Uses of Class de.jstacs.utils.IntList

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de.jstacs.utils.IntList

Packages that use IntList
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for `Classifier`s that are based on `SequenceScore`s.
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. model probabilities or scores independent of the position within a sequence
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`	`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`	`MultiDimensionalSequenceWrapperDiffSM.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)`

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. it converts the integer representation of the path in a String representation.
`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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