Uses of Class
weka.filters.Filter

Packages that use Filter

weka.associations
weka.classifiers.meta
weka.clusterers
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance
weka.gui.beans

Uses of Filter in weka.associations

Methods in weka.associations that return Filter

Filter

FilteredAssociator.getFilter() Gets the filter used.

Methods in weka.associations with parameters of type Filter

void	FilteredAssociator.setFilter(Filter value) Sets the filter

Constructors in weka.associations with parameters of type Filter

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

Filter

FilteredClassifier.getFilter() Gets the filter used.

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

void	FilteredClassifier.setFilter(Filter filter) Sets the filter

Uses of Filter in weka.clusterers

Methods in weka.clusterers that return Filter

Filter

FilteredClusterer.getFilter() Gets the filter used.

Methods in weka.clusterers with parameters of type Filter

void	FilteredClusterer.setFilter(Filter filter) Sets the filter.

Uses of Filter in weka.filters

Subclasses of Filter in weka.filters

class	AllFilter A simple instance filter that passes all instances directly through.
class	MultiFilter Applies several filters successively.
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

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

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

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	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	NominalToBinary Converts all nominal attributes into binary numeric attributes.

Uses of Filter in weka.filters.supervised.instance

Subclasses of Filter in weka.filters.supervised.instance

class	Resample Produces a random subsample of a dataset using either sampling with replacement or without replacement. The original dataset must fit entirely in memory.
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

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 attributes values.
class	AddValues Adds the labels from the given list to an attribute if they are missing.
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	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	InterquartileRange A filter for detecting outliers and extreme values based on interquartile ranges.
class	KernelFilter Converts the given set of predictor variables 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 expression Valid options are:
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	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 (e.g., 0) 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	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 (and string) attribute values.
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, where the number of bins is equal to the square root of the number of non-missing values. 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 (i.e.
class	RandomSubset Chooses a random subset of 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 attribute names. 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	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	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 string attribute (i.e.
class	StringToWordVector Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) 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.

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

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

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

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	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	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. Grammar: boolexpr_list ::= boolexpr_list boolexpr_part | boolexpr_part; boolexpr_part ::= boolexpr:e {: parser.setResult(e); :} ; boolexpr ::= BOOLEAN | true | false | expr < expr | expr <= expr | expr > expr | expr >= expr | expr = expr | ( boolexpr ) | not boolexpr | boolexpr and boolexpr | boolexpr or boolexpr | ATTRIBUTE is STRING ; expr ::= NUMBER | ATTRIBUTE | ( expr ) | opexpr | funcexpr ; opexpr ::= expr + expr | expr - expr | expr * expr | expr / expr ; funcexpr ::= abs ( expr ) | sqrt ( expr ) | log ( expr ) | exp ( expr ) | sin ( expr ) | cos ( expr ) | tan ( expr ) | rint ( expr ) | floor ( expr ) | pow ( expr for base , expr for exponent ) | ceil ( expr ) ; Notes: - NUMBER any integer or floating point number (but not in scientific notation!) - STRING any string surrounded by single quotes; the string may not contain a single quote though. - ATTRIBUTE the following placeholders are recognized for attribute values: - CLASS for the class value in case a class attribute is set. - ATTxyz with xyz a number from 1 to # of attributes in the dataset, representing the value of indexed attribute. 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

Filter

Filter.getFilter()

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

void	Filter.setFilter(Filter c) Set the filter to be wrapped by this bean