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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. |
void |
LabeledItemSet.upDateCounter(Instance instanceNoClass,
Instance instanceClass)
Updates counter of item set with respect to given transaction. |
| Uses of Instance in weka.associations.gsp |
|---|
| Methods in weka.associations.gsp with parameters of type Instance | |
|---|---|
boolean |
Element.isContainedBy(Instance instance)
Checks if an Element is contained by a given Instance. |
| 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 | |
|---|---|
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. |
abstract boolean |
LiteralSet.canKeep(Instance instance,
Literal newLit)
Test if an instance can be kept as a counter-instance, given a new literal. |
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. |
boolean |
Rule.counterInstance(Instance instance)
Test if an instance is a counter-instance of this rule. |
boolean |
LiteralSet.counterInstance(Instance instance)
Test if an instance is a counter-instance of this LiteralSet. |
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 |
AttributeValueLiteral.negationSatisfies(Instance instance)
|
abstract boolean |
Literal.negationSatisfies(Instance instance)
|
boolean |
AttributeValueLiteral.satisfies(Instance instance)
|
abstract boolean |
Literal.satisfies(Instance instance)
|
| 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 |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) format. |
Instance |
LatentSemanticAnalysis.convertInstance(Instance instance)
Transform an instance in original (unnormalized) format |
Instance |
AttributeTransformer.convertInstance(Instance instance)
Transforms an instance in the format of the original data to the transformed space |
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. |
| Methods in weka.attributeSelection with parameters of type Instance | |
|---|---|
Instance |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) format. |
Instance |
LatentSemanticAnalysis.convertInstance(Instance instance)
Transform an instance in original (unnormalized) format |
Instance |
AttributeTransformer.convertInstance(Instance instance)
Transforms an instance in the format of the original data to the transformed space |
abstract double |
HoldOutSubsetEvaluator.evaluateSubset(BitSet subset,
Instance holdOut,
boolean retrain)
Evaluates a subset of attributes with respect to a single instance. |
double |
ClassifierSubsetEval.evaluateSubset(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. |
| 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. |
double |
Evaluation.evaluateModelOnceAndRecordPrediction(Classifier classifier,
Instance instance)
Evaluates the classifier on a single instance and records the prediction (if the class is nominal). |
double |
Evaluation.evaluateModelOnceAndRecordPrediction(double[] dist,
Instance instance)
Evaluates the supplied distribution on a single instance. |
double[] |
CostMatrix.expectedCosts(double[] classProbs,
Instance inst)
Calculates the expected misclassification cost for each possible class value, given class probability estimates. |
double |
CostMatrix.getElement(int rowIndex,
int columnIndex,
Instance inst)
Return the value of a cell as a double. |
double |
CostMatrix.getMaxCost(int classVal,
Instance inst)
Gets the maximum cost for a particular class value. |
double[][] |
IntervalEstimator.predictInterval(Instance inst,
double confidenceLevel)
Returns an N*2 array, where N is the number of possible classes, that estimate the boundaries for the confidence interval with a confidence level specified by the second parameter. |
void |
UpdateableClassifier.updateClassifier(Instance instance)
Updates a classifier using the given instance. |
void |
Evaluation.updatePriors(Instance instance)
Updates the class prior probabilities (when incrementally training) |
| Uses of Instance in weka.classifiers.bayes |
|---|
| Methods in weka.classifiers.bayes with parameters of type Instance | |
|---|---|
double |
BayesianLogisticRegression.classifyInstance(Instance instance)
Classifies the given instance using the Bayesian Logistic Regression function. |
double |
ComplementNaiveBayes.classifyInstance(Instance instance)
Classifies a given instance. |
double[] |
BayesNet.countsForInstance(Instance instance)
Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance. |
double[] |
BayesNet.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
HNB.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance |
double[] |
DMNBtext.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
DMNBtext.DNBBinary.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
AODEsr.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
NaiveBayesMultinomialUpdateable.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
NaiveBayesMultinomial.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. |
double[] |
WAODE.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance |
double[] |
AODE.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
NaiveBayes.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double |
DMNBtext.DNBBinary.getLogProbForTargetClass(Instance ins)
Calculates the class membership probabilities for the given test instance. |
double |
AODEsr.NBconditionalProb(Instance instance,
int classVal)
Calculates the probability of the specified class for the given test instance, using naive Bayes. |
double |
AODE.NBconditionalProb(Instance instance,
int classVal)
Calculates the probability of the specified class for the given test instance, using naive Bayes. |
void |
BayesNet.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
void |
DMNBtext.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
void |
DMNBtext.DNBBinary.updateClassifier(Instance ins)
|
void |
AODEsr.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
void |
NaiveBayesMultinomialUpdateable.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
void |
AODE.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
void |
NaiveBayes.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
| Uses of Instance in weka.classifiers.bayes.net |
|---|
| Fields in weka.classifiers.bayes.net declared as Instance | |
|---|---|
Instance[] |
ADNode.m_Instances
list of Instance children (either m_Instances or m_VaryNodes is instantiated) |
| Uses of Instance in weka.classifiers.bayes.net.estimate |
|---|
| Methods in weka.classifiers.bayes.net.estimate with parameters of type Instance | |
|---|---|
double[] |
MultiNomialBMAEstimator.distributionForInstance(BayesNet bayesNet,
Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
SimpleEstimator.distributionForInstance(BayesNet bayesNet,
Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
BayesNetEstimator.distributionForInstance(BayesNet bayesNet,
Instance instance)
Calculates the class membership probabilities for the given test instance. |
void |
MultiNomialBMAEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance. |
void |
SimpleEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance. |
void |
BMAEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance. |
void |
BayesNetEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance. |
| Uses of Instance in weka.classifiers.evaluation |
|---|
| 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 |
|---|
| Methods in weka.classifiers.functions with parameters of type Instance | |
|---|---|
boolean |
PaceRegression.checkForMissing(Instance instance,
Instances model)
Checks if an instance has a missing value. |
double |
PLSClassifier.classifyInstance(Instance instance)
Classifies the given test instance. |
double |
LinearRegression.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function. |
double |
LeastMedSq.classifyInstance(Instance instance)
Classify a given instance using the best generated LinearRegression Classifier. |
double |
GaussianProcesses.classifyInstance(Instance inst)
Classifies a given instance. |
double |
IsotonicRegression.classifyInstance(Instance inst)
Generate a prediction for the supplied instance. |
double |
SimpleLinearRegression.classifyInstance(Instance inst)
Generate a prediction for the supplied instance. |
double |
Winnow.classifyInstance(Instance inst)
Outputs the prediction for the given instance. |
double |
SMOreg.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function. |
double |
PaceRegression.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function. |
double[] |
LibLINEAR.distributionForInstance(Instance instance)
Computes the distribution for a 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[] |
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[] |
LibSVM.distributionForInstance(Instance instance)
Computes the distribution for a given instance. |
double[] |
VotedPerceptron.distributionForInstance(Instance inst)
Outputs the distribution for the given output. |
double[] |
SPegasos.distributionForInstance(Instance inst)
Computes the distribution for a given instance |
double[] |
SMO.distributionForInstance(Instance inst)
Estimates class probabilities for given instance. |
double[] |
RBFNetwork.distributionForInstance(Instance instance)
Computes the distribution for a given instance |
double |
GaussianProcesses.getStandardDeviation(Instance inst)
Gives the variance of the prediction at the given instance |
int[] |
SMO.obtainVotes(Instance inst)
Returns an array of votes for the given instance. |
double[][] |
GaussianProcesses.predictInterval(Instance inst,
double confidenceLevel)
Predicts a confidence interval for the given instance and confidence level. |
double |
SMO.BinarySMO.SVMOutput(int index,
Instance inst)
Computes SVM output for given instance. |
void |
SPegasos.updateClassifier(Instance instance)
Updates the classifier with the given instance. |
void |
Winnow.updateClassifier(Instance instance)
Updates the classifier with a new learning example |
| Uses of Instance in weka.classifiers.functions.supportVector |
|---|
| Methods in weka.classifiers.functions.supportVector with parameters of type Instance | |
|---|---|
double |
PrecomputedKernelMatrixKernel.eval(int id1,
int id2,
Instance inst1)
|
double |
CachedKernel.eval(int id1,
int id2,
Instance inst1)
Implements the abstract function of Kernel using the cache. |
double |
NormalizedPolyKernel.eval(int id1,
int id2,
Instance inst1)
Computes the result of the kernel function for two instances. |
abstract double |
Kernel.eval(int id1,
int id2,
Instance inst1)
Computes the result of the kernel function for two instances. |
double |
StringKernel.eval(int id1,
int id2,
Instance inst1)
Computes the result of the kernel function for two instances. |
double |
RegOptimizer.SVMOutput(Instance inst)
|
| Uses of Instance in weka.classifiers.lazy |
|---|
| Methods in weka.classifiers.lazy with parameters of type Instance | |
|---|---|
double |
IB1.classifyInstance(Instance instance)
Classifies the given test instance. |
double[] |
LWL.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
LBR.distributionForInstance(Instance testInstance)
Calculates the class membership probabilities for the given test instance. |
double[] |
KStar.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
IBk.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
LBR.localDistributionForInstance(Instance instance,
LBR.Indexes instanceIndex)
Calculates the class membership probabilities. |
void |
IB1.updateClassifier(Instance instance)
Updates the classifier. |
void |
LWL.updateClassifier(Instance instance)
Adds the supplied instance to the training set. |
void |
KStar.updateClassifier(Instance instance)
Adds the supplied instance to the training set |
void |
IBk.updateClassifier(Instance instance)
Adds the supplied instance to the training set. |
| Uses of Instance in weka.classifiers.lazy.kstar |
|---|
| Constructors in weka.classifiers.lazy.kstar with parameters of type Instance | |
|---|---|
KStarNominalAttribute(Instance test,
Instance train,
int attrIndex,
Instances trainSet,
int[][] randClassCol,
KStarCache cache)
Constructor |
|
KStarNumericAttribute(Instance test,
Instance train,
int attrIndex,
Instances trainSet,
int[][] randClassCols,
KStarCache cache)
Constructor |
|
| Uses of Instance in weka.classifiers.meta |
|---|
| Methods in weka.classifiers.meta with parameters of type Instance | |
|---|---|
double |
AdditiveRegression.classifyInstance(Instance inst)
Classify an instance. |
double |
ClassificationViaClustering.classifyInstance(Instance instance)
Classifies the given test instance. |
double |
GridSearch.classifyInstance(Instance instance)
Classifies the given instance. |
double |
RegressionByDiscretization.classifyInstance(Instance instance)
Returns a predicted class for the test instance. |
double |
Vote.classifyInstance(Instance instance)
Classifies the given test instance. |
double[] |
MetaCost.distributionForInstance(Instance instance)
Classifies a given instance after filtering. |
double[] |
ClassificationViaRegression.distributionForInstance(Instance inst)
Returns the distribution for an instance. |
double[] |
FilteredClassifier.distributionForInstance(Instance instance)
Classifies a given instance after filtering. |
double[] |
AdaBoostM1.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
Stacking.distributionForInstance(Instance instance)
Returns class probabilities. |
double[] |
StackingC.distributionForInstance(Instance instance)
Classifies a given instance using the stacked classifier. |
double[] |
OrdinalClassClassifier.distributionForInstance(Instance inst)
Returns the distribution for an instance. |
double[] |
CVParameterSelection.distributionForInstance(Instance instance)
Predicts the class distribution for the given test instance. |
double[] |
RotationForest.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
MultiScheme.distributionForInstance(Instance instance)
Returns class probabilities. |
double[] |
Grading.distributionForInstance(Instance instance)
Returns class probabilities for a given instance using the stacked classifier. |
double[] |
MultiClassClassifier.distributionForInstance(Instance inst)
Returns the distribution for an instance. |
double[] |
LogitBoost.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
RandomSubSpace.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
Dagging.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
END.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
Vote.distributionForInstance(Instance instance)
Classifies a given instance using the selected combination rule. |
double[] |
AttributeSelectedClassifier.distributionForInstance(Instance instance)
Classifies a given instance after attribute selection |
double[] |
RandomCommittee.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
ThresholdSelector.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
Bagging.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
CostSensitiveClassifier.distributionForInstance(Instance instance)
Returns class probabilities. |
double[] |
Decorate.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
RacedIncrementalLogitBoost.distributionForInstance(Instance instance)
Computes class distribution of an instance using the best committee. |
double[] |
MultiClassClassifier.individualPredictions(Instance inst)
Returns the individual predictions of the base classifiers for an instance. |
void |
RacedIncrementalLogitBoost.updateClassifier(Instance instance)
Updates the classifier. |
| Uses of Instance in weka.classifiers.meta.nestedDichotomies |
|---|
| Methods in weka.classifiers.meta.nestedDichotomies with parameters of type Instance | |
|---|---|
double[] |
ClassBalancedND.distributionForInstance(Instance inst)
Predicts the class distribution for a given instance |
double[] |
DataNearBalancedND.distributionForInstance(Instance inst)
Predicts the class distribution for a given instance |
double[] |
ND.distributionForInstance(Instance inst)
Predicts the class distribution for a given instance |
| Uses of Instance in weka.classifiers.mi |
|---|
| Methods in weka.classifiers.mi that return Instance | |
|---|---|
Instance |
MINND.cleanse(Instance before)
Cleanse the given exemplar according to the valid and noise data statistics |
Instance |
MINND.preprocess(Instances data,
int pos)
Pre-process the given exemplar according to the other exemplars in the given exemplars. |
| Methods in weka.classifiers.mi with parameters of type Instance | |
|---|---|
double |
MINND.classifyInstance(Instance ex)
Use Kullback Leibler distance to find the nearest neighbours of the given exemplar. |
Instance |
MINND.cleanse(Instance before)
Cleanse the given exemplar according to the valid and noise data statistics |
void |
CitationKNN.countBagCiters(Instance bag)
calculates the citers associated to a bag |
void |
CitationKNN.countBagReferences(Instance bag)
Calculates the references of the exemplar bag |
double |
CitationKNN.distance(Instance first,
Instance second)
distance between two instances |
double |
CitationKNN.distanceSet(Instance first,
Instance second)
Calculates the distance between two instances |
double[] |
MDD.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
double[] |
MISVM.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
double[] |
MILR.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
double[] |
MIDD.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
double[] |
MISMO.distributionForInstance(Instance inst)
Estimates class probabilities for given instance. |
double[] |
SimpleMI.distributionForInstance(Instance newBag)
Computes the distribution for a given exemplar |
double[] |
MIEMDD.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
double[] |
MIBoost.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
double[] |
CitationKNN.distributionForInstance(Instance bag)
Computes the distribution for a given exemplar |
double[] |
MIOptimalBall.distributionForInstance(Instance newBag)
Computes the distribution for a given multiple instance |
double[] |
MIWrapper.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
boolean |
CitationKNN.equalExemplars(Instance exemplar1,
Instance exemplar2)
Wether the instances of two exemplars are or are not equal |
double |
MIOptimalBall.minBagDistance(Instance center,
Instance bag)
Calculate the distance from one data point to a bag |
void |
CitationKNN.updateNormalization(Instance bag)
Updates the normalization of each attribute. |
| Uses of Instance in weka.classifiers.misc |
|---|
| Methods in weka.classifiers.misc with parameters of type Instance | |
|---|---|
double[] |
SerializedClassifier.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
VFI.distributionForInstance(Instance instance)
Classifies the given test instance. |
double[] |
HyperPipes.distributionForInstance(Instance instance)
Classifies the given test instance. |
void |
HyperPipes.updateClassifier(Instance instance)
Updates the classifier. |
| Uses of Instance in weka.classifiers.pmml.consumer |
|---|
| Methods in weka.classifiers.pmml.consumer with parameters of type Instance | |
|---|---|
double[] |
NeuralNetwork.distributionForInstance(Instance inst)
Classifies the given test instance. |
double[] |
Regression.distributionForInstance(Instance inst)
Classifies the given test instance. |
double[] |
GeneralRegression.distributionForInstance(Instance inst)
Classifies the given test instance. |
| Uses of Instance in weka.classifiers.rules |
|---|
| Methods in weka.classifiers.rules with parameters of type Instance | |
|---|---|
double |
Ridor.classifyInstance(Instance datum)
Classify the test instance with the rule learner |
double |
PART.classifyInstance(Instance instance)
Classifies an instance. |
double |
Prism.classifyInstance(Instance inst)
Classifies a given instance. |
double |
NNge.classifyInstance(Instance instance)
Classifies a given instance. |
double |
ZeroR.classifyInstance(Instance instance)
Classifies a given instance. |
double |
OneR.classifyInstance(Instance inst)
Classifies a given instance. |
abstract boolean |
Rule.covers(Instance datum)
Whether the instance covered by this rule |
double[] |
DTNB.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
ConjunctiveRule.distributionForInstance(Instance instance)
Computes class distribution for the given instance. |
double[] |
JRip.distributionForInstance(Instance datum)
Classify the test instance with the rule learner and provide the class distributions |
double[] |
DecisionTable.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
PART.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double[] |
ZeroR.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
boolean |
ConjunctiveRule.isCover(Instance datum)
Whether the instance covered by this rule |
void |
NNge.updateClassifier(Instance instance)
Updates the classifier using the given instance. |
| Constructors in weka.classifiers.rules with parameters of type Instance | |
|---|---|
DecisionTableHashKey(Instance t,
int numAtts,
boolean ignoreClass)
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 |
ClassifierDecList.classifyInstance(Instance instance)
Classifies an instance. |
double[] |
MakeDecList.distributionForInstance(Instance instance)
Returns the class distribution for 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. |
| Uses of Instance in weka.classifiers.trees |
|---|
| Methods in weka.classifiers.trees with parameters of type Instance | |
|---|---|
double |
FT.classifyInstance(Instance instance)
Classifies an instance. |
double |
J48graft.classifyInstance(Instance instance)
Classifies an instance. |
double |
Id3.classifyInstance(Instance instance)
Classifies a given test instance using the decision tree. |
double |
NBTree.classifyInstance(Instance instance)
Classifies an instance. |
double |
J48.classifyInstance(Instance instance)
Classifies an instance. |
double |
LMT.classifyInstance(Instance instance)
Classifies an instance. |
double[] |
DecisionStump.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double[] |
FT.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double[] |
ADTree.distributionForInstance(Instance instance)
Returns the class probability distribution for an instance. |
double[] |
J48graft.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double[] |
LADTree.distributionForInstance(Instance instance)
Returns the class probability distribution for an instance. |
double[] |
Id3.distributionForInstance(Instance instance)
Computes class distribution for instance using decision tree. |
double[] |
REPTree.distributionForInstance(Instance instance)
Computes class distribution of an instance using the tree. |
double[] |
RandomForest.distributionForInstance(Instance instance)
Returns the class probability distribution for an instance. |
double[] |
NBTree.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double[] |
BFTree.distributionForInstance(Instance instance)
Computes class probabilities for instance using the decision tree. |
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[] |
SimpleCart.distributionForInstance(Instance instance)
Computes class probabilities for instance using the decision tree. |
double[] |
RandomTree.distributionForInstance(Instance instance)
Computes class distribution of an instance using the decision tree. |
double[] |
J48.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double[] |
LMT.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
| 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. |
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. |
abstract int |
Splitter.branchInstanceGoesDown(Instance i)
Gets the index of the branch that an instance applies to. |
| Uses of Instance in weka.classifiers.trees.ft |
|---|
| Methods in weka.classifiers.trees.ft with parameters of type Instance | |
|---|---|
double[] |
FTInnerNode.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the Functional tree. |
double[] |
FTLeavesNode.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the Functional Leaves tree. |
double[] |
FTNode.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the Functional Tree. |
abstract double[] |
FTtree.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the Functional tree. |
double[] |
FTtree.modelDistributionForInstance(Instance instance)
Returns the class probabilities for an instance according to the logistic model at the node. |
| Uses of Instance in weka.classifiers.trees.j48 |
|---|
| Methods in weka.classifiers.trees.j48 with parameters of type Instance | |
|---|---|
void |
Distribution.add(int bagIndex,
Instance instance)
Adds given instance to given bag. |
void |
Distribution.addWeights(Instance instance,
double[] weights)
Adds given instance to all bags weighting it according to given weights. |
double |
ClassifierSplitModel.classifyInstance(Instance instance)
Classifies a given instance. |
double |
ClassifierTree.classifyInstance(Instance instance)
Classifies an instance. |
double |
NBTreeNoSplit.classProb(int classIndex,
Instance instance,
int theSubset)
Return the probability for a class value |
double |
NBTreeSplit.classProb(int classIndex,
Instance instance,
int theSubset)
Return the probability for a class value |
double |
ClassifierSplitModel.classProb(int classIndex,
Instance instance,
int theSubset)
Gets class probability for instance. |
double |
BinC45Split.classProb(int classIndex,
Instance instance,
int theSubset)
Gets class probability for instance. |
double |
GraftSplit.classProb(int classIndex,
Instance instance,
int theSubset)
returns the probability for instance for the specified class |
double |
C45Split.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. |
void |
Distribution.del(int bagIndex,
Instance instance)
Deletes given instance from given bag. |
double[] |
ClassifierTree.distributionForInstance(Instance instance,
boolean useLaplace)
Returns class probabilities for a weighted instance. |
void |
Distribution.shift(int from,
int to,
Instance instance)
Shifts given instance from one bag to another one. |
void |
Distribution.sub(int bagIndex,
Instance instance)
Subtracts given instance from given bag. |
double[] |
NBTreeNoSplit.weights(Instance instance)
Always returns null because there is only one subset. |
double[] |
NoSplit.weights(Instance instance)
Always returns null because there is only one subset. |
double[] |
NBTreeSplit.weights(Instance instance)
Returns weights if instance is assigned to more than one subset. |
abstract double[] |
ClassifierSplitModel.weights(Instance instance)
Returns weights if instance is assigned to more than one subset. |
double[] |
BinC45Split.weights(Instance instance)
Returns weights if instance is assigned to more than one subset. |
double[] |
GraftSplit.weights(Instance instance)
|
double[] |
C45Split.weights(Instance instance)
Returns weights if instance is assigned to more than one subset. |
int |
NBTreeNoSplit.whichSubset(Instance instance)
Always returns 0 because only there is only one subset. |
int |
NoSplit.whichSubset(Instance instance)
Always returns 0 because only there is only one subset. |
int |
NBTreeSplit.whichSubset(Instance instance)
Returns index of subset instance is assigned to. |
abstract int |
ClassifierSplitModel.whichSubset(Instance instance)
Returns index of subset instance is assigned to. |
int |
BinC45Split.whichSubset(Instance instance)
Returns index of subset instance is assigned to. |
int |
GraftSplit.whichSubset(Instance instance)
|
int |
C45Split.whichSubset(Instance instance)
Returns index of subset instance is assigned to. |
| Uses of Instance in weka.classifiers.trees.lmt |
|---|
| Methods in weka.classifiers.trees.lmt with parameters of type Instance | |
|---|---|
double[] |
LogisticBase.distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
double[] |
LMTNode.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the logistic model tree. |
double[] |
LMTNode.modelDistributionForInstance(Instance instance)
Returns the class probabilities for an instance according to the logistic model at the node. |
double[] |
ResidualSplit.weights(Instance instance)
Method not in use |
int |
ResidualSplit.whichSubset(Instance instance)
|
| Uses of Instance in weka.classifiers.trees.m5 |
|---|
| Methods in weka.classifiers.trees.m5 with parameters of type Instance | |
|---|---|
double |
M5Base.classifyInstance(Instance inst)
Calculates a prediction for an instance using a set of rules or an M5 model tree |
double |
Rule.classifyInstance(Instance instance)
Calculates a prediction for an instance using this rule or M5 model tree |
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. |
| Uses of Instance in weka.clusterers |
|---|
| Methods in weka.clusterers that return Instance | |
|---|---|
Instance |
XMeans.getNextDebugVectorsInstance(Instances model)
Read an instance from debug vectors file. |
| Methods in weka.clusterers with parameters of type Instance | |
|---|---|
void |
Cobweb.addInstance(Instance newInstance)
Deprecated. updateClusterer(Instance) should be used instead |
int |
CLOPE.AddInstanceToBestCluster(Instance inst)
Add instance to best cluster |
int |
Cobweb.clusterInstance(Instance instance)
Classifies a given instance. |
int |
sIB.clusterInstance(Instance instance)
Cluster a given instance, this is the method defined in Clusterer interface do nothing but just return the cluster assigned to it |
int |
AbstractClusterer.clusterInstance(Instance instance)
Classifies a given instance. |
int |
FarthestFirst.clusterInstance(Instance instance)
Classifies a given instance. |
int |
CLOPE.clusterInstance(Instance instance)
Classifies a given instance. |
int |
HierarchicalClusterer.clusterInstance(Instance instance)
|
int |
DBScan.clusterInstance(Instance instance)
Classifies a given instance. |
int |
XMeans.clusterInstance(Instance instance)
Classifies a given instance. |
int |
OPTICS.clusterInstance(Instance instance)
Classifies a given instance. |
int |
SimpleKMeans.clusterInstance(Instance instance)
Classifies a given instance. |
int |
Clusterer.clusterInstance(Instance instance)
Classifies a given instance. |
DataObject |
DBScan.dataObjectForName(String database_distanceType,
Instance instance,
String key,
Database database)
Returns a new Class-Instance of the specified database |
DataObject |
OPTICS.dataObjectForName(String database_distanceType,
Instance instance,
String key,
Database database)
Returns a new Class-Instance of the specified database |
double[] |
AbstractClusterer.distributionForInstance(Instance instance)
Predicts the cluster memberships for a given instance. |
double[] |
FilteredClusterer.distributionForInstance(Instance instance)
Classifies a given instance after filtering. |
double[] |
HierarchicalClusterer.distributionForInstance(Instance instance)
|
double[] |
AbstractDensityBasedClusterer.distributionForInstance(Instance instance)
Returns the cluster probability distribution for an instance. |
double[] |
Clusterer.distributionForInstance(Instance instance)
Predicts the cluster memberships for a given instance. |
double |
DensityBasedClusterer.logDensityForInstance(Instance instance)
Computes the density for a given instance. |
double |
AbstractDensityBasedClusterer.logDensityForInstance(Instance instance)
Computes the density for a given instance. |
double[] |
EM.logDensityPerClusterForInstance(Instance inst)
Computes the log of the conditional density (per cluster) for a given instance. |
double[] |
MakeDensityBasedClusterer.logDensityPerClusterForInstance(Instance inst)
Computes the log of the conditional density (per cluster) for a given instance. |
double[] |
DensityBasedClusterer.logDensityPerClusterForInstance(Instance instance)
Computes the log of the conditional density (per cluster) for a given instance. |
abstract double[] |
AbstractDensityBasedClusterer.logDensityPerClusterForInstance(Instance instance)
Computes the log of the conditional density (per cluster) for a given instance. |
double[] |
DensityBasedClusterer.logJointDensitiesForInstance(Instance inst)
Returns the logs of the joint densities for a given instance. |
double[] |
AbstractDensityBasedClusterer.logJointDensitiesForInstance(Instance inst)
Returns the logs of the joint densities for a given instance. |
int |
CLOPE.MoveInstanceToBestCluster(Instance inst)
Move instance to best cluster |
void |
Cobweb.updateClusterer(Instance newInstance)
Adds an instance to the clusterer. |
void |
UpdateableClusterer.updateClusterer(Instance newInstance)
Adds an instance to the clusterer. |
| Uses of Instance in weka.clusterers.forOPTICSAndDBScan.DataObjects |
|---|
| Methods in weka.clusterers.forOPTICSAndDBScan.DataObjects that return Instance | |
|---|---|
Instance |
DataObject.getInstance()
Returns the original instance |
Instance |
EuclidianDataObject.getInstance()
Returns the original instance |
Instance |
ManhattanDataObject.getInstance()
Returns the original instance |
| Constructors in weka.clusterers.forOPTICSAndDBScan.DataObjects with parameters of type Instance | |
|---|---|
EuclidianDataObject(Instance originalInstance,
String key,
Database database)
Constructs a new DataObject. |
|
ManhattanDataObject(Instance originalInstance,
String key,
Database database)
Constructs a new DataObject. |
|
| 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 |
Instances.firstInstance()
Returns the first instance in the set. |
Instance |
AlgVector.getAsInstance(Instances model,
Random random)
Gets the elements of the vector as an instance. |
Instance |
Instances.instance(int index)
Returns the instance at the given position. |
Instance |
Instances.lastInstance()
Returns the last instance in the set. |
Instance |
BinarySparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns the result. |
Instance |
SparseInstance.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. |
| Methods in weka.core with parameters of type Instance | |
|---|---|
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. |
int |
EuclideanDistance.closestPoint(Instance instance,
Instances allPoints,
int[] pointList)
Returns the index of the closest point to the current instance. |
static void |
RelationalLocator.copyRelationalValues(Instance instance,
boolean instSrcCompat,
Instances srcDataset,
AttributeLocator srcLoc,
Instances destDataset,
AttributeLocator destLoc)
Takes relational values referenced by an Instance and copies them from a source dataset to a destination dataset. |
static void |
RelationalLocator.copyRelationalValues(Instance inst,
Instances destDataset,
AttributeLocator strAtts)
Copies relational values contained in the instance copied to a new dataset. |
static void |
StringLocator.copyStringValues(Instance instance,
boolean instSrcCompat,
Instances srcDataset,
AttributeLocator srcLoc,
Instances destDataset,
AttributeLocator destLoc)
Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset. |
static void |
StringLocator.copyStringValues(Instance inst,
Instances destDataset,
AttributeLocator strAtts)
Copies string values contained in the instance copied to a new dataset. |
double |
EuclideanDistance.distance(Instance first,
Instance second)
Calculates the distance between two instances. |
double |
NormalizableDistance.distance(Instance first,
Instance second)
Calculates the distance between two instances. |
double |
DistanceFunction.distance(Instance first,
Instance second)
Calculates the distance between two instances. |
double |
NormalizableDistance.distance(Instance first,
Instance second,
double cutOffValue)
Calculates the distance between two instances. |
double |
DistanceFunction.distance(Instance first,
Instance second,
double cutOffValue)
Calculates the distance between two instances. |
double |
NormalizableDistance.distance(Instance first,
Instance second,
double cutOffValue,
PerformanceStats stats)
Calculates the distance between two instances. |
double |
DistanceFunction.distance(Instance first,
Instance second,
double cutOffValue,
PerformanceStats stats)
Calculates the distance between two instances. |
double |
AbstractStringDistanceFunction.distance(Instance first,
Instance second,
double cutOffValue,
PerformanceStats stats)
Calculates the distance between two instances. |
double |
EuclideanDistance.distance(Instance first,
Instance second,
PerformanceStats stats)
Calculates the distance (or similarity) between two instances. |
double |
NormalizableDistance.distance(Instance first,
Instance second,
PerformanceStats stats)
Calculates the distance between two instances. |
double |
DistanceFunction.distance(Instance first,
Instance second,
PerformanceStats stats)
Calculates the distance between two instances. |
boolean |
Instance.equalHeaders(Instance inst)
Tests if the headers of two instances are equivalent. |
double |
AttributeExpression.evaluateExpression(Instance instance)
Evaluate the expression using the supplied Instance. |
boolean |
NormalizableDistance.inRanges(Instance instance,
double[][] ranges)
Test if an instance is within the given ranges. |
Instance |
BinarySparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns the result. |
Instance |
SparseInstance.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. |
void |
NormalizableDistance.update(Instance ins)
Update the distance function (if necessary) for the newly added instance. |
void |
DistanceFunction.update(Instance ins)
Update the distance function (if necessary) for the newly added instance. |
void |
NormalizableDistance.updateRanges(Instance instance)
Update the ranges if a new instance comes. |
double[][] |
NormalizableDistance.updateRanges(Instance instance,
double[][] ranges)
Updates the ranges given a new instance. |
void |
NormalizableDistance.updateRanges(Instance instance,
int numAtt,
double[][] ranges)
Updates the minimum and maximum and width values for all the attributes based on a new instance. |
void |
NormalizableDistance.updateRangesFirst(Instance instance,
int numAtt,
double[][] ranges)
Used to initialize the ranges. |
boolean |
EuclideanDistance.valueIsSmallerEqual(Instance instance,
int dim,
double value)
Returns true if the value of the given dimension is smaller or equal the value to be compared with. |
| Constructors in weka.core with parameters of type Instance | |
|---|---|
AlgVector(Instance instance)
Constructs a vector using an 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 |
DatabaseLoader.getNextInstance(Instances structure)
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 |
LibSVMLoader.getNextInstance(Instances structure)
LibSVmLoader is unable to process a data set incrementally. |
Instance |
ArffLoader.getNextInstance(Instances structure)
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 |
Loader.getNextInstance(Instances structure)
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(Instances structure)
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 |
XRFFLoader.getNextInstance(Instances structure)
XRFFLoader is unable to process a data set incrementally. |
Instance |
TextDirectoryLoader.getNextInstance(Instances structure)
TextDirectoryLoader is unable to process a data set incrementally. |
Instance |
SVMLightLoader.getNextInstance(Instances structure)
SVMLightLoader is unable to process a data set incrementally. |
abstract Instance |
AbstractLoader.getNextInstance(Instances structure)
|
Instance |
CSVLoader.getNextInstance(Instances structure)
CSVLoader is unable to process a data set incrementally. |
Instance |
SerializedInstancesLoader.getNextInstance(Instances structure)
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 |
ConverterUtils.DataSource.nextElement(Instances dataset)
returns the next element and sets the specified dataset, null if none available. |
Instance |
ArffLoader.ArffReader.readInstance(Instances structure)
Reads a single instance using the tokenizer and returns it. |
Instance |
ArffLoader.ArffReader.readInstance(Instances structure,
boolean flag)
Reads a single instance using the tokenizer and returns it. |
| Methods in weka.core.converters with parameters of type Instance | |
|---|---|
void |
ArffSaver.writeIncremental(Instance inst)
Saves an instances incrementally. |
void |
CSVSaver.writeIncremental(Instance inst)
Saves an instances incrementally. |
void |
AbstractSaver.writeIncremental(Instance i)
Method for incremental saving. |
void |
LibSVMSaver.writeIncremental(Instance inst)
Saves an instances incrementally. |
void |
SVMLightSaver.writeIncremental(Instance inst)
Saves an instances incrementally. |
void |
C45Saver.writeIncremental(Instance inst)
Saves an instances incrementally. |
void |
Saver.writeIncremental(Instance inst)
Writes to a destination in incremental mode. |
void |
DatabaseSaver.writeIncremental(Instance inst)
Saves an instances incrementally. |
| Uses of Instance in weka.core.neighboursearch |
|---|
| Methods in weka.core.neighboursearch that return Instance | |
|---|---|
Instance |
KDTree.nearestNeighbour(Instance target)
Returns the nearest neighbour of the supplied target instance. |
Instance |
LinearNNSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance. |
abstract Instance |
NearestNeighbourSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance. |
Instance |
CoverTree.nearestNeighbour(Instance target)
Returns the NN instance of a given target instance, from among the previously supplied training instances. |
Instance |
BallTree.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance. |
Instance |
CoverTree.CoverTreeNode.p()
Returns the instance represented by the node. |
| Methods in weka.core.neighboursearch with parameters of type Instance | |
|---|---|
void |
KDTree.addInstanceInfo(Instance instance)
Adds one instance to KDTree loosly. |
void |
LinearNNSearch.addInstanceInfo(Instance ins)
Adds the given instance info. |
void |
NearestNeighbourSearch.addInstanceInfo(Instance ins)
Adds information from the given instance without modifying the datastructure a lot. |
void |
CoverTree.addInstanceInfo(Instance ins)
Adds the given instance info. |
void |
BallTree.addInstanceInfo(Instance ins)
Adds the given instance's info. |
Instances |
KDTree.kNearestNeighbours(Instance target,
int k)
Returns the k nearest neighbours of the supplied instance. |
Instances |
LinearNNSearch.kNearestNeighbours(Instance target,
int kNN)
Returns k nearest instances in the current neighbourhood to the supplied instance. |
abstract Instances |
NearestNeighbourSearch.kNearestNeighbours(Instance target,
int k)
Returns k nearest instances in the current neighbourhood to the supplied instance. |
Instances |
CoverTree.kNearestNeighbours(Instance target,
int k)
Returns k-NNs of a given target instance, from among the previously supplied training instances (supplied through setInstances method) P.S.: May return more than k-NNs if more one instances have the same distance to the target as the kth NN. |
Instances |
BallTree.kNearestNeighbours(Instance target,
int k)
Returns k nearest instances in the current neighbourhood to the supplied instance. |
Instance |
KDTree.nearestNeighbour(Instance target)
Returns the nearest neighbour of the supplied target instance. |
Instance |
LinearNNSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance. |
abstract Instance |
NearestNeighbourSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance. |
Instance |
CoverTree.nearestNeighbour(Instance target)
Returns the NN instance of a given target instance, from among the previously supplied training instances. |
Instance |
BallTree.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance. |
void |
KDTree.update(Instance instance)
Adds one instance to the KDTree. |
void |
LinearNNSearch.update(Instance ins)
Updates the LinearNNSearch to cater for the new added instance. |
abstract void |
NearestNeighbourSearch.update(Instance ins)
Updates the NearNeighbourSearch algorithm for the new added instance. |
void |
CoverTree.update(Instance ins)
Adds an instance to the cover tree. |
void |
BallTree.update(Instance ins)
Adds one instance to the BallTree. |
| Uses of Instance in weka.core.neighboursearch.balltrees |
|---|
| Methods in weka.core.neighboursearch.balltrees that return Instance | |
|---|---|
static Instance |
BallNode.calcCentroidPivot(int[] instList,
Instances insts)
Calculates the centroid pivot of a node. |
static Instance |
BallNode.calcCentroidPivot(int start,
int end,
int[] instList,
Instances insts)
Calculates the centroid pivot of a node. |
static Instance |
BallNode.calcPivot(BallNode child1,
BallNode child2,
Instances insts)
Calculates the centroid pivot of a node based on its two child nodes (if merging two nodes). |
Instance |
BottomUpConstructor.calcPivot(weka.core.neighboursearch.balltrees.BottomUpConstructor.TempNode node1,
weka.core.neighboursearch.balltrees.BottomUpConstructor.TempNode node2,
Instances insts)
Calculates the centroid pivot of a node based on its two child nodes. |
Instance |
MiddleOutConstructor.calcPivot(weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList list1,
weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList list2,
Instances insts)
Calculates the centroid pivot of a node based on the list of points that it contains (tbe two lists of its children are provided). |
Instance |
MiddleOutConstructor.calcPivot(weka.core.neighboursearch.balltrees.MiddleOutConstructor.TempNode node1,
weka.core.neighboursearch.balltrees.MiddleOutConstructor.TempNode node2,
Instances insts)
/** Calculates the centroid pivot of a node based on its two child nodes (if merging two nodes). |
Instance |
BallNode.getPivot()
Returns the pivot/centre of the node's ball. |
| Methods in weka.core.neighboursearch.balltrees with parameters of type Instance | |
|---|---|
int[] |
BottomUpConstructor.addInstance(BallNode node,
Instance inst)
Adds an instance to the ball tree. |
abstract int[] |
BallTreeConstructor.addInstance(BallNode node,
Instance inst)
Adds an instance to the ball tree. |
int[] |
MiddleOutConstructor.addInstance(BallNode node,
Instance inst)
Adds an instance to the tree. |
int[] |
TopDownConstructor.addInstance(BallNode node,
Instance inst)
Adds an instance to the ball tree. |
static double |
BallNode.calcRadius(BallNode child1,
BallNode child2,
Instance pivot,
DistanceFunction distanceFunction)
Calculates the radius of a node based on its two child nodes (if merging two nodes). |
static double |
BallNode.calcRadius(int[] instList,
Instances insts,
Instance pivot,
DistanceFunction distanceFunction)
Calculates the radius of node. |
static double |
BallNode.calcRadius(int start,
int end,
int[] instList,
Instances insts,
Instance pivot,
DistanceFunction distanceFunction)
Calculates the radius of a node. |
double |
MiddleOutConstructor.calcRadius(weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList list1,
weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList list2,
Instance pivot,
Instances insts)
Calculates the radius of a node based on the list of points that it contains (the two lists of its children are provided). |
void |
BallNode.setPivot(Instance pivot)
Sets the pivot/centre of this nodes ball. |
| Constructors in weka.core.neighboursearch.balltrees with parameters of type Instance | |
|---|---|
BallNode(int start,
int end,
int nodeNumber,
Instance pivot,
double radius)
Creates a new instance of BallNode. |
|
| Uses of Instance in weka.core.pmml |
|---|
| Methods in weka.core.pmml with parameters of type Instance | |
|---|---|
double[] |
MappingInfo.instanceToSchema(Instance inst,
MiningSchema miningSchema)
Convert an Instance to an array of values that matches the
format of the mining schema. |
| Uses of Instance in weka.datagenerators |
|---|
| Methods in weka.datagenerators that return Instance | |
|---|---|
abstract Instance |
DataGenerator.generateExample()
Generates one example of the dataset. |
| Methods in weka.datagenerators with parameters of type Instance | |
|---|---|
boolean |
Test.passesTest(Instance inst)
Determines whether an instance passes the test. |
| Uses of Instance in weka.datagenerators.classifiers.classification |
|---|
| Methods in weka.datagenerators.classifiers.classification that return Instance | |
|---|---|
Instance |
LED24.generateExample()
Generates one example of the dataset. |
Instance |
BayesNet.generateExample()
Generates one example of the dataset. |
Instance |
RandomRBF.generateExample()
Generates one example of the dataset. |
Instance |
RDG1.generateExample()
Generate an example of the dataset dataset. |
Instance |
Agrawal.generateExample()
Generates one example of the dataset. |
| Uses of Instance in weka.datagenerators.classifiers.regression |
|---|
| Methods in weka.datagenerators.classifiers.regression that return Instance | |
|---|---|
Instance |
Expression.generateExample()
Generates one example of the dataset. |
Instance |
MexicanHat.generateExample()
Generates one example of the dataset. |
| Uses of Instance in weka.datagenerators.clusterers |
|---|
| Methods in weka.datagenerators.clusterers that return Instance | |
|---|---|
Instance |
BIRCHCluster.generateExample()
Generate an example of the dataset. |
Instance |
SubspaceCluster.generateExample()
Generate an example of the dataset. |
| Uses of Instance in weka.experiment |
|---|
| Methods in weka.experiment with parameters of type Instance | |
|---|---|
PairedStats |
Tester.calculateStatistics(Instance datasetSpecifier,
int resultset1Index,
int resultset2Index,
int comparisonColumn)
Computes a paired t-test comparison for a specified dataset between two resultsets. |
PairedStats |
PairedCorrectedTTester.calculateStatistics(Instance datasetSpecifier,
int resultset1Index,
int resultset2Index,
int comparisonColumn)
Computes a paired t-test comparison for a specified dataset between two resultsets. |
PairedStats |
PairedTTester.calculateStatistics(Instance datasetSpecifier,
int resultset1Index,
int resultset2Index,
int comparisonColumn)
Computes a paired t-test comparison for a specified dataset between two resultsets. |
| 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 | |
|---|---|
boolean |
AllFilter.input(Instance instance)
Input an instance for filtering. |
boolean |
SimpleStreamFilter.input(Instance instance)
Input an instance for filtering. |
boolean |
SimpleBatchFilter.input(Instance instance)
Input an instance for filtering. |
boolean |
Filter.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 |
NominalToBinary.input(Instance instance)
Input an instance for filtering. |
boolean |
AttributeSelection.input(Instance instance)
Input an instance for filtering. |
boolean |
ClassOrder.input(Instance instance)
Input an instance for filtering. |
boolean |
Discretize.input(Instance instance)
Input an instance for filtering. |
| Uses of Instance in weka.filters.supervised.instance |
|---|
| Methods in weka.filters.supervised.instance with parameters of type Instance | |
|---|---|
boolean |
Resample.input(Instance instance)
Input an instance for filtering. |
boolean |
StratifiedRemoveFolds.input(Instance instance)
Input an instance for filtering. |
boolean |
SpreadSubsample.input(Instance instance)
Input an instance for filtering. |
boolean |
SMOTE.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. |
| Methods in weka.filters.unsupervised.attribute with parameters of type Instance | |
|---|---|
boolean |
AddValues.input(Instance instance)
Input an instance for filtering. |
boolean |
RemoveType.input(Instance instance)
Input an instance for filtering. |
boolean |
ClusterMembership.input(Instance instance)
Input an instance for filtering. |
boolean |
PrincipalComponents.input(Instance instance)
Input an instance for filtering. |
boolean |
Obfuscate.input(Instance instance)
Input an instance for filtering. |
boolean |
AddExpression.input(Instance instance)
Input an instance for filtering. |
boolean |
FirstOrder.input(Instance instance)
Input an instance for filtering. |
boolean |
AbstractTimeSeries.input(Instance instance)
Input an instance for filtering. |
boolean |
Remove.input(Instance instance)
Input an instance for filtering. |
boolean |
NumericToBinary.input(Instance instance)
Input an instance for filtering. |
boolean |
MergeTwoValues.input(Instance instance)
Input an instance for filtering. |
boolean |
NominalToBinary.input(Instance instance)
Input an instance for filtering. |
boolean |
Copy.input(Instance instance)
Input an instance for filtering. |
boolean |
StringToNominal.input(Instance instance)
Input an instance for filtering. |
boolean |
NumericTransform.input(Instance instance)
Input an instance for filtering. |
boolean |
NominalToString.input(Instance instance)
Input an instance for filtering. |
boolean |
MathExpression.input(Instance instance)
Input an instance for filtering. |
boolean |
AddNoise.input(Instance instance)
Input an instance for filtering. |
boolean |
RandomProjection.input(Instance instance)
Input an instance for filtering. |
boolean |
ChangeDateFormat.input(Instance instance)
Input an instance for filtering. |
boolean |
Standardize.input(Instance instance)
Input an instance for filtering. |
boolean |
Normalize.input(Instance instance)
Input an instance for filtering. |
boolean |
MultiInstanceToPropositional.input(Instance instance)
Input an instance for filtering. |
boolean |
Reorder.input(Instance instance)
Input an instance for filtering. |
boolean |
Center.input(Instance instance)
Input an instance for filtering. |
boolean |
MakeIndicator.input(Instance instance)
Input an instance for filtering. |
boolean |
AddCluster.input(Instance instance)
Input an instance for filtering. |
boolean |
AddID.input(Instance instance)
Input an instance for filtering. |
boolean |
RemoveUseless.input(Instance instance)
Input an instance for filtering. |
boolean |
Discretize.input(Instance instance)
Input an instance for filtering. |
boolean |
Add.input(Instance instance)
Input an instance for filtering. |
boolean |
StringToWordVector.input(Instance instance)
Input an instance for filtering. |
boolean |
ReplaceMissingValues.input(Instance instance)
Input an instance for filtering. |
boolean |
SwapValues.input(Instance instance)
Input an instance for filtering. |
| Uses of Instance in weka.filters.unsupervised.instance |
|---|
| Methods in weka.filters.unsupervised.instance with parameters of type Instance | |
|---|---|
boolean |
RemoveMisclassified.input(Instance instance)
Input an instance for filtering. |
boolean |
SubsetByExpression.input(Instance instance)
Input an instance for filtering. |
boolean |
ReservoirSample.input(Instance instance)
Input an instance for filtering. |
boolean |
RemovePercentage.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 |
Randomize.input(Instance instance)
Input an instance for filtering. |
boolean |
NonSparseToSparse.input(Instance instance)
Input an instance for filtering. |
boolean |
RemoveFolds.input(Instance instance)
Input an instance for filtering. |
boolean |
RemoveRange.input(Instance instance)
Input an instance for filtering. |
boolean |
RemoveFrequentValues.input(Instance instance)
Input an instance for filtering. |
boolean |
Normalize.input(Instance instance)
Input an instance for filtering. |
boolean |
SparseToNonSparse.input(Instance instance)
Input an instance for filtering. |
| Uses of Instance in weka.filters.unsupervised.instance.subsetbyexpression |
|---|
| Methods in weka.filters.unsupervised.instance.subsetbyexpression with parameters of type Instance | |
|---|---|
static Object |
Parser.getValue(Instance instance,
int index)
Returns either a String object for nominal attributes or a Double for numeric ones. |
| Uses of Instance in weka.gui.beans |
|---|
| 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(Object source,
Classifier scheme,
Instance currentI,
int status)
Creates a new IncrementalClassifierEvent instance. |
|
InstanceEvent(Object source,
Instance instance,
int status)
Creates a new InstanceEvent instance that encapsulates
a single instance only. |
|
| Uses of Instance in weka.gui.boundaryvisualizer |
|---|
| Methods in weka.gui.boundaryvisualizer with parameters of type Instance | |
|---|---|
void |
BoundaryPanel.addTrainingInstance(Instance instance)
Adds a training instance to the visualization dataset. |
| Uses of Instance in weka.gui.explorer |
|---|
| Methods in weka.gui.explorer with parameters of type Instance | |
|---|---|
static void |
ClassifierPanel.processClassifierPrediction(Instance toPredict,
Classifier classifier,
Evaluation eval,
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. |
| Uses of Instance in weka.gui.streams |
|---|
| Methods in weka.gui.streams that return Instance | |
|---|---|
Instance |
InstanceProducer.outputPeek()
|
Instance |
InstanceLoader.outputPeek()
|
Instance |
InstanceJoiner.outputPeek()
Output an instance after filtering but do not remove from the output queue. |
| Methods in weka.gui.streams with parameters of type Instance | |
|---|---|
void |
InstanceTable.input(Instance instance)
|
void |
InstanceSavePanel.input(Instance instance)
|
void |
InstanceCounter.input(Instance instance)
|
void |
InstanceViewer.input(Instance instance)
|
boolean |
InstanceJoiner.input(Instance instance)
|
|
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