# All Classes and Interfaces

Class
Description
AbstractAssociator
Abstract scheme for learning associations.
AbstractClusterer
Abstract clusterer.
AbstractDataSink
Abstract class for objects that store instances to some destination.
AbstractDataSinkBeanInfo
Bean info class for the AbstractDataSink
AbstractDataSource
Abstract class for objects that can provide instances from some source
AbstractDataSourceBeanInfo
Bean info class for AbstractDataSource.
AbstractDensityBasedClusterer
Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
AbstractEvaluator
Abstract class for objects that can provide some kind of evaluation for classifier, clusterers etc.
AbstractFileLoader
Abstract superclass for all file loaders.
AbstractFileSaver
Abstract class for Savers that save to a file Valid options are: -i input arff file
The input filw in arff format.
AbstractLoader
Abstract class gives default implementation of setSource methods.
AbstractSaver
Abstract class for Saver
AbstractStringDistanceFunction
Represents the abstract ancestor for string-based distance functions, like EditDistance.
AbstractTestSetProducer
Abstract class for TestSetProducers that contains default implementations of add/remove listener methods and defualt visual representation.
AbstractTestSetProducerBeanInfo
BeanInfo class for AbstractTestSetProducer
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.
AbstractTrainAndTestSetProducer
Abstract base class for TrainAndTestSetProducers that contains default implementations of add/remove listener methods and defualt visual representation.
AbstractTrainAndTestSetProducerBeanInfo
Bean info class for AbstractTrainAndTestSetProducers
AbstractTrainingSetProducer
Abstract class for TrainingSetProducers that contains default implementations of add/remove listener methods and default visual representation
AbstractTrainingSetProducerBeanInfo
BeanInfo class for AbstractTrainingSetProducer
AdaBoostM1
Class for boosting a nominal class classifier using the Adaboost M1 method.
Add
An instance filter that adds a new attribute to the dataset.
AddClassification
A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
AddCluster
A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm.
AddExpression
An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
AddID
An instance filter that adds an ID attribute to the dataset.
AdditionalMeasureProducer
Interface to something that can produce measures other than those calculated by evaluation modules.
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
AddNoise
An instance filter that changes a percentage of a given attributes values.
AddValues
Adds the labels from the given list to an attribute if they are missing.
ADNode
The ADNode class implements the ADTree datastructure which increases the speed with which sub-contingency tables can be constructed from a data set in an Instances object.
ADTree
Class for generating an alternating decision tree.
Agrawal
Generates a people database and is based on the paper by Agrawal et al.:
R.
AlgorithmListPanel
This panel controls setting a list of algorithms for an experiment to iterate over.
AlgVector
Class for performing operations on an algebraic vector of floating-point values.
AllFilter
A simple instance filter that passes all instances directly through.
AllJavadoc
Applies all known Javadoc-derived classes to a source file.
AlphabeticTokenizer
Alphabetic string tokenizer, tokens are to be formed only from contiguous alphabetic sequences.
AODE
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
AODEsr
AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see:
Fei Zheng, Geoffrey I.
Apriori
Class implementing an Apriori-type algorithm.
AprioriItemSet
Class for storing a set of items.
ArffLoader
Reads a source that is in arff (attribute relation file format) format.
ArffLoader.ArffReader
Reads data from an ARFF file, either in incremental or batch mode.
ArffPanel
A Panel representing an ARFF-Table and the associated filename.
ArffSaver
Writes to a destination in arff text format.
ArffSortedTableModel
A sorter for the ARFF-Viewer - necessary because of the custom CellRenderer.
ArffTable
A specialized JTable for the Arff-Viewer.
ArffTableCellRenderer
Handles the background colors for missing values differently than the DefaultTableCellRenderer.
ArffTableModel
The model for the Arff-Viewer.
ArffViewer
A little tool for viewing ARFF files.
ArffViewerMainPanel
The main panel of the ArffViewer.
ASEvaluation
Abstract attribute selection evaluation class
ASSearch
Abstract attribute selection search class.
AssociationsPanel
This panel allows the user to select, configure, and run a scheme that learns associations.
Associator
Associator
Bean that wraps around weka.associations
AssociatorBeanInfo
BeanInfo class for the Associator wrapper bean
AssociatorCustomizer
GUI customizer for the associator wrapper bean
AssociatorEvaluation
Class for evaluating Associaters.
Attribute
Class for handling an attribute.
AttributeEvaluator
Interface for classes that evaluate attributes individually.
AttributeExpression
A general purpose class for parsing mathematical expressions involving attribute values.
AttributeListPanel
Creates a panel that displays the attributes contained in a set of instances, letting the user select a single attribute for inspection.
AttributeLocator
This class locates and records the indices of a certain type of attributes, recursively in case of Relational attributes.
AttributePanel
This panel displays one dimensional views of the attributes in a dataset.
AttributePanelEvent
Class encapsulating a change in the AttributePanel's selected x and y attributes.
AttributePanelListener
Interface for classes that want to listen for Attribute selection changes in the attribute panel
AttributeSelectedClassifier
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
AttributeSelection
Attribute selection class.
AttributeSelection
A supervised attribute filter that can be used to select attributes.
AttributeSelectionPanel
Creates a panel that displays the attributes contained in a set of instances, letting the user toggle whether each attribute is selected or not (eg: so that unselected attributes can be removed before classification).
AttributeSelectionPanel
This panel allows the user to select and configure an attribute evaluator and a search method, set the attribute of the current dataset to be used as the class, and perform attribute selection using one of two selection modes (select using all the training data or perform a n-fold cross validation---on each trial selecting features using n-1 folds of the data).
AttributeSetEvaluator
Abstract attribute set evaluator.
AttributeStats
A Utility class that contains summary information on an the values that appear in a dataset for a particular attribute.
AttributeSummarizer
Bean that encapsulates displays bar graph summaries for attributes in a data set.
AttributeSummarizerBeanInfo
Bean info class for the attribute summarizer bean
AttributeSummaryPanel
This panel displays summary statistics about an attribute: name, type number/% of missing/unique values, number of distinct values.
AttributeTransformer
Abstract attribute transformer.
AttributeValueLiteral
AttributeVisualizationPanel
Creates a panel that shows a visualization of an attribute in a dataset.
AveragingResultProducer
Takes the results from a ResultProducer and submits the average to the result listener.
Bagging
Class for bagging a classifier to reduce variance.
BallNode
Class representing a node of a BallTree.
BallSplitter
Abstract class for splitting a ball tree's BallNode.
BallTree
Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
The connection to dataset is only a reference.
BallTreeConstructor
Abstract class for constructing a BallTree .
BatchClassifierEvent
Class encapsulating a built classifier and a batch of instances to test on.
BatchClassifierListener
Interface to something that can process a BatchClassifierEvent
BatchClustererEvent
Class encapsulating a built clusterer and a batch of instances to test on.
BatchClustererListener
Interface to something that can process a BatchClustererEvent
BatchConverter
Marker interface for a loader/saver that can retrieve instances in batch mode
BayesianLogisticRegression
Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.
For more information, see
Alexander Genkin, David D.
BayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
BayesNet
Generates random instances based on a Bayes network.
BayesNetEstimator
BayesNetEstimator is the base class for estimating the conditional probability tables of a Bayes network once the structure has been learned.
BayesNetGenerator
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
BeanCommon
Interface specifying routines that all weka beans should implement in order to allow the bean environment to exercise some control over the bean and also to allow the bean to exercise some control over connections.
BeanConnection
Class for encapsulating a connection between two beans.
BeanInstance
Class that manages a set of beans.
BeanVisual
BeanVisual encapsulates icons and label for a given bean.
BestFirst
BestFirst:
Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
BFTree
Class for building a best-first decision tree classifier.
BIFFormatException
This is the Exception thrown by BIFParser, if there was an error in parsing the XMLBIF string or input stream.
BIFParser
This class parses an inputstream or a string in XMLBIF ver.
BIFReader
Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format.
For more details on XML BIF see:
Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).
BinarySparseInstance
Class for storing a binary-data-only instance as a sparse vector.
BinC45ModelSelection
Class for selecting a C4.5-like binary (!) split for a given dataset.
BinC45Split
Class implementing a binary C4.5-like split on an attribute.
BIRCHCluster
Cluster data generator designed for the BIRCH System
Dataset is generated with instances in K clusters.
Instances are 2-d data points.
Each cluster is characterized by the number of data points in itits radius and its center.
BMAEstimator
BMAEstimator estimates conditional probability tables of a Bayes network using Bayes Model Averaging (BMA).
BMPWriter
This class takes any JComponent and outputs it to a BMP-file.
Body
Class representing the body of a rule.
BottomUpConstructor
The class that constructs a ball tree bottom up.
BoundaryPanel
BoundaryPanel.
BoundaryPanelDistributed
This class extends BoundaryPanel with code for distributing the processing necessary to create a visualization among a list of remote machines.
BoundaryVisualizer
BoundaryVisualizer.
BrowserHelper
A little helper class for browser related stuff.
BuiltInArithmetic
Built-in function for +, -, *, /.
BuiltInMath
Built-in function for min, max, sum, avg, log10, ln, sqrt, abs, exp, pow, threshold, floor, ceil and round.
BuiltInString
Built-in function for uppercase, substring and trimblanks.
BVDecompose
Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
Ron Kohavi, David H.
BVDecomposeSegCVSub
This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
The Kohavi and Wolpert definition of bias and variance is specified in (2).
The Webb definition of bias and variance is specified in (3).
Geoffrey I.
C45Loader
Reads a file that is C45 format.
C45ModelSelection
Class for selecting a C4.5-type split for a given dataset.
C45PruneableClassifierTree
Class for handling a tree structure that can be pruned using C4.5 procedures.
C45PruneableClassifierTreeG
Class for handling a tree structure that can be pruned using C4.5 procedures and have nodes grafted on.
C45PruneableDecList
Class for handling a partial tree structure pruned using C4.5's pruning heuristic.
C45Saver
Writes to a destination that is in the format used by the C4.5 algorithm.
Therefore it outputs a names and a data file.
C45Split
Class implementing a C4.5-type split on an attribute.
CachedKernel
Base class for RBFKernel and PolyKernel that implements a simple LRU.
Capabilities
A class that describes the capabilites (e.g., handling certain types of attributes, missing values, types of classes, etc.) of a specific classifier.
Capabilities.Capability
enumeration of all capabilities
CapabilitiesHandler
Classes implementing this interface return their capabilities in regards to datasets.
CaRuleGeneration
Class implementing the rule generation procedure of the predictive apriori algorithm for class association rules.
CARuleMiner
Interface for learning class association rules.
Center
Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
CfsSubsetEval
CfsSubsetEval :
Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.
Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.
For more information see:
M.
ChangeDateFormat
Changes the date format used by a date attribute.
CharacterDelimitedTokenizer
Abstract superclass for tokenizers that take characters as delimiters.
ChartEvent
Event encapsulating info for plotting a data point on the StripChart
ChartListener
Interface to something that can process a ChartEvent
ChebyshevDistance
Implements the Chebyshev distance.
Check
Abstract general class for testing in Weka.
CheckAssociator
Class for examining the capabilities and finding problems with associators.
CheckAttributeSelection
Class for examining the capabilities and finding problems with attribute selection schemes.
CheckBoxList
An extended JList that contains CheckBoxes.
CheckClassifier
Class for examining the capabilities and finding problems with classifiers.
CheckClusterer
Class for examining the capabilities and finding problems with clusterers.
CheckEstimator
Class for examining the capabilities and finding problems with estimators.
CheckEstimator.AttrTypes
class that contains info about the attribute types the estimator can estimate estimator work on one attribute only
CheckEstimator.EstTypes
public class that contains info about the chosen attribute type estimator work on one attribute only
CheckGOE
Simple command line checking of classes that are editable in the GOE.
CheckKernel
Class for examining the capabilities and finding problems with kernels.
CheckOptionHandler
Simple command line checking of classes that implement OptionHandler.
CheckScheme
Abstract general class for testing schemes in Weka.
CheckScheme.PostProcessor
a class for postprocessing the test-data
CheckSource
A simple class for checking the source generated from Classifiers implementing the `weka.classifiers.Sourcable` interface.
CheckSource
A simple class for checking the source generated from Filters implementing the `weka.filters.Sourcable` interface.
ChisqMixture
Class for manipulating chi-square mixture distributions.
ChiSquaredAttributeEval
ChiSquaredAttributeEval :
Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.
CholeskyDecomposition
Cholesky Decomposition.
CISearchAlgorithm
The CISearchAlgorithm class supports Bayes net structure search algorithms that are based on conditional independence test (as opposed to for example score based of cross validation based search algorithms).
CitationKNN
Modified version of the Citation kNN multi instance classifier.
For more information see:
Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.
ClassAssigner
Filter that can set and unset the class index.
ClassAssigner
Bean that assigns a class attribute to a data set.
ClassAssignerBeanInfo
BeanInfo class for the class assigner bean
ClassAssignerCustomizer
GUI customizer for the class assigner bean
ClassBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.
For more info, check
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
ClassDiscovery
This class is used for discovering classes that implement a certain interface or a derived from a certain class.
ClassDiscovery.StringCompare
compares two strings.
ClassificationGenerator
Abstract class for data generators for classifiers.
ClassificationViaClustering
A simple meta-classifier that uses a clusterer for classification.
ClassificationViaRegression
Class for doing classification using regression methods.
Classifier
Abstract classifier.
Classifier
Bean that wraps around weka.classifiers
ClassifierBeanInfo
BeanInfo class for the Classifier wrapper bean
ClassifierCustomizer
GUI customizer for the classifier wrapper bean
ClassifierDecList
Class for handling a rule (partial tree) for a decision list.
ClassifierPanel
0* This panel allows the user to select and configure a classifier, set the attribute of the current dataset to be used as the class, and evaluate the classifier using a number of testing modes (test on the training data, train/test on a percentage split, n-fold cross-validation, test on a separate split).
ClassifierPerformanceEvaluator
A bean that evaluates the performance of batch trained classifiers
ClassifierPerformanceEvaluatorBeanInfo
Bean info class for the classifier performance evaluator
ClassifierSplitEvaluator
A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
ClassifierSplitModel
Abstract class for classification models that can be used recursively to split the data.
ClassifierSubsetEval
Classifier subset evaluator:
Evaluates attribute subsets on training data or a seperate hold out testing set.
ClassifierTree
Class for handling a tree structure used for classification.
ClassloaderUtil
Utility class that can add jar files to the classpath dynamically.
ClassOrder
Changes the order of the classes so that the class values are no longer of in the order specified in the header.
ClassPanel
This panel displays coloured labels for nominal attributes and a spectrum for numeric attributes.
ClassValuePicker
ClassValuePickerBeanInfo
BeanInfo class for the class value picker bean
ClassValuePickerCustomizer
CLOPE
Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
ClusterDefinition
Ancestor to all ClusterDefinitions, i.e., subclasses that handle their own parameters that the cluster generator only passes on.
Clusterer
Interface for clusterers.
Clusterer
Bean that wraps around weka.clusterers
ClustererBeanInfo
BeanInfo class for the Clusterer wrapper bean
ClustererCustomizer
GUI customizer for the Clusterer wrapper bean
ClustererPanel
This panel allows the user to select and configure a clusterer, and evaluate the clusterer using a number of testing modes (test on the training data, train/test on a percentage split, test on a separate split).
ClustererPerformanceEvaluator
A bean that evaluates the performance of batch trained clusterers
ClustererPerformanceEvaluatorBeanInfo
Bean info class for the clusterer performance evaluator
ClusterEvaluation
Class for evaluating clustering models.
ClusterGenerator
Abstract class for cluster data generators.
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).
Cobweb
Class implementing the Cobweb and Classit clustering algorithms.
Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
Colors
This class maintains a list that contains all the colornames from the dotty standard and what color (in RGB) they represent
ComplementNaiveBayes
Class for building and using a Complement class Naive Bayes classifier.
For more information see,
Jason D.
ComponentHelper
A helper class for some common tasks with Dialogs, Icons, etc.
Compute
Interface to something that can accept remote connections and execute a task.
ConditionalEstimator
Interface for conditional probability estimators.
ConfusionMatrix
Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.
ConjunctiveRule
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression.
ConnectionEvent
An event that is generated when a connection is established or dropped.
ConnectionListener
A listener for connect/disconnect events.
ConnectionNotificationConsumer
Interface for Beans that can receive (dis-)connection events generated when (dis-)connecting data processing nodes in the Weka KnowledgeFlow.
ConnectionPanel
Enables the user to insert a database URL, plus user/password to connect to this database.
ConsistencySubsetEval
ConsistencySubsetEval :
Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes.
ConsoleLogger
A simple logger that outputs the logging information in the console.
Constant
Class encapsulating a Constant Expression.
ContingencyTables
Class implementing some statistical routines for contingency tables.
ConverterFileChooser
A specialized JFileChooser that lists all available file Loaders and Savers.
ConverterUtils
Utility routines for the converter package.
ConverterUtils.DataSink
Helper class for saving data to files.
ConverterUtils.DataSource
Helper class for loading data from files and URLs.
Copy
An instance filter that copies a range of attributes in the dataset.
Copyable
Interface implemented by classes that can produce "shallow" copies of their objects.
Copyright
A class for providing centralized Copyright information.
CorrelationSplitInfo
Finds split points using correlation.
CostBenefitAnalysis
Bean that aids in analyzing cost/benefit tradeoffs.
CostBenefitAnalysisBeanInfo
Bean info class for the cost/benefit analysis
CostCurve
Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes.
CostMatrix
Class for storing and manipulating a misclassification cost matrix.
CostMatrixEditor
Class for editing CostMatrix objects.
CostSensitiveASEvaluation
Abstract base class for cost-sensitive subset and attribute evaluators.
CostSensitiveAttributeEval
A meta subset evaluator that makes its base subset evaluator cost-sensitive.
CostSensitiveClassifier
A metaclassifier that makes its base classifier cost-sensitive.
CostSensitiveClassifierSplitEvaluator
SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
CostSensitiveSubsetEval
A meta subset evaluator that makes its base subset evaluator cost-sensitive.
CoverTree
Class implementing the CoverTree datastructure.
The class is very much a translation of the c source code made available by the authors.
For more information and original source code see:
Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor.
CrossValidationFoldMaker
Bean for splitting instances into training ant test sets according to a cross validation
CrossValidationFoldMakerBeanInfo
BeanInfo class for the cross validation fold maker bean
CrossValidationFoldMakerCustomizer
GUI Customizer for the cross validation fold maker bean
CrossValidationResultProducer
Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
CSVLoader
Reads a source that is in comma separated or tab separated format.
CSVResultListener
Takes results from a result producer and assembles them into comma separated value form.
CSVSaver
Writes to a destination that is in csv format
CustomizerCloseRequester
Customizers who want to be able to close the customizer window themselves can implement this window.
CustomizerClosingListener
CustomPanelSupplier
An interface for objects that are capable of supplying their own custom GUI components.
CVParameterSelection
Class for performing parameter selection by cross-validation for any classifier.
For more information, see:
R.
Dagging
This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
Database
Database.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 20, 2004
Time: 1:03:43 PM
$ Revision 1.4 $
DatabaseConnection
Connects to a database.
DatabaseConnectionDialog
A dialog to enter URL, username and password for a database connection.
DatabaseConverter
Marker interface for a loader/saver that uses a database
DatabaseLoader
Reads Instances from a Database.
DatabaseResultListener
Takes results from a result producer and sends them to a database.
DatabaseResultProducer
Examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
DatabaseSaver
Writes to a database (tested with MySQL, InstantDB, HSQLDB).
DatabaseUtils
DatabaseUtils provides utility functions for accessing the experiment database.
DataFormatListener
Listener interface that customizer classes that are interested in data format changes can implement.
DataGenerator
Abstract superclass for data generators that generate data for classifiers and clusterers.
DataGenerator
Interface to something that can generate new instances based on a set of input instances
DataGeneratorPanel
A panel for generating artificial data via DataGenerators.
DataNearBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.
For more info, check
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
DataObject
DataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:48:59 PM
$ Revision 1.4 $
DataSetEvent
Event encapsulating a data set
DatasetListPanel
This panel controls setting a list of datasets for an experiment to iterate over.
DataSink
Indicator interface to something that can store instances to some destination
DataSource
Interface to something that is capable of being a source for data - either batch or incremental data
DataSourceListener
Interface to something that can accept DataSetEvents
DataVisualizer
Bean that encapsulates weka.gui.visualize.VisualizePanel
DataVisualizerBeanInfo
Bean info class for the data visualizer
DBSCAN
Basic implementation of DBSCAN clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
DbUtils
A little bit extended DatabaseUtils class.
DDConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon a discrete domain.
Debug
A helper class for debug output, logging, clocking, etc.
Debug.Clock
A little helper class for clocking and outputting times.
Debug.DBO
contains debug methods
Debug.Log
A helper class for logging stuff.
Debug.Random
This extended Random class enables one to print the generated random numbers etc., before they are returned.
Debug.SimpleLog
A little, simple helper class for logging stuff.
Debug.Timestamp
A class that can be used for timestamps in files, The toString() method simply returns the associated Date object in a timestamp format.
DecisionStump
Class for building and using a decision stump.
DecisionTable
Class for building and using a simple decision table majority classifier.
For more information see:
Ron Kohavi: The Power of Decision Tables.
DecisionTableHashKey
Class providing hash table keys for DecisionTable
Decorate
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
DefineFunction
Class encapsulating DefineFunction (used in TransformationDictionary).
DensityBasedClusterer
Interface for clusterers that can estimate the density for a given instance.
DensityBasedClustererSplitEvaluator
A SplitEvaluator that produces results for a density based clusterer.
DerivedFieldMetaInfo
DiscreteEstimator
Simple symbolic probability estimator based on symbol counts.
DiscreteEstimatorBayes
Symbolic probability estimator based on symbol counts and a prior.
DiscreteEstimatorFullBayes
Symbolic probability estimator based on symbol counts and a prior.
DiscreteFunction
Class for handling discrete functions.
Discretize
Class encapsulating a Discretize Expression.
Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
DistanceFunction
Interface for any class that can compute and return distances between two instances.
DistributeExperimentPanel
This panel enables an experiment to be distributed to multiple hosts; it also allows remote host names to be specified.
Distribution
Class for handling a distribution of class values.
DKConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DMNBtext
Class for building and using a Discriminative Multinomial Naive Bayes classifier.
DNConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DotParser
This class parses input in DOT format, and builds the datastructures that are passed to it.
DoubleVector
A vector specialized on doubles.
Drawable
Interface to something that can be drawn as a graph.
DTNB
Class for building and using a decision table/naive bayes hybrid classifier.
Edge
This class is used in conjunction with the Node class to form a tree structure.
EditableBayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
EditDistance
Computes the Levenshtein edit distance between two strings.
EigenvalueDecomposition
Eigenvalues and eigenvectors of a real matrix.
Element
Class representing an Element, i.e., a set of events/items.
EM
Simple EM (expectation maximisation) class.
EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
END
A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.
For more info, check
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
EntropyBasedSplitCrit
"Abstract" class for computing splitting criteria based on the entropy of a class distribution.
EntropySplitCrit
Class for computing the entropy for a given distribution.
Environment
This class encapsulates a map of all environment and java system properties.
EnvironmentHandler
Interface for something that can utilize environment variables.
EpsilonRange_ListElement
EpsilonRange_ListElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Sep 7, 2004
Time: 2:12:34 PM
$ Revision 1.4 $
ErrorBasedMeritEvaluator
Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme
ErrorVisualizePlugin
Interface implemented by classes loaded dynamically to visualize classifier errors in the explorer.
Estimator
Abstract class for all estimators.
EstimatorUtils
Contains static utility functions for Estimators.
EuclideanDataObject
EuclideanDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:50:22 PM
$ Revision 1.4 $
EuclideanDistance
Implementing Euclidean distance (or similarity) function.
One object defines not one distance but the data model in which the distances between objects of that data model can be computed.
Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.
For more information, see:
Wikipedia.
Evaluation
Class for evaluating machine learning models.
EvaluationUtils
Contains utility functions for generating lists of predictions in various manners.
EventConstraints
Interface for objects that want to be able to specify at any given time whether their current configuration allows a particular event to be generated.
ExhaustiveSearch
ExhaustiveSearch :
Performs an exhaustive search through the space of attribute subsets starting from the empty set of attrubutes.
Experiment
Holds all the necessary configuration information for a standard type experiment.
Experimenter
The main class for the experiment environment.
ExperimenterDefaults
This class offers get methods for the default Experimenter settings in the props file `weka/gui/experiment/Experimenter.props`.
Explorer
The main class for the Weka explorer.
Explorer.CapabilitiesFilterChangeEvent
This event can be fired in case the capabilities filter got changed
Explorer.CapabilitiesFilterChangeListener
Interface for classes that listen for filter changes.
Explorer.ExplorerPanel
A common interface for panels to be displayed in the Explorer
Explorer.LogHandler
A common interface for panels in the explorer that can handle logs
ExplorerDefaults
This class offers get methods for the default Explorer settings in the props file `weka/gui/explorer/Explorer.props`.
ExponentialFormat
Expression
Expression
A data generator for generating y according to a given expression out of randomly generated x.
E.g., the mexican hat can be generated like this:
sin(abs(a1)) / abs(a1)
In addition to this function, the amplitude can be changed and gaussian noise can be added.
ExtensionFileFilter
Provides a file filter for FileChoosers that accepts or rejects files based on their extension.
FarthestFirst
Cluster data using the FarthestFirst algorithm.
For more information see:
Hochbaum, Shmoys (1985).
FastVector
Implements a fast vector class without synchronized methods.
FieldMetaInfo
Abstract superclass for various types of field meta data.
FieldMetaInfo.Interval
Inner class for an Interval.
FieldMetaInfo.Interval.Closure
Enumerated type for the closure.
FieldMetaInfo.Optype
Enumerated type for the Optype
FieldMetaInfo.Value
Inner class for Values
FieldMetaInfo.Value.Property
Enumerated type for the property.
FieldRef
Class encapsulating a FieldRef Expression.
FileEditor
A PropertyEditor for File objects that lets the user select a file.
FileLogger
A simple file logger, that just logs to a single file.
FileSourcedConverter
Interface to a loader/saver that loads/saves from a file source.
Filter
An abstract class for instance filters: objects that take instances as input, carry out some transformation on the instance and then output the instance.
Filter
A wrapper bean for Weka filters
FilterBeanInfo
Bean info class for the Filter bean
FilterCustomizer
GUI customizer for the filter bean
FilteredAssociator
Class for running an arbitrary associator on data that has been passed through an arbitrary filter.
FilteredAttributeEval
Class for running an arbitrary attribute evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
FilteredClassifier
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
FilteredClusterer
Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
FilteredSubsetEval
Class for running an arbitrary subset evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
FindWithCapabilities
Locates all classes with certain capabilities.
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.
FlexibleDecimalFormat
FloatingPointFormat
Class for the format of floating point numbers
FlowRunner
Small utility class for executing KnowledgeFlow flows outside of the KnowledgeFlow application
FlowRunner.SimpleLogger
FPGrowth
Class implementing the FP-growth algorithm for finding large item sets without candidate generation.
FPGrowth.AssociationRule
FPGrowth.AssociationRule.METRIC_TYPE
Enum for holding different metric types
FPGrowth.BinaryItem
Inner class that handles a single binary item
FromFile
The FromFile reads the structure of a Bayes net from a file in BIFF format.
FT
Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
FTInnerNode
Class for Functional Inner tree structure.
FTLeavesNode
Class for Functional Leaves tree version.
FTNode
Class for Functional tree structure.
FTtree
Abstract class for Functional tree structure.
Function
Abstract superclass for PMML built-in and DefineFunctions.
GainRatioAttributeEval
GainRatioAttributeEval :
Evaluates the worth of an attribute by measuring the gain ratio with respect to the class.
GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute).
GainRatioSplitCrit
Class for computing the gain ratio for a given distribution.
GaussianPriorImpl
Implementation of the Gaussian Prior update function based on CLG Algorithm with a certain Trust Region Update.
GaussianProcesses
Implements Gaussian Processes for regression without hyperparameter-tuning.
GeneralizedSequentialPatterns
Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set.
The attribute identifying the distinct data sequences contained in the set can be determined by the respective option.
GeneralRegression
Class implementing import of PMML General Regression model.
GeneratorPropertyIteratorPanel
This panel controls setting a list of values for an arbitrary resultgenerator property for an experiment to iterate over.
GenericArrayEditor
A PropertyEditor for arrays of objects that themselves have property editors.
GenericObjectEditor
A PropertyEditor for objects.
GenericPropertiesCreator
This class can generate the properties object that is normally loaded from the `GenericObjectEditor.props` file (= PROPERTY_FILE).
GeneticSearch
GeneticSearch:
Performs a search using the simple genetic algorithm described in Goldberg (1989).
For more information see:
David E.
GeneticSearch
This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
GeneticSearch
This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
GlobalInfoJavadoc
Generates Javadoc comments from the class's globalInfo method.
GlobalScoreSearchAlgorithm
This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.
Grading
Implements Grading.
GraftSplit
Class implementing a split for nodes added to a tree during grafting.
GraphConstants
GraphConstants.java
GraphEdge
This class represents an edge in the graph
GraphEvent
Event for graphs
GraphListener
Describe interface `TextListener` here.
GraphNode
This class represents a node in the Graph.
GraphPanel
GraphPanel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 16, 2004
Time: 10:28:19 AM
$ Revision 1.4 $
GraphViewer
A bean encapsulating weka.gui.treevisualize.TreeVisualizer
GraphViewerBeanInfo
Bean info class for the graph viewer
GraphVisualizePlugin
Interface implemented by classes loaded dynamically to visualize graphs in the explorer.
GraphVisualizer
This class displays the graph we want to visualize.
GreedyStepwise
GreedyStepwise :
Performs a greedy forward or backward search through the space of attribute subsets.
GridSearch
Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.
The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).
GUI
GUI interface to Bayesian Networks.
GUIChooser
The main class for the Weka GUIChooser.
GUIChooser.ChildFrameSDI
Specialized JFrame class.
Head
Class representing the head of a rule.
HierarchicalBCEngine
This class lays out the vertices of a graph in a hierarchy of vertical levels, with a number of nodes in each level.
HierarchicalClusterer
Hierarchical clustering class.
HierarchyPropertyParser
This class implements a parser to read properties that have a hierarchy(i.e.
HierarchyVisualizer
HillClimber
This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
HillClimber
This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
HistoryChangedEvent
An event that is generated when a history is modified.
HistoryChangedListener
A listener for changes in a history.
HNB
Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC.
For more information refer to:
H.
HoldOutSubsetEvaluator
Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
HostListPanel
This panel controls setting a list of hosts for a RemoteExperiment to use.
HyperPipes
Class implementing a HyperPipe classifier.
IB1
Nearest-neighbour classifier.
IBk
K-nearest neighbours classifier.
ICSSearchAlgorithm
This Bayes Network learning algorithm uses conditional independence tests to find a skeleton, finds V-nodes and applies a set of rules to find the directions of the remaining arrows.
Id3
Class for constructing an unpruned decision tree based on the ID3 algorithm.
Impurity
Class for handling the impurity values when spliting the instances
IncrementalClassifierEvaluator
Bean that evaluates incremental classifiers
IncrementalClassifierEvaluatorBeanInfo
Bean info class for the incremental classifier evaluator bean
IncrementalClassifierEvaluatorCustomizer
GUI Customizer for the incremental classifier evaluator bean
IncrementalClassifierEvent
Class encapsulating an incrementally built classifier and current instance
IncrementalClassifierListener
Interface to something that can process a IncrementalClassifierEvent
IncrementalConverter
Marker interface for a loader/saver that can retrieve instances incrementally
IncrementalEstimator
Interface for an incremental probability estimators.
IndividualInstance
IndividualInstances
IndividualLiteral
InfoGainAttributeEval
InfoGainAttributeEval :
Evaluates the worth of an attribute by measuring the information gain with respect to the class.
InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).
InfoGainSplitCrit
Class for computing the information gain for a given distribution.
InfoPanel
A simple panel for displaying information, e.g.
InfoPanelCellRenderer
A specialized renderer that takes care of JLabels in a JList.
Instance
Class for handling an instance.
InstanceComparator
A comparator for the Instance class.
InstanceCounter
A bean that counts instances streamed to it.
InstanceEvent
Event that encapsulates a single instance or header information only
InstanceEvent
An event encapsulating an instance stream event.
InstanceJoiner
A bean that joins two streams of instances into one.
InstanceListener
Interface to something that can accept instance events
InstanceListener
An interface for objects interested in listening to streams of instances.
InstanceLoader
A bean that produces a stream of instances from a file.
InstanceProducer
An interface for objects capable of producing streams of instances.
InstanceQuery
Convert the results of a database query into instances.
Instances
Class for handling an ordered set of weighted instances.
InstanceSavePanel
A bean that saves a stream of instances to a file.
InstancesResultListener
Outputs the received results in arff format to a Writer.
InstancesSummaryPanel
This panel just displays relation name, number of instances, and number of attributes.
InstanceStreamToBatchMaker
Bean that converts an instance stream into a (batch) data set.
InstanceStreamToBatchMakerBeanInfo
BeanInfo class for the InstanceStreamToBatchMaker bean
InstanceTable
A bean that takes a stream of instances and displays in a table.
InstanceViewer
This is a very simple instance viewer - just displays the dataset as text output as it would be written to a file.
InterquartileRange
A filter for detecting outliers and extreme values based on interquartile ranges.
IntervalEstimator
Interface for classifiers that can output confidence intervals
IntVector
A vector specialized on integers.
IsotonicRegression
Learns an isotonic regression model.
ItemSet
Class for storing a set of items.
IteratedLovinsStemmer
An iterated version of the Lovins stemmer.
IteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
IterativeClassifier
Interface for classifiers that can induce models of growing complexity one step at a time.
J48
Class for generating a pruned or unpruned C4.5 decision tree.
J48graft
Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
Javadoc
Abstract superclass for classes that generate Javadoc comments and replace the content between certain comment tags.
JComponentWriter
This class takes any JComponent and outputs it to a file.
JListHelper
A helper class for JList GUI elements with DefaultListModel or derived models.
JPEGWriter
This class takes any JComponent and outputs it to a JPEG-file.
JRip
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
JTableHelper
A helper class for JTable, e.g.
Jython
A helper class for Jython.
JythonObject
An indicator interface for Jython objects.
JythonSerializableObject
An indicator interface for serializable Jython objects.
K2
This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.
For more information see:
G.F.
K2
This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.
For more information see:
G.F.
KDConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate kernel estimators for each discrete conditioning value).
KDDataGenerator
KDDataGenerator.
KDTree
Class implementing the KDTree search algorithm for nearest neighbour search.
The connection to dataset is only a reference.
KDTreeNode
A class representing a KDTree node.
KDTreeNodeSplitter
Class that splits up a KDTreeNode.
Kernel
Abstract kernel.
KernelEstimator
Simple kernel density estimator.
KernelEvaluation
Class for evaluating Kernels.
KernelFilter
Converts the given set of predictor variables into a kernel matrix.
KKConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon a numeric domain.
KMeansInpiredMethod
The class that splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum.
For more information see also:
Ashraf Masood Kibriya (2007).
KnowledgeFlow
Startup class for the KnowledgeFlow.
KnowledgeFlowApp
Main GUI class for the KnowledgeFlow.
KOML
This class is a helper class for XML serialization using KOML .
KStar
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
KStarCache
A class representing the caching system used to keep track of each attribute value and its corresponding scale factor or stop parameter.
KStarConstants
KStarNominalAttribute
A custom class which provides the environment for computing the transformation probability of a specified test instance nominal attribute to a specified train instance nominal attribute.
KStarNumericAttribute
A custom class which provides the environment for computing the transformation probability of a specified test instance numeric attribute to a specified train instance numeric attribute.
KStarWrapper
LabeledItemSet
Class for storing a set of items together with a class label.
LADTree
Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
LAGDHillClimber
This Bayes Network learning algorithm uses a Look Ahead Hill Climbing algorithm called LAGD Hill Climbing.
LaplacePriorImpl
Implementation of the Gaussian Prior update function based on modified CLG Algorithm (CLG-Lasso) with a certain Trust Region Update based on Laplace Priors.
LatentSemanticAnalysis
Performs latent semantic analysis and transformation of the data.
LayoutCompleteEvent
This is an event which is fired by a LayoutEngine once a LayoutEngine finishes laying out the graph, so that the Visualizer can repaint the screen to show the changes.
LayoutCompleteEventListener
This interface should be implemented by any class which needs to receive LayoutCompleteEvents from the LayoutEngine.
LayoutEngine
This interface class has been added to facilitate the addition of other layout engines to this package.
LBR
Lazy Bayesian Rules Classifier.
LearningRateResultProducer
Tells a sub-ResultProducer to reproduce the current run for varying sized subsamples of the dataset.
LeastMedSq
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
LED24
This generator produces data for a display with 7 LEDs.
LegendPanel
This panel displays legends for a list of plots.
LFSMethods
LibLINEAR
A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier).
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008).
LibSVM
A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier).
LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier.
LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool.
LibSVMLoader
Reads a source that is in libsvm format.
For more information about libsvm see:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
LibSVMSaver
Writes to a destination that is in libsvm format.
For more information about libsvm see:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
LinearForwardSelection
LinearForwardSelection:
Extension of BestFirst.
LinearNNSearch
Class implementing the brute force search algorithm for nearest neighbour search.
LinearRegression
Class for using linear regression for prediction.
LinearRegression
Class for performing (ridged) linear regression using Tikhonov regularization.
LinearUnit
This can be used by the neuralnode to perform all it's computations (as a Linear unit).
ListOptions
Lists the options of an OptionHandler
ListSelectorDialog
A dialog to present the user with a list of items, that the user can make a selection from, or cancel the selection.
Literal
LiteralSet
Class representing a set of literals, being either the body or the head of a rule.
LMT
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
LMTNode
Class for logistic model tree structure.
Loader
Interface to something that can load Instances from an input source in some format.
Loader
Loads data sets using weka.core.converter classes
Loader
This class is for loading resources from a JAR archive.
LoaderBeanInfo
Bean info class for the loader bean
LoaderCustomizer
GUI Customizer for the loader bean
LocalScoreSearchAlgorithm
The ScoreBasedSearchAlgorithm class supports Bayes net structure search algorithms that are based on maximizing scores (as opposed to for example conditional independence based search algorithms).
Logger
Abstract superclass for all loggers.
Logger
Interface for objects that display log (permanent historical) and status (transient) messages.
Logger.Level
The logging level.
Logistic
Class for building and using a multinomial logistic regression model with a ridge estimator.
There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992):
If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix.
The probability for class j with the exception of the last class is
Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1)
The last class has probability
1-(sum[j=1..(k-1)]Pj(Xi))
= 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)
The (negative) multinomial log-likelihood is thus:
L = -sum[i=1..n]{
sum[j=1..(k-1)](Yij * ln(Pj(Xi)))
+(1 - (sum[j=1..(k-1)]Yij))
* ln(1 - sum[j=1..(k-1)]Pj(Xi))
} + ridge * (B^2)
In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
LogisticBase
Base/helper class for building logistic regression models with the LogitBoost algorithm.
LogitBoost
Class for performing additive logistic regression.
LogPanel
Class for displaying a status area (made up of a variable number of lines) and a log area.
LogPanel
This panel allows log and status messages to be posted.
LogWindow
Frame that shows the output from stdout and stderr.
LogWriter
Interface to be implemented by classes that should be able to write their own output to the Weka logger.
LookAndFeel
A little helper class for setting the Look and Feel of the user interface.
LovinsStemmer
A stemmer based on the Lovins stemmer, described here:
Julie Beth Lovins (1968).
LUDecomposition
LU Decomposition.
LWL
Locally weighted learning.
M5Base
M5Base.
M5P
M5Base.
M5Rules
Generates a decision list for regression problems using separate-and-conquer.
MahalanobisEstimator
Simple probability estimator that places a single normal distribution over the observed values.
Main
Menu-based GUI for Weka, replacement for the GUIChooser.
Main.BackgroundDesktopPane
DesktopPane with background image.
Main.ChildFrameMDI
Specialized JInternalFrame class.
Main.ChildFrameSDI
Specialized JFrame class.
MainMenuExtension
Classes implementing this interface will be displayed in the "Extensions" menu in the main GUI of Weka.
MakeDecList
Class for handling a decision list.
MakeDensityBasedClusterer
Class for wrapping a Clusterer to make it return a distribution and density.
MakeIndicator
A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
ManhattanDataObject
ManhattanDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:50:22 PM
$ Revision 1.4 $
ManhattanDistance
Implements the Manhattan distance (or Taxicab geometry).
MappingInfo
Class that maintains the mapping between incoming data set structure and that of the mining schema.
MarginCalculator
MarginCurve
Generates points illustrating the prediction margin.
Matchable
Interface to something that can be matched with tree matching algorithms.
MathematicalExpression
Class for evaluating a string adhering the following grammar:
MathExpression
Modify numeric attributes according to a given expression
Maths
Utility class.
Matrix
Deprecated.
Use `weka.core.matrix.Matrix` instead - only for backwards compatibility.
Matrix
Jama = Java Matrix class.
MatrixPanel
This panel displays a plot matrix of the user selected attributes of a given data set.
MDD
Modified Diverse Density algorithm, with collective assumption.
More information about DD:
Oded Maron (1998).
MedianDistanceFromArbitraryPoint
Class that splits a BallNode of a ball tree using Uhlmann's described method.
For information see:
Jeffrey K.
MedianOfWidestDimension
Class that splits a BallNode of a ball tree based on the median value of the widest dimension of the points in the ball.
MedianOfWidestDimension
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.
For more information see also:
Jerome H.
Memory
A little helper class for Memory management.
MemoryUsagePanel
A panel for displaying the memory usage.
MergeTwoValues
Merges two values of a nominal attribute into one value.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
Messages
Messages.
MetaBean
A meta bean that encapsulates several other regular beans, useful for grouping large KnowledgeFlows.
MetaCost
This metaclassifier makes its base classifier cost-sensitive using the method specified in
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.
MethodHandler
This class handles relationships between display names of properties (or classes) and Methods that are associated with them.
MexicanHat
A data generator for the simple 'Mexian Hat' function:
y = sin|x| / |x|
In addition to this simple function, the amplitude can be changed and gaussian noise can be added.
MIBoost
MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.
For more information about Adaboost, see:
Yoav Freund, Robert E.
MIDD
Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998).
MiddleOutConstructor
The class that builds a BallTree middle out.
For more information see also:
Andrew W.
MidPointOfWidestDimension
The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.
For more information see also:
Andrew Moore (1991).
MIEMDD
EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
MILR
Uses either standard or collective multi-instance assumption, but within linear regression.
MiningFieldMetaInfo
Class encapsulating information about a MiningField.
MiningSchema
This class encapsulates the mining schema from a PMML xml file.
MINND
Multiple-Instance Nearest Neighbour with Distribution learner.
It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
MIOptimalBall
This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
MIPolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
MIRBFKernel
The RBF kernel.
MISMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
MISVM
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
MIWrapper
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see:
E.
MixtureDistribution
Abtract class for manipulating mixture distributions.
ModelPerformanceChart
Bean that can be used for displaying threshold curves (e.g.
ModelPerformanceChartBeanInfo
Bean info class for the model performance chart
ModelSelection
Abstract class for model selection criteria.
MultiBoostAB
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
MultiClassClassifier
A metaclassifier for handling multi-class datasets with 2-class classifiers.
MultiFilter
Applies several filters successively.
MultiInstanceCapabilitiesHandler
Multi-Instance classifiers can specify an additional Capabilities object for the data in the relational attribute, since the format of multi-instance data is fixed to "bag/NOMINAL,data/RELATIONAL,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.
MultilayerPerceptron
A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both.
MultiNomialBMAEstimator
Multinomial BMA Estimator.
MultipleClassifiersCombiner
Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
MultiScheme
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
NaiveBayes
Class for a Naive Bayes classifier using estimator classes.
NaiveBayes
The NaiveBayes class generates a fixed Bayes network structure with arrows from the class variable to each of the attribute variables.
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier.
NaiveBayesMultinomialUpdateable
Class for building and using a multinomial Naive Bayes classifier.
NaiveBayesSimple
Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution.
For more information, see
Richard Duda, Peter Hart (1973).
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
NamedColor
This class contains a color name and the rgb values of that color
NBTree
Class for generating a decision tree with naive Bayes classifiers at the leaves.
For more information, see
Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
NBTreeClassifierTree
Class for handling a naive bayes tree structure used for classification.
NBTreeModelSelection
Class for selecting a NB tree split.
NBTreeNoSplit
Class implementing a "no-split"-split (leaf node) for naive bayes trees.
NBTreeSplit
Class implementing a NBTree split on an attribute.
ND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.
For more info, check
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
NDConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate normal estimators for each discrete conditioning value).
NearestNeighbourSearch
Abstract class for nearest neighbour search.
NeuralConnection
Abstract unit in a NeuralNetwork.
NeuralMethod
This is an interface used to create classes that can be used by the neuralnode to perform all it's computations.
NeuralNetwork
Class implementing import of PMML Neural Network model.
NeuralNode
This class is used to represent a node in the neuralnet.
NGramTokenizer
Splits a string into an n-gram with min and max grams.
NNConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).
NNge
Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
Node
This class records all the data about a particular node for displaying.
NodePlace
This is an interface for classes that wish to take a node structure and arrange them
NominalPrediction
Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value.
NominalToBinary
Converts all nominal attributes into binary numeric attributes.
NominalToBinary
Converts all nominal attributes into binary numeric attributes.
NominalToString
Converts a nominal attribute (i.e.
NonSparseToSparse
An instance filter that converts all incoming instances into sparse format.
NormalEstimator
Simple probability estimator that places a single normal distribution over the observed values.
NormalizableDistance
Represents the abstract ancestor for normalizable distance functions, like Euclidean or Manhattan distance.
Normalize
Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
Normalize
An instance filter that normalize instances considering only numeric attributes and ignoring class index.
NormalizedPolyKernel
The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
NormalMixture
Class for manipulating normal mixture distributions.
NormContinuous
Class encapsulating a NormContinuous Expression.
NormDiscrete
Class encapsulating a NormDiscrete Expression.
NoSplit
Class implementing a "no-split"-split.
NoSupportForMissingValuesException
Exception that is raised by an object that is unable to process data with missing values.
NullStemmer
A dummy stemmer that performs no stemming at all.
NumberOfClustersRequestable
Interface to a clusterer that can generate a requested number of clusters
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.
NumericPrediction
Encapsulates an evaluatable numeric prediction: the predicted class value plus the actual class value.
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.
NumericToNominal
A filter for turning numeric attributes into nominal ones.
NumericTransform
Transforms numeric attributes using a given transformation method.
Obfuscate
A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
OneR
Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
OneRAttributeEval
OneRAttributeEval :
Evaluates the worth of an attribute by using the OneR classifier.
OPTICS
Basic implementation of OPTICS clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
OPTICS_Visualizer
Start the OPTICS Visualizer from command-line:
`java weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer [file.ser]`
Optimization
Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions.
Option
Class to store information about an option.
OptionHandler
Interface to something that understands options.
OptionHandlerJavadoc
Generates Javadoc comments from the OptionHandler's options.
OrdinalClassClassifier
Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.
For more information see:
Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification.
OutputFormatDialog
A dialog for setting various output format parameters.
OutputLogger
A logger that logs all output on stdout and stderr to a file.
OutputLogger.OutputPrintStream
A print stream class to capture all data from stdout and stderr.
OutputZipper
OutputZipper writes output to either gzipped files or to a multi entry zip file.
PaceMatrix
Class for matrix manipulation used for pace regression.
PaceRegression
Class for building pace regression linear models and using them for prediction.
PairedCorrectedTTester
Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic.
PairedStats
A class for storing stats on a paired comparison (t-test and correlation)
PairedStatsCorrected
A class for storing stats on a paired comparison.
PairedTTester
Calculates T-Test statistics on data stored in a set of instances.
ParentSet
Helper class for Bayes Network classifiers.
Parser
CUP v0.11a beta 20060608 generated parser.
Parser
CUP v0.11a beta 20060608 generated parser.
PART
Class for generating a PART decision list.
PartitionedMultiFilter
A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
PerformanceStats
The class that measures the performance of a nearest neighbour search (NNS) algorithm.
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.
PlaceNode1
This class will place the Nodes of a tree.
PlaceNode2
This class will place the Nodes of a tree.
Plot2D
This class plots datasets in two dimensions.
Plot2DCompanion
Interface for classes that need to draw to the Plot2D panel *before* Plot2D renders anything (eg.
PlotData2D
This class is a container for plottable data.
PLSClassifier
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
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).
PMMLClassifier
Abstract base class for all PMML classifiers.
PMMLFactory
This class is a factory class for reading/writing PMML models
PMMLModel
Interface for all PMML models
PMMLUtils
Utility routines.
PNGWriter
This class takes any JComponent and outputs it to a PNG-file.
PointsClosestToFurthestChildren
Implements the Moore's method to split a node of a ball tree.
For more information please see section 2 of the 1st and 3.2.3 of the 2nd:
Andrew W.
PoissonEstimator
Simple probability estimator that places a single Poisson distribution over the observed values.
PolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
PostscriptGraphics
The PostscriptGraphics class extends the Graphics2D class to produce an encapsulated postscript file rather than on-screen display.
PostscriptWriter
This class takes any Component and outputs it to a Postscript file.
PotentialClassIgnorer
This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
PrecomputedKernelMatrixKernel
This kernel is based on a static kernel matrix that is read from a file.
PreConstructedLinearModel
This class encapsulates a linear regression function.
Predicate
Prediction
Encapsulates a single evaluatable prediction: the predicted value plus the actual class value.
PredictionAppender
Bean that can can accept batch or incremental classifier events and produce dataset or instance events which contain instances with predictions appended.
PredictionAppenderBeanInfo
Bean info class for PredictionAppender.
PredictionAppenderCustomizer
GUI Customizer for the prediction appender bean
PredictionNode
Class representing a prediction node in an alternating tree.
PredictiveApriori
Class implementing the predictive apriori algorithm to mine association rules.
It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value.
For more information see:
Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence.
PreprocessPanel
This panel controls simple preprocessing of instances.
PrincipalComponents
Performs a principal components analysis and transformation of the data.
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.
PrintableComponent
This class extends the component which is handed over in the constructor by a print dialog.
PrintableHandler
This interface is for all JComponent classes that provide the ability to print itself to a file.
PrintablePanel
This Panel enables the user to print the panel to various file formats.
Prior
This is an interface to plug various priors into the Bayesian Logistic Regression Model.
PriorEstimation
Class implementing the prior estimattion of the predictive apriori algorithm for mining association rules.
PriorityQueue
PriorityQueue.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 27, 2004
Time: 5:36:35 PM
$ Revision 1.4 $
PriorityQueueElement
PriorityQueueElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 31, 2004
Time: 6:43:18 PM
$ Revision 1.4 $
Prism
Class for building and using a PRISM rule set for classification.
PropertyDialog
Support for PropertyEditors with custom editors: puts the editor into a separate frame.
PropertyHandler
This class stores information about properties to ignore or properties that are allowed for a certain class.
PropertyNode
Stores information on a property of an object: the class of the object with the property; the property descriptor, and the current value.
PropertyPanel
Support for drawing a property value in a component.
PropertyPath
A helper class for accessing properties in nested objects, e.g., accessing the "getRidge" method of a LinearRegression classifier part of MultipleClassifierCombiner, e.g., Vote.
PropertyPath.Path
Contains a (property) path structure
PropertyPath.PathElement
Represents a single element of a property path
PropertySelectorDialog
Allows the user to select any (supported) property of an object, including properties that any of it's property values may have.
PropertySheetPanel
Displays a property sheet where (supported) properties of the target object may be edited.
PropositionalToMultiInstance
Converts a propositional dataset into a multi-instance dataset (with relational attribute).
ProtectedProperties
Simple class that extends the Properties class so that the properties are unable to be modified.
PruneableClassifierTree
Class for handling a tree structure that can be pruned using a pruning set.
PruneableDecList
Class for handling a partial tree structure that can be pruned using a pruning set.
Puk
The Pearson VII function-based universal kernel.
For more information see:
B.
QRDecomposition
QR Decomposition.
QueryExecuteEvent
An event that is generated when a query is executed.
QueryExecuteListener
A listener for executing queries.
QueryPanel
Represents a panel for entering an SQL query.
Queue
Class representing a FIFO queue.
RacedIncrementalLogitBoost
Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
For more information see:
Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets.
RaceSearch
Races the cross validation error of competing attribute subsets.
RandomCommittee
Class for building an ensemble of randomizable base classifiers.
RandomForest
Class for constructing a forest of random trees.
For more information see:
Leo Breiman (2001).
Randomizable
Interface to something that has random behaviour that is able to be seeded with an integer.
RandomizableClassifier
Abstract utility class for handling settings common to randomizable classifiers.
RandomizableClusterer
Abstract utility class for handling settings common to randomizable clusterers.
RandomizableDensityBasedClusterer
Abstract utility class for handling settings common to randomizable clusterers.
RandomizableIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
RandomizableMultipleClassifiersCombiner
Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.
RandomizableSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
RandomizableSingleClustererEnhancer
Abstract utility class for handling settings common to randomizable clusterers.
Randomize
Randomly shuffles the order of instances passed through it.
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.
RandomRBF
RandomRBF data is generated by first creating a random set of centers for each class.
RandomSearch
RandomSearch :
Performs a Random search in the space of attribute subsets.
RandomSplitResultProducer
Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
RandomSubset
Chooses a random subset of attributes, either an absolute number or a percentage.
RandomSubSpace
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
RandomTree
Class for constructing a tree that considers K randomly chosen attributes at each node.
RandomVariates
Class implementing some simple random variates generator.
Range
Class representing a range of cardinal numbers.
RankedOutputSearch
Interface for search methods capable of producing a ranked list of attributes.
Ranker
Ranker :
Ranks attributes by their individual evaluations.
RankSearch
RankSearch :
Uses an attribute/subset evaluator to rank all attributes.
RBFKernel
The RBF kernel.
RBFNetwork
Class that implements a normalized Gaussian radial basisbasis function network.
It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that.
RDG1
A data generator that produces data randomly by producing a decision list.
The decision list consists of rules.
Instances are generated randomly one by one.
ReferenceInstances
Simple class that extends the Instances class making it possible to create subsets of instances that reference their source set.
RegOptimizer
Base class implementation for learning algorithm of SMOreg Valid options are:
Regression
Class implementing import of PMML Regression model.
RegressionByDiscretization
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
RegressionGenerator
Abstract class for data generators for regression classifiers.
RegressionSplitEvaluator
A SplitEvaluator that produces results for a classification scheme on a numeric class attribute.
RegSMO
Implementation of SMO for support vector regression as described in :
A.J.
RegSMOImproved
Learn SVM for regression using SMO with Shevade, Keerthi, et al.
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).
RelationalLocator
This class locates and records the indices of relational attributes,
ReliefFAttributeEval
ReliefFAttributeEval :
Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class.
RemoteBoundaryVisualizerSubTask
Class that encapsulates a sub task for distributed boundary visualization.
RemoteEngine
A general purpose server for executing Task objects sent via RMI.
RemoteExperiment
Holds all the necessary configuration information for a distributed experiment.
RemoteExperimentEvent
Class encapsulating information on progress of a remote experiment
RemoteExperimentListener
Interface for classes that want to listen for updates on RemoteExperiment progress
RemoteExperimentSubTask
Class to encapsulate an experiment as a task that can be executed on a remote host.
RemoteResult
Class that encapsulates a result (and progress info) for part of a distributed boundary visualization.
Remove
A filter that removes a range of attributes from the dataset.
RemoveFolds
This filter takes a dataset and outputs a specified fold for cross validation.
RemoveFrequentValues
Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
RemoveMisclassified
A filter that removes instances which are incorrectly classified.
RemovePercentage
A filter that removes a given percentage of a dataset.
RemoveRange
A filter that removes a given range of instances of a dataset.
RemoveType
Removes attributes of a given type.
RemoveUseless
This filter removes attributes that do not vary at all or that vary too much.
RemoveWithValues
Filters instances according to the value of an attribute.
Reorder
A filter that generates output with a new order of the attributes.
RepeatedHillClimber
This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
RepeatedHillClimber
This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
REPTree
Fast decision tree learner.
Resample
Produces a random subsample of a dataset using either sampling with replacement or without replacement.
The original dataset must fit entirely in memory.
Resample
Produces a random subsample of a dataset using either sampling with replacement or without replacement.
ReservoirSample
Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
ResidualModelSelection
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals.
ResidualSplit
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals of the LogitBoost algorithm.
ResultChangedEvent
An event that is generated when a different Result is activated in the ResultPanel.
ResultChangedListener
A listener that is notified if another Result is activated in the ResultPanel.
ResultHistoryPanel
A component that accepts named stringbuffers and displays the name in a list box.
ResultHistoryPanel.RKeyAdapter
Extension of KeyAdapter that implements Serializable.
ResultHistoryPanel.RMouseAdapter
Extension of MouseAdapter that implements Serializable.
ResultListener
Interface for objects able to listen for results obtained by a ResultProducer
ResultMatrix
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixCSV
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixGnuPlot
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixHTML
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixLatex
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixPlainText
This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixSignificance
This matrix is a container for the datasets and classifier setups and their statistics.
ResultPanel
Represents a panel for displaying the results of a query in table format.
ResultProducer
This interface defines the methods required for an object that produces results for different randomizations of a dataset.
ResultSetHelper
Represents an extended JTable, containing a table model based on a ResultSet and the corresponding query.
ResultSetTable
Represents an extended JTable, containing a table model based on a ResultSet and the corresponding query.
ResultSetTableCellRenderer
Handles the background colors for missing values differently than the DefaultTableCellRenderer.
ResultSetTableModel
The model for an SQL ResultSet.
ResultsPanel
This panel controls simple analysis of experimental results.
ResultVectorTableModel
ResultVectorTableModel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 12, 2004
Time: 9:23:31 PM
$ Revision 1.4 $
RevisionHandler
For classes that should return their source control revision.
RevisionUtils
Contains utility functions for handling revisions.
RevisionUtils.Type
Enumeration of source control types.
Ridor
An implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate.
RotationForest
Class for construction a Rotation Forest.
Rule
Class representing a rule with a body and a head.
Rule
Abstract class of generic rule
Rule
Generates a single m5 tree or rule
RuleGeneration
Class implementing the rule generation procedure of the predictive apriori algorithm.
RuleItem
Class for storing an (class) association rule.
RuleNode
Constructs a node for use in an m5 tree or rule
RuleStats
This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc.
RunNumberPanel
This panel controls configuration of lower and upper run numbers in an experiment.
RunPanel
This panel controls the running of an experiment.
SaveBuffer
This class handles the saving of StringBuffers to files.
Saver
Interface to something that can save Instances to an output destination in some format.
Saver
Saves data sets using weka.core.converter classes
SaverBeanInfo
Bean info class for the saver bean
SaverCustomizer
GUI Customizer for the saver bean
Scanner
A scanner for mathematical expressions.
Scanner
A scanner for evaluating whether an Instance is to be included in a subset or not.
ScatterPlotMatrix
Bean that encapsulates weka.gui.visualize.MatrixPanel for displaying a scatter plot matrix.
ScatterPlotMatrixBeanInfo
Bean info class for the scatter plot matrix bean
ScatterSearchV1
Class for performing the Sequential Scatter Search.
Scoreable
Interface for allowing to score a classifier
SearchAlgorithm
This is the base class for all search algorithms for learning Bayes networks.
SelectedTag
Represents a selected value from a finite set of values, where each value is a Tag (i.e.
SelectedTagEditor
A PropertyEditor that uses tags, where the tags are obtained from a weka.core.SelectedTag object.
Sequence
Class representing a sequence of elements/itemsets.
SequentialDatabase
SequentialDatabase.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 20, 2004
Time: 1:23:38 PM
$ Revision 1.4 $
SERFileFilter
SERFileFilter.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 15, 2004
Time: 6:54:56 PM
$ Revision 1.4 $
SerialInstanceListener
Defines an interface for objects able to produce two output streams of instances.
SerializationHelper
A helper class for determining serialVersionUIDs and checking whether classes contain one and/or need one.
SerializedClassifier
A wrapper around a serialized classifier model.
SerializedInstancesLoader
Reads a source that contains serialized Instances.
SerializedInstancesSaver
Serializes the instances to a file with extension bsi.
SerializedModelSaver
A bean that saves serialized models
SerializedModelSaverBeanInfo
Bean info class for the serialized model saver bean
SerializedModelSaverCustomizer
GUI Customizer for the SerializedModelSaver bean
SerializedObject
Class for storing an object in serialized form in memory.
SerialUIDChanger
This class enables one to change the UID of a serialized object and therefore not losing the data stored in the binary format.
SERObject
SERObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 15, 2004
Time: 9:43:00 PM
$ Revision 1.4 $
SetInstancesPanel
A panel that displays an instance summary for a set of instances and lets the user open a set of instances from either a file or URL.
SetupModePanel
This panel switches between simple and advanced experiment setup panels.
SetupPanel
This panel controls the configuration of an experiment.
sIB
Cluster data using the sequential information bottleneck algorithm.
Note: only hard clustering scheme is supported.
SigmoidUnit
This can be used by the neuralnode to perform all it's computations (as a sigmoid unit).
SimpleBatchFilter
This filter is a superclass for simple batch filters.
SimpleCart
Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method.
For more information, see:
Leo Breiman, Jerome H.
SimpleCLI
Creates a very simple command line for invoking the main method of classes.
SimpleCLIPanel
Creates a very simple command line for invoking the main method of classes.
SimpleCLIPanel.CommandlineCompletion
A class for commandline completion of classnames.
SimpleDateFormatEditor
Class for editing SimpleDateFormat strings.
SimpleEstimator
SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned.
SimpleFilter
This filter contains common behavior of the SimpleBatchFilter and the SimpleStreamFilter.
SimpleKMeans
Cluster data using the k means algorithm
SimpleLinearRegression
Learns a simple linear regression model.
SimpleLinkedList
SimpleLogistic
Classifier for building linear logistic regression models.
SimpleMI
Reduces MI data into mono-instance data.
SimpleSetupPanel
This panel controls the configuration of an experiment.
SimpleStreamFilter
This filter is a superclass for simple stream filters.
SimulatedAnnealing
This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.
For more information see:
R.R.
SimulatedAnnealing
This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.
For more information see:
R.R.
SingleAssociatorEnhancer
Abstract utility class for handling settings common to meta associators that use a single base associator.
SingleClassifierEnhancer
Abstract utility class for handling settings common to meta classifiers that use a single base learner.
SingleClustererEnhancer
Meta-clusterer for enhancing a base clusterer.
SingleIndex
Class representing a single cardinal number.
SingularValueDecomposition
Singular Value Decomposition.
SlidingMidPointOfWidestSide
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
SMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
SMOreg
SMOreg implements the support vector machine for regression.
SMOset
Stores a set of integer of a given size.
SMOTE
Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE).
SnowballStemmer
A wrapper class for the Snowball stemmers.
SortedTableModel
Represents a TableModel with sorting functionality.
SortedTableModel.SortContainer
Helper class for sorting the columns.
Sourcable
Interface for classifiers that can be converted to Java source.
Sourcable
Interface for filters that can be converted to Java source.
SparseInstance
Class for storing an instance as a sparse vector.
SparseToNonSparse
An instance filter that converts all incoming sparse instances into non-sparse format.
SpecialFunctions
Class implementing some mathematical functions.
SPegasos
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
SplashWindow
A Splash window.
SplitCriterion
Abstract class for computing splitting criteria with respect to distributions of class values.
SplitEvaluate
Interface for objects that determine a split point on an attribute
SplitEvaluator
Interface to objects able to generate a fixed set of results for a particular split of a dataset.
Splitter
Abstract class representing a splitter node in an alternating tree.
SpreadSubsample
Produces a random subsample of a dataset.
SqlViewer
Represents a little tool for querying SQL databases.
SqlViewerDialog
A little dialog containing the SqlViewer.
Stack<T>
Class implementing a stack.
Stacking
Combines several classifiers using the stacking method.
StackingC
Implements StackingC (more efficient version of stacking).
For more information, see
A.K.
Standardize
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
Startable
Interface to something that is a start point for a flow and can be launched programatically.
StartSetHandler
Interface for search methods capable of doing something sensible given a starting set of attributes.
StartUpListener
Interface to something that can be notified of a successful startup
Statistics
Class implementing some distributions, tests, etc.
Stats
Class implementing a statistical routine needed by J48 to compute its error estimate.
Stats
A class to store simple statistics
Stemmer
Interface for all stemming algorithms.
Stemming
A helper class for using the stemmers.
Stopwords
Class that can test whether a given string is a stop word.
StratifiedRemoveFolds
This filter takes a dataset and outputs a specified fold for cross validation.
StreamableFilter
Interface for filters can work with a stream of instances.
StringKernel
Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].
For more information, see
Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.
StringLocator
This class locates and records the indices of String attributes, recursively in case of Relational attributes.
StringToNominal
Converts a string attribute (i.e.
StringToWordVector
Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
StripChart
Bean that can display a horizontally scrolling strip chart.
StripChartBeanInfo
Bean info class for the strip chart bean
StripChartCustomizer
GUI Customizer for the strip chart bean
StructureProducer
Interface for something that can describe the structure of what is encapsulated in a named event type as an empty set of Instances (i.e.
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)
SubsetEvaluator
Interface for attribute subset evaluators.
SubsetSizeForwardSelection
SubsetSizeForwardSelection:
Extension of LinearForwardSelection.
SubspaceCluster
A data generator that produces data points in hyperrectangular subspace clusters.
SubspaceClusterDefinition
A single cluster for the SubspaceCluster datagenerator
Summarizable
Interface to something that provides a short textual summary (as opposed to toString() which is usually a fairly complete description) of itself.
SupervisedFilter
Interface for filters that make use of a class attribute.
SVMAttributeEval
SVMAttributeEval :
Evaluates the worth of an attribute by using an SVM classifier.
SVMLightLoader
Reads a source that is in svm light format.
For more information about svm light see:
http://svmlight.joachims.org/
SVMLightSaver
Writes to a destination that is in svm light format.
For more information about svm light see:
http://svmlight.joachims.org/
SwapValues
Swaps two values of a nominal attribute.
sym
CUP generated interface containing symbol constants.
sym
CUP generated interface containing symbol constants.
SymmetricalUncertAttributeEval
SymmetricalUncertAttributeEval :
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
SysErrLog
This Logger just sends messages to System.err.
SystemInfo
This class prints some information about the system setup, like Java version, JVM settings etc.
TabuSearch
This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
TabuSearch
This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
Tag
A `Tag` simply associates a numeric ID with a String description.
TAN
This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.
For more information see:
N.
TAN
This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.
For more information see:
N.
TargetMetaInfo
Class to encapsulate information about a Target.
Task
Interface to something that can be remotely executed as a task.
TaskLogger
Interface for objects that display log and display information on running tasks.
TaskStatusInfo
A class holding information for tasks being executed on RemoteEngines.
TechnicalInformation
Used for paper references in the Javadoc and for BibTex generation.
TechnicalInformation.Field
the possible fields
TechnicalInformation.Type
the different types of information
TechnicalInformationHandler
For classes that are based on some kind of publications.
TechnicalInformationHandlerJavadoc
Generates Javadoc comments from the TechnicalInformationHandler's data.
Tee
This class pipelines print/println's to several PrintStreams.
Tertius
Finds rules according to confirmation measure (Tertius-type algorithm).
For more information see:
P.
Test
Class to represent a test.
Tester
Interface for different kinds of Testers in the Experimenter.
TestInstances
Generates artificial datasets for testing.
TestSetEvent
Event encapsulating a test set
TestSetListener
Interface to something that can accpet test set events
TestSetMaker
Bean that accepts data sets and produces test sets
TestSetMakerBeanInfo
Bean info class for the test set maker bean.
TestSetProducer
Interface to something that can produce test sets
TextDirectoryLoader
Loads all text files in a directory and uses the subdirectory names as class labels.
TextEvent
Event that encapsulates some textual information
TextListener
Interface to something that can process a TextEvent
TextViewer
Bean that collects and displays pieces of text
TextViewerBeanInfo
Bean info class for the text viewer
ThresholdCurve
Generates points illustrating prediction tradeoffs that can be obtained by varying the threshold value between classes.
ThresholdDataEvent
Event encapsulating classifier performance data based on varying a threshold over the classifier's predicted probabilities
ThresholdDataListener
Interface to something that can accept ThresholdDataEvents
ThresholdSelector
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
ThresholdVisualizePanel
This panel is a VisualizePanel, with the added ablility to display the area under the ROC curve if an ROC curve is chosen.
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.
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.
Tokenizer
A superclass for all tokenizer algorithms.
TopDownConstructor
The class implementing the TopDown construction method of ball trees.
TrainingSetEvent
Event encapsulating a training set
TrainingSetListener
Interface to something that can accept and process training set events
TrainingSetMaker
Bean that accepts a data sets and produces a training set
TrainingSetMakerBeanInfo
Bean info class for the training set maker bean
TrainingSetProducer
Interface to something that can produce a training set
TrainTestSplitMaker
Bean that accepts data sets, training sets, test sets and produces both a training and test set by randomly spliting the data
TrainTestSplitMakerBeanInfo
Bean info class for the train test split maker bean
TrainTestSplitMakerCustomizer
GUI customizer for the train test split maker bean
TreeBuild
This class will parse a dotty file and construct a tree structure from it with Edge's and Node's
TreeDisplayEvent
An event containing the user selection from the tree display
TreeDisplayListener
Interface implemented by classes that wish to recieve user selection events from a tree displayer.
TreePerformanceStats
The class that measures the performance of a tree based nearest neighbour search algorithm.
TreeVisualizePlugin
Interface implemented by classes loaded dynamically to visualize classifier results in the explorer.
TreeVisualizer
Class for displaying a Node structure in Swing.
Trie
A class representing a Trie data structure for strings.
Trie.TrieIterator
Represents an iterator over a trie
Trie.TrieNode
Represents a node in the trie.
TwoClassStats
Encapsulates performance functions for two-class problems.
TwoWayNominalSplit
Class representing a two-way split on a nominal attribute, of the form: either 'is some_value' or 'is not some_value'.
TwoWayNumericSplit
Class representing a two-way split on a numeric attribute, of the form: either 'is invalid input: '<' some_value' or 'is >= some_value'.
UnassignedClassException
Exception that is raised when trying to use some data that has no class assigned to it, but a class is needed to perform the operation.
UnassignedDatasetException
Exception that is raised when trying to use something that has no reference to a dataset, when one is required.
Undoable
Interface implemented by classes that support undo.
UnsupervisedAttributeEvaluator
Abstract unsupervised attribute evaluator.
UnsupervisedFilter
Interface for filters that do not need a class attribute.
UnsupervisedSubsetEvaluator
Abstract unsupervised attribute subset evaluator.
UnsupportedAttributeTypeException
Exception that is raised by an object that is unable to process some of the attribute types it has been passed.
UnsupportedClassTypeException
Exception that is raised by an object that is unable to process the class type of the data it has been passed.
UpdateableClassifier
Interface to incremental classification models that can learn using one instance at a time.
UpdateableClusterer
Interface to incremental cluster models that can learn using one instance at a time.
UpdateQueue
UpdateQueue.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 27, 2004
Time: 5:36:35 PM
$ Revision 1.4 $
UpdateQueueElement
UpdateQueueElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 31, 2004
Time: 6:43:18 PM
$ Revision 1.4 $
URLSourcedLoader
Interface to a loader that can load from a http url
UserClassifier
Interactively classify through visual means.
UserRequestAcceptor
Interface to something that can accept requests from a user to perform some action
Utils
Class implementing some simple utility methods.
Values
Stores some statistics.
VaryNode
Part of ADTree implementation.
Version
This class contains the version number of the current WEKA release and some methods for comparing another version string.
VFI
Classification by voting feature intervals.
ViewerDialog
A downsized version of the ArffViewer, displaying only one Instances-Object.
Visible
Interface to something that has a visible (via BeanVisual) reprentation
VisualizableErrorEvent
Event encapsulating error information for a learning scheme that can be visualized in the DataVisualizer
VisualizableErrorListener
Interface to something that can accept VisualizableErrorEvents
VisualizePanel
A slightly extended MatrixPanel for better support in the Explorer.
VisualizePanel
This panel allows the user to visualize a dataset (and if provided) a classifier's/clusterer's predictions in two dimensions.
VisualizePanelEvent
This event Is fired to a listeners 'userDataEvent' function when The user on the VisualizePanel clicks submit.
VisualizePanelListener
Interface implemented by a class that is interested in receiving submited shapes from a visualize panel.
VisualizePlugin
Interface implemented by classes loaded dynamically to visualize classifier results in the explorer.
VisualizeUtils
This class contains utility routines for visualization
Vote
Class for combining classifiers.
VotedPerceptron
Implementation of the voted perceptron algorithm by Freund and Schapire.
WAODE
WAODE contructs the model called Weightily Averaged One-Dependence Estimators.
For more information, see
L.
Wavelet
A filter for wavelet transformation.
For more information see:
Wikipedia (2004).
WeightedInstancesHandler
Interface to something that makes use of the information provided by instance weights.
WekaException
Class for Weka-specific exceptions.
WekaTaskMonitor
This panel records the number of weka tasks running and displays a simple bird animation while their are active tasks
WekaWrapper
Interface to something that can wrap around a class of Weka algorithms (classifiers, filters etc).
Winnow
Implements Winnow and Balanced Winnow algorithms by Littlestone.
For more information, see
N.
WordTokenizer
A simple tokenizer that is using the java.util.StringTokenizer class to tokenize the strings.
WrapperSubsetEval
WrapperSubsetEval:
Evaluates attribute sets by using a learning scheme.
XMeans
Cluster data using the X-means algorithm.
X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region.
XMLBasicSerialization
This serializer contains some read/write methods for common classes that are not beans-conform.
XMLBeans
This class serializes and deserializes a KnowledgeFlow setup to and fro XML.
XMLClassifier
This class serializes and deserializes a Classifier instance to and fro XML.
XMLDocument
This class offers some methods for generating, reading and writing XML documents.
It can only handle UTF-8.
XMLExperiment
This class serializes and deserializes an Experiment instance to and fro XML.
It omits the `options` from the Experiment, since these are handled by the get/set-methods.
XMLInstances
XML representation of the Instances class.
XMLOptions
A class for transforming options listed in XML to a regular WEKA command line string.
XMLSerialization
With this class objects can be serialized to XML instead into a binary format.
XMLSerializationMethodHandler
This class handles relationships between display names of properties (or classes) and Methods that are associated with them.
XRFFLoader
Reads a source that is in the XML version of the ARFF format.
XRFFSaver
Writes to a destination that is in the XML version of the ARFF format.
XStream
This class is a helper class for XML serialization using XStream .
YongSplitInfo
Stores split information.
ZeroR
Class for building and using a 0-R classifier.