Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
boolean |
Element.isContainedBy(Instance instance)
Checks if an Element is contained by a given Instance.
|
Modifier and Type | Class and Description |
---|---|
class |
IndividualInstance |
Modifier and Type | Method and Description |
---|---|
abstract boolean |
LiteralSet.canKeep(Instance instance,
Literal newLit)
Test if an instance can be kept as a counter-instance,
given a new literal.
|
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.
|
boolean |
LiteralSet.counterInstance(Instance instance)
Test if an instance is a counter-instance of this LiteralSet.
|
boolean |
Rule.counterInstance(Instance instance)
Test if an instance is a counter-instance of this rule.
|
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.
|
abstract boolean |
Literal.negationSatisfies(Instance instance) |
boolean |
AttributeValueLiteral.negationSatisfies(Instance instance) |
abstract boolean |
Literal.satisfies(Instance instance) |
boolean |
AttributeValueLiteral.satisfies(Instance instance) |
Constructor and Description |
---|
IndividualInstance(Instance individual,
Instances parts) |
Modifier and Type | Method and Description |
---|---|
Instance |
AttributeTransformer.convertInstance(Instance instance)
Transforms an instance in the format of the original data to the
transformed space
|
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 |
AttributeSelection.reduceDimensionality(Instance in)
reduce the dimensionality of a single instance to include only those
attributes chosen by the last run of attribute selection.
|
Modifier and Type | Method and Description |
---|---|
Instance |
AttributeTransformer.convertInstance(Instance instance)
Transforms an instance in the format of the original data to the
transformed space
|
Instance |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) format.
|
Instance |
LatentSemanticAnalysis.convertInstance(Instance instance)
Transform an instance in original (unnormalized) format
|
double |
ClassifierSubsetEval.evaluateSubset(BitSet subset,
Instance holdOut,
boolean retrain)
Evaluates a subset of attributes with respect to a single instance.
|
abstract double |
HoldOutSubsetEvaluator.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.
|
Constructor and Description |
---|
hashKey(Instance t,
int numAtts)
Constructor for a hashKey
|
Modifier and Type | Method and Description |
---|---|
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)
|
Modifier and Type | Method and Description |
---|---|
double |
ComplementNaiveBayes.classifyInstance(Instance instance)
Classifies a given instance.
|
double |
BayesianLogisticRegression.classifyInstance(Instance instance)
Classifies the given instance using the Bayesian Logistic Regression function.
|
double[] |
BayesNet.countsForInstance(Instance instance)
Calculates the counts for Dirichlet distribution for 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[] |
BayesNet.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[] |
NaiveBayes.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[] |
NaiveBayesMultinomialUpdateable.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[] |
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[] |
NaiveBayesSimple.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 |
AODE.NBconditionalProb(Instance instance,
int classVal)
Calculates the probability of the specified class for the given test
instance, using naive Bayes.
|
double |
AODEsr.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 |
NaiveBayes.updateClassifier(Instance instance)
Updates the classifier with the given instance.
|
void |
AODE.updateClassifier(Instance instance)
Updates the classifier with the given instance.
|
void |
NaiveBayesMultinomialUpdateable.updateClassifier(Instance instance)
Updates the classifier with the given instance.
|
void |
AODEsr.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) |
Modifier and Type | Field and Description |
---|---|
Instance[] |
ADNode.m_Instances
list of Instance children (either m_Instances or m_VaryNodes is instantiated)
|
Modifier and Type | Method and Description |
---|---|
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 |
BayesNetEstimator.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.
|
Modifier and Type | Method and Description |
---|---|
Prediction |
EvaluationUtils.getPrediction(Classifier classifier,
Instance test)
Generate a single prediction for a test instance given the pre-trained
classifier.
|
Modifier and Type | Method and Description |
---|---|
boolean |
PaceRegression.checkForMissing(Instance instance,
Instances model)
Checks if an instance has a missing value.
|
double |
LeastMedSq.classifyInstance(Instance instance)
Classify a given instance using the best generated
LinearRegression Classifier.
|
double |
PaceRegression.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function.
|
double |
Winnow.classifyInstance(Instance inst)
Outputs the prediction for the given instance.
|
double |
LinearRegression.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function.
|
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 |
SMOreg.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function.
|
double |
GaussianProcesses.classifyInstance(Instance inst)
Classifies a given instance.
|
double |
PLSClassifier.classifyInstance(Instance instance)
Classifies the given test instance.
|
double[] |
LibSVM.distributionForInstance(Instance instance)
Computes the distribution for a given instance.
|
double[] |
SimpleLogistic.distributionForInstance(Instance inst)
Returns class probabilities for an instance.
|
double[] |
Logistic.distributionForInstance(Instance instance)
Computes the distribution for a given instance
|
double[] |
LibLINEAR.distributionForInstance(Instance instance)
Computes the distribution for a given instance.
|
double[] |
SMO.distributionForInstance(Instance inst)
Estimates class probabilities for given instance.
|
double[] |
SPegasos.distributionForInstance(Instance inst)
Computes the distribution for a given instance
|
double[] |
VotedPerceptron.distributionForInstance(Instance inst)
Outputs the distribution for the given output.
|
double[] |
RBFNetwork.distributionForInstance(Instance instance)
Computes the distribution for a given 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 |
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 |
Winnow.updateClassifier(Instance instance)
Updates the classifier with a new learning example
|
void |
SPegasos.updateClassifier(Instance instance)
Updates the classifier with the given instance.
|
Modifier and Type | Method and Description |
---|---|
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.
|
double |
StringKernel.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 |
PrecomputedKernelMatrixKernel.eval(int id1,
int id2,
Instance inst1) |
double |
RegOptimizer.SVMOutput(Instance inst) |
Modifier and Type | Method and Description |
---|---|
double |
IB1.classifyInstance(Instance instance)
Classifies the given test instance.
|
double[] |
IBk.distributionForInstance(Instance instance)
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[] |
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[] |
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.
|
void |
KStar.updateClassifier(Instance instance)
Adds the supplied instance to the training set
|
void |
LWL.updateClassifier(Instance instance)
Adds the supplied instance to the training set.
|
void |
IB1.updateClassifier(Instance instance)
Updates the classifier.
|
Constructor and Description |
---|
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
|
Modifier and Type | Method and Description |
---|---|
double |
AdditiveRegression.classifyInstance(Instance inst)
Classify an instance.
|
double |
ClassificationViaClustering.classifyInstance(Instance instance)
Classifies the given test instance.
|
double |
Vote.classifyInstance(Instance instance)
Classifies the given test instance.
|
double |
RegressionByDiscretization.classifyInstance(Instance instance)
Returns a predicted class for the test instance.
|
double[] |
RandomSubSpace.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
GridSearch.distributionForInstance(Instance instance)
Computes the distribution for a given instance
|
double[] |
FilteredClassifier.distributionForInstance(Instance instance)
Classifies a given instance after filtering.
|
double[] |
RacedIncrementalLogitBoost.distributionForInstance(Instance instance)
Computes class distribution of an instance using the best committee.
|
double[] |
OrdinalClassClassifier.distributionForInstance(Instance inst)
Returns the distribution for an instance.
|
double[] |
CostSensitiveClassifier.distributionForInstance(Instance instance)
Returns class probabilities.
|
double[] |
ThresholdSelector.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[] |
AttributeSelectedClassifier.distributionForInstance(Instance instance)
Classifies a given instance after attribute selection
|
double[] |
END.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
MultiScheme.distributionForInstance(Instance instance)
Returns class probabilities.
|
double[] |
RotationForest.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
Decorate.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance.
|
double[] |
MultiClassClassifier.distributionForInstance(Instance inst)
Returns the distribution for an instance.
|
double[] |
CVParameterSelection.distributionForInstance(Instance instance)
Predicts the class distribution for the given test instance.
|
double[] |
Grading.distributionForInstance(Instance instance)
Returns class probabilities for a given instance using the stacked classifier.
|
double[] |
Stacking.distributionForInstance(Instance instance)
Returns class probabilities.
|
double[] |
Bagging.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance.
|
double[] |
LogitBoost.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[] |
ClassificationViaRegression.distributionForInstance(Instance inst)
Returns the distribution for an instance.
|
double[] |
StackingC.distributionForInstance(Instance instance)
Classifies a given instance using the stacked classifier.
|
double[] |
MetaCost.distributionForInstance(Instance instance)
Classifies a given instance after filtering.
|
double[] |
RandomCommittee.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[] |
MultiClassClassifier.individualPredictions(Instance inst)
Returns the individual predictions of the base classifiers
for an instance.
|
void |
RacedIncrementalLogitBoost.updateClassifier(Instance instance)
Updates the classifier.
|
Modifier and Type | Method and Description |
---|---|
double[] |
ND.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[] |
ClassBalancedND.distributionForInstance(Instance inst)
Predicts the class distribution for a given instance
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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[] |
MIDD.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
MIOptimalBall.distributionForInstance(Instance newBag)
Computes the distribution for a given multiple instance
|
double[] |
MIBoost.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
MIWrapper.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
MDD.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[] |
MILR.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
MIEMDD.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
MISVM.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
CitationKNN.distributionForInstance(Instance bag)
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.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
double[] |
Regression.distributionForInstance(Instance inst)
Classifies the given test instance.
|
double[] |
NeuralNetwork.distributionForInstance(Instance inst)
Classifies the given test instance.
|
double[] |
GeneralRegression.distributionForInstance(Instance inst)
Classifies the given test instance.
|
Modifier and Type | Method and Description |
---|---|
double |
OneR.classifyInstance(Instance inst)
Classifies a given instance.
|
double |
Prism.classifyInstance(Instance inst)
Classifies a given instance.
|
double |
PART.classifyInstance(Instance instance)
Classifies an instance.
|
double |
NNge.classifyInstance(Instance instance)
Classifies a given instance.
|
double |
ZeroR.classifyInstance(Instance instance)
Classifies a given instance.
|
double |
Ridor.classifyInstance(Instance datum)
Classify the test instance with the rule learner
|
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 |
JRip.RipperRule.covers(Instance datum)
Whether the instance covered by this rule
|
abstract boolean |
Rule.covers(Instance datum)
Whether the instance covered by this rule
|
double[] |
JRip.distributionForInstance(Instance datum)
Classify the test instance with the rule learner and provide
the class distributions
|
double[] |
PART.distributionForInstance(Instance instance)
Returns class probabilities for an instance.
|
double[] |
DecisionTable.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given
test instance.
|
double[] |
ZeroR.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[] |
DTNB.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.
|
Constructor and Description |
---|
DecisionTableHashKey(Instance t,
int numAtts,
boolean ignoreClass)
Constructor for a hashKey
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
double |
J48.classifyInstance(Instance instance)
Classifies an instance.
|
double |
NBTree.classifyInstance(Instance instance)
Classifies an instance.
|
double |
FT.classifyInstance(Instance instance)
Classifies an instance.
|
double |
LMT.classifyInstance(Instance instance)
Classifies an instance.
|
double |
Id3.classifyInstance(Instance instance)
Classifies a given test instance using the decision tree.
|
double |
J48graft.classifyInstance(Instance instance)
Classifies an instance.
|
double[] |
RandomForest.distributionForInstance(Instance instance)
Returns the class probability distribution for 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 tree.
|
double[] |
LADTree.distributionForInstance(Instance instance)
Returns the class probability distribution for an instance.
|
double[] |
BFTree.distributionForInstance(Instance instance)
Computes class probabilities for instance using the decision tree.
|
double[] |
REPTree.distributionForInstance(Instance instance)
Computes class distribution of an instance using the tree.
|
double[] |
NBTree.distributionForInstance(Instance instance)
Returns class probabilities for an instance.
|
double[] |
DecisionStump.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance.
|
double[] |
SimpleCart.distributionForInstance(Instance instance)
Computes class probabilities for instance using the decision tree.
|
double[] |
FT.distributionForInstance(Instance instance)
Returns class probabilities for an instance.
|
double[] |
LMT.distributionForInstance(Instance instance)
Returns class probabilities for an instance.
|
double[] |
Id3.distributionForInstance(Instance instance)
Computes class distribution for instance using decision tree.
|
double[] |
ADTree.distributionForInstance(Instance instance)
Returns the class probability distribution for an 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[] |
J48graft.distributionForInstance(Instance instance)
Returns class probabilities for an instance.
|
double[] |
RandomTree.getMembershipValues(Instance instance)
Computes array that indicates node membership.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
abstract double[] |
FTtree.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[] |
FTInnerNode.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the Functional tree.
|
double[] |
FTNode.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.
|
Modifier and Type | Method and Description |
---|---|
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 |
ClassifierTree.classifyInstance(Instance instance)
Classifies an instance.
|
double |
ClassifierSplitModel.classifyInstance(Instance instance)
Classifies a given instance.
|
double |
GraftSplit.classProb(int classIndex,
Instance instance,
int theSubset)
returns the probability for instance for the specified class
|
double |
NBTreeNoSplit.classProb(int classIndex,
Instance instance,
int theSubset)
Return the probability for a class value
|
double |
C45Split.classProb(int classIndex,
Instance instance,
int theSubset)
Gets class probability for instance.
|
double |
ClassifierSplitModel.classProb(int classIndex,
Instance instance,
int theSubset)
Gets class probability for instance.
|
double |
NBTreeSplit.classProb(int classIndex,
Instance instance,
int theSubset)
Return the probability for a class value
|
double |
BinC45Split.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[] |
GraftSplit.weights(Instance instance) |
double[] |
NoSplit.weights(Instance instance)
Always returns null because there is only one subset.
|
double[] |
NBTreeNoSplit.weights(Instance instance)
Always returns null because there is only one subset.
|
double[] |
C45Split.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[] |
NBTreeSplit.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.
|
int |
GraftSplit.whichSubset(Instance instance) |
int |
NoSplit.whichSubset(Instance instance)
Always returns 0 because only there is only one subset.
|
int |
NBTreeNoSplit.whichSubset(Instance instance)
Always returns 0 because only there is only one subset.
|
int |
C45Split.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 |
NBTreeSplit.whichSubset(Instance instance)
Returns index of subset instance is assigned to.
|
int |
BinC45Split.whichSubset(Instance instance)
Returns index of subset instance is assigned to.
|
Modifier and Type | Method and Description |
---|---|
double[] |
LMTNode.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the logistic model tree.
|
double[] |
LogisticBase.distributionForInstance(Instance instance)
Returns class probabilities for an instance.
|
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) |
Modifier and Type | Method and Description |
---|---|
double |
RuleNode.classifyInstance(Instance inst)
Classify an instance using this node.
|
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 |
PreConstructedLinearModel.classifyInstance(Instance inst)
Predicts the class of the supplied instance using the linear model.
|
Modifier and Type | Method and Description |
---|---|
Instance |
XMeans.getNextDebugVectorsInstance(Instances model)
Read an instance from debug vectors file.
|
Modifier and Type | Method and Description |
---|---|
void |
Cobweb.addInstance(Instance newInstance)
Deprecated.
updateClusterer(Instance) should be used instead
|
int |
CLOPE.AddInstanceToBestCluster(Instance inst)
Add instance to best cluster
|
int |
XMeans.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
SimpleKMeans.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
DBSCAN.clusterInstance(Instance instance)
Classifies a given instance.
|
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 |
Clusterer.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
Cobweb.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
OPTICS.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
|
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[] |
DensityBasedClusterer.distributionForInstance(Instance instance)
Returns the cluster probability distribution for an instance.
|
double[] |
AbstractClusterer.distributionForInstance(Instance instance)
Predicts the cluster memberships for a given instance.
|
double[] |
AbstractDensityBasedClusterer.distributionForInstance(Instance instance)
Returns the cluster probability distribution for an instance.
|
double[] |
HierarchicalClusterer.distributionForInstance(Instance instance) |
double[] |
Clusterer.distributionForInstance(Instance instance)
Predicts the cluster memberships for a given instance.
|
double[] |
FilteredClusterer.distributionForInstance(Instance instance)
Classifies a given instance after filtering.
|
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[] |
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[] |
EM.logDensityPerClusterForInstance(Instance inst)
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 |
UpdateableClusterer.updateClusterer(Instance newInstance)
Adds an instance to the clusterer.
|
void |
Cobweb.updateClusterer(Instance newInstance)
Adds an instance to the clusterer.
|
Modifier and Type | Method and Description |
---|---|
Instance |
DataObject.getInstance()
Returns the original instance
|
Instance |
ManhattanDataObject.getInstance()
Returns the original instance
|
Instance |
EuclideanDataObject.getInstance()
Returns the original instance
|
Constructor and Description |
---|
EuclideanDataObject(Instance originalInstance,
String key,
Database database)
Constructs a new DataObject.
|
ManhattanDataObject(Instance originalInstance,
String key,
Database database)
Constructs a new DataObject.
|
Modifier and Type | Class and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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 |
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.
|
Instance |
BinarySparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns
the result.
|
Modifier and Type | Method and Description |
---|---|
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 |
DistanceFunction.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 |
EuclideanDistance.distance(Instance first,
Instance second)
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)
Calculates the distance between two instances.
|
double |
DistanceFunction.distance(Instance first,
Instance second,
double cutOffValue,
PerformanceStats stats)
Calculates the distance between two instances.
|
double |
NormalizableDistance.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 |
DistanceFunction.distance(Instance first,
Instance second,
PerformanceStats stats)
Calculates the distance between two instances.
|
double |
NormalizableDistance.distance(Instance first,
Instance second,
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.
|
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 |
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.
|
Instance |
BinarySparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns
the result.
|
void |
DistanceFunction.update(Instance ins)
Update the distance function (if necessary) for the newly added instance.
|
void |
NormalizableDistance.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.
|
Constructor and Description |
---|
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.
|
Modifier and Type | Method and Description |
---|---|
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 |
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 |
LibSVMLoader.getNextInstance(Instances structure)
LibSVmLoader is unable to process a data set incrementally.
|
Instance |
SVMLightLoader.getNextInstance(Instances structure)
SVMLightLoader is unable to process a data set incrementally.
|
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 |
TextDirectoryLoader.getNextInstance(Instances structure)
TextDirectoryLoader is unable to process a data set incrementally.
|
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.
|
abstract Instance |
AbstractLoader.getNextInstance(Instances structure) |
Instance |
XRFFLoader.getNextInstance(Instances structure)
XRFFLoader is unable to process a data set incrementally.
|
Instance |
CSVLoader.getNextInstance(Instances structure)
CSVLoader is unable to process a data set incrementally.
|
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 |
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.
|
Modifier and Type | Method and Description |
---|---|
void |
Saver.writeIncremental(Instance inst)
Writes to a destination in incremental mode.
|
void |
DatabaseSaver.writeIncremental(Instance inst)
Saves an instances incrementally.
|
void |
ArffSaver.writeIncremental(Instance inst)
Saves an instances incrementally.
|
void |
LibSVMSaver.writeIncremental(Instance inst)
Saves an instances incrementally.
|
void |
SVMLightSaver.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 |
C45Saver.writeIncremental(Instance inst)
Saves an instances incrementally.
|
Modifier and Type | Method and Description |
---|---|
Instance |
LinearNNSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied
instance.
|
Instance |
KDTree.nearestNeighbour(Instance target)
Returns the nearest neighbour of the supplied target
instance.
|
Instance |
BallTree.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.
|
abstract Instance |
NearestNeighbourSearch.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.
|
Modifier and Type | Method and Description |
---|---|
void |
LinearNNSearch.addInstanceInfo(Instance ins)
Adds the given instance info.
|
void |
KDTree.addInstanceInfo(Instance instance)
Adds one instance to KDTree loosly.
|
void |
BallTree.addInstanceInfo(Instance ins)
Adds the given instance's info.
|
void |
CoverTree.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.
|
Instances |
LinearNNSearch.kNearestNeighbours(Instance target,
int kNN)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
Instances |
KDTree.kNearestNeighbours(Instance target,
int k)
Returns the k nearest neighbours of the supplied instance.
|
Instances |
BallTree.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.
|
abstract Instances |
NearestNeighbourSearch.kNearestNeighbours(Instance target,
int k)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
Instance |
LinearNNSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied
instance.
|
Instance |
KDTree.nearestNeighbour(Instance target)
Returns the nearest neighbour of the supplied target
instance.
|
Instance |
BallTree.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.
|
abstract Instance |
NearestNeighbourSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied
instance.
|
void |
LinearNNSearch.update(Instance ins)
Updates the LinearNNSearch to cater for the new added instance.
|
void |
KDTree.update(Instance instance)
Adds one instance to the KDTree.
|
void |
BallTree.update(Instance ins)
Adds one instance to the BallTree.
|
void |
CoverTree.update(Instance ins)
Adds an instance to the cover tree.
|
abstract void |
NearestNeighbourSearch.update(Instance ins)
Updates the NearNeighbourSearch algorithm for the new added instance.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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[] |
TopDownConstructor.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.
|
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.
|
Constructor and Description |
---|
BallNode(int start,
int end,
int nodeNumber,
Instance pivot,
double radius)
Creates a new instance of BallNode.
|
Modifier and Type | Method and Description |
---|---|
double[] |
MappingInfo.instanceToSchema(Instance inst,
MiningSchema miningSchema)
Convert an
Instance to an array of values that matches the
format of the mining schema. |
Modifier and Type | Method and Description |
---|---|
abstract Instance |
DataGenerator.generateExample()
Generates one example of the dataset.
|
Modifier and Type | Method and Description |
---|---|
boolean |
Test.passesTest(Instance inst)
Determines whether an instance passes the test.
|
Modifier and Type | Method and Description |
---|---|
Instance |
RDG1.generateExample()
Generate an example of the dataset dataset.
|
Instance |
RandomRBF.generateExample()
Generates one example of the dataset.
|
Instance |
BayesNet.generateExample()
Generates one example of the dataset.
|
Instance |
LED24.generateExample()
Generates one example of the dataset.
|
Instance |
Agrawal.generateExample()
Generates one example of the dataset.
|
Modifier and Type | Method and Description |
---|---|
Instance |
MexicanHat.generateExample()
Generates one example of the dataset.
|
Instance |
Expression.generateExample()
Generates one example of the dataset.
|
Modifier and Type | Method and Description |
---|---|
Instance |
SubspaceCluster.generateExample()
Generate an example of the dataset.
|
Instance |
BIRCHCluster.generateExample()
Generate an example of the dataset.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
boolean |
Filter.input(Instance instance)
Input an instance for filtering.
|
boolean |
SimpleStreamFilter.input(Instance instance)
Input an instance for filtering.
|
boolean |
AllFilter.input(Instance instance)
Input an instance for filtering.
|
boolean |
SimpleBatchFilter.input(Instance instance)
Input an instance for filtering.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
boolean |
Resample.input(Instance instance)
Input an instance for filtering.
|
boolean |
StratifiedRemoveFolds.input(Instance instance)
Input an instance for filtering.
|
boolean |
SMOTE.input(Instance instance)
Input an instance for filtering.
|
boolean |
SpreadSubsample.input(Instance instance)
Input an instance for filtering.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
boolean |
ReplaceMissingValues.input(Instance instance)
Input an instance for filtering.
|
boolean |
MathExpression.input(Instance instance)
Input an instance for filtering.
|
boolean |
NominalToBinary.input(Instance instance)
Input an instance for filtering.
|
boolean |
StringToWordVector.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddNoise.input(Instance instance)
Input an instance for filtering.
|
boolean |
Remove.input(Instance instance)
Input an instance for filtering.
|
boolean |
StringToNominal.input(Instance instance)
Input an instance for filtering.
|
boolean |
MultiInstanceToPropositional.input(Instance instance)
Input an instance for filtering.
|
boolean |
Normalize.input(Instance instance)
Input an instance for filtering.
|
boolean |
NumericToBinary.input(Instance instance)
Input an instance for filtering.
|
boolean |
Add.input(Instance instance)
Input an instance for filtering.
|
boolean |
MakeIndicator.input(Instance instance)
Input an instance for filtering.
|
boolean |
AbstractTimeSeries.input(Instance instance)
Input an instance for filtering.
|
boolean |
Reorder.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddCluster.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveUseless.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 |
RandomProjection.input(Instance instance)
Input an instance for filtering.
|
boolean |
Discretize.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddExpression.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddValues.input(Instance instance)
Input an instance for filtering.
|
boolean |
FirstOrder.input(Instance instance)
Input an instance for filtering.
|
boolean |
Center.input(Instance instance)
Input an instance for filtering.
|
boolean |
ClusterMembership.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveType.input(Instance instance)
Input an instance for filtering.
|
boolean |
NumericTransform.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddID.input(Instance instance)
Input an instance for filtering.
|
boolean |
Copy.input(Instance instance)
Input an instance for filtering.
|
boolean |
SwapValues.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 |
NominalToString.input(Instance instance)
Input an instance for filtering.
|
boolean |
MergeTwoValues.input(Instance instance)
Input an instance for filtering.
|
Modifier and Type | Method and Description |
---|---|
boolean |
RemoveMisclassified.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 |
SubsetByExpression.input(Instance instance)
Input an instance for filtering.
|
boolean |
Normalize.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 |
ReservoirSample.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemovePercentage.input(Instance instance)
Input an instance for filtering.
|
boolean |
Randomize.input(Instance instance)
Input an instance for filtering.
|
boolean |
SparseToNonSparse.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveFrequentValues.input(Instance instance)
Input an instance for filtering.
|
boolean |
NonSparseToSparse.input(Instance instance)
Input an instance for filtering.
|
Modifier and Type | Method and Description |
---|---|
static Object |
Parser.getValue(Instance instance,
int index)
Returns either a String object for nominal attributes or a Double for numeric
ones.
|
Modifier and Type | Method and Description |
---|---|
Instance |
IncrementalClassifierEvent.getCurrentInstance()
Get the current instance
|
Instance |
InstanceEvent.getInstance()
Get the instance
|
Modifier and Type | Method and Description |
---|---|
void |
IncrementalClassifierEvent.setCurrentInstance(Instance i)
Set the current instance for this event
|
void |
InstanceEvent.setInstance(Instance i)
Set the instance
|
Constructor and Description |
---|
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. |
Modifier and Type | Method and Description |
---|---|
void |
BoundaryPanel.addTrainingInstance(Instance instance)
Adds a training instance to the visualization dataset.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
Instance |
InstanceLoader.outputPeek() |
Instance |
InstanceJoiner.outputPeek()
Output an instance after filtering but do not remove from the output
queue.
|
Instance |
InstanceProducer.outputPeek() |
Modifier and Type | Method and Description |
---|---|
void |
InstanceSavePanel.input(Instance instance) |
boolean |
InstanceJoiner.input(Instance instance) |
void |
InstanceTable.input(Instance instance) |
void |
InstanceViewer.input(Instance instance) |
void |
InstanceCounter.input(Instance instance) |
Copyright © 2019 University of Waikato, Hamilton, NZ. All rights reserved.