- s1 - Variable in class de.jstacs.algorithms.alignment.Alignment
-
The first sequence
- s2 - Variable in class de.jstacs.algorithms.alignment.Alignment
-
The second sequence
- SafeOutputStream - Class in de.jstacs.utils
-
This class is for any output.
- sameLength() - Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
Returns true
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.
- sample(SamplingScoreBasedClassifier.DiffSMSamplingComponent, DiffSSBasedOptimizableFunction) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- sample - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
The data set that was used in the last training.
- Sampled_RepeatedHoldOutAssessParameterSet - Class in de.jstacs.classifiers.assessment
-
- Sampled_RepeatedHoldOutAssessParameterSet() - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutAssessParameterSet
-
- 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
-
- 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
-
- Sampled_RepeatedHoldOutExperiment(AbstractClassifier...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
-
- Sampled_RepeatedHoldOutExperiment(boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
-
- Sampled_RepeatedHoldOutExperiment(AbstractClassifier[], boolean, TrainableStatisticalModel[]...) - Constructor for class de.jstacs.classifiers.assessment.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
-
- 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
-
- SamplingGenDisMixClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
-
- SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.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
-
- SamplingGenDisMixClassifierParameterSet(AlphabetContainer, int, int, int, int, String, int) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifierParameterSet
-
- 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
-
- SamplingScoreBasedClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- SamplingScoreBasedClassifier(SamplingScoreBasedClassifierParameterSet, BurnInTest, double[], SamplingDifferentiableStatisticalModel...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
-
- 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
-
- 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
-
- SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, SamplingScoreBasedClassifier.SamplingScheme, int, int, boolean, boolean, String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
-
- SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier>, AlphabetContainer, int, int, int, int, String) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifierParameterSet
-
- 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
-
- 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
-
- 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
-
- 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
-
- score - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
The internally used scoring functions.
- score - Variable in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
-
- 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
-
- ScoreClassifier(ScoreClassifierParameterSet, double, DifferentiableSequenceScore...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- ScoreClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
The standard constructor for the interface
Storable
.
- ScoreClassifierParameterSet - Class in de.jstacs.classifiers.differentiableSequenceScoreBased
-
- ScoreClassifierParameterSet(Class<? extends ScoreClassifier>, boolean, AlphabetContainer.AlphabetContainerType, boolean) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifierParameterSet
-
- 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
-
- 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
-
- SelectionParameter(DataType, String[], Object[], String[], String, String, boolean) - Constructor for class de.jstacs.parameters.SelectionParameter
-
- SelectionParameter(String, String, boolean, ParameterSet...) - Constructor for class de.jstacs.parameters.SelectionParameter
-
- SelectionParameter(String, String, boolean, Class<? extends ParameterSet>...) - Constructor for class de.jstacs.parameters.SelectionParameter
-
- 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
-
- SensitivityForFixedSpecificity(double) - Constructor for class de.jstacs.classifiers.performanceMeasures.SensitivityForFixedSpecificity
-
- 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
-
- 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
-
- 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
-
- SeqLogoPlotter.SeqLogoPlotGenerator - Class in de.jstacs.utils
-
- SeqLogoPlotter.SeqLogoPlotGenerator(double[][], int) - Constructor for class de.jstacs.utils.SeqLogoPlotter.SeqLogoPlotGenerator
-
- SeqLogoPlotter.SeqLogoPlotGenerator(StringBuffer) - Constructor for class de.jstacs.utils.SeqLogoPlotter.SeqLogoPlotGenerator
-
- seqs - Variable in class de.jstacs.clustering.distances.SequenceScoreDistance
-
The De Bruijn sequences
- 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
-
- Sequence.CompositeSequence<T> - Class in de.jstacs.data.sequences
-
- Sequence.CompositeSequence(Sequence, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
-
- Sequence.CompositeSequence(AlphabetContainer, Sequence<T>, int[], int[]) - Constructor for class de.jstacs.data.sequences.Sequence.CompositeSequence
-
- 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
-
- Sequence.RecursiveSequence(AlphabetContainer, Sequence<T>) - Constructor for class de.jstacs.data.sequences.Sequence.RecursiveSequence
-
- 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
-
- Sequence.SubSequence(Sequence, int, int) - Constructor for class de.jstacs.data.sequences.Sequence.SubSequence
-
- 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
-
- SequenceAnnotation(String, String, Result[]...) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
-
- SequenceAnnotation(String, String, SequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
-
- SequenceAnnotation(String, String, Collection<? extends Result>) - Constructor for class de.jstacs.data.sequences.annotation.SequenceAnnotation
-
- 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
-
- SequenceEnumeration - Class in de.jstacs.data
-
- 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
-
- sequenceIteratorToDataSet(SequenceIterator, FeatureFilter, AlphabetContainer) - Static method in class de.jstacs.data.bioJava.BioJavaAdapter
-
- SequenceScore - Interface in de.jstacs.sequenceScores
-
This interface defines a scoring function that assigns a score to each input sequence.
- SequenceScoreDistance - Class in de.jstacs.clustering.distances
-
Class for a distance metric between
StatisticalModel
s based on the correlation of score
profiles on De Bruijn sequences.
- SequenceScoreDistance(DiscreteAlphabet, int, boolean) - Constructor for class de.jstacs.clustering.distances.SequenceScoreDistance
-
Creates a new distance.
- SequenceScoreDistance(CyclicSequenceAdaptor[], boolean) - Constructor for class de.jstacs.clustering.distances.SequenceScoreDistance
-
Creates a new distance for a given set of sequences.
- SequenceScoringParameterSet<T extends InstantiableFromParameterSet> - Class in de.jstacs.parameters
-
- SequenceScoringParameterSet(Class<T>, AlphabetContainer.AlphabetContainerType, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
-
- SequenceScoringParameterSet(Class<T>, AlphabetContainer.AlphabetContainerType, boolean, boolean) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
-
- 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
-
- SequenceScoringParameterSet(Class<T>, AlphabetContainer) - Constructor for class de.jstacs.parameters.SequenceScoringParameterSet
-
- 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
-
- 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
-
- set(boolean, DifferentiableSequenceScore...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior
-
- set(boolean, DifferentiableSequenceScore...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior
-
- set(boolean, DifferentiableSequenceScore...) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLogPrior
-
- set(int, Parameter) - Method in class de.jstacs.parameters.ParameterSet.ParameterList
-
- set(int, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
Sets the (conditional) probability parameters at a specific position and sets the mixture parameters
(largely) to the unconditional PWM component.
- 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.
- setCurrent(double) - Method in class de.jstacs.tools.ProgressUpdater
-
Sets the value corresponding to the current progress
- 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
-
- 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
-
- 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.
- setElements() - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Sets the copy references of the leave nodes of this cluster
tree to the elements of its leaves in the current order.
- 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
-
- 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
:
.)
- setExport(boolean) - Method in class de.jstacs.results.ListResult
-
- setExtendedType(String) - Method in class de.jstacs.parameters.FileParameter
-
- setExtendedType(String) - Method in class de.jstacs.results.TextResult
-
- setExtension(String) - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
-
- setExtension(String) - Method in class de.jstacs.tools.ui.galaxy.GalaxyAdaptor.FileResult
-
Sets the filename extension
- setFilename(String) - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
-
- setFilename(String) - Method in class de.jstacs.tools.ui.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.
- setFromStoredParameters(ParameterSet) - Method in class de.jstacs.tools.ToolResult
-
Sets the values of all parameters in other
to those stored in the internal parameters
that have been supplied upon construction.
- setFurtherInformation(StringBuffer) - Method in class de.jstacs.sampling.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.tools.ui.galaxy.GalaxyAdaptor
-
Sets the help, i.e., a more detailed description of the program
to help
.
- setHelp(File) - Method in class de.jstacs.tools.ui.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
- setIndeterminate() - Method in class de.jstacs.tools.ProgressUpdater
-
Sets the progress to indeterminate.
- 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
:
.
- setLast(double) - Method in class de.jstacs.tools.ProgressUpdater
-
Sets the value that is reached upon completion of the monitored task.
- 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
-
Deprecated.
- setMaxTicks(double) - Method in class de.jstacs.utils.NiceScale
-
Sets maximum number of tick marks we're comfortable with
- setMeasure(T) - Method in class de.jstacs.classifiers.performanceMeasures.AbstractPerformanceMeasureParameterSet
-
- setMinMaxPoints(double, double) - Method in class de.jstacs.utils.NiceScale
-
Sets the minimum and maximum data points for the axis.
- 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
-
- setOriginalIndex(int) - Method in class de.jstacs.clustering.hierachical.ClusterTree
-
Sets the original index (e.g., if elements have been removed from the tree) referring to indexes
in the distance matrix that has been used to build a tree.
- setOutputStream(OutputStream) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier
-
- 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
-
- setOutputStream(OutputStream) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
-
- setOutputStream(OutputStream) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
-
- 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
-
- 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
-
- 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
-
- 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.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
-
- 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
-
- setParent(ParameterSet) - Method in class de.jstacs.parameters.Parameter
-
- setParent(Parameter) - Method in class de.jstacs.parameters.ParameterSet
-
- setParser(SequenceAnnotationParser) - Method in class de.jstacs.results.DataSetResult
-
- setPath(String) - Method in class de.jstacs.tools.ui.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
-
- setRangeable(boolean) - Method in class de.jstacs.parameters.SimpleParameter
-
- 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.
- setSkiptInit(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
Deprecated.
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
-
- 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
-
- SharedStructureMixture(FSDAGTrainSM[], StructureLearner.ModelType, byte, int, double[], double, TerminationCondition) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
-
- SharedStructureMixture(FSDAGTrainSM[], StructureLearner.ModelType, byte, int, boolean, double[], double, TerminationCondition) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture
-
- 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
-
- ShortSequence(AlphabetContainer, SequenceAnnotation[], String, String) - Constructor for class de.jstacs.data.sequences.ShortSequence
-
- ShortSequence(AlphabetContainer, SequenceAnnotation[], SymbolExtractor) - Constructor for class de.jstacs.data.sequences.ShortSequence
-
- 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
-
- SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.RandomSeqType, SignificantMotifOccurrencesFinder.JoinMethod, boolean, int, double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
- SignificantMotifOccurrencesFinder(MotifDiscoverer, DataSet, double[], double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
- SignificantMotifOccurrencesFinder(MotifDiscoverer, SignificantMotifOccurrencesFinder.JoinMethod, DataSet, double[], double) - Constructor for class de.jstacs.motifDiscovery.SignificantMotifOccurrencesFinder
-
- 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).
- SimpleCosts(StringBuffer) - Constructor for class de.jstacs.algorithms.alignment.cost.SimpleCosts
-
Restores
SimpleCosts
object from its XML representation.
- SimpleDifferentiableState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
-
- SimpleDifferentiableState(DifferentiableEmission, String, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleDifferentiableState
-
- 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
-
- 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
-
- 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
-
- SimpleParameter(DataType, String, String, boolean, Object) - Constructor for class de.jstacs.parameters.SimpleParameter
-
- SimpleParameter(DataType, String, String, boolean, ParameterValidator) - Constructor for class de.jstacs.parameters.SimpleParameter
-
- SimpleParameter(DataType, String, String, boolean, ParameterValidator, Object) - Constructor for class de.jstacs.parameters.SimpleParameter
-
- 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
-
- 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
-
- SimpleParameterSet - Class in de.jstacs.parameters
-
- 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
-
- 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
-
- SimpleSequenceAnnotationParser() - Constructor for class de.jstacs.data.sequences.annotation.SimpleSequenceAnnotationParser
-
- SimpleSequenceIterator - Class in de.jstacs.data.bioJava
-
- SimpleSequenceIterator(Sequence...) - Constructor for class de.jstacs.data.bioJava.SimpleSequenceIterator
-
- SimpleState - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states
-
- SimpleState(Emission, String, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.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
-
- SimpleStaticConstraint(String, int) - Constructor for class de.jstacs.parameters.validation.SimpleStaticConstraint
-
- 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
-
- 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
-
- SinglePositionSequenceAnnotation(SinglePositionSequenceAnnotation.Type, String, int, Result...) - Constructor for class de.jstacs.data.sequences.annotation.SinglePositionSequenceAnnotation
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- SparseSequence(AlphabetContainer, SymbolExtractor) - Constructor for class de.jstacs.data.sequences.SparseSequence
-
- SparseStringExtractor - Class in de.jstacs.io
-
- 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
-
- SplitSequenceAnnotationParser() - Constructor for class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
-
- SplitSequenceAnnotationParser(String, String) - Constructor for class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
-
- 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
-
- 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.
- startS1 - Variable in class de.jstacs.algorithms.alignment.Alignment
-
The start position in the first sequence
- startS2 - Variable in class de.jstacs.algorithms.alignment.Alignment
-
The start position in the second sequence
- 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
-
- 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
.
- StorableResultSaver - Class in de.jstacs.results.savers
-
- 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
-
- StorableValidator(Class<? extends Storable>) - Constructor for class de.jstacs.parameters.validation.StorableValidator
-
- 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
-
- 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
-
- StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
-
- StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, Collection<Result>) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
-
- StrandedLocatedSequenceAnnotationWithLength(int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, String, String, SequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
-
- StrandedLocatedSequenceAnnotationWithLength(String, String, StrandedLocatedSequenceAnnotationWithLength.Strand, LocatedSequenceAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.StrandedLocatedSequenceAnnotationWithLength
-
- 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
-
- 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
.
- stream - Variable in class de.jstacs.utils.graphics.EPSAdaptor
-
The stream for saving the results
- StringAlignment - Class in de.jstacs.algorithms.alignment
-
Class for the representation of an alignment of
String
s.
- StringAlignment(StringBuffer) - Constructor for class de.jstacs.algorithms.alignment.StringAlignment
-
- 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
-
- StructureLearner(AlphabetContainer, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner
-
- 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.tools.ui.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(int) - Method in class de.jstacs.data.DataSet
-
Randomly samples elements, i.e.
- subSampling(double, double[]) - Method in class de.jstacs.data.DataSet
-
Sub-samples sequences and corresponding weights from this
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
.
- sum(boolean[]) - Static method in class de.jstacs.utils.ToolBox
-
Counts the number of true
values in bools
(similar to sum on booleans in R).
- 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.
- SVGAdaptor - Class in de.jstacs.utils.graphics
-
- SVGAdaptor() - Constructor for class de.jstacs.utils.graphics.SVGAdaptor
-
Creates a new adaptor for plotting to an SVG device.
- 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
-
- SymbolExtractor(String) - Constructor for class de.jstacs.io.SymbolExtractor
-
- SymbolExtractor(String, String) - Constructor for class de.jstacs.io.SymbolExtractor
-
- 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
-
- 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.