Uses of Class
weka.filters.Filter

Packages that use Filter

Package	Description
weka.associations
weka.classifiers.meta
weka.clusterers
weka.core
weka.core.neighboursearch
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance
weka.gui.beans
weka.gui.explorer
weka.knowledgeflow.steps

Uses of Filter in weka.associations

Methods in weka.associations that return Filter

Modifier and Type	Method and Description
Filter	FilteredAssociator.getFilter() Gets the filter used.

Methods in weka.associations with parameters of type Filter

Modifier and Type	Method and Description
void	FilteredAssociator.setFilter(Filter value) Sets the filter

Constructors in weka.associations with parameters of type Filter

Constructor and Description
FilteredAssociationRules(Filter filter, AssociationRules rules) Constructs a new FilteredAssociationRules.
FilteredAssociationRules(Object producer, Filter filter, AssociationRules rules) Constructs a new FilteredAssociationRules.
FilteredAssociationRules(String producer, Filter filter, AssociationRules rules) Constructs a new FilteredAssociationRules.

Uses of Filter in weka.classifiers.meta

Methods in weka.classifiers.meta that return Filter

Modifier and Type	Method and Description
Filter	FilteredClassifier.getFilter() Gets the filter used.

Methods in weka.classifiers.meta with parameters of type Filter

Modifier and Type	Method and Description
void	FilteredClassifier.setFilter(Filter filter) Sets the filter

Uses of Filter in weka.clusterers

Methods in weka.clusterers that return Filter

Modifier and Type	Method and Description
Filter	FilteredClusterer.getFilter() Gets the filter used.

Methods in weka.clusterers with parameters of type Filter

Modifier and Type	Method and Description
static Canopy	Canopy.aggregateCanopies(List<Canopy> canopies, double aggregationT1, double aggregationT2, NormalizableDistance finalDistanceFunction, Filter missingValuesReplacer, int finalNumCanopies) Aggregate the canopies from a list of Canopy clusterers together into one final model.
void	FilteredClusterer.setFilter(Filter filter) Sets the filter.
void	Canopy.setMissingValuesReplacer(Filter missingReplacer) Set a ready-to-use missing values replacement filter

Uses of Filter in weka.core

Methods in weka.core that return Filter

Modifier and Type	Method and Description
Filter	FilteredDistance.getFilter() Gets the filter used.

Methods in weka.core with parameters of type Filter

Modifier and Type	Method and Description
void	FilteredDistance.setFilter(Filter filter) Sets the filter

Uses of Filter in weka.core.neighboursearch

Methods in weka.core.neighboursearch that return Filter

Modifier and Type	Method and Description
Filter	FilteredNeighbourSearch.getFilter() Gets the filter used.

Methods in weka.core.neighboursearch with parameters of type Filter

Modifier and Type	Method and Description
void	FilteredNeighbourSearch.setFilter(Filter filter) Sets the filter

Uses of Filter in weka.filters

Subclasses of Filter in weka.filters

Modifier and Type	Class and Description
class	AllFilter A simple instance filter that passes all instances directly through.
class	MultiFilter Applies several filters successively.
class	RenameRelation A simple filter that allows the relation name of a set of instances to be altered in various ways.
class	SimpleBatchFilter This filter is a superclass for simple batch filters.
class	SimpleFilter This filter contains common behavior of the SimpleBatchFilter and the SimpleStreamFilter.
class	SimpleStreamFilter This filter is a superclass for simple stream filters.

Methods in weka.filters that return Filter

Modifier and Type	Method and Description
Filter	CheckSource.getFilter() Gets the filter being used for the tests, can be null.
Filter	MultiFilter.getFilter(int index) Gets a single filter from the set of available filters.
Filter[]	MultiFilter.getFilters() Gets the list of possible filters to choose from.
Filter	CheckSource.getSourceCode() Gets the class to test.
static Filter[]	Filter.makeCopies(Filter model, int num) Creates a given number of deep copies of the given filter using serialization.
static Filter	Filter.makeCopy(Filter model) Creates a deep copy of the given filter using serialization.

Methods in weka.filters with parameters of type Filter

Modifier and Type	Method and Description
static void	Filter.batchFilterFile(Filter filter, String[] options) Method for testing filters ability to process multiple batches.
static void	Filter.filterFile(Filter filter, String[] options) Method for testing filters.
static Filter[]	Filter.makeCopies(Filter model, int num) Creates a given number of deep copies of the given filter using serialization.
static Filter	Filter.makeCopy(Filter model) Creates a deep copy of the given filter using serialization.
static void	Filter.runFilter(Filter filter, String[] options) runs the filter instance with the given options.
void	CheckSource.setFilter(Filter value) Sets the filter to use for the comparison.
void	MultiFilter.setFilters(Filter[] filters) Sets the list of possible filters to choose from.
void	CheckSource.setSourceCode(Filter value) Sets the class to test.
static Instances	Filter.useFilter(Instances data, Filter filter) Filters an entire set of instances through a filter and returns the new set.

Uses of Filter in weka.filters.supervised.attribute

Subclasses of Filter in weka.filters.supervised.attribute

Modifier and Type	Class and Description
class	AddClassification A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
class	AttributeSelection A supervised attribute filter that can be used to select attributes.
class	ClassConditionalProbabilities Converts the values of nominal and/or numeric attributes into class conditional probabilities.
class	ClassOrder Changes the order of the classes so that the class values are no longer of in the order specified in the header.
class	Discretize An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
class	MergeNominalValues Merges values of all nominal attributes among the specified attributes, excluding the class attribute, using the CHAID method, but without considering re-splitting of merged subsets.
class	PartitionMembership * A filter that uses a PartitionGenerator to generate partition membership values; filtered instances are composed of these values plus the class attribute (if set in the input data) and rendered as sparse instances.

Uses of Filter in weka.filters.supervised.instance

Subclasses of Filter in weka.filters.supervised.instance

Modifier and Type	Class and Description
class	ClassBalancer Reweights the instances in the data so that each class has the same total weight.
class	SpreadSubsample Produces a random subsample of a dataset.
class	StratifiedRemoveFolds This filter takes a dataset and outputs a specified fold for cross validation.

Uses of Filter in weka.filters.unsupervised.attribute

Subclasses of Filter in weka.filters.unsupervised.attribute

Modifier and Type	Class and Description
class	AbstractTimeSeries An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance.
class	Add An instance filter that adds a new attribute to the dataset.
class	AddCluster A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm. Either the clustering algorithm gets built with the first batch of data or one specifies are serialized clusterer model file to use instead.
class	AddExpression An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
class	AddID An instance filter that adds an ID attribute to the dataset.
class	AddNoise An instance filter that changes a percentage of a given attribute's values.
class	AddUserFields A filter that adds new attributes with user specified type and constant value.
class	AddValues Adds the labels from the given list to an attribute if they are missing.
class	CartesianProduct A filter for performing the Cartesian product of a set of nominal attributes.
class	Center Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
class	ChangeDateFormat Changes the date format used by a date attribute.
class	ClassAssigner Filter that can set and unset the class index.
class	ClusterMembership A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data).
class	Copy An instance filter that copies a range of attributes in the dataset.
class	DateToNumeric A filter for turning date attributes into numeric ones.
class	FirstOrder This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
class	FixedDictionaryStringToWordVector Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
class	InterquartileRange A filter for detecting outliers and extreme values based on interquartile ranges.
class	KernelFilter Converts the given set of data into a kernel matrix.
class	MakeIndicator A filter that creates a new dataset with a Boolean attribute replacing a nominal attribute.
class	MathExpression Modify numeric attributes according to a given mathematical expression.
class	MergeInfrequentNominalValues Merges all values of the specified nominal attributes that are insufficiently frequent.
class	MergeManyValues Merges many values of a nominal attribute into one value.
class	MergeTwoValues Merges two values of a nominal attribute into one value.
class	NominalToBinary Converts all nominal attributes into binary numeric attributes.
class	NominalToString Converts a nominal attribute (i.e.
class	Normalize Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
class	NumericCleaner A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value, and sets these values to a pre-defined default.
class	NumericToBinary Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
class	NumericToDate A filter for turning numeric attributes into date attributes.
class	NumericToNominal A filter for turning numeric attributes into nominal ones.
class	NumericTransform Transforms numeric attributes using a given transformation method.
class	Obfuscate A simple instance filter that renames the relation, all attribute names and all nominal attribute values.
class	OrdinalToNumeric An attribute filter that converts ordinal nominal attributes into numeric ones Valid options are:
class	PartitionedMultiFilter A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
class	PKIDiscretize Discretizes numeric attributes using equal frequency binning and forces the number of bins to be equal to the square root of the number of values of the numeric attribute. For more information, see: Ying Yang, Geoffrey I.
class	PotentialClassIgnorer This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
class	PrincipalComponents Performs a principal components analysis and transformation of the data. Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%). Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
class	RandomProjection Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
class	RandomSubset Chooses a random subset of non-class attributes, either an absolute number or a percentage.
class	Remove An filter that removes a range of attributes from the dataset.
class	RemoveByName Removes attributes based on a regular expression matched against their names.
class	RemoveType Removes attributes of a given type.
class	RemoveUseless This filter removes attributes that do not vary at all or that vary too much.
class	RenameAttribute This filter is used for renaming attributes. Regular expressions can be used in the matching and replacing. See Javadoc of java.util.regex.Pattern class for more information: http://java.sun.com/javase/6/docs/api/java/util/regex/Pattern.html Valid options are:
class	RenameNominalValues Renames the values of nominal attributes.
class	Reorder A filter that generates output with a new order of the attributes.
class	ReplaceMissingValues Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
class	ReplaceMissingWithUserConstant Replaces all missing values for nominal, string, numeric and date attributes in the dataset with user-supplied constant values.
class	ReplaceWithMissingValue A filter that can be used to introduce missing values in a dataset.
class	SortLabels A simple filter for sorting the labels of nominal attributes.
class	Standardize Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
class	StringToNominal Converts a range of string attributes (unspecified number of values) to nominal (set number of values).
class	StringToWordVector Converts string attributes into a set of numeric attributes representing word occurrence information from the text contained in the strings.
class	SwapValues Swaps two values of a nominal attribute.
class	TimeSeriesDelta An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
class	TimeSeriesTranslate An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
class	Transpose Transposes the data: instances become attributes and attributes become instances.

Methods in weka.filters.unsupervised.attribute that return Filter

Modifier and Type	Method and Description
Filter	PartitionedMultiFilter.getFilter(int index) Gets a single filter from the set of available filters.
Filter[]	PartitionedMultiFilter.getFilters() Gets the list of possible filters to choose from.
Filter	KernelFilter.getPreprocessing() Gets the filter used for preprocessing

Methods in weka.filters.unsupervised.attribute with parameters of type Filter

Modifier and Type	Method and Description
void	PartitionedMultiFilter.setFilters(Filter[] filters) Sets the list of possible filters to choose from.
void	KernelFilter.setPreprocessing(Filter value) Sets the filter to use for preprocessing (use the AllFilter for no preprocessing)

Uses of Filter in weka.filters.unsupervised.instance

Subclasses of Filter in weka.filters.unsupervised.instance

Modifier and Type	Class and Description
class	NonSparseToSparse An instance filter that converts all incoming instances into sparse format.
class	Randomize Randomly shuffles the order of instances passed through it.
class	RemoveDuplicates Removes all duplicate instances from the first batch of data it receives.
class	RemoveFolds This filter takes a dataset and outputs a specified fold for cross validation.
class	RemoveFrequentValues Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
class	RemoveMisclassified A filter that removes instances which are incorrectly classified.
class	RemovePercentage A filter that removes a given percentage of a dataset.
class	RemoveRange A filter that removes a given range of instances of a dataset.
class	RemoveWithValues Filters instances according to the value of an attribute.
class	Resample Produces a random subsample of a dataset using either sampling with replacement or without replacement.
class	ReservoirSample Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
class	SparseToNonSparse An instance filter that converts all incoming sparse instances into non-sparse format.
class	SubsetByExpression Filters instances according to a user-specified expression. Examples: - extracting only mammals and birds from the 'zoo' UCI dataset: (CLASS is 'mammal') or (CLASS is 'bird') - extracting only animals with at least 2 legs from the 'zoo' UCI dataset: (ATT14 >= 2) - extracting only instances with non-missing 'wage-increase-second-year' from the 'labor' UCI dataset: not ismissing(ATT3) Valid options are:

Uses of Filter in weka.gui.beans

Methods in weka.gui.beans that return Filter

Modifier and Type	Method and Description
Filter	Filter.getFilter()

Methods in weka.gui.beans with parameters of type Filter

Modifier and Type	Method and Description
void	Filter.setFilter(Filter c) Set the filter to be wrapped by this bean

Uses of Filter in weka.gui.explorer

Fields in weka.gui.explorer declared as Filter

Modifier and Type	Field and Description
static Filter	PreprocessPanel.PreprocessDefaults.FILTER

Uses of Filter in weka.knowledgeflow.steps

Methods in weka.knowledgeflow.steps that return Filter

Modifier and Type	Method and Description
Filter	Filter.getFilter() Get the filter.

Methods in weka.knowledgeflow.steps with parameters of type Filter

Modifier and Type	Method and Description
void	Filter.setFilter(Filter filter) Set the filter.