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 |
LatentSemanticAnalysis.convertInstance(Instance instance)
Transform an instance in original (unnormalized) format
|
Instance |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) 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 |
LatentSemanticAnalysis.convertInstance(Instance instance)
Transform an instance in original (unnormalized) format
|
Instance |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) format.
|
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.
|
Constructor and Description |
---|
hashKey(Instance t,
int numAtts)
Constructor for a hashKey
|
Modifier and Type | Method and Description |
---|---|
protected static String |
Evaluation.attributeValuesString(Instance instance,
Range attRange)
Builds a string listing the attribute values in a specified range of indices,
separated by commas and enclosed in brackets.
|
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.
|
protected static String |
Evaluation.predictionText(Classifier classifier,
Instance inst,
int instNum,
Range attributesToOutput,
boolean printDistribution)
store the prediction made by the classifier as a string
|
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)
|
protected void |
Evaluation.updateStatsForClassifier(double[] predictedDistribution,
Instance instance)
Updates all the statistics about a classifiers performance for
the current test instance.
|
protected void |
Evaluation.updateStatsForPredictor(double predictedValue,
Instance instance)
Updates all the statistics about a predictors performance for
the current test instance.
|
Modifier and Type | Method and Description |
---|---|
protected Instance |
BayesNet.normalizeInstance(Instance instance)
ensure that all variables are nominal and that there are no missing values
|
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[] |
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[] |
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[] |
NaiveBayesMultinomialUpdateable.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[] |
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[] |
HNB.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.
|
protected Instance |
BayesNet.normalizeInstance(Instance instance)
ensure that all variables are nominal and that there are no missing values
|
void |
AODE.updateClassifier(Instance instance)
Updates the classifier with the given instance.
|
void |
NaiveBayes.updateClassifier(Instance instance)
Updates the classifier with the given instance.
|
void |
NaiveBayesMultinomialUpdateable.updateClassifier(Instance instance)
Updates the classifier with the given instance.
|
void |
BayesNet.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[] |
SimpleEstimator.distributionForInstance(BayesNet bayesNet,
Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
MultiNomialBMAEstimator.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 |
SimpleEstimator.updateClassifier(BayesNet bayesNet,
Instance instance)
Updates the classifier with the given instance.
|
void |
MultiNomialBMAEstimator.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.
|
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 |
IsotonicRegression.classifyInstance(Instance inst)
Generate a prediction for the supplied instance.
|
double |
LeastMedSq.classifyInstance(Instance instance)
Classify a given instance using the best generated
LinearRegression Classifier.
|
double |
Winnow.classifyInstance(Instance inst)
Outputs the prediction for the given instance.
|
double |
GaussianProcesses.classifyInstance(Instance inst)
Classifies a given instance.
|
double |
SimpleLinearRegression.classifyInstance(Instance inst)
Generate a prediction for the supplied instance.
|
double |
PaceRegression.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function.
|
double |
LinearRegression.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function.
|
double |
SMOreg.classifyInstance(Instance instance)
Classifies the given instance using the linear regression function.
|
double |
PLSClassifier.classifyInstance(Instance instance)
Classifies the given test instance.
|
double[] |
Logistic.distributionForInstance(Instance instance)
Computes the distribution for a given instance
|
double[] |
SMO.distributionForInstance(Instance inst)
Estimates class probabilities for 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[] |
SimpleLogistic.distributionForInstance(Instance inst)
Returns class probabilities for an 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[] |
LibLINEAR.distributionForInstance(Instance instance)
Computes the distribution for a given instance.
|
double[] |
RBFNetwork.distributionForInstance(Instance instance)
Computes the distribution for a given instance
|
double[] |
LibSVM.distributionForInstance(Instance instance)
Computes the distribution for a given instance.
|
protected static double |
SPegasos.dotProd(Instance inst1,
double[] weights,
int classIndex) |
double |
GaussianProcesses.getStandardDeviation(Instance inst)
Gives the variance of the prediction at the given instance
|
protected Object |
LibLINEAR.instanceToArray(Instance instance)
returns an instance into a sparse liblinear array
|
protected Object |
LibSVM.instanceToArray(Instance instance)
returns an instance into a sparse libsvm array
|
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 |
---|---|
protected double |
CachedKernel.dotProd(Instance inst1,
Instance inst2)
Calculates a dot product between two instances
|
abstract double |
Kernel.eval(int id1,
int id2,
Instance inst1)
Computes the result of the kernel function for two instances.
|
double |
StringKernel.eval(int id1,
int id2,
Instance inst1)
Computes the result of the kernel function for two instances.
|
double |
NormalizedPolyKernel.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 |
CachedKernel.eval(int id1,
int id2,
Instance inst1)
Implements the abstract function of Kernel using the cache.
|
protected double |
Puk.evaluate(int id1,
int id2,
Instance inst1)
returns the dot product
|
protected double |
PolyKernel.evaluate(int id1,
int id2,
Instance inst1) |
protected double |
RBFKernel.evaluate(int id1,
int id2,
Instance inst1) |
protected abstract double |
CachedKernel.evaluate(int id1,
int id2,
Instance inst1)
This method is overridden in subclasses to implement specific kernels.
|
double |
RegOptimizer.SVMOutput(Instance inst) |
Modifier and Type | Method and Description |
---|---|
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[] |
IBk.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[] |
LBR.localDistributionForInstance(Instance instance,
LBR.Indexes instanceIndex)
Calculates the class membership probabilities.
|
void |
LWL.updateClassifier(Instance instance)
Adds the supplied instance to the training set.
|
void |
IBk.updateClassifier(Instance instance)
Adds the supplied instance to the training set.
|
void |
IB1.updateClassifier(Instance instance)
Updates the classifier.
|
void |
KStar.updateClassifier(Instance instance)
Adds the supplied instance to the training set
|
Modifier and Type | Field and Description |
---|---|
protected Instance |
KStarNumericAttribute.m_Test
The test instance
|
protected Instance |
KStarNominalAttribute.m_Test
The test instance
|
protected Instance |
KStarNumericAttribute.m_Train
The train instance
|
protected Instance |
KStarNominalAttribute.m_Train
The train instance
|
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 |
---|---|
protected Instance |
RotationForest.convertInstance(Instance instance,
int i)
Transforms an instance for the i-th classifier.
|
protected Instance |
Stacking.metaInstance(Instance instance)
Makes a level-1 instance from the given instance.
|
protected Instance |
Grading.metaInstance(Instance instance,
int k)
Makes a level-1 instance from the given instance.
|
Modifier and Type | Method and Description |
---|---|
double |
Vote.classifyInstance(Instance instance)
Classifies the given test instance.
|
double |
ClassificationViaClustering.classifyInstance(Instance instance)
Classifies the given test instance.
|
double |
RacedIncrementalLogitBoost.Committee.classifyInstance(Instance instance)
classifies an instance with the committee
|
double |
RegressionByDiscretization.classifyInstance(Instance instance)
Returns a predicted class for the test instance.
|
double |
AdditiveRegression.classifyInstance(Instance inst)
Classify an instance.
|
double |
GridSearch.classifyInstance(Instance instance)
Classifies the given instance.
|
protected double |
Vote.classifyInstanceMedian(Instance instance)
Classifies the given test instance, returning the median from all
classifiers.
|
protected Instance |
RotationForest.convertInstance(Instance instance,
int i)
Transforms an instance for the i-th classifier.
|
double[] |
MetaCost.distributionForInstance(Instance instance)
Classifies a given instance after filtering.
|
double[] |
Grading.distributionForInstance(Instance instance)
Returns class probabilities for a given instance using the stacked classifier.
|
double[] |
ClassificationViaRegression.distributionForInstance(Instance inst)
Returns the distribution for an instance.
|
double[] |
CostSensitiveClassifier.distributionForInstance(Instance instance)
Returns class probabilities.
|
double[] |
END.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[] |
Vote.distributionForInstance(Instance instance)
Classifies a given instance using the selected combination rule.
|
double[] |
RandomCommittee.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
Stacking.distributionForInstance(Instance instance)
Returns class probabilities.
|
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[] |
RacedIncrementalLogitBoost.distributionForInstance(Instance instance)
Computes class distribution of an instance using the best committee.
|
double[] |
RacedIncrementalLogitBoost.Committee.distributionForInstance(Instance instance)
returns the distribution the committee generates for an instance
|
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[] |
FilteredClassifier.distributionForInstance(Instance instance)
Classifies a given instance after filtering.
|
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[] |
RandomSubSpace.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
OrdinalClassClassifier.distributionForInstance(Instance inst)
Returns the distribution for an instance.
|
double[] |
MultiScheme.distributionForInstance(Instance instance)
Returns class probabilities.
|
double[] |
StackingC.distributionForInstance(Instance instance)
Classifies a given instance using the stacked classifier.
|
double[] |
Dagging.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
CVParameterSelection.distributionForInstance(Instance instance)
Predicts the class distribution for the given test instance.
|
double[] |
AttributeSelectedClassifier.distributionForInstance(Instance instance)
Classifies a given instance after attribute selection
|
protected double[] |
Vote.distributionForInstanceAverage(Instance instance)
Classifies a given instance using the Average of Probabilities
combination rule.
|
protected double[] |
Vote.distributionForInstanceMajorityVoting(Instance instance)
Classifies a given instance using the Majority Voting combination rule.
|
protected double[] |
Vote.distributionForInstanceMax(Instance instance)
Classifies a given instance using the Maximum Probability combination rule.
|
protected double[] |
Vote.distributionForInstanceMin(Instance instance)
Classifies a given instance using the Minimum Probability combination rule.
|
protected double[] |
Vote.distributionForInstanceProduct(Instance instance)
Classifies a given instance using the Product of Probabilities
combination rule.
|
double[] |
MultiClassClassifier.individualPredictions(Instance inst)
Returns the individual predictions of the base classifiers
for an instance.
|
protected Instance |
Stacking.metaInstance(Instance instance)
Makes a level-1 instance from the given instance.
|
protected Instance |
Grading.metaInstance(Instance instance,
int k)
Makes a level-1 instance from the given instance.
|
void |
RacedIncrementalLogitBoost.updateClassifier(Instance instance)
Updates the classifier.
|
double[] |
RacedIncrementalLogitBoost.Committee.updateFS(Instance instance,
Classifier[] newModel,
double[] Fs)
updates the Fs values given a new model in the committee
|
Modifier and Type | Method and Description |
---|---|
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
|
protected double[] |
ND.distributionForInstance(Instance inst,
ND.NDTree node)
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[] |
CitationKNN.distributionForInstance(Instance bag)
Computes the distribution for a given exemplar
|
double[] |
MDD.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
MIWrapper.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
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[] |
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[] |
MISMO.distributionForInstance(Instance inst)
Estimates class probabilities for given instance.
|
double[] |
MISVM.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
MIDD.distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
double[] |
MILR.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
|
protected weka.classifiers.mi.CitationKNN.NeighborList |
CitationKNN.findNeighbors(Instance bag,
int kNN,
Instances bags)
Build the list of nearest k neighbors to the given test instance.
|
double |
MIOptimalBall.minBagDistance(Instance center,
Instance bag)
Calculate the distance from one data point to a bag
|
protected double |
MISMO.BinaryMISMO.SVMOutput(int index,
Instance inst)
Computes SVM output for given instance.
|
void |
CitationKNN.updateNormalization(Instance bag)
Updates the normalization of each attribute.
|
Modifier and Type | Method and Description |
---|---|
protected double |
MIPolyKernel.evaluate(int id1,
int id2,
Instance inst1) |
protected double |
MIRBFKernel.evaluate(int id1,
int id2,
Instance inst1) |
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 |
NNge.classifyInstance(Instance instance)
Classifies a given instance.
|
double |
Prism.classifyInstance(Instance inst)
Classifies a given instance.
|
double |
ZeroR.classifyInstance(Instance instance)
Classifies a given instance.
|
double |
PART.classifyInstance(Instance instance)
Classifies an instance.
|
double |
OneR.classifyInstance(Instance inst)
Classifies a given instance.
|
double |
Ridor.classifyInstance(Instance datum)
Classify the test instance with the rule learner
|
abstract boolean |
Rule.covers(Instance datum)
Whether the instance covered by this rule
|
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
|
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[] |
PART.distributionForInstance(Instance instance)
Returns class probabilities for an instance.
|
double[] |
DTNB.distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given
test instance.
|
double[] |
JRip.distributionForInstance(Instance datum)
Classify the test instance with the rule learner and provide
the class distributions
|
double[] |
ConjunctiveRule.distributionForInstance(Instance instance)
Computes class distribution for the given 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 |
ClassifierDecList.classifyInstance(Instance instance)
Classifies an instance.
|
double |
MakeDecList.classifyInstance(Instance instance)
Classifies an instance.
|
double[] |
ClassifierDecList.distributionForInstance(Instance instance)
Returns class probabilities for a weighted instance.
|
double[] |
MakeDecList.distributionForInstance(Instance instance)
Returns the class distribution for an instance.
|
double |
ClassifierDecList.weight(Instance instance)
Returns the weight a rule assigns to an instance.
|
Modifier and Type | Class and Description |
---|---|
protected class |
LADTree.LADInstance
helper classes
|
Modifier and Type | Method and Description |
---|---|
abstract int |
LADTree.Splitter.branchInstanceGoesDown(Instance i) |
int |
LADTree.TwoWayNominalSplit.branchInstanceGoesDown(Instance inst) |
int |
LADTree.TwoWayNumericSplit.branchInstanceGoesDown(Instance inst) |
double |
J48.classifyInstance(Instance instance)
Classifies an instance.
|
double |
FT.classifyInstance(Instance instance)
Classifies an instance.
|
double |
J48graft.classifyInstance(Instance instance)
Classifies an instance.
|
double |
LMT.classifyInstance(Instance instance)
Classifies an instance.
|
double |
NBTree.classifyInstance(Instance instance)
Classifies an instance.
|
double |
Id3.classifyInstance(Instance instance)
Classifies a given test 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[] |
FT.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[] |
RandomForest.distributionForInstance(Instance instance)
Returns the class probability distribution for an instance.
|
double[] |
REPTree.distributionForInstance(Instance instance)
Computes class distribution of an instance using the tree.
|
protected double[] |
REPTree.Tree.distributionForInstance(Instance instance)
Computes class distribution of an instance using the tree.
|
double[] |
SimpleCart.distributionForInstance(Instance instance)
Computes class probabilities for instance using the decision tree.
|
double[] |
J48graft.distributionForInstance(Instance instance)
Returns class probabilities 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[] |
LMT.distributionForInstance(Instance instance)
Returns class probabilities 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[] |
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[] |
ADTree.distributionForInstance(Instance instance)
Returns the class probability distribution for an instance.
|
protected void |
REPTree.Tree.insertHoldOutInstance(Instance inst,
double weight,
REPTree.Tree parent)
Inserts an instance from the hold-out set into the tree.
|
protected double |
ADTree.predictionValueForInstance(Instance inst,
PredictionNode currentNode,
double currentValue)
Returns the class prediction value (vote) for an instance.
|
Constructor and Description |
---|
LADInstance(Instance instance) |
Modifier and Type | Method and Description |
---|---|
void |
ReferenceInstances.addReference(Instance instance)
Adds one instance reference to the end of the set.
|
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.
|
int |
TwoWayNominalSplit.branchInstanceGoesDown(Instance inst)
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[] |
FTNode.distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the Functional Tree.
|
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.
|
protected double[] |
FTtree.getFs(Instance instance)
Computes the F-values of LogitBoost for an instance from the current logistic model at the node
Note that this also takes into account the (partial) logistic model fit at higher levels in
the tree.
|
double[] |
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 |
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 |
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 |
C45Split.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 |
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[] |
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.
|
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[] |
C45Split.weights(Instance instance)
Returns weights if instance is assigned to more than one subset.
|
double[] |
GraftSplit.weights(Instance instance) |
double[] |
BinC45Split.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.
|
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 |
C45Split.whichSubset(Instance instance)
Returns index of subset instance is assigned to.
|
int |
GraftSplit.whichSubset(Instance instance) |
int |
BinC45Split.whichSubset(Instance instance)
Returns index of subset instance is assigned to.
|
Modifier and Type | Method and Description |
---|---|
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.
|
protected double[] |
LogisticBase.getFs(Instance instance)
Computes the F-values for a single instance.
|
protected double[] |
LMTNode.getFs(Instance instance)
Computes the F-values of LogitBoost for an instance from the current logistic model at the node
Note that this also takes into account the (partial) logistic model fit at higher levels in
the tree.
|
double[] |
LMTNode.modelDistributionForInstance(Instance instance)
Returns the class probabilities for an instance according to the logistic model at the node.
|
double[] |
ResidualSplit.weights(Instance instance)
Method not in use
|
int |
ResidualSplit.whichSubset(Instance instance) |
Modifier and Type | Method and Description |
---|---|
double |
M5Base.classifyInstance(Instance inst)
Calculates a prediction for an instance using a set of rules
or an M5 model tree
|
double |
RuleNode.classifyInstance(Instance inst)
Classify an instance using this node.
|
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
|
protected double |
XMeans.calculateBIC(int[] instList,
Instance center,
double mle,
Instances model)
Returns the BIC-value for the given center and instances.
|
int |
XMeans.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
CLOPE.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
FarthestFirst.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
OPTICS.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
Cobweb.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
DBScan.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
Clusterer.clusterInstance(Instance instance)
Classifies a given instance.
|
int |
SimpleKMeans.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 |
HierarchicalClusterer.clusterInstance(Instance instance) |
protected int |
XMeans.clusterProcessedInstance(Instance instance)
Clusters an instance that has been through the filters.
|
protected int |
FarthestFirst.clusterProcessedInstance(Instance instance)
clusters an instance that has been through the filters
|
protected int |
XMeans.clusterProcessedInstance(Instance instance,
Instances centers)
Clusters an instance.
|
DataObject |
OPTICS.dataObjectForName(String database_distanceType,
Instance instance,
String key,
Database database)
Returns a new Class-Instance of the specified database
|
DataObject |
DBScan.dataObjectForName(String database_distanceType,
Instance instance,
String key,
Database database)
Returns a new Class-Instance of the specified database
|
protected double |
FarthestFirst.distance(Instance first,
Instance second)
Calculates the distance between two instances
|
double[] |
FilteredClusterer.distributionForInstance(Instance instance)
Classifies a given instance after filtering.
|
double[] |
Clusterer.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[] |
AbstractClusterer.distributionForInstance(Instance instance)
Predicts the cluster memberships for a given instance.
|
double[] |
HierarchicalClusterer.distributionForInstance(Instance 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[] |
DensityBasedClusterer.logDensityPerClusterForInstance(Instance instance)
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.
|
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.
|
protected double |
XMeans.logLikelihoodEstimate(int numInst,
Instance center,
double distortion,
int numCent)
Calculates the log-likelihood of the data for the given model, taken
at the maximum likelihood point.
|
int |
CLOPE.MoveInstanceToBestCluster(Instance inst)
Move instance to best cluster
|
protected Instances |
XMeans.splitCenter(Random random,
Instance center,
double variance,
Instances model)
Split centers in their region.
|
void |
UpdateableClusterer.updateClusterer(Instance newInstance)
Adds an instance to the clusterer.
|
void |
Cobweb.updateClusterer(Instance newInstance)
Adds an instance to the clusterer.
|
protected void |
FarthestFirst.updateMinDistance(double[] minDistance,
boolean[] selected,
Instances data,
Instance center) |
Modifier and Type | Method and Description |
---|---|
Instance |
ManhattanDataObject.getInstance()
Returns the original instance
|
Instance |
EuclidianDataObject.getInstance()
Returns the original instance
|
Instance |
DataObject.getInstance()
Returns the original instance
|
Constructor and Description |
---|
EuclidianDataObject(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 |
BinarySparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns
the result.
|
Instance |
Instance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns
the result.
|
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 |
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 |
EuclideanDistance.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 |
AbstractStringDistanceFunction.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 |
DistanceFunction.distance(Instance first,
Instance second,
double cutOffValue,
PerformanceStats stats)
Calculates the distance 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.
|
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 |
BinarySparseInstance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns
the result.
|
Instance |
Instance.mergeInstance(Instance inst)
Merges this instance with the given instance and returns
the result.
|
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.
|
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 | Field and Description |
---|---|
protected Instance |
ConverterUtils.DataSource.m_IncrementalBuffer
the last internally read instance.
|
Modifier and Type | Method and Description |
---|---|
protected Instance |
ArffLoader.ArffReader.getInstance(Instances structure,
boolean flag)
Reads a single instance using the tokenizer and returns it.
|
protected Instance |
ArffLoader.ArffReader.getInstanceFull(boolean flag)
Reads a single instance using the tokenizer and returns it.
|
protected Instance |
ArffLoader.ArffReader.getInstanceSparse(boolean flag)
Reads a single instance using the tokenizer and returns it.
|
Instance |
TextDirectoryLoader.getNextInstance(Instances structure)
TextDirectoryLoader 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 |
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 |
XRFFLoader.getNextInstance(Instances structure)
XRFFLoader 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 |
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 |
LibSVMLoader.getNextInstance(Instances structure)
LibSVmLoader 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.
|
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 |
---|---|
protected String |
LibSVMSaver.instanceToLibsvm(Instance inst)
turns the instance into a libsvm row
|
protected String |
CSVSaver.instanceToString(Instance inst)
turns an instance into a string.
|
protected String |
SVMLightSaver.instanceToSvmlight(Instance inst)
turns the instance into a svm light row.
|
void |
C45Saver.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 |
DatabaseSaver.writeIncremental(Instance inst)
Saves an instances incrementally.
|
void |
LibSVMSaver.writeIncremental(Instance inst)
Saves an instances incrementally.
|
void |
Saver.writeIncremental(Instance inst)
Writes to a destination in incremental mode.
|
void |
ArffSaver.writeIncremental(Instance inst)
Saves an instances incrementally.
|
void |
SVMLightSaver.writeIncremental(Instance inst)
Saves an instances incrementally.
|
Modifier and Type | Field and Description |
---|---|
Instance |
NearestNeighbourSearch.NeighborNode.m_Instance
The neighbor instance.
|
Modifier and Type | Method and Description |
---|---|
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.
|
abstract Instance |
NearestNeighbourSearch.nearestNeighbour(Instance target)
Returns the nearest instance 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 |
CoverTree.nearestNeighbour(Instance target)
Returns the NN instance of a given target instance, from among
the previously supplied training instances.
|
Instance |
CoverTree.CoverTreeNode.p()
Returns the instance represented by the node.
|
Modifier and Type | Method and Description |
---|---|
void |
KDTree.addInstanceInfo(Instance instance)
Adds one instance to KDTree loosly.
|
void |
BallTree.addInstanceInfo(Instance ins)
Adds the given instance's info.
|
void |
NearestNeighbourSearch.addInstanceInfo(Instance ins)
Adds information from the given instance without modifying the
datastructure a lot.
|
void |
LinearNNSearch.addInstanceInfo(Instance ins)
Adds the given instance info.
|
void |
CoverTree.addInstanceInfo(Instance ins)
Adds the given instance info.
|
protected void |
KDTree.addInstanceToTree(Instance inst,
KDTreeNode node)
Recursively adds an instance to the tree starting from
the supplied KDTreeNode.
|
protected boolean |
KDTree.candidateIsFullOwner(KDTreeNode node,
Instance candidate,
Instance competitor)
Returns true if candidate is a full owner in respect to a competitor.
|
protected void |
KDTree.checkMissing(Instance ins)
Checks if there is any missing value in the given
instance.
|
protected boolean |
KDTree.clipToInsideHrect(KDTreeNode node,
Instance x)
Finds the closest point in the hyper rectangle to a given point.
|
protected double |
KDTree.distanceToHrect(KDTreeNode node,
Instance x)
Returns the distance between a point and an hyperrectangle.
|
protected NearestNeighbourSearch.NeighborList |
CoverTree.findKNearest(Instance target,
int k)
Performs k-NN serach for a single given query/test Instance.
|
protected void |
KDTree.findNearestNeighbours(Instance target,
KDTreeNode node,
int k,
NearestNeighbourSearch.MyHeap heap,
double distanceToParents)
Returns (in the supplied heap object) the k nearest
neighbours of the given instance starting from the give
tree node.
|
void |
NearestNeighbourSearch.NeighborList.insertSorted(double distance,
Instance instance)
Inserts an instance neighbor into the list, maintaining the list
sorted by distance.
|
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.
|
abstract Instances |
NearestNeighbourSearch.kNearestNeighbours(Instance target,
int k)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
Instances |
LinearNNSearch.kNearestNeighbours(Instance target,
int kNN)
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.
|
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.
|
abstract Instance |
NearestNeighbourSearch.nearestNeighbour(Instance target)
Returns the nearest instance 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 |
CoverTree.nearestNeighbour(Instance target)
Returns the NN instance of a given target instance, from among
the previously supplied training instances.
|
protected void |
BallTree.nearestNeighbours(NearestNeighbourSearch.MyHeap heap,
BallNode node,
Instance target,
int k)
Does NN search according to Moore's method.
|
void |
KDTree.update(Instance instance)
Adds one instance to the KDTree.
|
void |
BallTree.update(Instance ins)
Adds one instance to the BallTree.
|
abstract void |
NearestNeighbourSearch.update(Instance ins)
Updates the NearNeighbourSearch algorithm for the new added instance.
|
void |
LinearNNSearch.update(Instance ins)
Updates the LinearNNSearch to cater for the new added instance.
|
void |
CoverTree.update(Instance ins)
Adds an instance to the cover tree.
|
Constructor and Description |
---|
NeighborNode(double distance,
Instance instance)
Create a new neighbor node that doesn't link to any other nodes.
|
NeighborNode(double distance,
Instance instance,
NearestNeighbourSearch.NeighborNode next)
Create a new neighbor node.
|
Modifier and Type | Field and Description |
---|---|
protected Instance |
BallNode.m_Pivot
The pivot/centre of the ball.
|
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(BottomUpConstructor.TempNode node1,
BottomUpConstructor.TempNode node2,
Instances insts)
Calculates the centroid pivot of a node based on its
two child nodes.
|
Instance |
MiddleOutConstructor.calcPivot(MiddleOutConstructor.MyIdxList list1,
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(MiddleOutConstructor.TempNode node1,
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[] |
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(MiddleOutConstructor.MyIdxList list1,
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 |
---|---|
protected Instance |
XMLInstances.createInstance(Instances header,
Element parent)
creates an Instance from the given XML node
|
Modifier and Type | Method and Description |
---|---|
protected void |
XMLInstances.addInstance(Element parent,
Instance inst)
adds the instance to the XML structure
|
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 |
Expression.generateExample()
Generates one example of the dataset.
|
Instance |
MexicanHat.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 |
---|---|
protected Instance |
PairedTTester.DatasetSpecifiers.specifier(int i)
Get the template at the given position.
|
Modifier and Type | Method and Description |
---|---|
protected void |
PairedTTester.DatasetSpecifiers.add(Instance inst)
Add an instance to the list of specifiers (if necessary)
|
protected void |
PairedTTester.Dataset.add(Instance inst)
Adds the given instance to the dataset
|
void |
PairedTTester.Resultset.add(Instance newInst)
Adds an instance to this resultset
|
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 |
PairedTTester.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.
|
FastVector |
PairedTTester.Resultset.dataset(Instance inst)
Returns a vector containing all instances belonging to one dataset.
|
protected boolean |
PairedTTester.Dataset.matchesTemplate(Instance first)
Returns true if the two instances match on those attributes that have
been designated key columns (eg: scheme name and scheme options)
|
protected boolean |
PairedTTester.Resultset.matchesTemplate(Instance first)
Returns true if the two instances match on those attributes that have
been designated key columns (eg: scheme name and scheme options)
|
protected String |
PairedTTester.templateString(Instance template)
Returns a string descriptive of the key column values for
the "datasets
|
Constructor and Description |
---|
Dataset(Instance template)
Constructor
|
Resultset(Instance template)
Constructir
|
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.
|
protected abstract Instance |
SimpleStreamFilter.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
protected Instance |
MultiFilter.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
Modifier and Type | Method and Description |
---|---|
protected void |
Filter.bufferInput(Instance instance)
Adds the supplied input instance to the inputformat dataset for
later processing.
|
protected boolean |
CheckSource.compare(Instance inst1,
Instance inst2)
compares two Instance
|
protected void |
Filter.copyValues(Instance instance,
boolean isInput)
Copies string/relational values contained in the instance copied to a new
dataset.
|
protected void |
Filter.copyValues(Instance instance,
boolean instSrcCompat,
Instances srcDataset,
Instances destDataset)
Takes string/relational values referenced by an Instance and copies them
from a source dataset to a destination dataset.
|
boolean |
SimpleBatchFilter.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 |
Filter.input(Instance instance)
Input an instance for filtering.
|
protected abstract Instance |
SimpleStreamFilter.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
protected Instance |
MultiFilter.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
protected void |
Filter.push(Instance instance)
Adds an output instance to the queue.
|
Modifier and Type | Method and Description |
---|---|
protected void |
Discretize.convertInstance(Instance instance)
Convert a single instance over.
|
protected void |
AttributeSelection.convertInstance(Instance instance)
Convert a single instance over.
|
protected Matrix |
PLSFilter.getX(Instance instance)
returns the data minus the class column as matrix
|
protected Matrix |
PLSFilter.getY(Instance instance)
returns the data class column as matrix
|
boolean |
Discretize.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 |
NominalToBinary.input(Instance instance)
Input an instance for filtering.
|
Modifier and Type | Method and Description |
---|---|
boolean |
SpreadSubsample.input(Instance instance)
Input an instance for filtering.
|
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.
|
Modifier and Type | Method and Description |
---|---|
protected Instance |
RandomProjection.convertInstance(Instance currentInstance)
converts a single instance to the required format
|
protected Instance |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) format.
|
protected Instance |
AbstractTimeSeries.historyInput(Instance instance)
Adds an instance to the history buffer.
|
protected abstract Instance |
AbstractTimeSeries.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
|
protected Instance |
TimeSeriesDelta.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
|
protected Instance |
TimeSeriesTranslate.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
|
Instance |
RemoveType.output()
Output an instance after filtering and remove from the output queue.
|
Instance |
RemoveType.outputPeek()
Output an instance after filtering but do not remove from the
output queue.
|
protected Instance |
ClassAssigner.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
protected Instance |
RandomSubset.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
protected Instance |
NumericCleaner.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
Modifier and Type | Method and Description |
---|---|
protected double |
InterquartileRange.calculateMultiplier(Instance inst,
int index)
returns the mulitplier of the IQR the instance is off the median for this
particular attribute.
|
protected double |
RandomProjection.computeRandomProjection(int rpIndex,
int classIndex,
Instance instance)
computes one random projection for a given instance (skip missing values)
|
protected void |
Discretize.convertInstance(Instance instance)
Convert a single instance over.
|
protected void |
AddID.convertInstance(Instance instance)
Convert a single instance over.
|
protected void |
AddCluster.convertInstance(Instance instance)
Convert a single instance over.
|
protected Instance |
RandomProjection.convertInstance(Instance currentInstance)
converts a single instance to the required format
|
protected Instance |
PrincipalComponents.convertInstance(Instance instance)
Transform an instance in original (unormalized) format.
|
protected void |
Normalize.convertInstance(Instance instance)
Convert a single instance over.
|
protected void |
ClusterMembership.convertInstance(Instance instance)
Convert a single instance over.
|
protected Instance |
AbstractTimeSeries.historyInput(Instance instance)
Adds an instance to the history buffer.
|
boolean |
MakeIndicator.input(Instance instance)
Input an instance for filtering.
|
boolean |
Discretize.input(Instance instance)
Input an instance for filtering.
|
boolean |
MergeTwoValues.input(Instance instance)
Input an instance for filtering.
|
boolean |
Remove.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddID.input(Instance instance)
Input an instance for filtering.
|
boolean |
MultiInstanceToPropositional.input(Instance instance)
Input an instance for filtering.
|
boolean |
AbstractTimeSeries.input(Instance instance)
Input an instance for filtering.
|
boolean |
NumericTransform.input(Instance instance)
Input an instance for filtering.
|
boolean |
Center.input(Instance instance)
Input an instance for filtering.
|
boolean |
StringToNominal.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddCluster.input(Instance instance)
Input an instance for filtering.
|
boolean |
Obfuscate.input(Instance instance)
Input an instance for filtering.
|
boolean |
StringToWordVector.input(Instance instance)
Input an instance for filtering.
|
boolean |
Reorder.input(Instance instance)
Input an instance for filtering.
|
boolean |
RandomProjection.input(Instance instance)
Input an instance for filtering.
|
boolean |
MathExpression.input(Instance instance)
Input an instance for filtering.
|
boolean |
PrincipalComponents.input(Instance instance)
Input an instance for filtering.
|
boolean |
NominalToBinary.input(Instance instance)
Input an instance for filtering.
|
boolean |
SwapValues.input(Instance instance)
Input an instance for filtering.
|
boolean |
Normalize.input(Instance instance)
Input an instance for filtering.
|
boolean |
ClusterMembership.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddValues.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddExpression.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveType.input(Instance instance)
Input an instance for filtering.
|
boolean |
FirstOrder.input(Instance instance)
Input an instance for filtering.
|
boolean |
AddNoise.input(Instance instance)
Input an instance for filtering.
|
boolean |
NominalToString.input(Instance instance)
Input an instance for filtering.
|
boolean |
Add.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveUseless.input(Instance instance)
Input an instance for filtering.
|
boolean |
NumericToBinary.input(Instance instance)
Input an instance for filtering.
|
boolean |
Standardize.input(Instance instance)
Input an instance for filtering.
|
boolean |
ReplaceMissingValues.input(Instance instance)
Input an instance for filtering.
|
boolean |
Copy.input(Instance instance)
Input an instance for filtering.
|
boolean |
ChangeDateFormat.input(Instance instance)
Input an instance for filtering.
|
protected boolean |
InterquartileRange.isExtremeValue(Instance inst)
returns whether the instance is an extreme value or not
|
protected boolean |
InterquartileRange.isExtremeValue(Instance inst,
int index)
returns whether the instance has an extreme value in the specified
attribute or not
|
protected boolean |
InterquartileRange.isOutlier(Instance inst)
returns whether the instance is an outlier or not
|
protected boolean |
InterquartileRange.isOutlier(Instance inst,
int index)
returns whether the instance has an outlier in the specified attribute
or not
|
protected double[] |
ClusterMembership.logs2densities(int j,
Instance in)
Converts logs back to density values.
|
protected abstract Instance |
AbstractTimeSeries.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
|
protected Instance |
TimeSeriesDelta.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
|
protected Instance |
TimeSeriesTranslate.mergeInstances(Instance source,
Instance dest)
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
|
protected Instance |
ClassAssigner.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
protected Instance |
RandomSubset.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
protected Instance |
NumericCleaner.process(Instance instance)
processes the given instance (may change the provided instance) and
returns the modified version.
|
protected void |
PropositionalToMultiInstance.push(Instance instance)
Adds an output instance to the queue.
|
Modifier and Type | Field and Description |
---|---|
protected Instance[] |
ReservoirSample.m_subSample
Holds the sub-sample (reservoir)
|
Modifier and Type | Method and Description |
---|---|
boolean |
NonSparseToSparse.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveRange.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveFolds.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 |
SparseToNonSparse.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 |
Normalize.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveFrequentValues.input(Instance instance)
Input an instance for filtering.
|
boolean |
RemoveMisclassified.input(Instance instance)
Input an instance for filtering.
|
boolean |
Resample.input(Instance instance)
Input an instance for filtering.
|
boolean |
SubsetByExpression.input(Instance instance)
Input an instance for filtering.
|
protected void |
ReservoirSample.processInstance(Instance instance)
Decides whether the current instance gets retained in the
reservoir.
|
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 | Field and Description |
---|---|
protected Instance |
IncrementalClassifierEvent.m_currentInstance |
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 |
---|---|
protected String |
ClassifierPanel.predictionText(Classifier classifier,
Instance inst,
int instNum)
generates a prediction row for an 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.
|
Modifier and Type | Method and Description |
---|---|
Instance |
InstanceJoiner.outputPeek()
Output an instance after filtering but do not remove from the output
queue.
|
Instance |
InstanceLoader.outputPeek() |
Instance |
InstanceProducer.outputPeek() |
Modifier and Type | Method and Description |
---|---|
boolean |
InstanceJoiner.input(Instance instance) |
void |
InstanceTable.input(Instance instance) |
void |
InstanceViewer.input(Instance instance) |
void |
InstanceSavePanel.input(Instance instance) |
void |
InstanceCounter.input(Instance instance) |
Modifier and Type | Method and Description |
---|---|
boolean |
VisualizePanel.PlotPanel.inSplit(Instance i)
This will check if an instance is inside or outside of the current
shapes.
|
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