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| Uses of Instance in weka.associations |
| Methods in weka.associations with parameters of type Instance | |
boolean |
ItemSet.containedBy(Instance instance)
Checks if an instance contains an item set. |
void |
ItemSet.upDateCounter(Instance instance)
Updates counter of item set with respect to given transaction. |
| Uses of Instance in weka.associations.tertius |
| Subclasses of Instance in weka.associations.tertius | |
class |
IndividualInstance
|
| Methods in weka.associations.tertius with parameters of type Instance | |
abstract boolean |
Literal.satisfies(Instance instance)
|
abstract boolean |
Literal.negationSatisfies(Instance instance)
|
boolean |
LiteralSet.counterInstance(Instance individual,
Instance part)
Test if an individual instance, given a part instance of this individual, is a counter-instance of this LiteralSet. |
boolean |
LiteralSet.counterInstance(Instance instance)
Test if an instance is a counter-instance of this LiteralSet. |
abstract boolean |
LiteralSet.canKeep(Instance instance,
Literal newLit)
Test if an instance can be kept as a counter-instance, given a new literal. |
boolean |
AttributeValueLiteral.satisfies(Instance instance)
|
boolean |
AttributeValueLiteral.negationSatisfies(Instance instance)
|
boolean |
Rule.counterInstance(Instance instance)
Test if an instance is a counter-instance of this rule. |
boolean |
Body.canKeep(Instance instance,
Literal newLit)
Test if an instance can be kept as a counter-instance, if a new literal is added to this body. |
boolean |
Head.canKeep(Instance instance,
Literal newLit)
Test if an instance can be kept as a counter-instance, if a new literal is added to this head. |
| Constructors in weka.associations.tertius with parameters of type Instance | |
IndividualInstance(Instance individual,
Instances parts)
|
|
| Uses of Instance in weka.attributeSelection |
| Methods in weka.attributeSelection that return Instance | |
Instance |
AttributeSelection.reduceDimensionality(Instance in)
reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection. |
Instance |
AttributeTransformer.convertInstance(Instance instance)
Transforms an instance in the format of the original data to the transformed space |
private Instance |
PrincipalComponents.convertInstanceToOriginal(Instance inst)
Convert a pc transformed instance back to the original space |
Instance |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) format. |
| Methods in weka.attributeSelection with parameters of type Instance | |
abstract double |
HoldOutSubsetEvaluator.evaluateSubset(java.util.BitSet subset,
Instance holdOut,
boolean retrain)
Evaluates a subset of attributes with respect to a single instance. |
Instance |
AttributeSelection.reduceDimensionality(Instance in)
reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection. |
private void |
ConsistencySubsetEval.insertIntoTable(Instance inst,
double[] instA)
Inserts an instance into the hash table |
Instance |
AttributeTransformer.convertInstance(Instance instance)
Transforms an instance in the format of the original data to the transformed space |
double |
ClassifierSubsetEval.evaluateSubset(java.util.BitSet subset,
Instance holdOut,
boolean retrain)
Evaluates a subset of attributes with respect to a single instance. |
private Instance |
PrincipalComponents.convertInstanceToOriginal(Instance inst)
Convert a pc transformed instance back to the original space |
Instance |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) format. |
private void |
ReliefFAttributeEval.updateMinMax(Instance instance)
Updates the minimum and maximum values for all the attributes based on a new instance. |
private double |
ReliefFAttributeEval.distance(Instance first,
Instance second)
Calculates the distance between two instances |
| Constructors in weka.attributeSelection with parameters of type Instance | |
ConsistencySubsetEval.hashKey(Instance t,
int numAtts)
Constructor for a hashKey |
|
| Uses of Instance in weka.classifiers |
| Methods in weka.classifiers with parameters of type Instance | |
double |
Classifier.classifyInstance(Instance instance)
Classifies the given test instance. |
double[] |
Classifier.distributionForInstance(Instance instance)
Predicts the class memberships for a given instance. |
double |
Evaluation.evaluateModelOnce(Classifier classifier,
Instance instance)
Evaluates the classifier on a single instance. |
double |
Evaluation.evaluateModelOnce(double[] dist,
Instance instance)
Evaluates the supplied distribution on a single instance. |
void |
Evaluation.evaluateModelOnce(double prediction,
Instance instance)
Evaluates the supplied prediction on a single instance. |
void |
Evaluation.updatePriors(Instance instance)
Updates the class prior probabilities (when incrementally training) |
protected static java.lang.String |
Evaluation.attributeValuesString(Instance instance,
Range attRange)
Builds a string listing the attribute values in a specified range of indices, separated by commas and enclosed in brackets. |
protected void |
Evaluation.updateStatsForClassifier(double[] predictedDistribution,
Instance instance)
Updates all the statistics about a classifiers performance for the current test instance. |
protected void |
Evaluation.updateStatsForPredictor(double predictedValue,
Instance instance)
Updates all the statistics about a predictors performance for the current test instance. |
void |
UpdateableClassifier.updateClassifier(Instance instance)
Updates a classifier using the given instance. |
| Uses of Instance in weka.classifiers.bayes |
| Fields in weka.classifiers.bayes declared as Instance | |
Instance[] |
ADNode.m_Instances
list of Instance children (either m_Instances or m_VaryNodes is instantiated) |
| Methods in weka.classifiers.bayes with parameters of type Instance | |
double |
ComplementNaiveBayes.classifyInstance(Instance instance)
Classifies a given instance. |
void |
BayesNet.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
double[] |
BayesNet.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
BayesNet.countsForInstance(Instance instance)
Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance. |
private void |
AODE.addToCounts(Instance instance)
Puts an instance's values into m_CondiCounts, m_ClassCounts and m_SumInstances. |
double[] |
AODE.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double |
AODE.NBconditionalProb(Instance instance,
int classVal)
Calculates the probability of the specified class for the given test instance, using naive Bayes. |
double[] |
NaiveBayesMultinomial.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
private double |
NaiveBayesMultinomial.probOfDocGivenClass(Instance inst,
int classIndex)
log(N!) |
void |
NaiveBayes.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
double[] |
NaiveBayes.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
NaiveBayesSimple.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
| Uses of Instance in weka.classifiers.evaluation |
| Methods in weka.classifiers.evaluation that return Instance | |
private Instance |
ThresholdCurve.makeInstance(TwoClassStats tc,
double prob)
|
private Instance |
MarginCurve.makeInstance(double margin,
int current,
int cumulative)
Creates an Instance object with the attributes calculated. |
| Methods in weka.classifiers.evaluation with parameters of type Instance | |
Prediction |
EvaluationUtils.getPrediction(Classifier classifier,
Instance test)
Generate a single prediction for a test instance given the pre-trained classifier. |
| Uses of Instance in weka.classifiers.functions |
| Fields in weka.classifiers.functions declared as Instance | |
private Instance |
MultilayerPerceptron.m_currentInstance
The current instance running through the network. |
| Methods in weka.classifiers.functions with parameters of type Instance | |
double[] |
MultilayerPerceptron.distributionForInstance(Instance i)
Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call. |
double |
SMOreg.classifyInstance(Instance inst)
Classifies a given instance. |
double |
LeastMedSq.classifyInstance(Instance instance)
Classify a given instance using the best generated LinearRegression Classifier. |
private double |
SMO.BinarySMO.SVMOutput(int index,
Instance inst)
Computes SVM output for given instance. |
double[] |
Logistic.distributionForInstance(Instance instance)
Computes the distribution for a given instance |
double[] |
SimpleLogistic.distributionForInstance(Instance inst)
Returns class probabilities for an instance. |
double[] |
VotedPerceptron.distributionForInstance(Instance inst)
Outputs the distribution for the given output. |
private double |
VotedPerceptron.innerProduct(Instance i1,
Instance i2)
Computes the inner product of two instances |
private int |
VotedPerceptron.makePrediction(int k,
Instance inst)
Compute a prediction from a perceptron |
void |
Winnow.updateClassifier(Instance instance)
Updates the classifier with a new learning example |
private void |
Winnow.actualUpdateClassifier(Instance inst)
Actual update routine for prefiltered instances |
private void |
Winnow.actualUpdateClassifierBalanced(Instance inst)
Actual update routine (balanced) for prefiltered instances |
double |
Winnow.classifyInstance(Instance inst)
Outputs the prediction for the given instance. |
private double |
Winnow.makePrediction(Instance inst)
Compute the actual prediction for prefiltered instance |
private double |
Winnow.makePredictionBalanced(Instance inst)
Compute our prediction (Balanced) for prefiltered instance |
double |
SimpleLinearRegression.classifyInstance(Instance inst)
Generate a prediction for the supplied instance. |
double |
LinearRegression.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function. |
private double |
LinearRegression.regressionPrediction(Instance transformedInstance,
boolean[] selectedAttributes,
double[] coefficients)
Calculate the dependent value for a given instance for a given regression model. |
boolean |
PaceRegression.checkForMissing(Instance instance,
Instances model)
Checks if an instance has a missing value. |
double |
PaceRegression.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function. |
private double |
PaceRegression.regressionPrediction(Instance transformedInstance,
double[] coefficients)
Calculate the dependent value for a given instance for a given regression model. |
double[] |
SMO.distributionForInstance(Instance inst)
Estimates class probabilities for given instance. |
int[] |
SMO.obtainVotes(Instance inst)
Returns an array of votes for the given instance. |
double[] |
RBFNetwork.distributionForInstance(Instance instance)
Computes the distribution for a given instance |
| Uses of Instance in weka.classifiers.functions.supportVector |
| Methods in weka.classifiers.functions.supportVector with parameters of type Instance | |
double |
NormalizedPolyKernel.eval(int id1,
int id2,
Instance inst1)
Redefines the eval function of PolyKernel. |
double |
RBFKernel.eval(int id1,
int id2,
Instance inst1)
Implements the abstract function of Kernel. |
private double |
RBFKernel.dotProd(Instance inst1,
Instance inst2)
Calculates a dot product between two instances |
abstract double |
Kernel.eval(int id1,
int id2,
Instance inst1)
Computes the result of the kernel function for two instances. |
double |
PolyKernel.eval(int id1,
int id2,
Instance inst1)
Implements the abstract function of Kernel. |
private double |
PolyKernel.dotProd(Instance inst1,
Instance inst2)
Calculates a dot product between two instances |
| Uses of Instance in weka.classifiers.lazy |
| Fields in weka.classifiers.lazy declared as Instance | |
private Instance |
IBk.NeighborNode.m_Instance
The neighbor instance |
| Methods in weka.classifiers.lazy with parameters of type Instance | |
void |
IBk.NeighborList.insertSorted(double distance,
Instance instance)
Inserts an instance neighbor into the list, maintaining the list sorted by distance. |
void |
LWL.updateClassifier(Instance instance)
Adds the supplied instance to the training set |
double[] |
LWL.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
private double |
LWL.distance(Instance first,
Instance second)
Calculates the distance between two instances |
private void |
LWL.updateMinMax(Instance instance)
Updates the minimum and maximum values for all the attributes based on a new instance. |
void |
IB1.updateClassifier(Instance instance)
Updates the classifier. |
double |
IB1.classifyInstance(Instance instance)
Classifies the given test instance. |
private double |
IB1.distance(Instance first,
Instance second)
Calculates the distance between two instances |
private void |
IB1.updateMinMax(Instance instance)
Updates the minimum and maximum values for all the attributes based on a new instance. |
void |
KStar.updateClassifier(Instance instance)
Adds the supplied instance to the training set |
double[] |
KStar.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
private double |
KStar.instanceTransformationProbability(Instance first,
Instance second)
Calculate the probability of the first instance transforming into the second instance: the probability is the product of the transformation probabilities of the attributes normilized over the number of instances used. |
private double |
KStar.attrTransProb(Instance first,
Instance second,
int col)
Calculates the transformation probability of the indexed test attribute to the indexed train attribute. |
double[] |
LBR.distributionForInstance(Instance testInstance)
Calculates the class membership probabilities for the given test instance. |
double[] |
LBR.localDistributionForInstance(Instance instance,
LBR.Indexes instanceIndex)
Calculates the class membership probabilities. |
void |
IBk.updateClassifier(Instance instance)
Adds the supplied instance to the training set |
double[] |
IBk.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
protected double |
IBk.distance(Instance first,
Instance second)
Calculates the distance between two instances |
protected void |
IBk.updateMinMax(Instance instance)
Updates the minimum and maximum values for all the attributes based on a new instance. |
protected IBk.NeighborList |
IBk.findNeighbors(Instance instance)
Build the list of nearest k neighbors to the given test instance. |
| Constructors in weka.classifiers.lazy with parameters of type Instance | |
IBk.NeighborNode(double distance,
Instance instance,
IBk.NeighborNode next)
Create a new neighbor node. |
|
IBk.NeighborNode(double distance,
Instance instance)
Create a new neighbor node that doesn't link to any other nodes. |
|
| Uses of Instance in weka.classifiers.lazy.kstar |
| Fields in weka.classifiers.lazy.kstar declared as Instance | |
protected Instance |
KStarNumericAttribute.m_Test
The test instance |
protected Instance |
KStarNumericAttribute.m_Train
The train instance |
protected Instance |
KStarNominalAttribute.m_Test
The test instance |
protected Instance |
KStarNominalAttribute.m_Train
The train instance |
| Methods in weka.classifiers.lazy.kstar with parameters of type Instance | |
private double |
KStarNominalAttribute.PStar(Instance test,
Instance train,
int col,
double stop)
Calculates the nominal probability function defined as: P(i|j) = (1-stop) * P(i) + ((i==j) ? |
| Constructors in weka.classifiers.lazy.kstar with parameters of type Instance | |
KStarNumericAttribute(Instance test,
Instance train,
int attrIndex,
Instances trainSet,
int[][] randClassCols,
KStarCache cache)
Constructor |
|
KStarNominalAttribute(Instance test,
Instance train,
int attrIndex,
Instances trainSet,
int[][] randClassCol,
KStarCache cache)
Constructor |
|
| Uses of Instance in weka.classifiers.meta |
| Methods in weka.classifiers.meta that return Instance | |
protected Instance |
Stacking.metaInstance(Instance instance)
Makes a level-1 instance from the given instance. |
protected Instance |
Grading.metaInstance(Instance instance,
int k)
Makes a level-1 instance from the given instance. |
| Methods in weka.classifiers.meta with parameters of type Instance | |
double[] |
LogitBoost.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
Stacking.distributionForInstance(Instance instance)
Returns class probabilities. |
protected Instance |
Stacking.metaInstance(Instance instance)
Makes a level-1 instance from the given instance. |
double[] |
HND.distributionForInstance(Instance instance)
Returns a class probability estimation for the given instance. |
private void |
HND.distributionForInstance(Instance instance,
java.util.Map classDistribution,
double factor)
Computes levelwise class probability estimation for the given instance and fills in the respective values to the classDistribution, using the factor (accumulated class probability for the respective superclass). |
double[] |
AttributeSelectedClassifier.distributionForInstance(Instance instance)
Classifies a given instance after attribute selection |
double[] |
Vote.distributionForInstance(Instance instance)
Classifies a given instance using the selected classifier. |
double[] |
FilteredClassifier.distributionForInstance(Instance instance)
Classifies a given instance after filtering. |
double[] |
CostSensitiveClassifier.distributionForInstance(Instance instance)
Returns class probabilities. |
double[] |
MultiScheme.distributionForInstance(Instance instance)
Returns class probabilities. |
double[] |
RandomCommittee.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
CVParameterSelection.distributionForInstance(Instance instance)
Predicts the class distribution for the given test instance. |
double[] |
MultiClassClassifier.individualPredictions(Instance inst)
Returns the individual predictions of the base classifiers for an instance. |
double[] |
MultiClassClassifier.distributionForInstance(Instance inst)
Returns the distribution for an instance. |
double[] |
Grading.distributionForInstance(Instance instance)
Returns class probabilities for a given instance using the stacked classifier. |
protected Instance |
Grading.metaInstance(Instance instance,
int k)
Makes a level-1 instance from the given instance. |
double[] |
ND.distributionForInstance(Instance inst)
Predicts the class distribution for a given instance |
protected double[] |
ND.distributionForInstance(Instance inst,
ND.NDTree node)
Predicts the class distribution for a given instance |
double |
RacedIncrementalLogitBoost.Committee.classifyInstance(Instance instance)
|
double[] |
RacedIncrementalLogitBoost.Committee.updateFS(Instance instance,
Classifier[] newModel,
double[] Fs)
|
double[] |
RacedIncrementalLogitBoost.Committee.distributionForInstance(Instance instance)
|
double[] |
ClassificationViaRegression.distributionForInstance(Instance inst)
Returns the distribution for an instance. |
double |
RegressionByDiscretization.classifyInstance(Instance instance)
Returns a predicted class for the test instance. |
double[] |
StackingC.distributionForInstance(Instance instance)
Classifies a given instance using the stacked classifier. |
double |
MetaCost.classifyInstance(Instance instance)
Classifies a given test instance. |
double |
AdditiveRegression.classifyInstance(Instance inst)
Classify an instance. |
double[] |
END.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
void |
RacedIncrementalLogitBoost.updateClassifier(Instance instance)
Updates the classifier. |
double[] |
RacedIncrementalLogitBoost.distributionForInstance(Instance instance)
Computes class distribution of an instance using the best committee. |
double[] |
Bagging.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
OrdinalClassClassifier.distributionForInstance(Instance inst)
Returns the distribution for an instance. |
double[] |
Decorate.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
AdaBoostM1.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
TreeBasedMultiClassClassifier.distributionForInstance(Instance inst)
Predicts the class distribution for a given instance * * @param inst the (multi-class) instance to be classified |
double[] |
ThresholdSelector.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
| Uses of Instance in weka.classifiers.misc |
| Methods in weka.classifiers.misc with parameters of type Instance | |
double[] |
VFI.distributionForInstance(Instance instance)
Classifies the given test instance. |
double |
FLR.classifyInstance(Instance instance)
Classifies a given instance using the FLR Classifier model |
void |
HyperPipes.updateClassifier(Instance instance)
Updates the classifier. |
double[] |
HyperPipes.distributionForInstance(Instance instance)
Classifies the given test instance. |
void |
HyperPipes.HyperPipe.addInstance(Instance instance)
Updates the bounds arrays with a single instance. |
double |
HyperPipes.HyperPipe.partialContains(Instance instance)
Returns the fraction of the dimensions of a given instance with values lying within the corresponding bounds of the HyperPipe. |
| Constructors in weka.classifiers.misc with parameters of type Instance | |
FLR.FuzzyLattice(Instance dR,
FLR.FuzzyLattice bounds)
Constructs a Fuzzy Lattice from a instance |
|
| Uses of Instance in weka.classifiers.rules |
| Fields in weka.classifiers.rules declared as Instance | |
private Instance |
NNge.Exemplar.m_PreInst
|
| Methods in weka.classifiers.rules with parameters of type Instance | |
double |
NNge.classifyInstance(Instance instance)
Classifies a given instance. |
void |
NNge.updateClassifier(Instance instance)
Updates the classifier using the given instance. |
private void |
NNge.update(Instance instance)
Performs the update of the classifier |
private NNge.Exemplar |
NNge.nearestExemplar(Instance inst)
Returns the nearest Exemplar |
private NNge.Exemplar |
NNge.nearestExemplar(Instance inst,
double c)
Returns the nearest Exemplar with class c |
private void |
NNge.generalise(Instance newInst)
Generalise an Exemplar (not necessarily predictedExemplar) to match instance. |
private void |
NNge.adjust(Instance newInst,
NNge.Exemplar predictedExemplar)
Adjust the NNge. |
private void |
NNge.prune(NNge.Exemplar predictedExemplar,
Instance newInst)
Prunes an Exemplar that matches an Instance |
private boolean |
NNge.notEqualFeatures(Instance inst1,
Instance inst2)
Returns true if the instance don't have the same feature values |
private void |
NNge.updateMinMax(Instance instance)
Updates the minimum, maximum, sum, sumSquare values for all the attributes |
private void |
NNge.updateMI(Instance inst)
Updates the data for computing the mutual information MUST be called AFTER adding inst in m_Train |
private void |
NNge.Exemplar.generalise(Instance inst)
Generalise the Exemplar with inst |
private void |
NNge.Exemplar.preGeneralise(Instance inst)
pre-generalise the Exemplar with inst i.e. the boundaries of the Exemplar include inst but the Exemplar still doesn't 'own' inst. |
private boolean |
NNge.Exemplar.holds(Instance inst)
return true if inst is held by this Exemplar, false otherwise |
private double |
NNge.Exemplar.attrDistance(Instance inst,
int attrIndex)
Compute the distance between the projection of inst and this Exemplar along the attribute attrIndex. |
private double |
NNge.Exemplar.squaredDistance(Instance inst)
Returns the square of the distance between inst and the Exemplar. |
abstract boolean |
ConjunctiveRule.Antd.isCover(Instance inst)
|
private boolean |
Prism.Test.satisfies(Instance inst)
Returns whether a given instance satisfies this test. |
double[] |
ConjunctiveRule.distributionForInstance(Instance instance)
Computes class distribution for the given instance. |
boolean |
ConjunctiveRule.isCover(Instance datum)
Whether the instance covered by this rule |
boolean |
ConjunctiveRule.NumericAntd.isCover(Instance inst)
Whether the instance is covered by this antecedent |
boolean |
ConjunctiveRule.NominalAntd.isCover(Instance inst)
Whether the instance is covered by this antecedent |
int |
Prism.PrismRule.resultRule(Instance inst)
Returns the result assigned by this rule to a given instance. |
int |
Prism.PrismRule.resultRules(Instance inst)
Returns the result assigned by these rules to a given instance. |
boolean |
JRip.RipperRule.covers(Instance datum)
Whether the instance covered by this rule |
double |
OneR.classifyInstance(Instance inst)
Classifies a given instance. |
abstract boolean |
Rule.covers(Instance datum)
Whether the instance covered by this rule |
double |
Prism.classifyInstance(Instance inst)
Classifies a given instance. |
double |
ZeroR.classifyInstance(Instance instance)
Classifies a given instance. |
double[] |
ZeroR.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
boolean |
Ridor.RidorRule.isCover(Instance datum)
Whether the instance covered by this rule |
abstract boolean |
Ridor.Antd.isCover(Instance inst)
|
boolean |
Ridor.NumericAntd.isCover(Instance inst)
Whether the instance is covered by this antecedent |
double[] |
JRip.distributionForInstance(Instance datum)
Classify the test instance with the rule learner and provide the class distributions |
abstract boolean |
JRip.Antd.covers(Instance inst)
|
boolean |
JRip.NumericAntd.covers(Instance inst)
Whether the instance is covered by this antecedent |
boolean |
JRip.NominalAntd.covers(Instance inst)
Whether the instance is covered by this antecedent |
boolean |
Ridor.NominalAntd.isCover(Instance inst)
Whether the instance is covered by this antecedent |
private void |
DecisionTable.insertIntoTable(Instance inst,
double[] instA)
Inserts an instance into the hash table |
(package private) double |
DecisionTable.classifyInstanceLeaveOneOut(Instance instance,
double[] instA)
Classifies an instance for internal leave one out cross validation of feature sets |
double[] |
DecisionTable.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double |
PART.classifyInstance(Instance instance)
Classifies an instance. |
double[] |
PART.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double |
Ridor.classifyInstance(Instance datum)
Classify the test instance with the rule learner |
private double |
Ridor.classify(Ridor.Ridor_node node,
Instance datum)
Classify the test instance with one node of Ridor |
| Constructors in weka.classifiers.rules with parameters of type Instance | |
DecisionTable.hashKey(Instance t,
int numAtts)
Constructor for a hashKey |
|
| Uses of Instance in weka.classifiers.rules.part |
| Methods in weka.classifiers.rules.part with parameters of type Instance | |
double |
MakeDecList.classifyInstance(Instance instance)
Classifies an instance. |
double[] |
MakeDecList.distributionForInstance(Instance instance)
Returns the class distribution for an instance. |
double |
ClassifierDecList.classifyInstance(Instance instance)
Classifies an instance. |
double[] |
ClassifierDecList.distributionForInstance(Instance instance)
Returns class probabilities for a weighted instance. |
double |
ClassifierDecList.weight(Instance instance)
Returns the weight a rule assigns to an instance. |
private double |
ClassifierDecList.getProbs(int classIndex,
Instance instance,
double weight)
Help method for computing class probabilities of a given instance. |
| Uses of Instance in weka.classifiers.trees |
| Methods in weka.classifiers.trees with parameters of type Instance | |
double[] |
LMT.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double |
LMT.classifyInstance(Instance instance)
Classifies an instance. |
double[] |
REPTree.distributionForInstance(Instance instance)
Computes class distribution of an instance using the tree. |
protected double[] |
REPTree.Tree.distributionForInstance(Instance instance)
Computes class distribution of an instance using the tree. |
protected void |
REPTree.Tree.insertHoldOutInstance(Instance inst,
double weight,
REPTree.Tree parent)
Inserts an instance from the hold-out set into the tree. |
protected void |
REPTree.Tree.backfitHoldOutInstance(Instance inst,
double weight,
REPTree.Tree parent)
Inserts an instance from the hold-out set into the tree. |
double[] |
ADTree.distributionForInstance(Instance instance)
Returns the class probability distribution for an instance. |
protected double |
ADTree.predictionValueForInstance(Instance inst,
PredictionNode currentNode,
double currentValue)
Returns the class prediction value (vote) for an instance. |
double[] |
RandomForest.distributionForInstance(Instance instance)
Returns the class probability distribution for an instance. |
double |
J48.classifyInstance(Instance instance)
Classifies an instance. |
double[] |
J48.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double[] |
RandomTree.distributionForInstance(Instance instance)
Computes class distribution of an instance using the decision tree. |
double |
Id3.classifyInstance(Instance instance)
Classifies a given test instance using the decision tree. |
double[] |
Id3.distributionForInstance(Instance instance)
Computes class distribution for instance using decision tree. |
double[] |
UserClassifier.TreeClass.calcClassType(Instance i)
This will recursively go through the tree and return inside the array the weightings of each of the class types for this instance. |
double[] |
UserClassifier.distributionForInstance(Instance i)
Call this function to get a double array filled with the probability of how likely each class type is the class of the instance. |
double[] |
DecisionStump.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
private int |
DecisionStump.whichSubset(Instance instance)
Returns the subset an instance falls into. |
| Uses of Instance in weka.classifiers.trees.adtree |
| Methods in weka.classifiers.trees.adtree with parameters of type Instance | |
void |
ReferenceInstances.addReference(Instance instance)
Adds one instance reference to the end of the set. |
abstract int |
Splitter.branchInstanceGoesDown(Instance i)
Gets the index of the branch that an instance applies to. |
int |
TwoWayNominalSplit.branchInstanceGoesDown(Instance inst)
Gets the index of the branch that an instance applies to. |
int |
TwoWayNumericSplit.branchInstanceGoesDown(Instance inst)
Gets the index of the branch that an instance applies to. |
| Uses of Instance in weka.classifiers.trees.j48 |
| Methods in weka.classifiers.trees.j48 with parameters of type Instance | |
double |
ClassifierSplitModel.classifyInstance(Instance instance)
Classifies a given instance. |
double |
ClassifierSplitModel.classProb(int classIndex,
Instance instance,
int theSubset)
Gets class probability for instance. |
double |
ClassifierSplitModel.classProbLaplace(int classIndex,
Instance instance,
int theSubset)
Gets class probability for instance. |
abstract double[] |
ClassifierSplitModel.weights(Instance instance)
Returns weights if instance is assigned to more than one subset. |
abstract int |
ClassifierSplitModel.whichSubset(Instance instance)
Returns index of subset instance is assigned to. |
double |
C45Split.classProb(int classIndex,
Instance instance,
int theSubset)
Gets class probability for instance. |
double[] |
C45Split.weights(Instance instance)
Returns weights if instance is assigned to more than one subset. |
int |
C45Split.whichSubset(Instance instance)
Returns index of subset instance is assigned to. |
double |
BinC45Split.classProb(int classIndex,
Instance instance,
int theSubset)
Gets class probability for instance. |
double[] |
BinC45Split.weights(Instance instance)
Returns weights if instance is assigned to more than one subset. |
int |
BinC45Split.whichSubset(Instance instance)
Returns index of subset instance is assigned to. |
double |
ClassifierTree.classifyInstance(Instance instance)
Classifies an instance. |
double[] |
ClassifierTree.distributionForInstance(Instance instance,
boolean useLaplace)
Returns class probabilities for a weighted instance. |
private double |
ClassifierTree.getProbsLaplace(int classIndex,
Instance instance,
double weight)
Help method for computing class probabilities of a given instance. |
private double |
ClassifierTree.getProbs(int classIndex,
Instance instance,
double weight)
Help method for computing class probabilities of a given instance. |
void |
Distribution.add(int bagIndex,
Instance instance)
Adds given instance to given bag. |
void |
Distribution.sub(int bagIndex,
Instance instance)
Subtracts given instance from given bag. |
void |
Distribution.addWeights(Instance instance,
double[] weights)
Adds given instance to all bags weighting it according to given weights. |
void |
Distribution.del(int bagIndex,
Instance instance)
Deletes given instance from given bag. |
void |
Distribution.shift(int from,
int to,
Instance instance)
Shifts given instance from one bag to another one. |
int |
NoSplit.whichSubset(Instance instance)
Always returns 0 because only there is only one subset. |
double[] |
NoSplit.weights(Instance instance)
Always returns null because there is only one subset. |
| Uses of Instance in weka.classifiers.trees.lmt |
| Methods in weka.classifiers.trees.lmt with parameters of type Instance | |
protected double[] |
LMTNode.getFs(Instance instance)
Computes the F-values of LogitBoost for an instance from the current logistic model at the node Note that this also takes into account the (partial) logistic model fit at higher levels in the tree. |
double[] |
LMTNode.modelDistributionForInstance(Instance instance)
Returns the class probabilities for an instance according to the logistic model at the node. |
double[] |
LMTNode.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the logistic model tree. |
protected double[] |
LogisticBase.getFs(Instance instance)
Computes the F-values for a single instance. |
double[] |
LogisticBase.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
int |
ResidualSplit.whichSubset(Instance instance)
|
double[] |
ResidualSplit.weights(Instance instance)
Method not in use |
| Uses of Instance in weka.classifiers.trees.m5 |
| Methods in weka.classifiers.trees.m5 with parameters of type Instance | |
double |
RuleNode.classifyInstance(Instance inst)
Classify an instance using this node. |
double |
PreConstructedLinearModel.classifyInstance(Instance inst)
Predicts the class of the supplied instance using the linear model. |
double |
Rule.classifyInstance(Instance instance)
Calculates a prediction for an instance using this rule or M5 model tree |
double |
M5Base.classifyInstance(Instance inst)
Calculates a prediction for an instance using a set of rules or an M5 model tree |
| Uses of Instance in weka.clusterers |
| Methods in weka.clusterers with parameters of type Instance | |
abstract double[] |
DensityBasedClusterer.logDensityPerClusterForInstance(Instance instance)
Computes the log of the conditional density (per cluster) for a given instance. |
double |
DensityBasedClusterer.logDensityForInstance(Instance instance)
Computes the density for a given instance. |
double[] |
DensityBasedClusterer.distributionForInstance(Instance instance)
Returns the cluster probability distribution for an instance. |
protected double[] |
DensityBasedClusterer.logJointDensitiesForInstance(Instance inst)
Returns the logs of the joint densities for a given instance. |
double[] |
MakeDensityBasedClusterer.logDensityPerClusterForInstance(Instance inst)
Computes the log of the conditional density (per cluster) for a given instance. |
protected void |
FarthestFirst.updateMinDistance(double[] minDistance,
boolean[] selected,
Instances data,
Instance center)
|
private void |
FarthestFirst.updateMinMax(Instance instance)
Updates the minimum and maximum values for all the attributes based on a new instance. |
protected int |
FarthestFirst.clusterProcessedInstance(Instance instance)
clusters an instance that has been through the filters |
int |
FarthestFirst.clusterInstance(Instance instance)
Classifies a given instance. |
protected double |
FarthestFirst.distance(Instance first,
Instance second)
Calculates the distance between two instances |
private void |
EM.updateMinMax(Instance instance)
Updates the minimum and maximum values for all the attributes based on a new instance. |
double[] |
EM.logDensityPerClusterForInstance(Instance inst)
Computes the log of the conditional density (per cluster) for a given instance. |
private static java.lang.String |
ClusterEvaluation.attributeValuesString(Instance instance,
Range attRange)
Builds a string listing the attribute values in a specified range of indices, separated by commas and enclosed in brackets. |
int |
Cobweb.clusterInstance(Instance instance)
Classifies a given instance. |
void |
Cobweb.addInstance(Instance newInstance)
Adds an instance to the Cobweb tree. |
protected void |
Cobweb.CNode.addInstance(Instance newInstance)
Adds an instance to this cluster. |
private double[] |
Cobweb.CNode.cuScoresForChildren(Instance newInstance)
Temporarily adds a new instance to each of this nodes children in turn and computes the category utility. |
private double |
Cobweb.CNode.cuScoreForBestTwoMerged(Cobweb.CNode merged,
Cobweb.CNode a,
Cobweb.CNode b,
Instance newInstance)
|
private Cobweb.CNode |
Cobweb.CNode.findHost(Instance newInstance,
boolean structureFrozen)
Finds a host for the new instance in this nodes children. |
protected void |
Cobweb.CNode.updateStats(Instance updateInstance,
boolean delete)
Update attribute stats using the supplied instance. |
private int |
SimpleKMeans.clusterProcessedInstance(Instance instance)
clusters an instance that has been through the filters |
int |
SimpleKMeans.clusterInstance(Instance instance)
Classifies a given instance. |
private double |
SimpleKMeans.distance(Instance first,
Instance second)
Calculates the distance between two instances |
private void |
SimpleKMeans.updateMinMax(Instance instance)
Updates the minimum and maximum values for all the attributes based on a new instance. |
int |
Clusterer.clusterInstance(Instance instance)
Classifies a given instance. |
double[] |
Clusterer.distributionForInstance(Instance instance)
Predicts the cluster memberships for a given instance. |
| Constructors in weka.clusterers with parameters of type Instance | |
Cobweb.CNode(int numAttributes,
Instance leafInstance)
Creates a new leaf CNode instance. |
|
| Uses of Instance in weka.core |
| Subclasses of Instance in weka.core | |
class |
BinarySparseInstance
Class for storing a binary-data-only instance as a sparse vector. |
class |
SparseInstance
Class for storing an instance as a sparse vector. |
| Methods in weka.core that return Instance | |
Instance |
ClassHierarchy.mergeClasses(Instance instance)
Returns a new Instance with classes selected and merged to superclasses according to the superclasses of this hierarchy. |
Instance |
BinarySparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns the result. |
Instance |
Instance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns the result. |
Instance |
ClassTree.mergeClasses(Instance instance)
Returns a new Instance with classes selected and merged to superclasses according to the superclasses of this ClassTree. |
Instance |
SparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns the result. |
Instance |
Instances.firstInstance()
Returns the first instance in the set. |
Instance |
Instances.instance(int index)
Returns the instance at the given position. |
Instance |
Instances.lastInstance()
Returns the last instance in the set. |
| Methods in weka.core with parameters of type Instance | |
Instance |
ClassHierarchy.mergeClasses(Instance instance)
Returns a new Instance with classes selected and merged to superclasses according to the superclasses of this hierarchy. |
Instance |
BinarySparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns the result. |
boolean |
Instance.equalHeaders(Instance inst)
Tests if the headers of two instances are equivalent. |
Instance |
Instance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns the result. |
Instance |
ClassTree.mergeClasses(Instance instance)
Returns a new Instance with classes selected and merged to superclasses according to the superclasses of this ClassTree. |
Instance |
SparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns the result. |
void |
Instances.add(Instance instance)
Adds one instance to the end of the set. |
boolean |
Instances.checkInstance(Instance instance)
Checks if the given instance is compatible with this dataset. |
| Constructors in weka.core with parameters of type Instance | |
BinarySparseInstance(Instance instance)
Constructor that generates a sparse instance from the given instance. |
|
Instance(Instance instance)
Constructor that copies the attribute values and the weight from the given instance. |
|
SparseInstance(Instance instance)
Constructor that generates a sparse instance from the given instance. |
|
| Uses of Instance in weka.core.converters |
| Methods in weka.core.converters that return Instance | |
Instance |
ArffLoader.getNextInstance()
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get. |
Instance |
CSVLoader.getNextInstance()
CSVLoader is unable to process a data set incrementally. |
Instance |
Loader.getNextInstance()
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get. |
abstract Instance |
TreeLoader.getNextInstance()
|
Instance |
SerializedInstancesLoader.getNextInstance()
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get. |
Instance |
C45Loader.getNextInstance()
Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get. |
private Instance |
C45Loader.getInstance(java.io.StreamTokenizer tokenizer)
Reads an instance using the supplied tokenizer. |
abstract Instance |
AbstractLoader.getNextInstance()
|
| Uses of Instance in weka.datagenerators |
| Methods in weka.datagenerators that return Instance | |
Instance |
BIRCHCluster.generateExample()
Generate an example of the dataset. |
private Instance |
BIRCHCluster.generateInstance(Instances format,
java.util.Random randomG,
double stdDev,
double[] center,
java.lang.String cName)
Generate an example of the dataset. |
(package private) abstract Instance |
ClusterGenerator.generateExample()
Generates one example of the dataset. |
Instance |
RDG1.generateExample()
Generate an example of the dataset dataset. |
private Instance |
RDG1.updateDecisionList(java.util.Random random,
Instance example)
Generates a new rule for the decision list. |
private Instance |
RDG1.generateExample(java.util.Random random,
Instances format)
Generates an example with its classvalue set to missing and binds it to the datasets. |
private Instance |
RDG1.votedReclassifyExample(Instance example)
Classify example with maximum vote the following way. |
(package private) abstract Instance |
Generator.generateExample()
Generates one example of the dataset. |
| Methods in weka.datagenerators with parameters of type Instance | |
private Instance |
RDG1.updateDecisionList(java.util.Random random,
Instance example)
Generates a new rule for the decision list. |
private FastVector |
RDG1.generateTestList(java.util.Random random,
Instance example)
Generates a new rule for the decision list and classifies the new example. |
private boolean |
RDG1.classifyExample(Instance example)
Tries to classify an example. |
private Instance |
RDG1.votedReclassifyExample(Instance example)
Classify example with maximum vote the following way. |
private double |
RDG1.RuleList.classifyInstance(Instance example)
|
boolean |
Test.passesTest(Instance inst)
Determines whether an instance passes the test. |
| Uses of Instance in weka.experiment |
| Fields in weka.experiment declared as Instance | |
(package private) Instance |
PairedTTester.Dataset.m_Template
|
(package private) Instance |
PairedTTester.Resultset.m_Template
|
| Methods in weka.experiment that return Instance | |
protected Instance |
PairedTTester.DatasetSpecifiers.specifier(int i)
Get the template at the given position. |
| Methods in weka.experiment with parameters of type Instance | |
protected void |
PairedTTester.DatasetSpecifiers.add(Instance inst)
Add an instance to the list of specifiers (if necessary) |
PairedStats |
PairedCorrectedTTester.calculateStatistics(Instance datasetSpecifier,
int resultset1Index,
int resultset2Index,
int comparisonColumn)
Computes a paired t-test comparison for a specified dataset between two resultsets. |
protected boolean |
PairedTTester.Dataset.matchesTemplate(Instance first)
Returns true if the two instances match on those attributes that have been designated key columns (eg: scheme name and scheme options) |
protected void |
PairedTTester.Dataset.add(Instance inst)
Adds the given instance to the dataset |
protected boolean |
PairedTTester.Resultset.matchesTemplate(Instance first)
Returns true if the two instances match on those attributes that have been designated key columns (eg: scheme name and scheme options) |
FastVector |
PairedTTester.Resultset.dataset(Instance inst)
Returns a vector containing all instances belonging to one dataset. |
void |
PairedTTester.Resultset.add(Instance newInst)
Adds an instance to this resultset |
protected java.lang.String |
PairedTTester.templateString(Instance template)
Returns a string descriptive of the key column values for the "datasets |
PairedStats |
PairedTTester.calculateStatistics(Instance datasetSpecifier,
int resultset1Index,
int resultset2Index,
int comparisonColumn)
Computes a paired t-test comparison for a specified dataset between two resultsets. |
| Constructors in weka.experiment with parameters of type Instance | |
PairedTTester.Dataset(Instance template)
|
|
PairedTTester.Resultset(Instance template)
|
|
| Uses of Instance in weka.filters |
| Methods in weka.filters that return Instance | |
Instance |
Filter.output()
Output an instance after filtering and remove from the output queue. |
Instance |
Filter.outputPeek()
Output an instance after filtering but do not remove from the output queue. |
| Methods in weka.filters with parameters of type Instance | |
protected void |
Filter.push(Instance instance)
Adds an output instance to the queue. |
protected void |
Filter.bufferInput(Instance instance)
Adds the supplied input instance to the inputformat dataset for later processing. |
private void |
Filter.copyStringValues(Instance inst,
Instances destDataset,
int[] strAtts)
Copies string values contained in the instance copied to a new dataset. |
protected void |
Filter.copyStringValues(Instance instance,
boolean instSrcCompat,
Instances srcDataset,
Instances destDataset)
Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset. |
protected void |
Filter.copyStringValues(Instance instance,
boolean instSrcCompat,
Instances srcDataset,
int[] srcStrAtts,
Instances destDataset,
int[] destStrAtts)
Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset. |
boolean |
Filter.input(Instance instance)
Input an instance for filtering. |
boolean |
AllFilter.input(Instance instance)
Input an instance for filtering. |
boolean |
NullFilter.input(Instance instance)
Input an instance for filtering. |
| Uses of Instance in weka.filters.supervised.attribute |
| Methods in weka.filters.supervised.attribute with parameters of type Instance | |
boolean |
ClassOrder.input(Instance instance)
Input an instance for filtering. |
boolean |
NominalToBinary.input(Instance instance)
Input an instance for filtering. |
private void |
NominalToBinary.convertInstance(Instance inst)
Convert a single instance over. |
private void |
NominalToBinary.convertInstanceNominal(Instance instance)
Convert a single instance over if the class is nominal. |
private void |
NominalToBinary.convertInstanceNumeric(Instance instance)
Convert a single instance over if the class is numeric. |
boolean |
AttributeSelection.input(Instance instance)
Input an instance for filtering. |
protected void |
AttributeSelection.convertInstance(Instance instance)
Convert a single instance over. |
boolean |
Discretize.input(Instance instance)
Input an instance for filtering. |
protected void |
Discretize.convertInstance(Instance instance)
Convert a single instance over. |
| Uses of Instance in weka.filters.supervised.instance |
| Methods in weka.filters.supervised.instance with parameters of type Instance | |
boolean |
SpreadSubsample.input(Instance instance)
Input an instance for filtering. |
boolean |
Resample.input(Instance instance)
Input an instance for filtering. |
| Uses of Instance in weka.filters.unsupervised.attribute |
| Methods in weka.filters.unsupervised.attribute that return Instance | |
Instance |
RemoveType.output()
Output an instance after filtering and remove from the output queue. |
Instance |
RemoveType.outputPeek()
Output an instance after filtering but do not remove from the output queue. |
protected Instance |
TimeSeriesDelta.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source") |
private Instance |
RandomProjection.convertInstance(Instance currentInstance)
converts a single instance to the required format |
protected Instance |
TimeSeriesTranslate.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source") |
protected Instance |
AbstractTimeSeries.historyInput(Instance instance)
Adds an instance to the history buffer. |
protected abstract Instance |
AbstractTimeSeries.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source") |
| Methods in weka.filters.unsupervised.attribute with parameters of type Instance | |
boolean |
RemoveType.input(Instance instance)
Input an instance for filtering. |
boolean |
Add.input(Instance instance)
Input an instance for filtering. |
boolean |
NominalToBinary.input(Instance instance)
Input an instance for filtering. |
private void |
NominalToBinary.convertInstance(Instance instance)
Convert a single instance over if the class is nominal. |
boolean |
ReplaceMissingValues.input(Instance instance)
Input an instance for filtering. |
private void |
ReplaceMissingValues.convertInstance(Instance instance)
Convert a single instance over. |
boolean |
AddNoise.input(Instance instance)
Input an instance for filtering. |
private void |
AddNoise.changeValueRandomly(java.util.Random r,
int numOfValues,
int indexOfAtt,
Instance instance,
boolean useMissing)
method to set a new value |
boolean |
Standardize.input(Instance instance)
Input an instance for filtering. |
private void |
Standardize.convertInstance(Instance instance)
Convert a single instance over. |
boolean |
StringToNominal.input(Instance instance)
Input an instance for filtering. |
boolean |
Normalize.input(Instance instance)
Input an instance for filtering. |
private void |
Normalize.convertInstance(Instance instance)
Convert a single instance over. |
boolean |
Copy.input(Instance instance)
Input an instance for filtering. |
protected Instance |
TimeSeriesDelta.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source") |
boolean |
RandomProjection.input(Instance instance)
Input an instance for filtering. |
private Instance |
RandomProjection.convertInstance(Instance currentInstance)
converts a single instance to the required format |
boolean |
AddCluster.input(Instance instance)
Input an instance for filtering. |
protected void |
AddCluster.convertInstance(Instance instance)
Convert a single instance over. |
boolean |
ClusterMembership.input(Instance instance)
Input an instance for filtering. |
protected double[] |
ClusterMembership.logs2densities(Instance in)
Converts logs back to density values. |
protected void |
ClusterMembership.convertInstance(Instance instance)
Convert a single instance over. |
boolean |
MakeIndicator.input(Instance instance)
Input an instance for filtering. |
boolean |
NumericToBinary.input(Instance instance)
Input an instance for filtering. |
private void |
NumericToBinary.convertInstance(Instance instance)
Convert a single instance over. |
boolean |
Discretize.input(Instance instance)
Input an instance for filtering. |
protected void |
Discretize.convertInstance(Instance instance)
Convert a single instance over. |
boolean |
Obfuscate.input(Instance instance)
Input an instance for filtering. |
boolean |
RemoveUseless.input(Instance instance)
Input an instance for filtering. |
boolean |
NumericTransform.input(Instance instance)
Input an instance for filtering. |
protected Instance |
TimeSeriesTranslate.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source") |
boolean |
SwapValues.input(Instance instance)
Input an instance for filtering. |
boolean |
AddExpression.input(Instance instance)
Input an instance for filtering. |
boolean |
MergeTwoValues.input(Instance instance)
Input an instance for filtering. |
boolean |
FirstOrder.input(Instance instance)
Input an instance for filtering. |
boolean |
StringToWordVector.input(Instance instance)
Input an instance for filtering. |
private void |
StringToWordVector.convertInstance(Instance instance)
|
private int |
StringToWordVector.convertInstancewoDocNorm(Instance instance,
FastVector v)
|
boolean |
Remove.input(Instance instance)
Input an instance for filtering. |
boolean |
AbstractTimeSeries.input(Instance instance)
Input an instance for filtering. |
protected Instance |
AbstractTimeSeries.historyInput(Instance instance)
Adds an instance to the history buffer. |
protected abstract Instance |
AbstractTimeSeries.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source") |
| Uses of Instance in weka.filters.unsupervised.instance |
| Methods in weka.filters.unsupervised.instance with parameters of type Instance | |
boolean |
SparseToNonSparse.input(Instance instance)
Input an instance for filtering. |
boolean |
Resample.input(Instance instance)
Input an instance for filtering. |
boolean |
RemoveWithValues.input(Instance instance)
Input an instance for filtering. |
boolean |
NonSparseToSparse.input(Instance instance)
Input an instance for filtering. |
boolean |
RemoveMisclassified.input(Instance instance)
Input an instance for filtering. |
| Uses of Instance in weka.gui.beans |
| Fields in weka.gui.beans declared as Instance | |
protected Instance |
IncrementalClassifierEvent.m_currentInstance
|
private Instance |
InstanceEvent.m_instance
|
| Methods in weka.gui.beans that return Instance | |
Instance |
IncrementalClassifierEvent.getCurrentInstance()
Get the current instance |
Instance |
InstanceEvent.getInstance()
Get the instance |
| Methods in weka.gui.beans with parameters of type Instance | |
void |
IncrementalClassifierEvent.setCurrentInstance(Instance i)
Set the current instance for this event |
void |
InstanceEvent.setInstance(Instance i)
Set the instance |
| Constructors in weka.gui.beans with parameters of type Instance | |
IncrementalClassifierEvent(java.lang.Object source,
Classifier scheme,
Instance currentI,
int status)
Creates a new IncrementalClassifierEvent instance. |
|
InstanceEvent(java.lang.Object source,
Instance instance,
int status)
Creates a new InstanceEvent instance. |
|
| Uses of Instance in weka.gui.boundaryvisualizer |
| Fields in weka.gui.boundaryvisualizer declared as Instance | |
private Instance |
RemoteBoundaryVisualizerSubTask.m_predInst
|
(package private) Instance |
BoundaryPanel.PlotThread.m_predInst
|
| Methods in weka.gui.boundaryvisualizer with parameters of type Instance | |
private double |
KDDataGenerator.distance(Instance first,
Instance second)
Calculates the distance between two instances |
private void |
KDDataGenerator.updateMinMax(Instance instance)
Updates the minimum and maximum values for all the attributes based on a new instance. |
| Uses of Instance in weka.gui.explorer |
| Methods in weka.gui.explorer with parameters of type Instance | |
private void |
ClassifierPanel.processClassifierPrediction(Instance toPredict,
Classifier classifier,
Evaluation eval,
FastVector predictions,
Instances plotInstances,
FastVector plotShape,
FastVector plotSize)
Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info. plotInfo for nominal class datasets holds shape types (actual data points have automatic shape type assignment; classifier error data points have box shape type). |
protected java.lang.String |
ClassifierPanel.predictionText(Classifier classifier,
Instance inst,
int instNum)
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| Uses of Instance in weka.gui.streams |
| Fields in weka.gui.streams declared as Instance | |
private Instance |
InstanceJoiner.m_OutputInstance
The current output instance |
private Instance |
InstanceLoader.m_OutputInstance
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| Methods in weka.gui.streams that return Instance | |
Instance |
InstanceProducer.outputPeek()
|
Instance |
InstanceJoiner.outputPeek()
Output an instance after filtering but do not remove from the output queue. |
Instance |
InstanceLoader.outputPeek()
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| Methods in weka.gui.streams with parameters of type Instance | |
void |
InstanceTable.input(Instance instance)
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void |
InstanceCounter.input(Instance instance)
|
boolean |
InstanceJoiner.input(Instance instance)
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void |
InstanceSavePanel.input(Instance instance)
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void |
InstanceViewer.input(Instance instance)
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| Uses of Instance in weka.gui.visualize |
| Methods in weka.gui.visualize with parameters of type Instance | |
boolean |
VisualizePanel.PlotPanel.inSplit(Instance i)
This will check if an instance is inside or outside of the current shapes. |
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