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## N

NA - Static variable in class de.jstacs.results.StorableResult
  The `Storable` cannot be trained anyway.
name - Variable in class de.jstacs.parameters.SimpleParameter
  The name of the parameter
name - Variable in class de.jstacs.results.Result
  The name of the result.
nameTemplate - Variable in class de.jstacs.parameters.ExpandableParameterSet
  A template for the name of the enclosing `ParameterSetContainer`
neededReference - Variable in class de.jstacs.parameters.Parameter
  In cases, when the validity of some `ParameterSet` depends on the value of this `Parameter`, this variable holds a reference to that `ParameterSet`.
neededReferenceId - Variable in class de.jstacs.parameters.Parameter
  In addition to the reference, the id of the `ParameterSet` is saved, which assists reconstruction from XML.
NegativeDifferentiableFunction - Class in de.jstacs.algorithms.optimization
  The negative function `-f` for a given `DifferentiableFunction` `f`.
NegativeDifferentiableFunction(DifferentiableFunction) - Constructor for class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction
  Creates the `DifferentiableFunction` `f` for which `-f` should be calculated.
NegativeFunction - Class in de.jstacs.algorithms.optimization
  The negative function -f for a given `Function` `f`.
NegativeFunction(Function) - Constructor for class de.jstacs.algorithms.optimization.NegativeFunction
  Creates the `Function` `f` for which `-f` should be calculated.
NegativeOneDimensionalFunction - Class in de.jstacs.algorithms.optimization
  This class extends the class `OneDimensionalFunction`.
NegativeOneDimensionalFunction(OneDimensionalFunction) - Constructor for class de.jstacs.algorithms.optimization.NegativeOneDimensionalFunction
  Creates the `OneDimensionalFunction` `f` for which `-f` should be calculated.
next() - Method in class de.jstacs.data.Sample.ElementEnumerator
next() - Method in class de.jstacs.models.discrete.inhomogeneous.CombinationIterator
  Steps to the next combination.
next() - Method in class de.jstacs.models.discrete.inhomogeneous.SequenceIterator
  Changes the internal sequence representation to the next sequence.
next() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
next() - Method in class de.jstacs.parameters.ParameterSet
next() - Method in class de.jstacs.parameters.ParameterSetContainer
next() - Method in interface de.jstacs.parameters.RangeIterator
  Switches to the next value in the collection of values in the specified range.
next() - Method in class de.jstacs.parameters.RangeParameter
  Returns `true` if the next element still exists and can be fetched using `RangeParameter.getValue()`, `false` otherwise.
next() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
next() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
  This method steps to the next reasonable outcome if possible.
next(int) - Method in class de.jstacs.utils.random.RandomNumberGenerator
nextBeta(double, double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from Beta(alpha,beta) (E(X)=a/(a+b) ; Var(X)=ab/[(a+b+1)(a+b)^2])
nextBoolean() - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random boolean variable
nextBoolean(double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates true with probability p
nextChiSq() - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from ChiSq(1) (E(X)=1 ; Var(X)=2)
nextChiSq(int) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from ChiSq(df) (E(X)=df ; Var(X)=2*df)
nextChiSq(int, double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from shifted-ChiSq(df) (E(X)=df+lambda ; Var(X)=2*df)
nextElement() - Method in class de.jstacs.data.DiscreteSequenceEnumerator
nextElement() - Method in class de.jstacs.data.Sample.ElementEnumerator
nextElement() - Method in class de.jstacs.data.SampleKMerEnumerator
nextElement() - Method in class de.jstacs.data.SequenceEnumeration
nextElement() - Method in class de.jstacs.io.InfixStringExtractor
nextElement() - Method in class de.jstacs.io.LimitedStringExtractor
nextElement() - Method in class de.jstacs.io.SimpleStringExtractor
nextElement() - Method in class de.jstacs.io.SparseStringExtractor
nextElement() - Method in class de.jstacs.io.StringExtractor
nextElement() - Method in class de.jstacs.io.SymbolExtractor
nextExp() - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from Exp(1) (E(X)=1 ; Var(X)=1)
nextExp(double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from Exp(beta) (E(X)=beta ; Var(X)=beta^2)
nextExp(double, double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from shifted-Exp(beta) (E(X)=beta+lambda ; Var(X)=beta^2)
nextGamma() - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from Gamma(1,1) (E(X)=1 ; Var(X)=1)
nextGamma(double, double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from Gamma(alpha,beta) (E(X)=alpha*beta ; Var(X)=alpha*beta^2)
nextGamma(double, double, double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from shifted-Gamma(alpha,beta) (E(X)=alpha*beta+lambda ; Var(X)=alpha*beta^2)
nextGaussian() - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from N(0,1) (E(X)=0 ; Var(X)=1)
nextGaussian(double, double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from N(m,s2) (E(X)=m ; Var(X)=s2)
nextInt() - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates an integer between 0 and 2^32
nextInt(int) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates an integer between 0 and n-1 (inclusive)
nextPoisson(double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from Poisson(lambda) (E(X)=lambda ; Var(X)=lambda)
nextPoisson() - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from Poisson(1) (E(X)=1 ; Var(X)=1)
nextSequence() - Method in class de.jstacs.data.bioJava.SimpleSequenceIterator
nextUniform() - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from U(0,1) (E(X)=1/2 ; Var(X)=1/12)
nextUniform(double, double) - Method in class de.jstacs.utils.random.RandomNumberGenerator
  generates a random number from U(a,b) (E(X)=(b-a)/2 ; Var(X)=(b-a)^2/12)
NonParsableException - Exception in de.jstacs
  A `NonParsableException` is thrown if some object could not be restored (parsed) from a `StringBuffer`.
NonParsableException() - Constructor for exception de.jstacs.NonParsableException
  Creates a new `NonParsableException` with standard error message ("StringBuffer not parsable.
NonParsableException(String) - Constructor for exception de.jstacs.NonParsableException
  Creates a new `NonParsableException` with given error `message`.
NoRevertHistory - Class in de.jstacs.motifDiscovery.history
  This class implements a history that allows operations, that are not a priorily forbidden and do not create a configuration that has already be considered.
NoRevertHistory() - Constructor for class de.jstacs.motifDiscovery.history.NoRevertHistory
  This constructor creates an instance that allows to shift shrink and expand the motif.
NoRevertHistory(boolean, boolean, boolean) - Constructor for class de.jstacs.motifDiscovery.history.NoRevertHistory
  This constructor creates an instance with user specified allowed operations.
norm - Variable in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
  Indicates whether a normalization should be done or not.
norm - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This `double` contains the normalization constant of the instance.
Normalisation - Class in de.jstacs.utils
  This class can be used for normalisation of any `double` array or a part of a `double` array.
Normalisation() - Constructor for class de.jstacs.utils.Normalisation
normalisation(double[], double) - Static method in class de.jstacs.utils.Normalisation
  The method does a normalisation on `d` using the value `v` for normalisation.
normalisation(double[], double, double[], int) - Static method in class de.jstacs.utils.Normalisation
  The method does a normalisation on `d` writing the result in `dest` starting at position `start` while `d` remains unchanged.
normalisation(double[], double, int, int) - Static method in class de.jstacs.utils.Normalisation
  The method does a sum normalisation on `d` between start index `start` and end index `end` using the value `v` for the normalisation.
NormalizableScoringFunction - Interface in de.jstacs.scoringFunctions
  The interface for normalizable `ScoringFunction`s.
NormalizableScoringFunctionModel - Class in de.jstacs.models
  This model can be used to use a NormalizableScoringFunction as model.
NormalizableScoringFunctionModel(NormalizableScoringFunction, int, byte, double, double, double) - Constructor for class de.jstacs.models.NormalizableScoringFunctionModel
  The main constructor that creates an instance with the user given parameters.
NormalizableScoringFunctionModel(StringBuffer) - Constructor for class de.jstacs.models.NormalizableScoringFunctionModel
  The standard constructor for the interface `Storable`.
NormalizedScoringFunction - Class in de.jstacs.scoringFunctions
  This class makes an unnormalized ScoringFunction to a normalized ScoringFunction.
NormalizedScoringFunction(NormalizableScoringFunction, int) - Constructor for class de.jstacs.scoringFunctions.NormalizedScoringFunction
  Creates a new instance using a given NormalizableScoringFunction.
NormalizedScoringFunction(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.NormalizedScoringFunction
  This is the constructor for `Storable`.
normalizeParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Normalizes the parameter values to the corresponding log-probabilities.
normalizePlugInParameters() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Starts the normalization of the plug-in parameters to the logarithm of the MAP-estimates.
NOT_TRAINED_VALUE - Static variable in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  This value should be used in `ScoreClassifier.getLastScore()` if the classifier is not trained.
NotTrainedException - Exception in de.jstacs
  A `NotTrainedException` is thrown if the user tries to use an untrained model.
NotTrainedException() - Constructor for exception de.jstacs.NotTrainedException
  Creates a new `NotTrainedException` with standard error message ("The model is not trained yet.
NotTrainedException(String) - Constructor for exception de.jstacs.NotTrainedException
  Creates a new `NotTrainedException` with given error `message`.
NullProgressUpdater - Class in de.jstacs.utils
  This class implements a `ProgressUpdater` doing nothing but forces a crossvalidation that is used with an instance of this class to continue to its end.
NullSequenceAnnotationParser - Class in de.jstacs.data.sequences.annotation
  This `SequenceAnnotationParser` returns always `null` as `SequenceAnnotation`.
NumberValidator<E extends Comparable<? extends Number>> - Class in de.jstacs.parameters.validation
  Class that validates all subclasses of `Number` that implement `Comparable` (e.g.
NumberValidator(E, E) - Constructor for class de.jstacs.parameters.validation.NumberValidator
  Constructs a `NumberValidator` for a given upper and lower bound.
NumberValidator(StringBuffer) - Constructor for class de.jstacs.parameters.validation.NumberValidator
  The standard constructor for the interface `Storable`.
NumericalDifferentiableFunction - Class in de.jstacs.algorithms.optimization
  This class is the framework for any numerical differentiable function `f: R^n -> R`.
NumericalDifferentiableFunction(double) - Constructor for class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction
  Sets the value for epsilon for this `NumericalDifferentiableFunction`.
NumericalResult - Class in de.jstacs.results
  Class for numerical `Result` values.
NumericalResult(StringBuffer) - Constructor for class de.jstacs.results.NumericalResult
  The standard constructor for the interface `Storable`.
NumericalResult(String, String, double) - Constructor for class de.jstacs.results.NumericalResult
  The simplified constructor for the primitive type `double`.
NumericalResult(String, String, int) - Constructor for class de.jstacs.results.NumericalResult
  The simplified constructor for the primitive type `int`.
NumericalResult(String, String, Integer) - Constructor for class de.jstacs.results.NumericalResult
  The simplified constructor for the type `Integer`.
NumericalResult(String, String, long) - Constructor for class de.jstacs.results.NumericalResult
  The simplified constructor for the primitive type `long`.
NumericalResultSet - Class in de.jstacs.results
  Class for a set of numerical result values, which are all of the type `NumericalResult`.
NumericalResultSet(NumericalResult) - Constructor for class de.jstacs.results.NumericalResultSet
  Constructs a `NumericalResultSet` containing one `NumericalResult`.
NumericalResultSet(NumericalResult[]...) - Constructor for class de.jstacs.results.NumericalResultSet
  Constructs a `NumericalResultSet` from some arrays of `NumericalResult`s.
NumericalResultSet(LinkedList<? extends NumericalResult>) - Constructor for class de.jstacs.results.NumericalResultSet
  Constructs a `NumericalResultSet` from a `LinkedList` of `NumericalResult`s.
NumericalResultSet(StringBuffer) - Constructor for class de.jstacs.results.NumericalResultSet
  The standard constructor for the interface `Storable`.
numFreePars - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
  The number of free parameters.
nums - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
  Used internally.

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