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N

NaiveBayes class weka.classifiers.bayes.NaiveBayes.

Class for a Naive Bayes classifier using estimator classes.

NaiveBayes(). Constructor for class weka.classifiers.bayes.NaiveBayes

NaiveBayesSimple class weka.classifiers.bayes.NaiveBayesSimple.

Class for building and using a simple Naive Bayes classifier.

NaiveBayesSimple(). Constructor for class weka.classifiers.bayes.NaiveBayesSimple

NaiveBayesUpdateable class weka.classifiers.bayes.NaiveBayesUpdateable.

Class for a Naive Bayes classifier using estimator classes.

NaiveBayesUpdateable(). Constructor for class weka.classifiers.bayes.NaiveBayesUpdateable

name(). Method in class weka.core.Attribute

Returns the attribute's name.

name(). Method in class weka.core.Option

Returns the option's name.

NamedColor class weka.gui.treevisualizer.NamedColor.

This class contains a color name and the rgb values of that color

NamedColor(String, int, int, int). Constructor for class weka.gui.treevisualizer.NamedColor

nameTipText(). Method in class weka.filters.unsupervised.attribute.AddExpression

Returns the tip text for this property

NBconditionalProb(Instance, int). Method in class weka.classifiers.bayes.AODE

Calculates the class membership probabilities for the given test instance, using naive Bayes.

NDConditionalEstimator class weka.estimators.NDConditionalEstimator.

Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate normal estimators for each discrete conditioning value).

NDConditionalEstimator(int, double). Constructor for class weka.estimators.NDConditionalEstimator

Constructor

needExponentialFormat(double). Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat

nestedEstimate(DoubleVector). Method in class weka.classifiers.functions.pace.NormalMixture

Returns the optimal nested model estimate of a vector.

NeuralConnection class weka.classifiers.functions.neural.NeuralConnection.

Abstract unit in a NeuralNetwork.

NeuralConnection(String). Constructor for class weka.classifiers.functions.neural.NeuralConnection

Constructs The unit with the basic connection information prepared for use.

NeuralMethod interface weka.classifiers.functions.neural.NeuralMethod.

This is an interface used to create classes that can be used by the neuralnode to perform all it's computations.

NeuralNetwork class weka.classifiers.functions.neural.NeuralNetwork.

A Classifier that uses backpropagation to classify instances.

NeuralNetwork(). Constructor for class weka.classifiers.functions.neural.NeuralNetwork

The constructor.

NeuralNode class weka.classifiers.functions.neural.NeuralNode.

This class is used to represent a node in the neuralnet.

NeuralNode(String, Random, NeuralMethod). Constructor for class weka.classifiers.functions.neural.NeuralNode

NEW_BATCH. Static variable in class weka.gui.beans.IncrementalClassifierEvent

newEnt(Distribution). Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit

Computes entropy of distribution after splitting.

newNominalRule(Attribute, Instances, int[]). Method in class weka.classifiers.rules.OneR

Create a rule branching on this nominal attribute.

newNumericRule(Attribute, Instances, int[]). Method in class weka.classifiers.rules.OneR

Create a rule branching on this numeric attribute

newRule(Attribute, Instances). Method in class weka.classifiers.rules.OneR

Create a rule branching on this attribute.

next. Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry

next table entry (separate chaining)

next(int). Method in interface weka.classifiers.IterativeClassifier

Performs one iteration.

next(int). Method in class weka.classifiers.trees.adtree.ADTree

Performs one iteration.

nextElement(). Method in class weka.core.FastVector.FastVectorEnumeration

Returns the next element.

nextErlang(int). Method in class weka.core.RandomVariates

Generate a value of a variate following standard Erlang distribution.

nextExponential(). Method in class weka.core.RandomVariates

Generate a value of a variate following standard exponential distribution using simple inverse method.

Variates related to standard Exponential can be generated using simple transformations.

nextGamma(double). Method in class weka.core.RandomVariates

Generate a value of a variate following standard Gamma distribution with shape parameter a.

If a>1, it uses a rejection method developed by Minh(1988)"Generating Gamma Variates", ACM Trans.

nextIteration(). Method in class weka.experiment.Experiment

Carries out the next iteration of the experiment.

nextIteration(). Method in class weka.experiment.RemoteExperiment

Overides the one in Experiment

nextSplitAddedOrder(). Method in class weka.classifiers.trees.adtree.ADTree

Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.

NNConditionalEstimator class weka.estimators.NNConditionalEstimator.

Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).

NNConditionalEstimator(). Constructor for class weka.estimators.NNConditionalEstimator

Nnge class weka.classifiers.rules.Nnge.

Nnge classifier.

Nnge(). Constructor for class weka.classifiers.rules.Nnge

nnls(PaceMatrix, IntVector). Method in class weka.classifiers.functions.pace.PaceMatrix

Solves the nonnegative linear squares problem.

nnlse(PaceMatrix, PaceMatrix, PaceMatrix, IntVector). Method in class weka.classifiers.functions.pace.PaceMatrix

Solves the nonnegative least squares problem with equality constraint.

nnlse1(PaceMatrix, IntVector). Method in class weka.classifiers.functions.pace.PaceMatrix

Solves the nonnegative least squares problem with equality constraint.

NNMMethod. Static variable in class weka.classifiers.functions.pace.MixtureDistribution

The nonnegative-measure-based method

NO_COMMAND. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent

Node class weka.gui.treevisualizer.Node.

This class records all the data about a particular node for displaying.

Node(String, String, int, int, Color, String). Constructor for class weka.gui.treevisualizer.Node

This will setup all the values of the node except for its top and center.

NodePlace interface weka.gui.treevisualizer.NodePlace.

This is an interface for classes that wish to take a node structure and arrange them

nodeToString(). Method in class weka.classifiers.trees.m5.RuleNode

Returns a description of this node (debugging purposes)

NOMINAL. Static variable in class weka.core.Attribute

Constant set for nominal attributes.

nominalCounts. Variable in class weka.core.AttributeStats

Counts of each nominal value

nominalLabelsTipText(). Method in class weka.filters.unsupervised.attribute.Add

Returns the tip text for this property

NominalPrediction class weka.classifiers.evaluation.NominalPrediction.

Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value.

NominalPrediction(double, double[]). Constructor for class weka.classifiers.evaluation.NominalPrediction

Creates the NominalPrediction object with a default weight of 1.0.

NominalPrediction(double, double[], double). Constructor for class weka.classifiers.evaluation.NominalPrediction

Creates the NominalPrediction object.

NominalToBinary class weka.filters.supervised.attribute.NominalToBinary.

Converts all nominal attributes into binary numeric attributes.

NominalToBinary class weka.filters.unsupervised.attribute.NominalToBinary.

Converts all nominal attributes into binary numeric attributes.

NominalToBinary(). Constructor for class weka.filters.supervised.attribute.NominalToBinary

NominalToBinary(). Constructor for class weka.filters.unsupervised.attribute.NominalToBinary

nominalToBinaryFilterTipText(). Method in class weka.classifiers.functions.neural.NeuralNetwork

NONE. Static variable in class weka.gui.visualize.VisualizePanelEvent

No longer used

NonSparseToSparse class weka.filters.unsupervised.instance.NonSparseToSparse.

A filter that converts all incoming instances into sparse format.

NonSparseToSparse(). Constructor for class weka.filters.unsupervised.instance.NonSparseToSparse

NORM_EXPECTED_COST_NAME. Static variable in class weka.classifiers.evaluation.CostCurve

norm(). Method in class weka.core.AlgVector

Returns the norm of the vector

norm(). Method in class weka.clusterers.AlgVector

Returns the norm of the vector

norm1(). Method in class weka.classifiers.functions.pace.Matrix

One norm

norm1(). Method in class weka.classifiers.functions.pace.DoubleVector

Returns the L1-norm of the vector

norm2(). Method in class weka.classifiers.functions.pace.DoubleVector

Returns the L2-norm of the vector

normalDistribution. Static variable in class weka.classifiers.functions.pace.Maths

Distribution type: noraml

NormalEstimator class weka.estimators.NormalEstimator.

Simple probability estimator that places a single normal distribution over the observed values.

NormalEstimator(double). Constructor for class weka.estimators.NormalEstimator

Constructor that takes a precision argument.

normalInverse(double). Static method in class weka.core.Statistics

Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).

Normalize class weka.filters.unsupervised.attribute.Normalize.

Normalizes all numeric values in the given dataset.

normalize(). Method in class weka.classifiers.CostMatrix

Normalizes the matrix so that the diagonal contains zeros.

normalize(). Method in class weka.classifiers.functions.pace.DiscreteFunction

Normalizes the function values with L1-norm.

Normalize(). Constructor for class weka.filters.unsupervised.attribute.Normalize

normalize(double[]). Static method in class weka.core.Utils

Normalizes the doubles in the array by their sum.

normalize(double[], double). Static method in class weka.core.Utils

Normalizes the doubles in the array using the given value.

normalizeAttributesTipText(). Method in class weka.classifiers.functions.neural.NeuralNetwork

NormalizedPolyKernel class weka.classifiers.functions.supportVector.NormalizedPolyKernel.

The normalized polynomial kernel.

NormalizedPolyKernel(Instances, int, double, boolean). Constructor for class weka.classifiers.functions.supportVector.NormalizedPolyKernel

Creates a new NormalizedPolyKernel instance.

normalizeNumericClassTipText(). Method in class weka.classifiers.functions.neural.NeuralNetwork

normalizeTipText(). Method in class weka.attributeSelection.PrincipalComponents

Returns the tip text for this property

NormalMixture class weka.classifiers.functions.pace.NormalMixture.

Class for manipulating normal mixture distributions.

NormalMixture(). Constructor for class weka.classifiers.functions.pace.NormalMixture

Contructs an empty NormalMixture

normalProbability(double). Static method in class weka.core.Statistics

Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).

normF(). Method in class weka.classifiers.functions.pace.Matrix

Frobenius norm

normInf(). Method in class weka.classifiers.functions.pace.Matrix

Infinity norm

normVector(). Method in class weka.core.AlgVector

Norms this vector to length 1.0

normVector(). Method in class weka.clusterers.AlgVector

Norms this vector to length 1.0

NORTH_CONNECTOR. Static variable in class weka.gui.beans.BeanVisual

NoSplit class weka.classifiers.trees.j48.NoSplit.

Class implementing a "no-split"-split.

NoSplit(Distribution). Constructor for class weka.classifiers.trees.j48.NoSplit

Creates "no-split"-split for given distribution.

NoSupportForMissingValuesException exception weka.core.NoSupportForMissingValuesException.

Exception that is raised by an object that is unable to process data with missing values.

NoSupportForMissingValuesException(). Constructor for class weka.core.NoSupportForMissingValuesException

Creates a new NoSupportForMissingValuesException with no message.

NoSupportForMissingValuesException(String). Constructor for class weka.core.NoSupportForMissingValuesException

Creates a new NoSupportForMissingValuesException.

notCoveredInstances(). Method in class weka.classifiers.trees.m5.Rule

Get the instances not covered by this rule

NullFilter class weka.filters.NullFilter.

A simple instance filter that allows no instances to pass through.

NullFilter(). Constructor for class weka.filters.NullFilter

NUM_RAND_COLS. Static variable in interface weka.classifiers.lazy.kstar.KStarConstants

numAllConditions(Instances). Static method in class weka.classifiers.rules.RuleStats

Compute the number of all possible conditions that could appear in a rule of a given data.

numArguments(). Method in class weka.core.Option

Returns the option's number of arguments.

numAttributes(). Method in class weka.core.Instance

Returns the number of attributes.

numAttributes(). Method in class weka.core.SparseInstance

Returns the number of attributes.

numAttributes(). Method in class weka.core.Instances

Returns the number of attributes.

numBags(). Method in class weka.classifiers.trees.j48.Distribution

Returns number of bags.

numberAttributesSelected(). Method in class weka.attributeSelection.AttributeSelection

Return the number of attributes selected from the most recent run of attribute selection

numberOfClusters(). Method in class weka.clusterers.Clusterer

Returns the number of clusters.

numberOfClusters(). Method in class weka.clusterers.EM

Returns the number of clusters.

numberOfClusters(). Method in class weka.clusterers.FarthestFirst

Returns the number of clusters.

numberOfClusters(). Method in class weka.clusterers.SimpleKMeans

Returns the number of clusters.

numberOfClusters(). Method in class weka.clusterers.XMeans

Returns the number of clusters.

numberOfClusters(). Method in class weka.clusterers.Cobweb

Returns the number of clusters.

numberOfClusters(). Method in class weka.clusterers.DistributionMetaClusterer

Returns the number of clusters.

numberOfLinearModels(). Method in class weka.classifiers.trees.m5.RuleNode

Get the number of linear models in the tree

numChildren(). Method in class weka.gui.HierarchyPropertyParser

The number of the children nodes.

numClassAttributeValues(). Method in class weka.classes()">numClasses(). Method in class weka.core.Instances

Returns the number of class labels.

numClasses(). Method in class weka.classifiers.trees.j48.Distribution

Returns number of classes.

numClustersTipText(). Method in class weka.clusterers.EM

Returns the tip text for this property

numClustersTipText(). Method in class weka.clusterers.FarthestFirst

Returns the tip text for this property

numClustersTipText(). Method in class weka.clusterers.SimpleKMeans

Returns the tip text for this property

numColumns(). Method in class weka.core.Matrix

Returns the number of columns in the matrix.

numCorrect(). Method in class weka.classifiers.trees.j48.Distribution

Returns perClass(maxClass()).

numCorrect(int). Method in class weka.classifiers.trees.j48.Distribution

Returns perClassPerBag(index,maxClass(index)).

numDistinctValues(Attribute). Method in class weka.core.Instances

Returns the number of distinct values of a given attribute.

numDistinctValues(int). Method in class weka.core.Instances

Returns the number of distinct values of a given attribute.

numElements(). Method in class weka.core.AlgVector

Returns the number of elements in the vector.

numElements(). Method in class weka.clusterers.AlgVector

Returns the number of elements in the vector.

numElements(). Method in class weka.classifiers.functions.supportVector.SMOset

Returns the number of elements in the set.

NUMERIC. Static variable in class weka.core.Attribute

Constant set for numeric attributes.

NumericPrediction class weka.classifiers.evaluation.NumericPrediction.

Encapsulates an evaluatable numeric prediction: the predicted class value plus the actual class value.

NumericPrediction(double, double). Constructor for class weka.classifiers.evaluation.NumericPrediction

Creates the NumericPrediction object with a default weight of 1.0.

NumericPrediction(double, double, double). Constructor for class weka.classifiers.evaluation.NumericPrediction

Creates the NumericPrediction object.

numericStats. Variable in class weka.core.AttributeStats

Stats on numeric value distributions

numericTipText(). Method in class weka.filters.unsupervised.attribute.MakeIndicator

NumericToBinary class weka.filters.unsupervised.attribute.NumericToBinary.

Converts all numeric attributes into binary attributes (apart from the class attribute): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.

NumericToBinary(). Constructor for class weka.filters.unsupervised.attribute.NumericToBinary

NumericTransform class weka.filters.unsupervised.attribute.NumericTransform.

Transforms numeric attributes using a given transformation method.

Valid filter-specific options are:

-R index1,index2-index4,...
Specify list of columns to transform.

NumericTransform(). Constructor for class weka.filters.unsupervised.attribute.NumericTransform

Default constructor -- sets the default transform method to java.lang.Math.abs().

numEvals(). Method in class weka.classifiers.functions.supportVector.Kernel

Returns the number of kernel evaluation performed.

numEvals(). Method in class weka.classifiers.functions.supportVector.PolyKernel

Returns the number of time Eval has been called.

numEvals(). Method in class weka.classifiers.functions.supportVector.RBFKernel

Returns the number of time Eval has been called.

numFalseNegatives(int). Method in class weka.classifiers.Evaluation

Calculate number of false negatives with respect to a particular class.

numFalsePositives(int). Method in class weka.classifiers.Evaluation

Calculate number of false positives with respect to a particular class.

numFoldsTipText(). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds

Returns the tip text for this property

numFoldsTipText(). Method in class weka.filters.unsupervised.instance.RemoveFolds

Returns the tip text for this property

numFoldsTipText(). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Returns the tip text for this property

numFoldsTipText(). Method in class weka.experiment.CrossValidationResultProducer

Returns the tip text for this property

numIncorrect(). Method in class weka.classifiers.trees.j48.Distribution

Returns total-numCorrect().

numIncorrect(int). Method in class weka.classifiers.trees.j48.Distribution

Returns perBag(index)-numCorrect(index).

numInstances(). Method in class weka.core.Instances

Returns the number of instances in the dataset.

numInstances(). Method in class weka.core.KDTree

Gets number of instances in KDTree.

numInstances(). Method in class weka.classifiers.Evaluation

Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).

numLeaves(). Method in class weka.classifiers.trees.j48.ClassifierTree

Returns number of leaves in tree structure.

numLeaves(int). Method in class weka.classifiers.trees.m5.RuleNode

Sets the leaves' numbers

numNeighboursTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval

Returns the tip text for this property

numNodes(). Method in class weka.classifiers.trees.REPTree

Computes size of the tree.

numNodes(). Method in class weka.classifiers.trees.RandomTree

Computes size of the tree.

numNodes(). Method in class weka.classifiers.trees.j48.ClassifierTree

Returns number of nodes in tree structure.

numOfBoostingIterationsTipText(). Method in class weka.classifiers.trees.adtree.ADTree

numParameters(). Method in class weka.classifiers.functions.LinearRegression

Get the number of coefficients used in the model

numParameters(). Method in class weka.classifiers.functions.pace.PaceRegression

Get the number of coefficients used in the model

numPendingOutput(). Method in class weka.filters.Filter

Returns the number of instances pending output

numPendingOutput(). Method in class weka.filters.unsupervised.attribute.RemoveType

Returns the number of instances pending output

numRows(). Method in class weka.core.Matrix

Returns the number of rows in the matrix.

numRules(). Method in class weka.classifiers.rules.part.MakeDecList

Outputs the number of rules in the classifier.

numRulesTipText(). Method in class weka.associations.Apriori

Returns the tip text for this property

numSubsets(). Method in class weka.classifiers.trees.j48.ClassifierSplitModel

Returns the number of created subsets for the split.

numToSelectTipText(). Method in class weka.attributeSelection.RaceSearch

Returns the tip text for this property

numToSelectTipText(). Method in class weka.attributeSelection.ForwardSelection

Returns the tip text for this property

numToSelectTipText(). Method in class weka.attributeSelection.Ranker

Returns the tip text for this property

numTrueNegatives(int). Method in class weka.classifiers.Evaluation

Calculate the number of true negatives with respect to a particular class.

numTruePositives(int). Method in class weka.classifiers.Evaluation

Calculate the number of true positives with respect to a particular class.

numValues(). Method in class weka.core.Instance

Returns the number of values present.

numValues(). Method in class weka.core.SparseInstance

Returns the number of values in the sparse vector.

numValues(). Method in class weka.core.Attribute

Returns the number of attribute values.

numXValFoldsTipText(). Method in class weka.classifiers.meta.ThresholdSelector

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