Modifier and Type | Method and Description |
---|---|
Filter |
FilteredAssociator.getFilter()
Gets the filter used.
|
Modifier and Type | Method and Description |
---|---|
void |
FilteredAssociator.setFilter(Filter value)
Sets the filter
|
Modifier and Type | Method and Description |
---|---|
Filter |
FilteredSubsetEval.getFilter()
Get the filter to use
|
Filter |
FilteredAttributeEval.getFilter()
Get the filter to use
|
Modifier and Type | Method and Description |
---|---|
void |
FilteredSubsetEval.setFilter(Filter newFilter)
Set the filter to use
|
void |
FilteredAttributeEval.setFilter(Filter newFilter)
Set the filter to use
|
Modifier and Type | Field and Description |
---|---|
Filter |
BayesianLogisticRegression.m_Filter
Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
|
Modifier and Type | Method and Description |
---|---|
Filter |
PLSClassifier.getFilter()
Get the PLS filter.
|
Modifier and Type | Method and Description |
---|---|
void |
PLSClassifier.setFilter(Filter value)
Set the PLS filter (only used for setup).
|
Modifier and Type | Method and Description |
---|---|
Filter |
GridSearch.getBestFilter()
returns the best filter setup
|
Filter |
GridSearch.getFilter()
Get the kernel filter.
|
Filter |
FilteredClassifier.getFilter()
Gets the filter used.
|
Filter |
RotationForest.getProjectionFilter()
Gets the filter used to project the data.
|
Modifier and Type | Method and Description |
---|---|
void |
GridSearch.setFilter(Filter value)
Set the kernel filter (only used for setup).
|
void |
FilteredClassifier.setFilter(Filter filter)
Sets the filter
|
void |
RotationForest.setProjectionFilter(Filter projectionFilter)
Sets the filter used to project the data.
|
Modifier and Type | Method and Description |
---|---|
Filter |
FilteredClusterer.getFilter()
Gets the filter used.
|
Modifier and Type | Method and Description |
---|---|
void |
FilteredClusterer.setFilter(Filter filter)
Sets the filter.
|
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 |
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.
|
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.
|
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.
|
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.
|
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 |
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.
|
class |
PLSFilter
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002). |
Modifier and Type | Class and Description |
---|---|
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 |
SMOTE
Resamples a dataset by applying the Synthetic
Minority Oversampling TEchnique (SMOTE).
|
class |
SpreadSubsample
Produces a random subsample of a dataset.
|
class |
StratifiedRemoveFolds
This filter takes a dataset and outputs a specified fold for cross validation.
|
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.
|
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 |
MergeTwoValues
Merges two values of a nominal attribute into one value.
|
class |
MultiInstanceToPropositional
Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.
Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId. |
class |
NominalToString
Converts a nominal attribute (i.e.
|
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 |
PropositionalToMultiInstance
Converts a propositional dataset into a multi-instance dataset (with relational attribute).
|
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 |
RELAGGS
A propositionalization filter inspired by the RELAGGS algorithm.
It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output). |
class |
Remove
A filter that removes a range of attributes from the dataset.
|
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 |
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 |
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.
|
class |
Wavelet
A filter for wavelet transformation.
For more information see: Wikipedia (2004). |
Modifier and Type | Method and Description |
---|---|
Filter |
Wavelet.getFilter()
Get the preprocessing 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
|
Modifier and Type | Method and Description |
---|---|
void |
Wavelet.setFilter(Filter value)
Set the preprocessing filter (only used for setup).
|
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)
|
Modifier and Type | Class and Description |
---|---|
class |
NonSparseToSparse
An instance filter that converts all incoming instances into sparse format.
|
class |
Normalize
An instance filter that normalize instances considering only numeric attributes and ignoring class index.
|
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: |
Modifier and Type | Method and Description |
---|---|
Filter |
Filter.getFilter() |
Modifier and Type | Method and Description |
---|---|
void |
Filter.setFilter(Filter c)
Set the filter to be wrapped by this bean
|
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