de.jstacs.data.sequences
- This class is an implementation for sequences on one alphabet with length 4.
- SparseSequence(AlphabetContainer, String) -
Constructor for class de.jstacs.data.sequences.SparseSequence
- Creates a new
SparseSequence
from a String
representation.
- SparseSequence(AlphabetContainer, SymbolExtractor) -
Constructor for class de.jstacs.data.sequences.SparseSequence
- Creates a new
SparseSequence
from a SymbolExtractor
.
- SparseStringExtractor - Class in de.jstacs.io
- This
StringExtractor
reads the String
s from a File
as
the user asks for the String
. - SparseStringExtractor(String) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file.
- SparseStringExtractor(File) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file.
- SparseStringExtractor(String, SequenceAnnotationParser) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file.
- SparseStringExtractor(String, char) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file and ignores
those starting with the comment character
ignore
.
- SparseStringExtractor(File, char) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file and ignores
those starting with the comment character
ignore
.
- SparseStringExtractor(String, char, SequenceAnnotationParser) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file and ignores
those starting with the comment character
ignore
.
- SparseStringExtractor(String, String, SequenceAnnotationParser) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file and sets the
annotation of the source to
annotation
.
- SparseStringExtractor(String, char, String, SequenceAnnotationParser) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file, ignores those
starting with the comment character
ignore
and sets the
annotation of the source to annotation
.
- SparseStringExtractor(File, char, String, SequenceAnnotationParser) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a file, ignores those
starting with the comment character
ignore
and sets the
annotation of the source to annotation
.
- SparseStringExtractor(Reader, char, String, SequenceAnnotationParser) -
Constructor for class de.jstacs.io.SparseStringExtractor
- A constructor that reads the lines from a
Reader
, ignores those
starting with the comment character ignore
and sets the
annotation of the source to annotation
.
- spearmanCorrelation(double[], double[]) -
Static method in class de.jstacs.utils.ToolBox
- The method computes the Spearman correlation of two vectors.
- SplitSequenceAnnotationParser - Class in de.jstacs.data.sequences.annotation
- This class implements a simple
SequenceAnnotationParser
which simply splits the comments by specific delimiters. - SplitSequenceAnnotationParser() -
Constructor for class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
- Creates a new
SplitSequenceAnnotationParser
with specific delimiters, i.e., key value
delimiter "=" and annotation delimiter ";".
- SplitSequenceAnnotationParser(String, String) -
Constructor for class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
- Creates a new
SplitSequenceAnnotationParser
with user-specified delimiters.
- standardDeviation -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.PluginGaussianEmission
- Initial standard deviation.
- start -
Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- This array specifies the start positions of the specific parts.
- START_NODE -
Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- The
String
for the start node used in Graphviz annotation.
- STARTDISTANCE -
Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools
- The start distance for the line search in an optimization using the
Optimizer
.
- StartDistanceForecaster - Interface in de.jstacs.algorithms.optimization
- This interface is used to determine the next start distance that will be used
in a line search.
- startIndexOfParams -
Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
- This array contains the start indices for
DifferentiableSequenceScore.setParameters(double[], int)
on IndependentProductDiffSS.score
.
- starts -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
- The start indices.
- starts -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- The number of starts.
- State - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
- This interface declares the methods of any state used in a hidden Markov model.
- stateList -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
- Helper variable = only for internal use.
- states -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
- The (hidden) states of the HMM.
- states -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- The states that can be visited
- StationaryDistribution - Class in de.jstacs.utils
- This class can be used to determine the stationary distribution.
- StationaryDistribution() -
Constructor for class de.jstacs.utils.StationaryDistribution
-
- stationaryIteration -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- The number of (stationary) iterations of the Gibbs Sampler.
- statistic -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
- The array for storing the statistics for
each parameter
- statistic -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement
- The sufficient statistic for determining the parameters during sampling, viterbi or Baum-Welch training.
- StatisticalModel - Interface in de.jstacs.sequenceScores.statisticalModels
- This interface declares methods of a statistical model, i.e., a
SequenceScore
that defines a proper likelihood
over the input Sequence
s. - StatisticalModelTester - Class in de.jstacs.utils
- This class is useful for some test for any (discrete) models.
- StatisticalModelTester() -
Constructor for class de.jstacs.utils.StatisticalModelTester
-
- StatisticalTest - Class in de.jstacs.utils
- This class enables the user to compute some divergences.
- StatisticalTest() -
Constructor for class de.jstacs.utils.StatisticalTest
-
- statisticsTransitionProb -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement
- Represents the summarized epsilons required for estimating the transition probabilities from the
context
.
- statisticsTransitionProb -
Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement
- Represents the gammas required for estimating the transition probabilities not including pseudocounts.
- STEEPEST_DESCENT -
Static variable in class de.jstacs.algorithms.optimization.Optimizer
- This constant can be used to specify that the steepest descent should be
used in the
optimize
-method.
- steepestDescent(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) -
Static method in class de.jstacs.algorithms.optimization.Optimizer
- The steepest descent.
- stopThreads() -
Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction
- This method can and should be used to stop all threads if they are not needed any longer.
- Storable - Interface in de.jstacs
- This is the root interface for all immutable objects that must be stored in
e.g.
- StorableResult - Class in de.jstacs.results
- Class for
Result
s that are Storable
s. - StorableResult(String, String, Storable) -
Constructor for class de.jstacs.results.StorableResult
- Creates a result for an XML representation of an object.
- StorableResult(StringBuffer) -
Constructor for class de.jstacs.results.StorableResult
- The standard constructor for the interface
Storable
.
- StorableValidator - Class in de.jstacs.parameters.validation
- Class for a validator that validates instances and XML representations for
the correct class types (e.g.
- StorableValidator(Class<? extends Storable>, boolean) -
Constructor for class de.jstacs.parameters.validation.StorableValidator
- Creates a new
StorableValidator
for a subclass of
AbstractTrainableStatisticalModel
or AbstractClassifier
.
- StorableValidator(Class<? extends Storable>) -
Constructor for class de.jstacs.parameters.validation.StorableValidator
- Creates a new
StorableValidator
for a subclass of
Storable
.
- StorableValidator(StringBuffer) -
Constructor for class de.jstacs.parameters.validation.StorableValidator
- The standard constructor for the interface
Storable
.
- StrandDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
- This class enables the user to search on both strand.
- StrandDiffSM(DifferentiableStatisticalModel, double, int, boolean, StrandDiffSM.InitMethod) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
- This constructor creates a StrandDiffSM that optimizes the usage of each strand.
- StrandDiffSM(DifferentiableStatisticalModel, int, boolean, StrandDiffSM.InitMethod, double) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
- This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage.
- StrandDiffSM(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
- This is the constructor for
Storable
.
- StrandDiffSM.InitMethod - Enum in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
- This enum defines the different types of plug-in initialization of a
StrandDiffSM
. - StrandedLocatedSequenceAnnotationWithLength - Class in de.jstacs.data.sequences.annotation
- Class for a
SequenceAnnotation
that has a position, a length and an
orientation on the strand of a Sequence
. - StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, Result...) -
Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
- Creates a new
StrandedLocatedSequenceAnnotationWithLength
of type
type
with identifier identifier
and additional
annotation (that does not fit the SequenceAnnotation
definitions)
given as an array of Result
s results
.
- StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, Collection<Result>) -
Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
- Creates a new
StrandedLocatedSequenceAnnotationWithLength
of type
type
with identifier identifier
and additional
annotation (that does not fit the SequenceAnnotation
definitions)
given as a Collection
of Result
s results
.
- StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, SequenceAnnotation[], Result...) -
Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
- Creates a new
StrandedLocatedSequenceAnnotationWithLength
of type
type
with identifier identifier
, additional
annotation (that does not fit the SequenceAnnotation
definitions)
given as an array of Result
s additionalAnnotations
and sub-annotations annotations
.
- StrandedLocatedSequenceAnnotationWithLength(String, String, StrandedLocatedSequenceAnnotationWithLength.Strand, LocatedSequenceAnnotation[], Result...) -
Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
- Creates a new
StrandedLocatedSequenceAnnotationWithLength
of type
type
with identifier identifier
, additional
annotation (that does not fit the SequenceAnnotation
definitions)
given as an array of Result
s additionalAnnotations
and sub-annotations annotations
.
- StrandedLocatedSequenceAnnotationWithLength(StringBuffer) -
Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
- The standard constructor for the interface
Storable
.
- StrandedLocatedSequenceAnnotationWithLength.Strand - Enum in de.jstacs.data.sequences.annotation
- This enum defines possible orientations on the strands.
- strandedness() -
Method in enum de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength.Strand
- Returns the strandedness, i.e.
- StrandTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.mixture
- This model handles sequences that can either lie on the forward strand or on
the reverse complementary strand.
- StrandTrainSM(TrainableStatisticalModel, int, boolean, double[], double, AbstractMixtureTrainSM.Algorithm, double, TerminationCondition, AbstractMixtureTrainSM.Parameterization, int, int, BurnInTest) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
- Creates a new
StrandTrainSM
.
- StrandTrainSM(TrainableStatisticalModel, int, double[], double, TerminationCondition, AbstractMixtureTrainSM.Parameterization) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
- Creates an instance using EM and estimating the component probabilities.
- StrandTrainSM(TrainableStatisticalModel, int, double, double, TerminationCondition, AbstractMixtureTrainSM.Parameterization) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
- Creates an instance using EM and fixed component probabilities.
- StrandTrainSM(TrainableStatisticalModel, int, double[], int, int, BurnInTest) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
- Creates an instance using Gibbs Sampling and sampling the component
probabilities.
- StrandTrainSM(TrainableStatisticalModel, int, double, int, int, BurnInTest) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
- Creates an instance using Gibbs Sampling and fixed component
probabilities.
- StrandTrainSM(StringBuffer) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM
- The constructor for the interface
Storable
.
- StringAlignment - Class in de.jstacs.algorithms.alignment
- Class for the representation of an alignment of
String
s. - StringAlignment(double, String...) -
Constructor for class de.jstacs.algorithms.alignment.StringAlignment
- This constructor creates an instance storing the aligned Strings and the costs of the alignment.
- StringAlignment(double, String[], Result) -
Constructor for class de.jstacs.algorithms.alignment.StringAlignment
- This constructor creates an instance storing the aligned Strings and the costs of the alignment.
- StringExtractor - Class in de.jstacs.io
- This class implements the reader that extracts
String
s from either a
File
or a String
. - StringExtractor(File, int) -
Constructor for class de.jstacs.io.StringExtractor
- A constructor that reads the lines from
file
.
- StringExtractor(File, int, char) -
Constructor for class de.jstacs.io.StringExtractor
- A constructor that reads the lines from
file
and ignores
those starting with the comment character ignore
.
- StringExtractor(File, int, String) -
Constructor for class de.jstacs.io.StringExtractor
- A constructor that reads the lines from
file
and sets the
annotation of the source to annotation
.
- StringExtractor(File, int, char, String) -
Constructor for class de.jstacs.io.StringExtractor
- A constructor that reads the lines from
file
, ignores those
starting with the comment character ignore
and sets the
annotation of the source to annotation
.
- StringExtractor(String, int, String) -
Constructor for class de.jstacs.io.StringExtractor
- A constructor that reads the lines from a
String
content
and sets the annotation of the source to
annotation
.
- StringExtractor(String, int, char, String) -
Constructor for class de.jstacs.io.StringExtractor
- A constructor that reads the lines from a
String
content
, ignores those starting with the comment character
ignore
and sets the annotation of the source to
annotation
.
- StructureLearner - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
- This class can be used to learn the structure of any discrete model.
- StructureLearner(AlphabetContainer, int, double) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
- Creates a new
StructureLearner
for a given
AlphabetContainer
, a given length and a given equivalent
sample size (ess).
- StructureLearner(AlphabetContainer, int) -
Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
- Creates a
StructureLearner
with equivalent sample
size (ess) = 0.
- StructureLearner.LearningType - Enum in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
- This
enum
defines the different types of learning that are
possible with the StructureLearner
. - StructureLearner.ModelType - Enum in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
- This
enum
defines the different types of models that can be
learned with the StructureLearner
. - structureMeasure -
Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM
Measure
that defines the network structure.
- stylesheet -
Static variable in class de.jstacs.utils.galaxy.GalaxyAdaptor
- The stylesheet used for the Galaxy HTML output.
- SubclassFinder - Class in de.jstacs.utils
- Utility-class with static methods to
find all sub-classes of a certain class (or interface) within the scope
of the current class-loader
find all sub-classes of a certain class (or interface) within the scope
of the current class-loader that can be instantiated, i.e.
- SubclassFinder() -
Constructor for class de.jstacs.utils.SubclassFinder
-
- subSampling(double) -
Method in class de.jstacs.data.DataSet
- Randomly samples elements, i.e.
- subSampling(double, double[]) -
Method in class de.jstacs.data.DataSet
-
- SubTensor - Class in de.jstacs.algorithms.graphs.tensor
- This Tensor can be used to extract or use only a part of a complete
Tensor
. - SubTensor(Tensor, int, int) -
Constructor for class de.jstacs.algorithms.graphs.tensor.SubTensor
- This constructor creates a
SubTensor
using the Tensor
t
for the nodes offset, offset+1, ..., offset+length-1
.
- sum -
Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction
- The sums of the weighted data per class and additional the total weight
sum.
- sum(double[]) -
Static method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
- Computes the sum of all elements in the array
ar
.
- sum(double...) -
Static method in class de.jstacs.utils.ToolBox
- Computes the sum of the values in
array
- sum(int, int, double[]) -
Static method in class de.jstacs.utils.ToolBox
- Computes the sum of the values in
array
starting at
start
until end
.
- sumNormalisation(double[]) -
Static method in class de.jstacs.utils.Normalisation
- The method does a sum-normalisation on
d
, i.e.
- sumNormalisation(double[], double[], int) -
Static method in class de.jstacs.utils.Normalisation
- The method does a sum-normalisation on
d
, i.e.
- swap() -
Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
- This method swaps the current component models with the alternative
model.
- symbol -
Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter
- The symbol (out of some
Alphabet
) this parameter
is responsible for.
- SymbolExtractor - Class in de.jstacs.io
- This class enables you to extract elements (symbols) from a given
String
similar to a StringTokenizer
. - SymbolExtractor(String) -
Constructor for class de.jstacs.io.SymbolExtractor
- Creates a new
SymbolExtractor
using delim
as
delimiter.
- SymbolExtractor(String, String) -
Constructor for class de.jstacs.io.SymbolExtractor
- Creates a new
SymbolExtractor
using delim
as
delimiter and string
as the String
to be parsed.
- SymmetricTensor - Class in de.jstacs.algorithms.graphs.tensor
- This class can be used for
Tensor
s with a special symmetry property. - SymmetricTensor(int, byte) -
Constructor for class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
- This constructor creates an empty symmetric tensor with given dimension.
- SymmetricTensor(SymmetricTensor[], double[]) -
Constructor for class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
- The constructor can be used creating a new
SymmetricTensor
as
weighted sum of SymmetricTensor
s.
- SymmetricTensor(AsymmetricTensor) -
Constructor for class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
- This constructor creates and checks a filled asymmetric tensor from an
AsymmetricTensor
instance.
- SymmetricTensor(double[][][], int, byte) -
Constructor for class de.jstacs.algorithms.graphs.tensor.SymmetricTensor
- This constructor creates and checks a filled asymmetric tensor with given
dimension.
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