| Modifier and Type | Method and Description |
|---|---|
static Instances |
LabeledItemSet.divide(Instances instances,
boolean invert)
Splits the class attribute away.
|
Instances |
CARuleMiner.getInstancesNoClass()
Gets the instances without the class attribute
|
Instances |
Apriori.getInstancesNoClass()
Gets the instances without the class atrribute.
|
Instances |
CARuleMiner.getInstancesOnlyClass()
Gets the class attribute and its values for all instances
|
Instances |
Apriori.getInstancesOnlyClass()
Gets only the class attribute of the instances.
|
| Modifier and Type | Method and Description |
|---|---|
void |
FPGrowth.buildAssociations(Instances data)
Method that generates all large item sets with a minimum support, and from
these all association rules with a minimum metric (i.e.
|
void |
FilteredAssociator.buildAssociations(Instances data)
Build the associator on the filtered data.
|
void |
Associator.buildAssociations(Instances data)
Generates an associator.
|
void |
Apriori.buildAssociations(Instances instances)
Method that generates all large itemsets with a minimum support, and from
these all association rules with a minimum confidence.
|
static Instances |
LabeledItemSet.divide(Instances instances,
boolean invert)
Splits the class attribute away.
|
String |
AssociatorEvaluation.evaluate(Associator associator,
Instances data)
Evaluates the associator with the given commandline options and returns
the evaluation string.
|
FastVector[] |
CARuleMiner.mineCARs(Instances data)
Method for mining class association rules.
|
FastVector[] |
Apriori.mineCARs(Instances data)
Method that mines all class association rules with minimum support and with
a minimum confidence.
|
static FastVector |
ItemSet.singletons(Instances instances)
Converts the header info of the given set of instances into a set of item
sets (singletons).
|
static FastVector |
AprioriItemSet.singletons(Instances instances,
boolean treatZeroAsMissing)
Converts the header info of the given set of instances into a set of item
sets (singletons).
|
static FastVector |
LabeledItemSet.singletons(Instances instancesNoClass,
Instances classes)
Converts the header info of the given set of instances into a set of item
sets (singletons).
|
String |
ItemSet.toString(Instances instances)
Returns the contents of an item set as a string.
|
String |
AprioriItemSet.toString(Instances instances)
Returns the contents of an item set as a string.
|
String |
ItemSet.toString(Instances instances,
char outerDelim,
char innerDelim)
Returns the contents of an item set as a delimited string.
|
static void |
ItemSet.upDateCounters(FastVector itemSets,
Instances instances)
Updates counters for a set of item sets and a set of instances.
|
static void |
LabeledItemSet.upDateCounters(FastVector itemSets,
Instances instancesNoClass,
Instances instancesClass)
Updates counter of a specific item set
|
static void |
ItemSet.upDateCountersTreatZeroAsMissing(FastVector itemSets,
Instances instances)
Updates counters for a set of item sets and a set of instances.
|
static void |
LabeledItemSet.upDateCountersTreatZeroAsMissing(FastVector itemSets,
Instances instancesNoClass,
Instances instancesClass)
Updates counter of a specific item set
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
AttributeSelection.reduceDimensionality(Instances in)
reduce the dimensionality of a set of instances to include only those
attributes chosen by the last run of attribute selection.
|
Instances |
PrincipalComponents.transformedData(Instances data)
Gets the transformed training data.
|
Instances |
AttributeTransformer.transformedData(Instances data)
Transform the supplied data set (assumed to be the same format
as the training data)
|
Instances |
PrincipalComponents.transformedHeader()
Returns just the header for the transformed data (ie.
|
Instances |
AttributeTransformer.transformedHeader()
Returns just the header for the transformed data (ie.
|
| Modifier and Type | Method and Description |
|---|---|
void |
WrapperSubsetEval.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
void |
SymmetricalUncertAttributeEval.buildEvaluator(Instances data)
Initializes a symmetrical uncertainty attribute evaluator.
|
void |
ReliefFAttributeEval.buildEvaluator(Instances data)
Initializes a ReliefF attribute evaluator.
|
void |
PrincipalComponents.buildEvaluator(Instances data)
Initializes principal components and performs the analysis
|
void |
OneRAttributeEval.buildEvaluator(Instances data)
Initializes a OneRAttribute attribute evaluator.
|
void |
InfoGainAttributeEval.buildEvaluator(Instances data)
Initializes an information gain attribute evaluator.
|
void |
GainRatioAttributeEval.buildEvaluator(Instances data)
Initializes a gain ratio attribute evaluator.
|
void |
CorrelationAttributeEval.buildEvaluator(Instances data)
Initializes an information gain attribute evaluator.
|
void |
CfsSubsetEval.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
abstract void |
ASEvaluation.buildEvaluator(Instances data)
Generates a attribute evaluator.
|
abstract double |
HoldOutSubsetEvaluator.evaluateSubset(BitSet subset,
Instances holdOut)
Evaluates a subset of attributes with respect to a set of instances.
|
Instances |
AttributeSelection.reduceDimensionality(Instances in)
reduce the dimensionality of a set of instances to include only those
attributes chosen by the last run of attribute selection.
|
int[] |
Ranker.search(ASEvaluation ASEval,
Instances data)
Kind of a dummy search algorithm.
|
int[] |
GreedyStepwise.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space by forward selection.
|
int[] |
BestFirst.search(ASEvaluation ASEval,
Instances data)
Searches the attribute subset space by best first search
|
abstract int[] |
ASSearch.search(ASEvaluation ASEvaluator,
Instances data)
Searches the attribute subset/ranking space.
|
static String |
AttributeSelection.SelectAttributes(ASEvaluation ASEvaluator,
String[] options,
Instances train)
Perform attribute selection with a particular evaluator and
a set of options specifying search method and options for the
search method and evaluator.
|
void |
AttributeSelection.SelectAttributes(Instances data)
Perform attribute selection on the supplied training instances.
|
void |
AttributeSelection.selectAttributesCVSplit(Instances split)
Select attributes for a split of the data.
|
Instances |
PrincipalComponents.transformedData(Instances data)
Gets the transformed training data.
|
Instances |
AttributeTransformer.transformedData(Instances data)
Transform the supplied data set (assumed to be the same format
as the training data)
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
CostMatrix.applyCostMatrix(Instances data,
Random random)
Applies the cost matrix to a set of instances.
|
Instances |
Evaluation.getHeader()
Returns the header of the underlying dataset.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
CostMatrix.applyCostMatrix(Instances data,
Random random)
Applies the cost matrix to a set of instances.
|
void |
ParallelMultipleClassifiersCombiner.buildClassifier(Instances data)
Stump method for building the classifiers
|
void |
ParallelIteratedSingleClassifierEnhancer.buildClassifier(Instances data)
Stump method for building the classifiers
|
void |
IteratedSingleClassifierEnhancer.buildClassifier(Instances data)
Stump method for building the classifiers.
|
void |
Classifier.buildClassifier(Instances data)
Generates a classifier.
|
void |
Evaluation.crossValidateModel(Classifier classifier,
Instances data,
int numFolds,
Random random,
Object... forPredictionsPrinting)
Performs a (stratified if class is nominal) cross-validation for a
classifier on a set of instances.
|
void |
Evaluation.crossValidateModel(String classifierString,
Instances data,
int numFolds,
String[] options,
Random random)
Performs a (stratified if class is nominal) cross-validation for a
classifier on a set of instances.
|
double[] |
Evaluation.evaluateModel(Classifier classifier,
Instances data,
Object... forPredictionsPrinting)
Evaluates the classifier on a given set of instances.
|
void |
IterativeClassifier.initClassifier(Instances instances)
Inits an iterative classifier.
|
void |
Evaluation.setPriors(Instances train)
Sets the class prior probabilities.
|
| Constructor and Description |
|---|
AggregateableEvaluation(Instances data)
Constructs a new AggregateableEvaluation object
|
AggregateableEvaluation(Instances data,
CostMatrix costMatrix)
Constructs a new AggregateableEvaluation object
|
Evaluation(Instances data) |
Evaluation(Instances data,
CostMatrix costMatrix) |
| Modifier and Type | Field and Description |
|---|---|
Instances |
BayesNet.m_Instances
The dataset header for the purposes of printing out a semi-intelligible
model
|
| Modifier and Type | Method and Description |
|---|---|
void |
NaiveBayesMultinomialUpdateable.buildClassifier(Instances instances)
Generates the classifier.
|
void |
NaiveBayesMultinomialText.buildClassifier(Instances data)
Generates the classifier.
|
void |
NaiveBayesMultinomial.buildClassifier(Instances instances)
Generates the classifier.
|
void |
NaiveBayes.buildClassifier(Instances instances)
Generates the classifier.
|
void |
BayesNet.buildClassifier(Instances instances)
Generates the classifier.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ParentSet.addParent(int nParent,
Instances _Instances)
Add parent to parent set and update internals (specifically the cardinality of the parent set)
|
void |
ParentSet.addParent(int nParent,
int iParent,
Instances _Instances)
Add parent to parent set at specific location
and update internals (specifically the cardinality of the parent set)
|
void |
ParentSet.deleteLastParent(Instances _Instances)
Delete last added parent from parent set and update internals (specifically the cardinality of the parent set)
|
int |
ParentSet.deleteParent(int nParent,
Instances _Instances)
delete node from parent set
|
int |
ParentSet.getFreshCardinalityOfParents(Instances _Instances)
returns cardinality of parents after recalculation
|
static ADNode |
ADNode.makeADTree(Instances instances)
create AD tree from set of instances
|
static ADNode |
ADNode.makeADTree(int iNode,
FastVector nRecords,
Instances instances)
create sub tree
|
static VaryNode |
ADNode.makeVaryNode(int iNode,
FastVector nRecords,
Instances instances)
create sub tree
|
void |
EditableBayesNet.setData(Instances instances)
Assuming a network structure is defined and we want to learn from data,
the data set must be put if correct order first and possibly discretized/missing
values filled in before proceeding to CPT learning.
|
| Constructor and Description |
|---|
EditableBayesNet(Instances instances)
constructor, creates empty network with nodes based on the attributes in a data set
|
| Modifier and Type | Method and Description |
|---|---|
void |
SearchAlgorithm.buildStructure(BayesNet bayesNet,
Instances instances)
buildStructure determines the network structure/graph of the network.
|
| Modifier and Type | Method and Description |
|---|---|
void |
NaiveBayes.buildStructure(BayesNet bayesNet,
Instances instances) |
void |
FromFile.buildStructure(BayesNet bayesNet,
Instances instances) |
| Modifier and Type | Method and Description |
|---|---|
void |
TAN.buildStructure(BayesNet bayesNet,
Instances instances)
buildStructure determines the network structure/graph of the network
using the maximimum weight spanning tree algorithm of Chow and Liu
|
void |
SimulatedAnnealing.search(BayesNet bayesNet,
Instances instances) |
void |
K2.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with the K2 algorithm, restricted by its initial structure (which can
be an empty graph, or a Naive Bayes graph.
|
| Modifier and Type | Method and Description |
|---|---|
void |
TAN.buildStructure(BayesNet bayesNet,
Instances instances)
buildStructure determines the network structure/graph of the network
using the maximimum weight spanning tree algorithm of Chow and Liu
|
void |
LocalScoreSearchAlgorithm.buildStructure(BayesNet bayesNet,
Instances instances)
buildStructure determines the network structure/graph of the network
with the K2 algorithm, restricted by its initial structure (which can
be an empty graph, or a Naive Bayes graph.
|
void |
SimulatedAnnealing.search(BayesNet bayesNet,
Instances instances) |
void |
K2.search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network
with the K2 algorithm, restricted by its initial structure (which can
be an empty graph, or a Naive Bayes graph.
|
| Constructor and Description |
|---|
LocalScoreSearchAlgorithm(BayesNet bayesNet,
Instances instances)
constructor
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
ThresholdCurve.getCurve(FastVector predictions)
Calculates the performance stats for the default class and return
results as a set of Instances.
|
Instances |
MarginCurve.getCurve(FastVector predictions)
Calculates the cumulative margin distribution for the set of
predictions, returning the result as a set of Instances.
|
Instances |
CostCurve.getCurve(FastVector predictions)
Calculates the performance stats for the default class and return
results as a set of Instances.
|
Instances |
ThresholdCurve.getCurve(FastVector predictions,
int classIndex)
Calculates the performance stats for the desired class and return
results as a set of Instances.
|
Instances |
CostCurve.getCurve(FastVector predictions,
int classIndex)
Calculates the performance stats for the desired class and return
results as a set of Instances.
|
Instances |
Evaluation.getHeader()
Returns the header of the underlying dataset.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Evaluation.crossValidateModel(Classifier classifier,
Instances data,
int numFolds,
Random random,
Object... forPredictionsPrinting)
Performs a (stratified if class is nominal) cross-validation for a
classifier on a set of instances.
|
void |
Evaluation.crossValidateModel(String classifierString,
Instances data,
int numFolds,
String[] options,
Random random)
Performs a (stratified if class is nominal) cross-validation for a
classifier on a set of instances.
|
double[] |
Evaluation.evaluateModel(Classifier classifier,
Instances data,
Object... forPredictionsPrinting)
Evaluates the classifier on a given set of instances.
|
FastVector |
EvaluationUtils.getCVPredictions(Classifier classifier,
Instances data,
int numFolds)
Generate a bunch of predictions ready for processing, by performing a
cross-validation on the supplied dataset.
|
static double |
ThresholdCurve.getNPointPrecision(Instances tcurve,
int n)
Calculates the n point precision result, which is the precision averaged
over n evenly spaced (w.r.t recall) samples of the curve.
|
static double |
ThresholdCurve.getPRCArea(Instances tcurve)
Calculates the area under the precision-recall curve (AUPRC).
|
static double |
ThresholdCurve.getROCArea(Instances tcurve)
Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
|
FastVector |
EvaluationUtils.getTestPredictions(Classifier classifier,
Instances test)
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set assuming the classifier is already trained.
|
static int |
ThresholdCurve.getThresholdInstance(Instances tcurve,
double threshold)
Gets the index of the instance with the closest threshold value to the
desired target
|
FastVector |
EvaluationUtils.getTrainTestPredictions(Classifier classifier,
Instances train,
Instances test)
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set after training on the given training set.
|
void |
Evaluation.setPriors(Instances train)
Sets the class prior probabilities.
|
| Constructor and Description |
|---|
AggregateableEvaluation(Instances data)
Constructs a new AggregateableEvaluation object
|
AggregateableEvaluation(Instances data,
CostMatrix costMatrix)
Constructs a new AggregateableEvaluation object
|
Evaluation(Instances data)
Initializes all the counters for the evaluation.
|
Evaluation(Instances data,
CostMatrix costMatrix)
Initializes all the counters for the evaluation and also takes a cost
matrix as parameter.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
AbstractOutput.getHeader()
Returns the header of the dataset.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractOutput.print(Classifier classifier,
Instances testset)
Prints the header, classifications and footer to the buffer.
|
void |
AbstractOutput.printClassifications(Classifier classifier,
Instances testset)
Prints the classifications to the buffer.
|
void |
AbstractOutput.setHeader(Instances value)
Sets the header of the dataset.
|
| Modifier and Type | Method and Description |
|---|---|
void |
VotedPerceptron.buildClassifier(Instances insts)
Builds the ensemble of perceptrons.
|
void |
SMOreg.buildClassifier(Instances instances)
Method for building the classifier.
|
void |
SMO.buildClassifier(Instances insts)
Method for building the classifier.
|
void |
SimpleLogistic.buildClassifier(Instances data)
Builds the logistic regression using LogitBoost.
|
void |
SimpleLinearRegression.buildClassifier(Instances insts)
Builds a simple linear regression model given the supplied training data.
|
void |
SGDText.buildClassifier(Instances data)
Method for building the classifier.
|
void |
SGD.buildClassifier(Instances data)
Method for building the classifier.
|
void |
MultilayerPerceptron.buildClassifier(Instances i)
Call this function to build and train a neural network for the training
data provided.
|
void |
Logistic.buildClassifier(Instances train)
Builds the classifier
|
void |
LinearRegression.buildClassifier(Instances data)
Builds a regression model for the given data.
|
void |
GaussianProcesses.buildClassifier(Instances insts)
Method for building the classifier.
|
| Modifier and Type | Method and Description |
|---|---|
void |
RegSMOImproved.buildClassifier(Instances instances)
learn SVM parameters from data using Keerthi's SMO algorithm.
|
void |
RegSMO.buildClassifier(Instances instances)
learn SVM parameters from data using Smola's SMO algorithm.
|
void |
RegOptimizer.buildClassifier(Instances data)
learn SVM parameters from data.
|
void |
StringKernel.buildKernel(Instances data)
builds the kernel with the given data.
|
void |
RBFKernel.buildKernel(Instances data)
builds the kernel with the given data.
|
void |
Puk.buildKernel(Instances data)
builds the kernel with the given data.
|
void |
Kernel.buildKernel(Instances data)
builds the kernel with the given data
|
void |
CachedKernel.buildKernel(Instances data)
builds the kernel with the given data.
|
String |
KernelEvaluation.evaluate(Kernel kernel,
Instances data)
Evaluates the Kernel with the given commandline options and returns
the evaluation string.
|
| Constructor and Description |
|---|
NormalizedPolyKernel(Instances dataset,
int cacheSize,
double exponent,
boolean lowerOrder)
Creates a new
NormalizedPolyKernel instance. |
PolyKernel(Instances data,
int cacheSize,
double exponent,
boolean lowerOrder)
Creates a new
PolyKernel instance. |
Puk(Instances data,
int cacheSize,
double omega,
double sigma)
Constructor.
|
RBFKernel(Instances data,
int cacheSize,
double gamma)
Constructor.
|
StringKernel(Instances data,
int cacheSize,
int subsequenceLength,
double lambda,
boolean debug)
creates a new StringKernel object.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
IBk.pruneToK(Instances neighbours,
double[] distances,
int k)
Prunes the list to contain the k nearest neighbors.
|
| Modifier and Type | Method and Description |
|---|---|
void |
LWL.buildClassifier(Instances instances)
Generates the classifier.
|
void |
KStar.buildClassifier(Instances instances)
Generates the classifier.
|
void |
IBk.buildClassifier(Instances instances)
Generates the classifier.
|
Instances |
IBk.pruneToK(Instances neighbours,
double[] distances,
int k)
Prunes the list to contain the k nearest neighbors.
|
| 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 |
|---|---|
void |
Vote.buildClassifier(Instances data)
Buildclassifier selects a classifier from the set of classifiers by
minimising error on the training data.
|
void |
Stacking.buildClassifier(Instances data)
Buildclassifier selects a classifier from the set of classifiers
by minimising error on the training data.
|
void |
RegressionByDiscretization.buildClassifier(Instances instances)
Generates the classifier.
|
void |
RandomSubSpace.buildClassifier(Instances data)
builds the classifier.
|
void |
RandomCommittee.buildClassifier(Instances data)
Builds the committee of randomizable classifiers.
|
void |
MultiScheme.buildClassifier(Instances data)
Buildclassifier selects a classifier from the set of classifiers
by minimising error on the training data.
|
void |
MultiClassClassifierUpdateable.buildClassifier(Instances insts) |
void |
MultiClassClassifier.buildClassifier(Instances insts)
Builds the classifiers.
|
void |
LogitBoost.buildClassifier(Instances data)
Builds the boosted classifier
|
void |
FilteredClassifier.buildClassifier(Instances data)
Build the classifier on the filtered data.
|
void |
CVParameterSelection.buildClassifier(Instances instances)
Generates the classifier.
|
void |
CostSensitiveClassifier.buildClassifier(Instances data)
Builds the model of the base learner.
|
void |
ClassificationViaRegression.buildClassifier(Instances insts)
Builds the classifiers.
|
void |
Bagging.buildClassifier(Instances data)
Bagging method.
|
void |
AttributeSelectedClassifier.buildClassifier(Instances data)
Build the classifier on the dimensionally reduced data.
|
void |
AdditiveRegression.buildClassifier(Instances data)
Build the classifier on the supplied data
|
void |
AdaBoostM1.buildClassifier(Instances data)
Boosting method.
|
void |
RandomCommittee.generatePartition(Instances data)
Builds the classifier to generate a partition.
|
void |
FilteredClassifier.generatePartition(Instances data)
Builds the classifier to generate a partition.
|
void |
Bagging.generatePartition(Instances data)
Builds the classifier to generate a partition.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
InputMappedClassifier.getModelHeader(Instances defaultH)
Return the instance structure that the encapsulated model was built with.
|
| Modifier and Type | Method and Description |
|---|---|
void |
SerializedClassifier.buildClassifier(Instances data)
loads only the serialized classifier
|
void |
InputMappedClassifier.buildClassifier(Instances data)
Build the classifier
|
Instances |
InputMappedClassifier.getModelHeader(Instances defaultH)
Return the instance structure that the encapsulated model was built with.
|
void |
InputMappedClassifier.setModelHeader(Instances modelHeader)
Set the structure of the data used to create the model.
|
void |
InputMappedClassifier.setTestStructure(Instances testStructure)
Set the test structure (if known in advance) that we are likely
to see.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
PMMLClassifier.getDataDictionary()
Get the data dictionary.
|
| Modifier and Type | Method and Description |
|---|---|
void |
PMMLClassifier.buildClassifier(Instances data)
Throw an exception - PMML models are pre-built.
|
void |
PMMLClassifier.mapToMiningSchema(Instances dataSet)
Map mining schema to incoming instances.
|
| Constructor and Description |
|---|
GeneralRegression(Element model,
Instances dataDictionary,
MiningSchema miningSchema)
Constructs a GeneralRegression classifier.
|
NeuralNetwork(Element model,
Instances dataDictionary,
MiningSchema miningSchema) |
Regression(Element model,
Instances dataDictionary,
MiningSchema miningSchema)
Constructs a new PMML Regression.
|
RuleSetModel(Element model,
Instances dataDictionary,
MiningSchema miningSchema)
Constructor for a RuleSetModel
|
SupportVectorMachineModel(Element model,
Instances dataDictionary,
MiningSchema miningSchema)
Construct a new SupportVectorMachineModel encapsulating the information provided
in the PMML document.
|
TreeModel(Element model,
Instances dataDictionary,
MiningSchema miningSchema) |
| Modifier and Type | Method and Description |
|---|---|
Instances |
RuleStats.getData()
Get the data of the stats
|
Instances[] |
RuleStats.getFiltered(int index)
Get the data after filtering the given rule
|
static Instances[] |
RuleStats.partition(Instances data,
int numFolds)
Patition the data into 2, first of which has (numFolds-1)/numFolds of
the data and the second has 1/numFolds of the data
|
static Instances |
RuleStats.rmCoveredBySuccessives(Instances data,
FastVector rules,
int index)
Static utility function to count the data covered by the
rules after the given index in the given rules, and then
remove them.
|
abstract Instances[] |
JRip.Antd.splitData(Instances data,
double defAcRt,
double cla) |
Instances[] |
JRip.NumericAntd.splitData(Instances insts,
double defAcRt,
double cl)
Implements the splitData function.
|
Instances[] |
JRip.NominalAntd.splitData(Instances data,
double defAcRt,
double cl)
Implements the splitData function.
|
static Instances |
RuleStats.stratify(Instances data,
int folds,
Random rand)
Stratify the given data into the given number of bags based on the class
values.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ZeroR.buildClassifier(Instances instances)
Generates the classifier.
|
void |
PART.buildClassifier(Instances instances)
Generates the classifier.
|
void |
OneR.buildClassifier(Instances instances)
Generates the classifier.
|
void |
JRip.buildClassifier(Instances instances)
Builds Ripper in the order of class frequencies.
|
void |
DecisionTable.buildClassifier(Instances data)
Generates the classifier.
|
void |
RuleStats.countData(int index,
Instances uncovered,
double[][] prevRuleStats)
Count data from the position index in the ruleset
assuming that given data are not covered by the rules
in position 0...(index-1), and the statistics of these
rules are provided.
This procedure is typically useful when a temporary object of RuleStats is constructed in order to efficiently calculate the relative DL of rule in position index, thus all other stuff is not needed. |
abstract void |
Rule.grow(Instances data)
Build this rule
|
void |
JRip.RipperRule.grow(Instances data)
Build one rule using the growing data
|
weka.classifiers.rules.OneR.OneRRule |
OneR.newNominalRule(Attribute attr,
Instances data,
int[] missingValueCounts)
Create a rule branching on this nominal attribute.
|
weka.classifiers.rules.OneR.OneRRule |
OneR.newNumericRule(Attribute attr,
Instances data,
int[] missingValueCounts)
Create a rule branching on this numeric attribute
|
weka.classifiers.rules.OneR.OneRRule |
OneR.newRule(Attribute attr,
Instances data)
Create a rule branching on this attribute.
|
static double |
RuleStats.numAllConditions(Instances data)
Compute the number of all possible conditions that could
appear in a rule of a given data.
|
static Instances[] |
RuleStats.partition(Instances data,
int numFolds)
Patition the data into 2, first of which has (numFolds-1)/numFolds of
the data and the second has 1/numFolds of the data
|
void |
JRip.RipperRule.prune(Instances pruneData,
boolean useWhole)
Prune all the possible final sequences of the rule using the
pruning data.
|
static Instances |
RuleStats.rmCoveredBySuccessives(Instances data,
FastVector rules,
int index)
Static utility function to count the data covered by the
rules after the given index in the given rules, and then
remove them.
|
void |
RuleStats.setData(Instances data)
Set the data of the stats, overwriting the old one if any
|
abstract Instances[] |
JRip.Antd.splitData(Instances data,
double defAcRt,
double cla) |
Instances[] |
JRip.NumericAntd.splitData(Instances insts,
double defAcRt,
double cl)
Implements the splitData function.
|
Instances[] |
JRip.NominalAntd.splitData(Instances data,
double defAcRt,
double cl)
Implements the splitData function.
|
static Instances |
RuleStats.stratify(Instances data,
int folds,
Random rand)
Stratify the given data into the given number of bags based on the class
values.
|
String |
DecisionTableHashKey.toString(Instances t,
int maxColWidth)
Convert a hash entry to a string
|
| Constructor and Description |
|---|
RuleStats(Instances data,
FastVector rules)
Constructor that provides ruleset and data
|
| Modifier and Type | Method and Description |
|---|---|
void |
MakeDecList.buildClassifier(Instances data)
Builds dec list.
|
void |
ClassifierDecList.buildDecList(Instances data,
boolean leaf)
Builds the partial tree without hold out set.
|
void |
C45PruneableDecList.buildDecList(Instances data,
boolean leaf)
Builds the partial tree without hold out set.
|
void |
PruneableDecList.buildDecList(Instances train,
Instances test,
boolean leaf)
Builds the partial tree with hold out set
|
void |
ClassifierDecList.buildRule(Instances data)
Method for building a pruned partial tree.
|
void |
PruneableDecList.buildRule(Instances train,
Instances test)
Method for building a pruned partial tree.
|
void |
ClassifierDecList.cleanup(Instances justHeaderInfo)
Cleanup in order to save memory.
|
| Modifier and Type | Method and Description |
|---|---|
void |
REPTree.buildClassifier(Instances data)
Builds classifier.
|
void |
RandomTree.buildClassifier(Instances data)
Builds classifier.
|
void |
RandomForest.buildClassifier(Instances data)
Builds a classifier for a set of instances.
|
void |
LMT.buildClassifier(Instances data)
Builds the classifier.
|
void |
J48.buildClassifier(Instances instances)
Generates the classifier.
|
void |
HoeffdingTree.buildClassifier(Instances data)
Builds the classifier.
|
void |
DecisionStump.buildClassifier(Instances instances)
Generates the classifier.
|
void |
REPTree.generatePartition(Instances data)
Builds the classifier to generate a partition.
|
void |
RandomTree.generatePartition(Instances data)
Builds the classifier to generate a partition.
|
void |
RandomForest.generatePartition(Instances data)
Builds the classifier to generate a partition.
|
void |
J48.generatePartition(Instances data)
Builds the classifier to generate a partition.
|
| Constructor and Description |
|---|
NBNode(Instances header,
double nbWeightThreshold)
Construct a new NBNode
|
NBNodeAdaptive(Instances header,
double nbWeightThreshold)
Constructor
|
| Modifier and Type | Method and Description |
|---|---|
Instances[] |
ClassifierSplitModel.split(Instances data)
Splits the given set of instances into subsets.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Distribution.addInstWithUnknown(Instances source,
int attIndex)
Adds all instances with unknown values for given attribute, weighted
according to frequency of instances in each bag.
|
void |
Distribution.addRange(int bagIndex,
Instances source,
int startIndex,
int lastPlusOne)
Adds all instances in given range to given bag.
|
void |
PruneableClassifierTree.buildClassifier(Instances data)
Method for building a pruneable classifier tree.
|
void |
NoSplit.buildClassifier(Instances instances)
Creates a "no-split"-split for a given set of instances.
|
void |
NBTreeSplit.buildClassifier(Instances trainInstances)
Creates a NBTree-type split on the given data.
|
void |
NBTreeNoSplit.buildClassifier(Instances instances)
Build the no-split node
|
void |
NBTreeClassifierTree.buildClassifier(Instances data)
Method for building a naive bayes classifier tree
|
void |
ClassifierTree.buildClassifier(Instances data)
Method for building a classifier tree.
|
abstract void |
ClassifierSplitModel.buildClassifier(Instances instances)
Builds the classifier split model for the given set of instances.
|
void |
C45Split.buildClassifier(Instances trainInstances)
Creates a C4.5-type split on the given data.
|
void |
C45PruneableClassifierTree.buildClassifier(Instances data)
Method for building a pruneable classifier tree.
|
void |
BinC45Split.buildClassifier(Instances trainInstances)
Creates a C4.5-type split on the given data.
|
void |
ClassifierTree.buildTree(Instances data,
boolean keepData)
Builds the tree structure.
|
void |
ClassifierTree.buildTree(Instances train,
Instances test,
boolean keepData)
Builds the tree structure with hold out set
|
void |
ClassifierTree.cleanup(Instances justHeaderInfo)
Cleanup in order to save memory.
|
static double |
NBTreeNoSplit.crossValidate(NaiveBayesUpdateable fullModel,
Instances trainingSet,
Random r)
Utility method for fast 5-fold cross validation of a naive bayes
model
|
void |
Distribution.delRange(int bagIndex,
Instances source,
int startIndex,
int lastPlusOne)
Deletes all instances in given range from given bag.
|
String |
ClassifierSplitModel.dumpLabel(int index,
Instances data)
Prints label for subset index of instances (eg class).
|
String |
ClassifierSplitModel.dumpModel(Instances data)
Prints the split model.
|
String |
NoSplit.leftSide(Instances instances)
Does nothing because no condition has to be satisfied.
|
String |
NBTreeSplit.leftSide(Instances data)
Prints left side of condition..
|
String |
NBTreeNoSplit.leftSide(Instances instances)
Does nothing because no condition has to be satisfied.
|
abstract String |
ClassifierSplitModel.leftSide(Instances data)
Prints left side of condition satisfied by instances.
|
String |
C45Split.leftSide(Instances data)
Prints left side of condition..
|
String |
BinC45Split.leftSide(Instances data)
Prints left side of condition.
|
double[][] |
C45Split.minsAndMaxs(Instances data,
double[][] minsAndMaxs,
int index)
Returns the minsAndMaxs of the index.th subset.
|
void |
ClassifierSplitModel.resetDistribution(Instances data)
Sets distribution associated with model.
|
void |
C45Split.resetDistribution(Instances data)
Sets distribution associated with model.
|
void |
BinC45Split.resetDistribution(Instances data)
Sets distribution associated with model.
|
String |
NoSplit.rightSide(int index,
Instances instances)
Does nothing because no condition has to be satisfied.
|
String |
NBTreeSplit.rightSide(int index,
Instances data)
Prints the condition satisfied by instances in a subset.
|
String |
NBTreeNoSplit.rightSide(int index,
Instances instances)
Does nothing because no condition has to be satisfied.
|
abstract String |
ClassifierSplitModel.rightSide(int index,
Instances data)
Prints left side of condition satisfied by instances in subset index.
|
String |
C45Split.rightSide(int index,
Instances data)
Prints the condition satisfied by instances in a subset.
|
String |
BinC45Split.rightSide(int index,
Instances data)
Prints the condition satisfied by instances in a subset.
|
ClassifierSplitModel |
NBTreeModelSelection.selectModel(Instances data)
Selects NBTree-type split for the given dataset.
|
abstract ClassifierSplitModel |
ModelSelection.selectModel(Instances data)
Selects a model for the given dataset.
|
ClassifierSplitModel |
C45ModelSelection.selectModel(Instances data)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
BinC45ModelSelection.selectModel(Instances data)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
NBTreeModelSelection.selectModel(Instances train,
Instances test)
Selects NBTree-type split for the given dataset.
|
ClassifierSplitModel |
ModelSelection.selectModel(Instances train,
Instances test)
Selects a model for the given train data using the given test data
|
ClassifierSplitModel |
C45ModelSelection.selectModel(Instances train,
Instances test)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
BinC45ModelSelection.selectModel(Instances train,
Instances test)
Selects C4.5-type split for the given dataset.
|
void |
C45Split.setSplitPoint(Instances allInstances)
Sets split point to greatest value in given data smaller or equal to
old split point.
|
void |
BinC45Split.setSplitPoint(Instances allInstances)
Sets split point to greatest value in given data smaller or equal to
old split point.
|
void |
Distribution.shiftRange(int from,
int to,
Instances source,
int startIndex,
int lastPlusOne)
Shifts all instances in given range from one bag to another one.
|
String |
ClassifierSplitModel.sourceClass(int index,
Instances data) |
String |
NoSplit.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
String |
NBTreeSplit.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
String |
NBTreeNoSplit.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
abstract String |
ClassifierSplitModel.sourceExpression(int index,
Instances data) |
String |
C45Split.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
String |
BinC45Split.sourceExpression(int index,
Instances data)
Returns a string containing java source code equivalent to the test
made at this node.
|
Instances[] |
ClassifierSplitModel.split(Instances data)
Splits the given set of instances into subsets.
|
| Constructor and Description |
|---|
BinC45ModelSelection(int minNoObj,
Instances allData,
boolean useMDLcorrection)
Initializes the split selection method with the given parameters.
|
C45ModelSelection(int minNoObj,
Instances allData,
boolean useMDLcorrection)
Initializes the split selection method with the given parameters.
|
Distribution(Instances source)
Creates a distribution with only one bag according
to instances in source.
|
Distribution(Instances source,
ClassifierSplitModel modelToUse)
Creates a distribution according to given instances and
split model.
|
NBTreeModelSelection(int minNoObj,
Instances allData)
Initializes the split selection method with the given parameters.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ResidualSplit.buildClassifier(Instances data)
Method not in use
|
void |
LogisticBase.buildClassifier(Instances data)
Builds the logistic regression model usiing LogitBoost.
|
void |
LMTNode.buildClassifier(Instances data)
Method for building a logistic model tree (only called for the root node).
|
void |
ResidualSplit.buildClassifier(Instances data,
double[][] dataZs,
double[][] dataWs)
Builds the split.
|
void |
LMTNode.buildTree(Instances data,
SimpleLinearRegression[][] higherRegressions,
double totalInstanceWeight,
double higherNumParameters)
Method for building the tree structure.
|
String |
ResidualSplit.leftSide(Instances data)
Returns name of splitting attribute (left side of condition).
|
int |
LMTNode.prune(double[] alphas,
double[] errors,
Instances test)
Method for performing one fold in the cross-validation of the cost-complexity parameter.
|
String |
ResidualSplit.rightSide(int index,
Instances data)
Prints the condition satisfied by instances in a subset.
|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances train)
Method not in use
|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances data,
double[][] dataZs,
double[][] dataWs)
Selects split based on residuals for the given dataset.
|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances train,
Instances test)
Method not in use
|
String |
ResidualSplit.sourceExpression(int index,
Instances data)
Method not in use
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
Rule.notCoveredInstances()
Get the instances not covered by this rule
|
| Modifier and Type | Method and Description |
|---|---|
void |
YongSplitInfo.attrSplit(int attr,
Instances inst)
Finds the best splitting point for an attribute in the instances
|
void |
SplitEvaluate.attrSplit(int attr,
Instances inst)
Finds the best splitting point for an attribute in the instances
|
void |
CorrelationSplitInfo.attrSplit(int attr,
Instances inst)
Finds the best splitting point for an attribute in the instances
|
void |
RuleNode.buildClassifier(Instances data)
Build this node (find an attribute and split point)
|
void |
Rule.buildClassifier(Instances data)
Generates a single rule or m5 model tree.
|
void |
PreConstructedLinearModel.buildClassifier(Instances instances)
Builds the classifier.
|
void |
M5Base.buildClassifier(Instances data)
Generates the classifier.
|
String |
YongSplitInfo.toString(Instances inst)
Converts the spliting information to string
|
| Constructor and Description |
|---|
Impurity(int partition,
int attribute,
Instances inst,
int k)
Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
|
Values(int low,
int high,
int attribute,
Instances inst)
Constructs an object which stores some statistics of the instances such
as sum, squared sum, variance, standard deviation
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
SimpleKMeans.getClusterCentroids()
Gets the the cluster centroids.
|
Instances |
SimpleKMeans.getClusterStandardDevs()
Gets the standard deviations of the numeric attributes in each cluster.
|
| Modifier and Type | Method and Description |
|---|---|
void |
SimpleKMeans.buildClusterer(Instances data)
Generates a clusterer.
|
void |
MakeDensityBasedClusterer.buildClusterer(Instances data)
Builds a clusterer for a set of instances.
|
void |
HierarchicalClusterer.buildClusterer(Instances data) |
void |
FilteredClusterer.buildClusterer(Instances data)
Build the clusterer on the filtered data.
|
void |
FarthestFirst.buildClusterer(Instances data)
Generates a clusterer.
|
void |
EM.buildClusterer(Instances data)
Generates a clusterer.
|
void |
Cobweb.buildClusterer(Instances data)
Builds the clusterer.
|
void |
Clusterer.buildClusterer(Instances data)
Generates a clusterer.
|
abstract void |
AbstractClusterer.buildClusterer(Instances data)
Generates a clusterer.
|
static double |
ClusterEvaluation.crossValidateModel(DensityBasedClusterer clusterer,
Instances data,
int numFolds,
Random random)
Perform a cross-validation for DensityBasedClusterer on a set of instances.
|
static String |
ClusterEvaluation.crossValidateModel(String clustererString,
Instances data,
int numFolds,
String[] options,
Random random)
Performs a cross-validation
for a DensityBasedClusterer clusterer on a set of instances.
|
void |
ClusterEvaluation.evaluateClusterer(Instances test)
Evaluate the clusterer on a set of instances.
|
void |
ClusterEvaluation.evaluateClusterer(Instances test,
String testFileName)
Evaluate the clusterer on a set of instances.
|
void |
ClusterEvaluation.evaluateClusterer(Instances test,
String testFileName,
boolean outputModel)
Evaluate the clusterer on a set of instances.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
Instance.dataset()
Returns the dataset this instance has access to.
|
Instances |
AbstractInstance.dataset()
Returns the dataset this instance has access to.
|
Instances |
TestInstances.generate()
Generates a new dataset
|
Instances |
TestInstances.generate(String namePrefix)
generates a new dataset.
|
Instances |
TestInstances.getData()
returns the current dataset, can be null
|
Instances |
AttributeLocator.getData()
returns the underlying data
|
Instances |
NormalizableDistance.getInstances()
returns the instances currently set.
|
Instances |
DistanceFunction.getInstances()
returns the instances currently set.
|
Instances |
TestInstances.getRelationalClassFormat()
returns the current strcuture of the relational class attribute, can
be null
|
Instances |
TestInstances.getRelationalFormat(int index)
returns the format for the specified relational attribute, can be null
|
static Instances |
Instances.mergeInstances(Instances first,
Instances second)
Merges two sets of Instances together.
|
Instances |
CheckScheme.PostProcessor.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
Instances |
Attribute.relation()
Returns the header info for a relation-valued attribute,
null if the attribute is not relation-valued.
|
Instances |
Attribute.relation(int valIndex)
Returns a value of a relation-valued attribute.
|
Instances |
Instance.relationalValue(Attribute att)
Returns the relational value of a relational attribute.
|
Instances |
AbstractInstance.relationalValue(Attribute att)
Returns the relational value of a relational attribute.
|
Instances |
Instance.relationalValue(int attIndex)
Returns the relational value of a relational attribute.
|
Instances |
AbstractInstance.relationalValue(int attIndex)
Returns the relational value of a relational attribute.
|
Instances |
Instances.resample(Random random)
Creates a new dataset of the same size using random sampling
with replacement.
|
Instances |
Instances.resampleWithWeights(Random random)
Creates a new dataset of the same size using random sampling
with replacement according to the current instance weights.
|
Instances |
Instances.resampleWithWeights(Random random,
boolean[] sampled)
Creates a new dataset of the same size using random sampling
with replacement according to the current instance weights.
|
Instances |
Instances.resampleWithWeights(Random random,
double[] weights)
Creates a new dataset of the same size using random sampling
with replacement according to the given weight vector.
|
Instances |
Instances.resampleWithWeights(Random random,
double[] weights,
boolean[] sampled)
Creates a new dataset of the same size using random sampling
with replacement according to the given weight vector.
|
Instances |
Instances.stringFreeStructure()
Create a copy of the structure if the data has string or
relational attributes, "cleanses" string types (i.e.
|
Instances |
Instances.testCV(int numFolds,
int numFold)
Creates the test set for one fold of a cross-validation on
the dataset.
|
Instances |
Instances.trainCV(int numFolds,
int numFold)
Creates the training set for one fold of a cross-validation
on the dataset.
|
Instances |
Instances.trainCV(int numFolds,
int numFold,
Random random)
Creates the training set for one fold of a cross-validation
on the dataset.
|
| Modifier and Type | Method and Description |
|---|---|
int |
Attribute.addRelation(Instances value)
Adds a relation to a relation-valued attribute.
|
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[][] |
BatchPredictor.distributionsForInstances(Instances insts)
Batch scoring method
|
boolean |
Instances.equalHeaders(Instances dataset)
Checks if two headers are equivalent.
|
String |
Instances.equalHeadersMsg(Instances dataset)
Checks if two headers are equivalent.
|
static Capabilities |
Capabilities.forInstances(Instances data)
returns a Capabilities object specific for this data.
|
static Capabilities |
Capabilities.forInstances(Instances data,
boolean multi)
returns a Capabilities object specific for this data.
|
void |
PartitionGenerator.generatePartition(Instances data)
Builds the classifier to generate a partition.
|
Instance |
AlgVector.getAsInstance(Instances model,
Random random)
Gets the elements of the vector as an instance.
|
static Instances |
Instances.mergeInstances(Instances first,
Instances second)
Merges two sets of Instances together.
|
Instances |
CheckScheme.PostProcessor.process(Instances data)
Provides a hook for derived classes to further modify the data.
|
void |
Instance.setDataset(Instances instances)
Sets the reference to the dataset.
|
void |
AbstractInstance.setDataset(Instances instances)
Sets the reference to the dataset.
|
void |
NormalizableDistance.setInstances(Instances insts)
Sets the instances.
|
void |
DistanceFunction.setInstances(Instances insts)
Sets the instances.
|
void |
TestInstances.setRelationalClassFormat(Instances value)
sets the structure for the relational class attribute
|
void |
TestInstances.setRelationalFormat(int index,
Instances value)
sets the structure for the bags for the relational attribute
|
boolean |
Capabilities.test(Instances data)
Tests the given data, whether it can be processed by the handler,
given its capabilities.
|
boolean |
Capabilities.test(Instances data,
int fromIndex,
int toIndex)
Tests a certain range of attributes of the given data, whether it can be
processed by the handler, given its capabilities.
|
void |
Capabilities.testWithFail(Instances data)
tests the given data by calling the test(Instances) method and throws
an exception if the test fails.
|
void |
Capabilities.testWithFail(Instances data,
int fromIndex,
int toIndex)
tests the given data by calling the test(Instances,int,int) method and
throws an exception if the test fails.
|
| Constructor and Description |
|---|
AlgVector(Instances format,
Random random)
Constructs a vector using a given data format.
|
Attribute(String attributeName,
Instances header)
Constructor for relation-valued attributes.
|
Attribute(String attributeName,
Instances header,
int index)
Constructor for a relation-valued attribute with a particular index.
|
Attribute(String attributeName,
Instances header,
ProtectedProperties metadata)
Constructor for relation-valued attributes.
|
AttributeLocator(Instances data,
int type)
Initializes the AttributeLocator with the given data for the specified
type of attribute.
|
AttributeLocator(Instances data,
int type,
int[] indices)
initializes the AttributeLocator with the given data for the specified
type of attribute.
|
AttributeLocator(Instances data,
int type,
int fromIndex,
int toIndex)
Initializes the AttributeLocator with the given data for the specified
type of attribute.
|
ChebyshevDistance(Instances data)
Constructs an Chebyshev Distance object and automatically initializes the
ranges.
|
EuclideanDistance(Instances data)
Constructs an Euclidean Distance object and automatically initializes the
ranges.
|
Instances(Instances dataset)
Constructor copying all instances and references to
the header information from the given set of instances.
|
Instances(Instances dataset,
int capacity)
Constructor creating an empty set of instances.
|
Instances(Instances source,
int first,
int toCopy)
Creates a new set of instances by copying a
subset of another set.
|
ManhattanDistance(Instances data)
Constructs an Manhattan Distance object and automatically initializes the
ranges.
|
MinkowskiDistance(Instances data)
Constructs an Minkowski Distance object and automatically initializes the
ranges.
|
NormalizableDistance(Instances data)
Initializes the distance function and automatically initializes the
ranges.
|
RelationalLocator(Instances data)
Initializes the RelationalLocator with the given data.
|
RelationalLocator(Instances data,
int[] indices)
Initializes the RelationalLocator with the given data.
|
RelationalLocator(Instances data,
int fromIndex,
int toIndex)
Initializes the RelationalLocator with the given data.
|
StringLocator(Instances data)
initializes the StringLocator with the given data
|
StringLocator(Instances data,
int[] indices)
Initializes the AttributeLocator with the given data.
|
StringLocator(Instances data,
int fromIndex,
int toIndex)
Initializes the StringLocator with the given data.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
ArffLoader.ArffReader.getData()
Returns the data that was read
|
Instances |
XRFFLoader.getDataSet()
Return the full data set.
|
Instances |
TextDirectoryLoader.getDataSet()
Return the full data set.
|
Instances |
SVMLightLoader.getDataSet()
Return the full data set.
|
Instances |
SerializedInstancesLoader.getDataSet()
Return the full data set.
|
Instances |
MatlabLoader.getDataSet()
Return the full data set.
|
Instances |
Loader.getDataSet()
Return the full data set.
|
Instances |
LibSVMLoader.getDataSet()
Return the full data set.
|
Instances |
JSONLoader.getDataSet()
Return the full data set.
|
Instances |
DatabaseLoader.getDataSet()
Return the full data set in batch mode (header and all intances at once).
|
Instances |
CSVLoader.getDataSet() |
Instances |
ConverterUtils.DataSource.getDataSet()
returns the full dataset, can be null in case of an error.
|
Instances |
C45Loader.getDataSet()
Return the full data set.
|
Instances |
ArffLoader.getDataSet()
Return the full data set.
|
abstract Instances |
AbstractLoader.getDataSet() |
Instances |
ConverterUtils.DataSource.getDataSet(int classIndex)
returns the full dataset with the specified class index set,
can be null in case of an error.
|
Instances |
AbstractSaver.getInstances()
Gets instances that should be stored.
|
Instances |
XRFFLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
TextDirectoryLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
SVMLightLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
SerializedInstancesLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
MatlabLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
Loader.getStructure()
Determines and returns (if possible) the structure (internally the header)
of the data set as an empty set of instances.
|
Instances |
LibSVMLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
JSONLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
DatabaseLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
CSVLoader.getStructure() |
Instances |
ConverterUtils.DataSource.getStructure()
returns the structure of the data.
|
Instances |
C45Loader.getStructure()
Determines and returns (if possible) the structure (internally the header)
of the data set as an empty set of instances.
|
Instances |
ArffLoader.getStructure()
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
|
Instances |
ArffLoader.ArffReader.getStructure()
Returns the header format
|
abstract Instances |
AbstractLoader.getStructure() |
Instances |
ConverterUtils.DataSource.getStructure(int classIndex)
returns the structure of the data, with the defined class index.
|
static Instances |
ConverterUtils.DataSource.read(InputStream stream)
convencience method for loading a dataset in batch mode from a stream.
|
static Instances |
ConverterUtils.DataSource.read(Loader loader)
convencience method for loading a dataset in batch mode.
|
static Instances |
ConverterUtils.DataSource.read(String location)
convencience method for loading a dataset in batch mode.
|
| Modifier and Type | Method and Description |
|---|---|
Instance |
XRFFLoader.getNextInstance(Instances structure)
XRFFLoader is unable to process a data set incrementally.
|
Instance |
TextDirectoryLoader.getNextInstance(Instances structure)
TextDirectoryLoader is unable to process a data set incrementally.
|
Instance |
SVMLightLoader.getNextInstance(Instances structure)
SVMLightLoader is unable to process a data set incrementally.
|
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 |
MatlabLoader.getNextInstance(Instances structure)
MatlabLoader 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 |
JSONLoader.getNextInstance(Instances structure)
JSONLoader 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 |
CSVLoader.getNextInstance(Instances structure) |
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.
|
abstract Instance |
AbstractLoader.getNextInstance(Instances structure) |
boolean |
ConverterUtils.DataSource.hasMoreElements(Instances structure)
returns whether there are more Instance objects in the data.
|
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.
|
void |
XRFFSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
void |
SVMLightSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
void |
Saver.setInstances(Instances instances)
Sets the instances to be saved
|
void |
LibSVMSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
void |
JSONSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
void |
AbstractSaver.setInstances(Instances instances)
Sets instances that should be stored.
|
int |
AbstractSaver.setStructure(Instances headerInfo)
Sets the strcuture of the instances for the first step of incremental saving.
|
void |
ConverterUtils.DataSink.write(Instances data)
writes the given data either via the saver or to the defined
output stream (depending on the constructor).
|
static void |
ConverterUtils.DataSink.write(OutputStream stream,
Instances data)
writes the data to the given stream (always in ARFF format).
|
static void |
ConverterUtils.DataSink.write(Saver saver,
Instances data)
writes the data via the given saver.
|
static void |
ConverterUtils.DataSink.write(String filename,
Instances data)
writes the data to the given file.
|
| Constructor and Description |
|---|
ArffLoader.ArffReader(Reader reader,
Instances template,
int lines)
Reads the data without header according to the specified template.
|
ArffLoader.ArffReader(Reader reader,
Instances template,
int lines,
int capacity)
Initializes the reader without reading the header according to the
specified template.
|
ArffLoader.ArffReader(Reader reader,
Instances template,
int lines,
int capacity,
boolean batch)
Initializes the reader without reading the header according to the
specified template.
|
ConverterUtils.DataSource(Instances inst)
Initializes the datasource with the given dataset.
|
| Modifier and Type | Method and Description |
|---|---|
static Instances |
JSONInstances.toHeader(JSONNode json)
Turns a JSON object, if possible, into an Instances object (only header).
|
static Instances |
JSONInstances.toInstances(JSONNode json)
Turns a JSON object, if possible, into an Instances object.
|
| Modifier and Type | Method and Description |
|---|---|
static JSONNode |
JSONInstances.toJSON(Instances inst)
Turns the Instances object into a JSON object.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
NearestNeighbourSearch.getInstances()
returns the instances currently set.
|
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 |
KDTree.kNearestNeighbours(Instance target,
int k)
Returns the k nearest neighbours of the supplied instance.
|
Instances |
CoverTree.kNearestNeighbours(Instance target,
int k)
Returns k-NNs of a given target instance, from among the previously
supplied training instances (supplied through setInstances method)
P.S.: May return more than k-NNs if more one instances have
the same distance to the target as the kth NN.
|
Instances |
BallTree.kNearestNeighbours(Instance target,
int k)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
| Modifier and Type | Method and Description |
|---|---|
void |
KDTree.assignSubToCenters(KDTreeNode node,
Instances centers,
int[] centList,
int[] assignments)
Assigns instances of this node to center.
|
void |
KDTree.centerInstances(Instances centers,
int[] assignments,
double pc)
Assigns instances to centers using KDTree.
|
void |
NearestNeighbourSearch.setInstances(Instances insts)
Sets the instances.
|
void |
LinearNNSearch.setInstances(Instances insts)
Sets the instances comprising the current neighbourhood.
|
void |
KDTree.setInstances(Instances instances)
Builds the KDTree on the given set of instances.
|
void |
CoverTree.setInstances(Instances instances)
Builds the Cover Tree on the given set of instances.
|
void |
BallTree.setInstances(Instances insts)
Builds the BallTree based on the given set of instances.
|
| Constructor and Description |
|---|
BallTree(Instances insts)
Creates a new instance of BallTree.
|
KDTree(Instances insts)
Creates a new instance of KDTree.
|
LinearNNSearch(Instances insts)
Constructor that uses the supplied set of
instances.
|
NearestNeighbourSearch(Instances insts)
Constructor.
|
| 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).
|
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 |
MiddleOutConstructor.setInstances(Instances insts)
Sets the instances on which the tree is to be built.
|
void |
BallTreeConstructor.setInstances(Instances inst)
Sets the instances on which the tree is to be built.
|
void |
BallSplitter.setInstances(Instances inst)
Sets the training instances on which the tree is
(or is to be) built.
|
| Constructor and Description |
|---|
BallSplitter(int[] instList,
Instances insts,
EuclideanDistance e)
Creates a new instance of BallSplitter.
|
MedianDistanceFromArbitraryPoint(int[] instList,
Instances insts,
EuclideanDistance e)
Constructor.
|
MedianOfWidestDimension(int[] instList,
Instances insts,
EuclideanDistance e)
Constructor.
|
PointsClosestToFurthestChildren(int[] instList,
Instances insts,
EuclideanDistance e)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
void |
KDTreeNodeSplitter.setInstances(Instances inst)
Sets the training instances on which the tree is (or is
to be) built.
|
| Constructor and Description |
|---|
KDTreeNodeSplitter(int[] instList,
Instances insts,
EuclideanDistance e)
Creates a new instance of KDTreeNodeSplitter.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
MiningSchema.getFieldsAsInstances()
Get the all the fields (both mining schema and derived) as Instances.
|
Instances |
MiningSchema.getMiningSchemaAsInstances()
Get the mining schema fields as an Instances object.
|
| Modifier and Type | Method and Description |
|---|---|
static String |
PMMLFactory.applyClassifier(PMMLModel model,
Instances test) |
void |
DerivedFieldMetaInfo.setFieldDefs(Instances fields)
Upadate the field definitions for this derived field
|
| Constructor and Description |
|---|
MappingInfo(Instances dataSet,
MiningSchema miningSchema,
Logger log) |
MiningSchema(Element model,
Instances dataDictionary,
weka.core.pmml.TransformationDictionary transDict)
Constructor for MiningSchema.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
XMLInstances.getInstances()
returns the current instances, either the ones that were set or the ones
that were generated from the XML structure.
|
| Modifier and Type | Method and Description |
|---|---|
void |
XMLInstances.setInstances(Instances data)
builds up the XML structure based on the given data
|
| Constructor and Description |
|---|
XMLInstances(Instances data)
generates the XML structure based on the given data
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
DataGenerator.defineDataFormat()
Initializes the format for the dataset produced.
|
abstract Instances |
DataGenerator.generateExamples()
Generates all examples of the dataset.
|
Instances |
DataGenerator.getDatasetFormat()
Gets the format of the dataset that is to be generated.
|
| Modifier and Type | Method and Description |
|---|---|
void |
DataGenerator.setDatasetFormat(Instances newFormat)
Sets the format of the dataset that is to be generated.
|
| Constructor and Description |
|---|
Test(int i,
double s,
Instances dataset)
Constructor
|
Test(int i,
double s,
Instances dataset,
boolean n)
Constructor
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
RDG1.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
RandomRBF.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
LED24.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
BayesNet.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
Agrawal.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
RDG1.generateExamples()
Generate all examples of the dataset.
|
Instances |
RandomRBF.generateExamples()
Generates all examples of the dataset.
|
Instances |
LED24.generateExamples()
Generates all examples of the dataset.
|
Instances |
BayesNet.generateExamples()
Generates all examples of the dataset.
|
Instances |
Agrawal.generateExamples()
Generates all examples of the dataset.
|
Instances |
RDG1.generateExamples(int num,
Random random,
Instances format)
Generate all examples of the dataset.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
RDG1.generateExamples(int num,
Random random,
Instances format)
Generate all examples of the dataset.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
MexicanHat.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
Expression.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
MexicanHat.generateExamples()
Generates all examples of the dataset.
|
Instances |
Expression.generateExamples()
Generates all examples of the dataset.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
SubspaceCluster.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
BIRCHCluster.defineDataFormat()
Initializes the format for the dataset produced.
|
Instances |
SubspaceCluster.generateExamples()
Generate all examples of the dataset.
|
Instances |
BIRCHCluster.generateExamples()
Generate all examples of the dataset.
|
Instances |
BIRCHCluster.generateExamples(Random random,
Instances format)
Generate all examples of the dataset.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
BIRCHCluster.generateExamples(Random random,
Instances format)
Generate all examples of the dataset.
|
| Modifier and Type | Method and Description |
|---|---|
static Instances |
EstimatorUtils.getInstancesFromClass(Instances data,
int classIndex,
double classValue)
Returns a dataset that contains of all instances of a certain class value.
|
static Instances |
EstimatorUtils.getInstancesFromValue(Instances data,
int index,
double v)
Returns a dataset that contains of all instances of a certain value
for the given attribute.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Estimator.addValues(Instances data,
int attrIndex)
Initialize the estimator with a new dataset.
|
void |
Estimator.addValues(Instances data,
int attrIndex,
double min,
double max,
double factor)
Initialize the estimator with all values of one attribute of a dataset.
|
void |
Estimator.addValues(Instances data,
int attrIndex,
int classIndex,
int classValue)
Initialize the estimator using only the instance of one class.
|
void |
Estimator.addValues(Instances data,
int attrIndex,
int classIndex,
int classValue,
double min,
double max)
Initialize the estimator using only the instance of one class.
|
static void |
Estimator.buildEstimator(Estimator est,
Instances instances,
int attrIndex,
int classIndex,
int classValueIndex,
boolean isIncremental) |
static double |
EstimatorUtils.findMinDistance(Instances inst,
int attrIndex)
Find the minimum distance between values
|
static Instances |
EstimatorUtils.getInstancesFromClass(Instances data,
int classIndex,
double classValue)
Returns a dataset that contains of all instances of a certain class value.
|
static Vector |
EstimatorUtils.getInstancesFromClass(Instances data,
int attrIndex,
int classIndex,
double classValue,
Instances workData)
Returns a dataset that contains all instances of a certain class value.
|
static Instances |
EstimatorUtils.getInstancesFromValue(Instances data,
int index,
double v)
Returns a dataset that contains of all instances of a certain value
for the given attribute.
|
static int |
EstimatorUtils.getMinMax(Instances inst,
int attrIndex,
double[] minMax)
Find the minimum and the maximum of the attribute and return it in
the last parameter..
|
static int |
CheckEstimator.getMinMax(Instances inst,
int attrIndex,
double[] minMax)
Find the minimum and the maximum of the attribute and return it in
the last parameter..
|
void |
Estimator.testCapabilities(Instances data,
int attrIndex)
Test if the estimator can handle the data.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
Tester.getInstances()
Get the value of Instances.
|
Instances |
PairedTTester.getInstances()
Get the value of Instances.
|
Instances |
InstanceQuery.retrieveInstances()
Makes a database query using the query set through the -Q option
to convert a table into a set of instances
|
static Instances |
InstanceQuery.retrieveInstances(InstanceQueryAdapter adapter,
ResultSet rs) |
Instances |
InstanceQuery.retrieveInstances(String query)
Makes a database query to convert a table into a set of instances
|
| Modifier and Type | Method and Description |
|---|---|
Object[] |
SplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
Object[] |
RegressionSplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
Object[] |
DensityBasedClustererSplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
Object[] |
CostSensitiveClassifierSplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
Object[] |
ClassifierSplitEvaluator.getResult(Instances train,
Instances test)
Gets the results for the supplied train and test datasets.
|
void |
Tester.setInstances(Instances newInstances)
Set the value of Instances.
|
void |
ResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
RandomSplitResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
PairedTTester.setInstances(Instances newInstances)
Set the value of Instances.
|
void |
LearningRateResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
ExplicitTestsetResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
DatabaseResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
CrossValidationResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
void |
AveragingResultProducer.setInstances(Instances instances)
Sets the dataset that results will be obtained for.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
Filter.getOutputFormat()
Gets the format of the output instances.
|
static Instances |
Filter.useFilter(Instances data,
Filter filter)
Filters an entire set of instances through a filter and returns
the new set.
|
| Modifier and Type | Method and Description |
|---|---|
Capabilities |
Filter.getCapabilities(Instances data)
Returns the Capabilities of this filter, customized based on the data.
|
boolean |
SimpleFilter.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Filter.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AllFilter.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
String |
Sourcable.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
String |
AllFilter.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
static Instances |
Filter.useFilter(Instances data,
Filter filter)
Filters an entire set of instances through a filter and returns
the new set.
|
static String |
Filter.wekaStaticWrapper(Sourcable filter,
String className,
Instances input,
Instances output)
generates source code from the filter
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
PartitionMembership.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NominalToBinary.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Discretize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ClassOrder.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
StratifiedRemoveFolds.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
SpreadSubsample.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Resample.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
PotentialClassIgnorer.getOutputFormat()
Gets the format of the output instances.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AddNoise.addNoise(Instances instances,
int seed,
int percent,
int attIndex,
boolean useMissing)
add noise to the dataset
a given percentage of the instances are changed in the way, that
a set of instances are randomly selected using seed.
|
Capabilities |
ClusterMembership.getCapabilities(Instances data)
Returns the Capabilities of this filter, makes sure that the class is
never set (for the clusterer).
|
Capabilities |
AddCluster.getCapabilities(Instances data)
Returns the Capabilities of this filter, makes sure that the class is
never set (for the clusterer).
|
void |
KernelFilter.initFilter(Instances instances)
initializes the filter with the given dataset, i.e., the kernel gets
built.
|
boolean |
TimeSeriesTranslate.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
TimeSeriesDelta.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
SwapValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
StringToWordVector.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
StringToNominal.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Standardize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ReplaceMissingWithUserConstant.setInputFormat(Instances instanceInfo) |
boolean |
ReplaceMissingValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Reorder.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveUseless.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveType.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Remove.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RandomProjection.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
PrincipalComponents.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
PotentialClassIgnorer.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
PKIDiscretize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Obfuscate.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NumericTransform.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NumericToBinary.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Normalize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NominalToString.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NominalToBinary.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
MergeTwoValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
MergeManyValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
MathExpression.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
MakeIndicator.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
FirstOrder.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Discretize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Copy.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ClusterMembership.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ChangeDateFormat.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Center.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddUserFields.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddNoise.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddID.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddExpression.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AddCluster.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Add.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
AbstractTimeSeries.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
String |
Standardize.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
String |
ReplaceMissingValues.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
String |
Normalize.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
String |
Center.toSource(String className,
Instances data)
Returns a string that describes the filter as source.
|
| Modifier and Type | Method and Description |
|---|---|
void |
RemoveFrequentValues.determineValues(Instances inst)
determines the values to retain, it is always at least 1
and up to the maximum number of distinct values
|
boolean |
SparseToNonSparse.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
ReservoirSample.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Resample.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveWithValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveRange.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemovePercentage.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveMisclassified.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveFrequentValues.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
RemoveFolds.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
Randomize.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
boolean |
NonSparseToSparse.setInputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
| Modifier and Type | Method and Description |
|---|---|
static Instances |
Parser.filter(String expression,
Instances input)
Filters the input dataset against the provided expression.
|
| Modifier and Type | Method and Description |
|---|---|
static Instances |
Parser.filter(String expression,
Instances input)
Filters the input dataset against the provided expression.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
ViewerDialog.getInstances()
returns the currently displayed instances
|
Instances |
SetInstancesPanel.getInstances()
Gets the set of instances currently held by the panel.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ViewerDialog.setInstances(Instances inst)
sets the instances to display
|
void |
SetInstancesPanel.setInstances(Instances i)
Updates the set of instances that is currently held by the panel.
|
void |
InstancesSummaryPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AttributeVisualizationPanel.setInstances(Instances newins)
Sets the instances for use
|
void |
AttributeSummaryPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AttributeSelectionPanel.setInstances(Instances newInstances)
Sets the instances who's attribute names will be displayed.
|
void |
AttributeListPanel.setInstances(Instances newInstances)
Sets the instances who's attribute names will be displayed.
|
int |
ViewerDialog.showDialog(Instances inst)
Pops up the modal dialog and waits for Cancel or OK.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
ArffTableModel.getInstances()
returns the data
|
Instances |
ArffSortedTableModel.getInstances()
returns the data
|
Instances |
ArffPanel.getInstances()
returns the instances of the panel, if none then NULL
|
| Modifier and Type | Method and Description |
|---|---|
void |
ArffTableModel.setInstances(Instances data)
sets the data
|
void |
ArffSortedTableModel.setInstances(Instances data)
sets the data
|
void |
ArffPanel.setInstances(Instances data)
displays the given instances, i.e.
|
| Constructor and Description |
|---|
ArffPanel(Instances data)
initializes the panel with the given data
|
ArffSortedTableModel(Instances data)
initializes the sorter w/o a model, but uses the given data to create
a model from that
|
ArffTableModel(Instances data)
initializes the model with the given data
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
Sorter.getConnectedFormat()
Returns the structure of the incoming instances (if any)
|
Instances |
FlowByExpression.getConnectedFormat()
Returns the structure of the incoming instances (if any)
|
Instances |
ClassValuePicker.getConnectedFormat()
Returns the structure of the incoming instances (if any)
|
Instances |
ClassAssigner.getConnectedFormat()
Returns the structure of the incoming instances (if any)
|
Instances |
DataSetEvent.getDataSet()
Return the instances of the data set
|
Instances |
InstanceEvent.getStructure()
Get the instances structure (may be null if this is not a FORMAT_AVAILABLE
event)
|
Instances |
IncrementalClassifierEvent.getStructure()
Get the instances structure (may be null if this is not
a NEW_BATCH event)
|
Instances |
TrainTestSplitMaker.getStructure(String eventName)
Get the structure of the output encapsulated in the named
event.
|
Instances |
TrainingSetMaker.getStructure(String eventName)
Get the structure of the output encapsulated in the named event.
|
Instances |
TestSetMaker.getStructure(String eventName)
Get the structure of the output encapsulated in the named event.
|
Instances |
StructureProducer.getStructure(String eventName)
Get the structure of the output encapsulated in the named
event.
|
Instances |
Sorter.getStructure(String eventName)
Get the structure of the output encapsulated in the named event.
|
Instances |
Loader.getStructure(String eventName)
Get the structure of the output encapsulated in the named event.
|
Instances |
FlowByExpression.getStructure(String eventName)
Get the structure of the output encapsulated in the named event.
|
Instances |
CrossValidationFoldMaker.getStructure(String eventName)
Get the structure of the output encapsulated in the named
event.
|
Instances |
ClassValuePicker.getStructure(String eventName) |
Instances |
ClassAssigner.getStructure(String eventName)
Get the structure of the output encapsulated in the named
event.
|
Instances |
Associator.getStructure(String eventName)
Get the structure of the output encapsulated in the named
event.
|
Instances |
TestSetEvent.getTestSet()
Get the test set instances
|
Instances |
TrainingSetEvent.getTrainingSet()
Get the training instances
|
| Modifier and Type | Method and Description |
|---|---|
static void |
SerializedModelSaver.saveBinary(File saveTo,
Object model,
Instances header)
Save a model in binary form.
|
static void |
SerializedModelSaver.saveKOML(File saveTo,
Object model,
Instances header)
Save a model in KOML deep object serialized XML form.
|
static void |
SerializedModelSaver.saveXStream(File saveTo,
Object model,
Instances header)
Save a model in XStream deep object serialized XML form.
|
void |
SQLViewerPerspective.setInstances(Instances insts)
Set instances (if the perspective accepts them)
|
void |
ScatterPlotMatrix.setInstances(Instances inst)
Set instances for this bean.
|
void |
KnowledgeFlowApp.KFPerspective.setInstances(Instances insts)
Set instances (if the perspective accepts them)
|
void |
KnowledgeFlowApp.MainKFPerspective.setInstances(Instances insts) |
void |
DataVisualizer.setInstances(Instances inst)
Set instances for this bean.
|
void |
AttributeSummarizer.setInstances(Instances inst)
Set instances for this bean.
|
void |
InstanceEvent.setStructure(Instances structure)
Set the instances structure
|
void |
IncrementalClassifierEvent.setStructure(Instances structure)
Set the instances structure
|
| Modifier and Type | Method and Description |
|---|---|
BufferedImage |
WekaOffscreenChartRenderer.renderHistogram(int width,
int height,
List<Instances> series,
String attToPlot,
List<String> optionalArgs)
Render histogram(s) (numeric attribute) or pie chart (nominal attribute).
|
BufferedImage |
OffscreenChartRenderer.renderHistogram(int width,
int height,
List<Instances> series,
String attsToPlot,
List<String> optionalArgs)
Render histogram(s) (numeric attribute) or bar chart(s) (nominal attribute).
|
BufferedImage |
WekaOffscreenChartRenderer.renderXYLineChart(int width,
int height,
List<Instances> series,
String xAxis,
String yAxis,
List<String> optionalArgs)
Render an XY line chart
|
BufferedImage |
OffscreenChartRenderer.renderXYLineChart(int width,
int height,
List<Instances> series,
String xAxis,
String yAxis,
List<String> optionalArgs)
Render an XY line chart
|
BufferedImage |
WekaOffscreenChartRenderer.renderXYScatterPlot(int width,
int height,
List<Instances> series,
String xAxis,
String yAxis,
List<String> optionalArgs)
Render an XY scatter plot
|
BufferedImage |
OffscreenChartRenderer.renderXYScatterPlot(int width,
int height,
List<Instances> series,
String xAxis,
String yAxis,
List<String> optionalArgs)
Render an XY scatter plot
|
| Constructor and Description |
|---|
DataSetEvent(Object source,
Instances dataSet) |
IncrementalClassifierEvent(Object source,
Classifier scheme,
Instances structure)
Creates a new incremental classifier event that encapsulates
header information and classifier.
|
InstanceEvent(Object source,
Instances structure)
Creates a new
InstanceEvent instance which encapsulates header
information only. |
TestSetEvent(Object source,
Instances testSet)
Creates a new
TestSetEvent |
TestSetEvent(Object source,
Instances testSet,
int setNum,
int maxSetNum)
Creates a new
TestSetEvent |
TestSetEvent(Object source,
Instances testSet,
int runNum,
int maxRunNum,
int setNum,
int maxSetNum)
Creates a new
TestSetEvent |
TrainingSetEvent(Object source,
Instances trainSet)
Creates a new
TrainingSetEvent |
TrainingSetEvent(Object source,
Instances trainSet,
int setNum,
int maxSetNum)
Creates a new
TrainingSetEvent |
TrainingSetEvent(Object source,
Instances trainSet,
int runNum,
int maxRunNum,
int setNum,
int maxSetNum)
Creates a new
TrainingSetEvent |
| Modifier and Type | Method and Description |
|---|---|
Instances |
BoundaryVisualizer.getInstances()
Get the training instances
|
| Modifier and Type | Method and Description |
|---|---|
void |
KDDataGenerator.buildGenerator(Instances inputInstances)
Initialize the generator using the supplied instances
|
void |
DataGenerator.buildGenerator(Instances inputInstances)
Build the data generator
|
static void |
BoundaryVisualizer.createNewVisualizerWindow(Classifier classifier,
Instances instances)
Creates a new GUI window with all of the BoundaryVisualizer trappings,
|
void |
RemoteBoundaryVisualizerSubTask.setInstances(Instances i)
Set the training data
|
void |
BoundaryVisualizer.setInstances(Instances inst)
Set the training instances
|
void |
BoundaryPanel.setTrainingData(Instances trainingData)
Set the training data to use
|
| Modifier and Type | Method and Description |
|---|---|
void |
ResultsPanel.setInstances(Instances newInstances)
Sets up the panel with a new set of instances, attempting
to guess the correct settings for various columns.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
PreprocessPanel.getInstances()
Gets the working set of instances.
|
Instances |
DataGeneratorPanel.getInstances()
returns the generated instances, null if the process was cancelled.
|
Instances |
AbstractPlotInstances.getInstances()
Returns the training data.
|
Instances |
AbstractPlotInstances.getPlotInstances()
Returns the generated plot instances.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ClassifierErrorsPlotInstances.process(Instances batch,
double[][] predictions,
Evaluation eval) |
void |
PreprocessPanel.saveInstancesToFile(AbstractFileSaver saver,
Instances inst)
saves the data with the specified saver
|
void |
PreprocessPanel.setInstances(Instances inst)
Tells the panel to use a new base set of instances.
|
void |
Explorer.ExplorerPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
ClustererPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
ClassifierPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AttributeSelectionPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AssociationsPanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
AbstractPlotInstances.setInstances(Instances value)
Sets the instances that are the basis for the plot instances.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
InstanceProducer.outputFormat() |
Instances |
InstanceLoader.outputFormat() |
Instances |
InstanceJoiner.outputFormat()
Gets the format of the output instances.
|
| Modifier and Type | Method and Description |
|---|---|
void |
InstanceViewer.inputFormat(Instances instanceInfo) |
void |
InstanceTable.inputFormat(Instances instanceInfo) |
void |
InstanceSavePanel.inputFormat(Instances instanceInfo) |
boolean |
InstanceJoiner.inputFormat(Instances instanceInfo)
Sets the format of the input instances.
|
void |
InstanceCounter.inputFormat(Instances instanceInfo) |
| Modifier and Type | Method and Description |
|---|---|
Instances |
Node.getInstances()
This will return the Instances object related to this node.
|
| Modifier and Type | Method and Description |
|---|---|
Instances |
VisualizePanel.getInstances()
Get the master plot's instances
|
Instances |
VisualizePanelEvent.getInstances1() |
Instances |
VisualizePanelEvent.getInstances2() |
Instances |
PlotData2D.getPlotInstances()
Returns the instances for this plot
|
| Modifier and Type | Method and Description |
|---|---|
Vector<Instances> |
InstanceInfoFrame.getInfoData()
Returns the underlying data.
|
Vector<Instances> |
InstanceInfo.getInfoData()
Returns the underlying data.
|
| Modifier and Type | Method and Description |
|---|---|
void |
VisualizePanel.setInstances(Instances inst)
Tells the panel to use a new set of instances.
|
void |
Plot2D.setInstances(Instances inst)
Sets the master plot from a set of instances
|
void |
MatrixPanel.setInstances(Instances newInst)
This method changes the Instances object of this class to a new one.
|
void |
ClassPanel.setInstances(Instances insts)
Set the instances.
|
void |
AttributePanel.setInstances(Instances ins)
This sets the instances to be drawn into the attribute panel
|
void |
VisualizePanel.setUpComboBoxes(Instances inst)
initializes the comboboxes based on the data
|
void |
ThresholdVisualizePanel.setUpComboBoxes(Instances inst)
This overloads VisualizePanel's setUpComboBoxes to add
ActionListeners to watch for when the X/Y Axis comboboxes
are changed.
|
| Modifier and Type | Method and Description |
|---|---|
void |
InstanceInfoFrame.setInfoData(Vector<Instances> data)
Sets the underlying data.
|
void |
InstanceInfo.setInfoData(Vector<Instances> data)
Sets the underlying data.
|
| Constructor and Description |
|---|
PlotData2D(Instances insts)
Construct a new PlotData2D using the supplied instances
|
VisualizePanelEvent(FastVector ar,
Instances i,
Instances i2,
int at1,
int at2)
This constructor creates the event with all the parameters set.
|
| Modifier and Type | Method and Description |
|---|---|
JMenuItem |
ErrorVisualizePlugin.getVisualizeMenuItem(Instances predInst)
Get a JMenu or JMenuItem which contain action listeners
that perform the visualization of the classifier errors.
|
Copyright © 2013 University of Waikato, Hamilton, NZ. All Rights Reserved.