Uses of Class de.jstacs.utils.IntList

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Uses of Class
de.jstacs.utils.IntList

Packages that use IntList
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for `Classifier`s that are based on `SequenceScore`s. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
de.jstacs.motifDiscovery	This package provides the framework including the interface for any de novo motif discoverer.
de.jstacs.sequenceScores.differentiable
de.jstacs.sequenceScores.differentiable.logistic
de.jstacs.sequenceScores.statisticalModels.differentiable	Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides `DifferentiableStatisticalModel`s that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous	Provides `DifferentiableStatisticalModel`s that are homogeneous, i.e.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides `DifferentiableSequenceScore`s that are mixtures of other `DifferentiableSequenceScore`s.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm	The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models	The package provides different implementations of hidden Markov models based on `AbstractHMM`.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states	The package provides all interfaces and classes for states used in hidden Markov models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions	The package provides all interfaces and classes for transitions used in hidden Markov models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements
de.jstacs.utils	This package contains a bundle of useful classes and interfaces like ...

Uses of IntList in de.jstacs.classifiers.differentiableSequenceScoreBased

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased declared as IntList
`protected IntList[][]`	`DiffSSBasedOptimizableFunction.iList` These `IntList`s are used during the parallel computation of the gradient.

Uses of IntList in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery that return IntList
`IntList`	`SignificantMotifOccurrencesFinder.getStartPositions(int startPos, DataSet data, int motifIndex, int addMax)` This method returns a list of start positions of binding sites.

Uses of IntList in de.jstacs.sequenceScores.differentiable

Methods in de.jstacs.sequenceScores.differentiable with parameters of type IntList
`double`	`DifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.
`double`	`AbstractDifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, int startpos, int endpos, IntList indices, DoubleList partialDer)`
`double`	`UniformDiffSS.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)`
`double`	`MultiDimensionalSequenceWrapperDiffSS.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double`	`IndependentProductDiffSS.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double`	`DifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.
`double`	`DifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` `seq` and fills lists with the indices and the partial derivations.
`double`	`AbstractDifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)`

Uses of IntList in de.jstacs.sequenceScores.differentiable.logistic

Methods in de.jstacs.sequenceScores.differentiable.logistic with parameters of type IntList
`double`	`LogisticDiffSS.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.differentiable

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable with parameters of type IntList
`double`	`VariableLengthDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int startpos, int endpos, IntList indices, DoubleList partialDer)`
`double`	`CyclicMarkovModelDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)`
`abstract double`	`AbstractVariableLengthDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int startpos, int endpos, IntList indices, DoubleList partialDer)`
`double`	`UniformDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)`
`double`	`NormalizedDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double`	`MarkovRandomFieldDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double`	`MappingDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double`	`AbstractVariableLengthDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)`

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels with parameters of type IntList
`double`	`BayesianNetworkDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous with parameters of type IntList
`double`	`UniformHomogeneousDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)`
`double`	`HomogeneousMMDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)`
`double`	`HomogeneousMM0DiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)`

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Fields in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture declared as IntList
`protected IntList[]`	`AbstractMixtureDiffSM.iList` This array contains some `IntList`s that are used while computing the partial derivation.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture with parameters of type IntList
`double`	`VariableLengthMixtureDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList partialDer)`
`double`	`StrandDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double`	`MixtureDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif with parameters of type IntList
`double`	`UniformDurationDiffSM.getLogScoreAndPartialDerivation(IntList indices, DoubleList partialDer, int... values)`
`double`	`SkewNormalLikeDurationDiffSM.getLogScoreAndPartialDerivation(IntList indices, DoubleList partialDer, int... values)`
`abstract double`	`PositionDiffSM.getLogScoreAndPartialDerivation(IntList indices, DoubleList partialDer, int... values)` This method enables the user to get the log-score and the partial derivations without using a sequence object.
`double`	`MixtureDurationDiffSM.getLogScoreAndPartialDerivation(IntList indices, DoubleList partialDer, int... values)`
`double`	`PositionDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double`	`ExtendedZOOPSDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double`	`PositionDiffSM.getLogScoreAndPartialDerivationForInternal(IntList indices, DoubleList partialDer)` This method enables the user to get the log-score and the partial derivations without using a sequence object by using the internal iterator.

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous with parameters of type IntList
`void`	`MEMConstraint.addParameters(int offset, IntList list, MEMConstraint[] constraint, double[] params, int[] start)` This method implements a heuristic to modify a constraint if a number of constraints should be delete.

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that return types with arguments of type IntList
`abstract Pair<IntList,Double>`	`AbstractHMM.getViterbiPathFor(int startPos, int endPos, Sequence seq)`
`Pair<IntList,Double>`	`AbstractHMM.getViterbiPathFor(Sequence seq)`

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm with parameters of type IntList
`String[]`	`AbstractHMM.decodePath(IntList path)` This method decodes any path of the HMM, i.e.
`abstract double`	`AbstractHMM.getLogProbForPath(IntList path, int startPos, Sequence seq)`

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models declared as IntList
`protected IntList[]`	`DifferentiableHigherOrderHMM.indicesState` Help array for the indexes of the parameters of the states
`protected IntList[]`	`DifferentiableHigherOrderHMM.indicesTransition` Help array for the indexes of the parameters of the transition
`protected IntList`	`HigherOrderHMM.stateList` Helper variable = only for internal use.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that return types with arguments of type IntList
`Pair<IntList,Double>`	`SamplingHigherOrderHMM.getViterbiPath(int startPos, int endPos, Sequence seq, SamplingHigherOrderHMM.ViterbiComputation compute)` This method returns a viterbi path that is the optimum for the choosen ViterbiComputation method
`Pair<IntList,Double>`	`SamplingHigherOrderHMM.getViterbiPathFor(int startPos, int endPos, Sequence seq)`
`Pair<IntList,Double>`	`HigherOrderHMM.getViterbiPathFor(int startPos, int endPos, Sequence seq)`

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models with parameters of type IntList
`double`	`SamplingHigherOrderHMM.getLogProbForPath(IntList path, int startPos, Sequence seq)`
`double`	`HigherOrderHMM.getLogProbForPath(IntList path, int startPos, Sequence seq)`
`double`	`DifferentiableHigherOrderHMM.getLogScoreAndPartialDerivation(Sequence seq, int startPos, int endPos, IntList indices, DoubleList partialDer)`
`double`	`DifferentiableHigherOrderHMM.getLogScoreAndPartialDerivation(Sequence seq, int startPos, IntList indices, DoubleList partialDer)`
`double`	`DifferentiableHigherOrderHMM.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)`
`void`	`HigherOrderHMM.samplePath(IntList path, int startPos, int endPos, Sequence seq)` This method samples a valid path for the given sequence `seq` using the internal parameters.
`protected double`	`HigherOrderHMM.viterbi(IntList path, int startPos, int endPos, double weight, Sequence seq)` This method computes the viterbi score of a given sequence `seq`.

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states with parameters of type IntList
`double`	`SimpleDifferentiableState.getLogScoreAndPartialDerivation(int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double`	`DifferentiableState.getLogScoreAndPartialDerivation(int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)` This method allows to compute the logarithm of the score and the gradient for the given subsequences.

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions with parameters of type IntList
`double`	`UniformEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double`	`SilentEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double`	`DifferentiableEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous with parameters of type IntList
`double`	`GaussianEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete with parameters of type IntList
`double`	`PhyloDiscreteEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double`	`AbstractConditionalDiscreteEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions with parameters of type IntList
`double`	`HigherOrderTransition.getLogScoreAndPartialDerivation(int layer, int index, int childIdx, IntList indices, DoubleList partDer, Sequence sequence, int sequencePosition)`
`double`	`DifferentiableTransition.getLogScoreAndPartialDerivation(int layer, int index, int childIdx, IntList indices, DoubleList partDer, Sequence sequence, int sequencePosition)` This method allows to compute the logarithm of the score and the gradient for a specific transition.

Uses of IntList in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements with parameters of type IntList
`double`	`TransitionElement.getLogScoreAndPartialDerivation(int childIdx, IntList indices, DoubleList partialDer, Sequence sequence, int sequencePosition)` Returns the logarithmic score and fills lists with the indices and the partial derivations.

Uses of IntList in de.jstacs.utils

Methods in de.jstacs.utils that return IntList
`IntList`	`IntList.clone()`

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