All Classes and Interfaces
Class
Description
Abstract scheme for learning associations.
Abstract classifier.
Abstract clusterer.
Ancestor for command.
Abstract class for objects that store instances to some destination.
Bean info class for the AbstractDataSink
Abstract class for objects that can provide instances from some source
Bean info class for AbstractDataSource.
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie.
Abstract base class for pluggable classification/regression evaluation
metrics.
Abstract class for objects that can provide some kind of evaluation for
classifier, clusterers etc.
Ancestor for file-based stopword schemes.
Abstract superclass for all file loaders.
Abstract class for Savers that save to a file
Valid options are:
-i input arff file
The input filw in arff format.
The input filw in arff format.
Base class for a graphical command
Base class for GUI applications in Weka
Abstract class providing common functionality for the original instance
implementations.
Abstract class gives default implementation of setSource methods.
Abstract base class for offscreen chart renderers.
A superclass for outputting the classifications of a classifier.
Base classes for GUI perspectives to extend.
Abstract superclass for generating plottable instances.
Abstract base class for PMMLProducer helper classes to extend.
Abstract class for Saver
Ancestor for setup panels for experiments.
Ancestor for stopwords classes.
Abstract class for TestSetProducers that contains default implementations of
add/remove listener methods and defualt visual representation.
BeanInfo class for AbstractTestSetProducer
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.
Abstract base class for TrainAndTestSetProducers that contains default
implementations of add/remove listener methods and defualt visual
representation.
Bean info class for AbstractTrainAndTestSetProducers
Abstract class for TrainingSetProducers that contains default implementations
of add/remove listener methods and default visual representation
BeanInfo class for AbstractTrainingSetProducer
Java class for ACTIVATION-FUNCTION.
Node that is "active" (i.e.
Class for boosting a nominal class classifier using
the Adaboost M1 method.
An instance filter that adds a new attribute to the
dataset.
A filter for adding the classification, the class
distribution and an error flag to a dataset with a classifier.
A filter that adds a new nominal attribute
representing the cluster assigned to each instance by the specified
clustering algorithm.
Either the clustering algorithm gets built with the first batch of data or one specifies are serialized clusterer model file to use instead.
Either the clustering algorithm gets built with the first batch of data or one specifies are serialized clusterer model file to use instead.
An instance filter that creates a new attribute by
applying a mathematical expression to existing attributes.
An instance filter that adds an ID attribute to the
dataset.
Interface to something that can produce measures other than those
calculated by evaluation modules.
Meta classifier that enhances the performance of a regression base classifier.
An instance filter that changes a percentage of a
given attribute's values.
A filter that adds new attributes with user
specified type and constant value.
Inner class encapsulating a new user-specified attribute to create.
Bean info class for the AddUserFields filter.
Customizer for the AddUserFields filter.
Adds the labels from the given list to an attribute
if they are missing.
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.
Java class for Aggregate element declaration.
Interface to something that can aggregate an object of the same type with
itself.
Subclass of Evaluation that provides a method for aggregating the results
stored in another Evaluation object.
Subclass of Evaluation that provides a method for aggregating the results
stored in another Evaluation object.
Generates a people database and is based on the
paper by Agrawal et al.:
R.
R.
This panel controls setting a list of algorithms for an experiment to iterate
over.
Class for performing operations on an algebraic vector
of floating-point values.
A simple instance filter that passes all instances directly
through.
Applies all known Javadoc-derived classes to a source file.
Alphabetic string tokenizer, tokens are to be
formed only from contiguous alphabetic sequences.
Java class for Alternate element declaration.
Step that alters the relation name for data received via instance, dataSet,
trainingSet and testSet connections
Java class for Annotation element declaration.
Java class for Anova element declaration.
Java class for AnovaRow element declaration.
Java class for AntecedentSequence element declaration.
Java class for AnyDistribution element declaration.
A bean that appends multiple incoming data connections into a single data
set.
A bean that appends multiple incoming data connections into a single data
set.
Bean info class for the appender bean
Java class for Application element declaration.
Java class for Apply element declaration.
Class implementing an Apriori-type algorithm.
Class for storing a set of items.
Reads a source that is in arff (attribute relation
file format) format.
Reads data from an ARFF file, either in incremental or batch mode.
A Panel representing an ARFF-Table and the associated filename.
Writes to a destination in arff text format.
A sorter for the ARFF-Viewer - necessary because of the custom CellRenderer.
A specialized JTable for the Arff-Viewer.
Handles the background colors for missing values differently than the
DefaultTableCellRenderer.
The model for the Arff-Viewer.
A little tool for viewing ARFF files.
The main panel of the ArffViewer.
Class for encapsulating a PMML Array element.
Java class for ArrayType complex type.
Abstract attribute selection evaluation class
Step that wraps a Weka attribute or subset evaluator.
Step editor dialog for the ASEvaluator step
Abstract attribute selection search class.
Step that wraps a Weka attribute selection search strategy.
Java class for AssociationModel element declaration.
Abstract class for storing and manipulating an association rule.
Java class for AssociationRule element declaration.
Class encapsulating a list of association rules.
Interface to something that can provide a list of
AssociationRules.
Interface implemented by classes loaded dynamically to
visualize association results in the explorer.
This panel allows the user to select, configure, and run a scheme that learns
associations.
Bean that wraps around weka.associations.
Step that wraps a Weka associator.
BeanInfo class for the Associator wrapper bean
GUI customizer for the associator wrapper bean
Class for evaluating Associaters.
Class for handling an attribute.
Java class for Attribute element declaration.
Interface for classes that evaluate attributes individually.
Creates a panel that displays the attributes contained in a set of instances,
letting the user select a single attribute for inspection.
This class locates and records the indices of a certain type of attributes,
recursively in case of Relational attributes.
This panel displays one dimensional views of the attributes in a dataset.
Class encapsulating a change in the AttributePanel's selected x and y
attributes.
Interface for classes that want to listen for Attribute selection
changes in the attribute panel
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
Attribute selection class.
A supervised attribute filter that can be used to
select attributes.
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).
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).
Abstract attribute set evaluator.
A Utility class that contains summary information on an
the values that appear in a dataset for a particular attribute.
Bean that encapsulates displays bar graph summaries for attributes in a data
set.
Step that collects data to display in a summary overview of attribute
distributions
Bean info class for the attribute summarizer bean
GUI customizer for attribute summarizer.
Interactive viewer for the AttributeSummarizer step
Step editor dialog for the attribute summarizer step
This panel displays summary statistics about an attribute: name, type
number/% of missing/unique values, number of distinct values.
Knowledge Flow perspective that provides a matrix of
AttributeVisualizationPanels
Default settings for the AttributeSummaryPerspective
Abstract attribute transformer.
Creates a panel that shows a visualization of an attribute in a dataset.
Takes the results from a ResultProducer and submits
the average to the result listener.
Class for bagging a classifier to reduce variance.
Class representing a node of a BallTree.
Abstract class for splitting a ball tree's BallNode.
Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
The connection to dataset is only a reference.
The connection to dataset is only a reference.
Abstract class for constructing a BallTree .
Java class for BaseCumHazardTables element declaration.
Base class for execution environments
Defaults for the base execution environment
Base class than clients can extend when implementing
StepInteractiveViewer.Java class for Baseline element declaration.
Java class for BaselineCell element declaration.
Java class for BaselineModel element declaration.
Java class for BaselineStratum element declaration.
Java class for BASELINE-TEST-STATISTIC.
Abstract base class for simple data visualization steps that just collect
data sets for visualization.
Base class for implementations of Step to use.
A minimal set of methods, duplicated from the Step interface, that a simple
subclass of BaseStep would need to implement in order to function as a start
and/or main processing step in the Knowledge Flow.
Class encapsulating a set of association rules.
Interface to something that can process a BatchAssociationRulesEvent.
Class encapsulating a built classifier and a batch of instances to test on.
Interface to something that can process a BatchClassifierEvent
Class encapsulating a built clusterer and a batch of instances to
test on.
Interface to something that can process a BatchClustererEvent
Marker interface for a loader/saver that can retrieve instances in batch mode
Interface to something that can produce predictions in a batch manner
when presented with a set of Instances.
Java class for BayesInput element declaration.
Java class for BayesInputs element declaration.
Bayes Network learning using various search
algorithms and quality measures.
Base class for a Bayes Network classifier.
Base class for a Bayes Network classifier.
Generates random instances based on a Bayes network.
BayesNetEstimator is the base class for estimating
the conditional probability tables of a Bayes network once the structure has
been learned.
Bayes Network learning using various search
algorithms and quality measures.
Base class for a Bayes Network classifier.
Base class for a Bayes Network classifier.
Java class for BayesOutput element declaration.
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.
Class for encapsulating a connection between two beans.
Extends java.beans.Customizer and provides a method to register
a listener interested in notification about whether the customizer
has modified the object that it is customizing.
Interface for something that is interested in the modified status
of a source object (typically a BeanCustomizer that is editing an
object)
Class that manages a set of beans.
Utility class encapsulating various properties for the KnowledgeFlow and
providing methods to register and deregister plugin Bean components
BeanVisual encapsulates icons and label for a given bean.
BestFirst:
Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
This is the Exception thrown by BIFParser, if there
was an error in parsing the XMLBIF string or input
stream.
This class parses an inputstream or a string in XMLBIF ver.
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).
For more details on XML BIF see:
Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).
Class that encapsulates an item whose backing Attribute is
binary or unary.
Java class for binarySimilarity element declaration.
Class for storing a binary-data-only instance as a sparse vector.
Class for selecting a C4.5-like binary (!) split for a given dataset.
Class implementing a binary C4.5-like split on an attribute.
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.
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.
A step that waits for a specified step to finish processing before allowing
incoming data to proceed downstream.
Step editor dialog for the Block step
BMAEstimator estimates conditional probability
tables of a Bayes network using Bayes Model Averaging (BMA).
This class takes any JComponent and outputs it to a BMP-file.
The class that constructs a ball tree bottom up.
BoundaryPanel.
This class extends BoundaryPanel with code for distributing the processing
necessary to create a visualization among a list of remote machines.
A step that computes visualization data for class/cluster decision
boundaries.
Interface for something that wants to be informed of rendering progress
updates
Interactive viewer component for the boundary plotter step
Editor dialog for the boundary plotter step
Java class for BoundaryValueMeans element declaration.
Java class for BoundaryValues element declaration.
BoundaryVisualizer.
A little helper class for browser related stuff.
Built-in function for +, -, *, /.
Built-in function for min, max, sum, avg, log10,
ln, sqrt, abs, exp, pow, threshold, floor, ceil and round.
Built-in function for uppercase, substring and trimblanks.
Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
Ron Kohavi, David H.
Ron Kohavi, David H.
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.
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.
Reads a file that is C45 format.
Class for selecting a C4.5-type split for a given dataset.
Class for handling a tree structure that can
be pruned using C4.5 procedures.
Class for handling a partial tree structure pruned using C4.5's pruning
heuristic.
Writes to a destination that is in the format used
by the C4.5 algorithm.
Therefore it outputs a names and a data file.
Therefore it outputs a names and a data file.
Class implementing a C4.5-type split on an attribute.
Base class for RBFKernel and PolyKernel that implements a simple LRU.
Interface to something that can notify a Step that a Task submitted by
ExecutionEnvironment.submitTask() has finished.
Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
A class that describes the capabilites (e.g., handling certain types of
attributes, missing values, types of classes, etc.) of a specific classifier.
Outputs the capabilities of the specified class.
enumeration of all capabilities
Classes implementing this interface return their capabilities in regards
to datasets.
Classes implementing this interface make it possible to turn off
capabilities checking.
Helper class for Capabilities.
A filter for performing the Cartesian product of a set of nominal attributes.
Interface for learning class association rules.
Java class for CategoricalPredictor element declaration.
Java class for Categories element declaration.
Java class for Category element declaration.
Java class for CAT-SCORING-METHOD.
Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
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.
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.
Changes the date format used by a date attribute.
Abstract superclass for tokenizers that take characters as delimiters.
Java class for Characteristic element declaration.
Java class for Characteristics element declaration.
Splits a string into an n-gram with min and max
grams.
Event encapsulating info for plotting a data point on the StripChart
Interface to something that can process a ChartEvent
Java class for chebychev element declaration.
Implements the Chebyshev distance.
Abstract general class for testing in Weka.
Class for examining the capabilities and finding problems with associators.
Class for examining the capabilities and finding problems with attribute
selection schemes.
An extended JList that contains CheckBoxes.
Class for examining the capabilities and finding problems with classifiers.
Class for examining the capabilities and finding problems with clusterers.
Class for examining the capabilities and finding problems with estimators.
class that contains info about the attribute types the estimator can
estimate estimator work on one attribute only
public class that contains info about the chosen attribute type estimator
work on one attribute only
Simple command line checking of classes that are editable in the GOE.
Class for examining the capabilities and finding problems with kernels.
Simple command line checking of classes that implement OptionHandler.
Abstract general class for testing schemes in Weka.
a class for postprocessing the test-data
A simple class for checking the source generated from Classifiers
implementing the
weka.classifiers.Sourcable interface.A simple class for checking the source generated from Filters implementing
the
weka.filters.Sourcable interface.Java class for ChildParent element declaration.
Cholesky Decomposition.
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).
Java class for cityBlock element declaration.
Filter that can set and unset the class index.
Bean that assigns a class attribute to a data set.
Knowledge Flow step for assigning a class attribute in incoming data
BeanInfo class for the class assigner bean
GUI customizer for the class assigner bean
Step editor dialog for the ClassAssigner step
Reweights the instances in the data so that each class has the same total weight.
A singleton that stores all classes on the classpath.
For filtering classes.
For filtering classes.
Converts the values of nominal and/or numeric attributes into class conditional probabilities.
This class is used for discovering classes that implement a certain interface
or a derived from a certain class.
compares two strings.
Abstract class for data generators for classifiers.
Class for doing classification using regression methods.
Classifier interface.
Bean that wraps around weka.classifiers
Step that wraps a Weka classifier.
ClassifierAttributeEval :
Evaluates the worth of an attribute by using a user-specified classifier.
Evaluates the worth of an attribute by using a user-specified classifier.
BeanInfo class for the Classifier wrapper bean
GUI customizer for the classifier wrapper bean
Class for handling a rule (partial tree) for a decision list.
A class for generating plottable visualization errors.
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).
Interface to plugin that can take the current state of the
Classifier panel and execute it.
A bean that evaluates the performance of batch trained classifiers
Step that implements batch classifier evaluation
Bean info class for the classifier performance evaluator
GUI customizer for the classifier performance evaluator component
GUI step editor dialog for the ClassifierPerformanceEvaluator step
A SplitEvaluator that produces results for a
classification scheme on a nominal class attribute.
Abstract class for classification models that can be used
recursively to split the data.
Classifier subset evaluator:
Evaluates attribute subsets on training data or a separate hold out testing set.
Evaluates attribute subsets on training data or a separate hold out testing set.
Class for handling a tree structure used for classification.
Java class for ClassLabels element declaration.
Utility class that can add jar files to the classpath dynamically.
Changes the order of the classes so that the class
values are no longer of in the order specified in the header.
This panel displays coloured labels for nominal attributes and a spectrum for
numeric attributes.
Step that allows the selection of the class label that is to be considered as
the "positive" class when computing threshold curves.
BeanInfo class for the class value picker bean
Editor dialog for the ClassValuePicker step.
Tab title widget that allows the user to click a little cross in order to
close the tab
Interface for a callback for notification of a tab's close widget being
clicked
Clears the output area.
Java class for Cluster element declaration.
Ancestor to all ClusterDefinitions, i.e., subclasses that handle their own
parameters that the cluster generator only passes on.
Interface for clusterers.
Bean that wraps around weka.clusterers
Step that wraps a Weka clusterer.
A class for generating plottable cluster assignments.
BeanInfo class for the Clusterer wrapper bean
GUI customizer for the Clusterer wrapper bean
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).
Interface to plugin that can take the current state of the
Clusterer panel and execute it.
A bean that evaluates the performance of batch trained clusterers
A step that evaluates the performance of batch trained clusterers
Bean info class for the clusterer performance evaluator
Class for evaluating clustering models.
Abstract class for cluster data generators.
Java class for ClusteringField element declaration.
Java class for ClusteringModel element declaration.
Java class for ClusteringModelQuality element declaration.
A filter that uses a density-based clusterer to
generate cluster membership values; filtered instances are composed of these
values plus the class attribute (if set in the input data).
Class 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.
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.
Java class for Coefficient element declaration.
Java class for Coefficients element declaration.
A property editor for colors that uses JColorChooser as the
underlying editor.
This class maintains a list that contains all the colornames from the
dotty standard and what color (in RGB) they represent
Interface to something that can be run from the command line.
Java class for COMPARE-FUNCTION.
Java class for ComparisonMeasure element declaration.
Java class for Comparisons element declaration.
A helper class for some common tasks with Dialogs, Icons, etc.
Java class for CompoundPredicate element declaration.
Java class for CompoundRule element declaration.
Interface to something that can accept remote connections and execute
a task.
Java class for Con element declaration.
Interface for numeric prediction schemes that can output conditional
density estimates.
Interface for conditional probability estimators.
Records sufficient stats for an attribute
Matching event for ConfigurationListener.
Matching listener for ConfigurationEvent.
Marker interface for components that can share their configuration.
Cells of this matrix correspond to counts of the number (or weight) of
predictions for each actual value / predicted value combination.
Java class for ConfusionMatrix element declaration.
This subclass of Optimization.java implements conjugate gradient descent
rather than BFGS updates, by overriding findArgmin(), with the same tests for
convergence, and applies the same line search code.
An event that is generated when a connection is established or dropped.
A listener for connect/disconnect events.
Interface for Beans that can receive (dis-)connection events generated when
(dis-)connecting data processing nodes in the Weka KnowledgeFlow.
Enables the user to insert a database URL, plus user/password to connect to
this database.
Java class for ConsequentSequence element declaration.
A simple logger that outputs the logging information in the console.
Class encapsulating a Constant Expression.
Java class for Constant element declaration.
Java class for Constraints element declaration.
Class implementing some statistical routines for contingency tables.
Java class for CONT-SCORING-METHOD.
Java class for ContStats element declaration.
A specialized JFileChooser that lists all available file Loaders and Savers.
Helper class for dealing with Converter resources.
Utility routines for the converter package.
Helper class for saving data to files.
Helper class for loading data from files and URLs.
An instance filter that copies a range of
attributes in the dataset.
Interface implemented by classes that can produce "shallow" copies
of their objects.
A class for providing centralized Copyright information.
CorrelationAttributeEval :
Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class.
Nominal attributes are considered on a value by value basis by treating each value as an indicator.
Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class.
Nominal attributes are considered on a value by value basis by treating each value as an indicator.
Java class for CorrelationFields element declaration.
Java class for CorrelationMethods element declaration.
Java class for Correlations element declaration.
Finds split points using correlation.
Java class for CorrelationValues element declaration.
Bean that aids in analyzing cost/benefit tradeoffs.
Step for storing and viewing threshold data in a cost-benefit visualization
Bean info class for the cost/benefit analysis
Interactive view for the CostBenefitAnalysis step
Panel for displaying the cost-benefit plots and all control widgets.
Generates points illustrating probablity cost tradeoffs that can be obtained
by varying the threshold value between classes.
Class for storing and manipulating a misclassification cost matrix.
Class for editing CostMatrix objects.
A metaclassifier that makes its base classifier cost sensitive.
SplitEvaluator that produces results for a
classification scheme on a nominal class attribute, including weighted
misclassification costs.
Java class for Counts element declaration.
Java class for COUNT-TABLE-TYPE complex type.
Java class for Covariances element declaration.
Java class for CovariateList element declaration.
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.
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.
Bean for splitting instances into training ant test sets according to a cross
validation
Step for generating cross-validation splits
BeanInfo class for the cross validation fold maker bean
GUI Customizer for the cross validation fold maker bean
Generates for each run, carries out an n-fold
cross-validation, using the set SplitEvaluator to generate some results.
Carries out one split of a repeated k-fold
cross-validation, using the set SplitEvaluator to generate some results.
Outputs the predictions as CSV.
Reads a source that is in comma separated format (the default).
Takes results from a result producer and assembles
them into comma separated value form.
Writes to a destination that is in CSV
(comma-separated values) format.
Java class for CUMULATIVE-LINK-FUNCTION.
For classes that do not implement the OptionHandler interface and want to
provide a custom display string in the GenericObjectEditor, which is more
descriptive than the class name.
Customizers who want to be able to close the customizer window
themselves can implement this window.
An interface for objects that are capable of supplying their own
custom GUI components.
Class for performing parameter selection by cross-validation for any classifier.
For more information, see:
R.
For more information, see:
R.
Class for encapsulating data to be transferred between Knowledge Flow steps
over a particular connection type.
Connects to a database.
A dialog to enter URL, username and password for a database connection.
Marker interface for a loader/saver that uses a database
Reads Instances from a Database.
Takes results from a result producer and sends them
to a database.
Examines a database and extracts out the results
produced by the specified ResultProducer and submits them to the specified
ResultListener.
Writes to a database (tested with MySQL, InstantDB,
HSQLDB).
DatabaseUtils provides utility functions for accessing the experiment
database.
Auxiliary interface for steps that collect data results of some type - e.g.
Java class for DataDictionary element declaration.
Java class for DataField element declaration.
Listener interface that customizer classes that are interested
in data format changes can implement.
Abstract superclass for data generators that generate data for classifiers
and clusterers.
Interface to something that can generate new instances based on
a set of input instances
Step that wraps a Weka DataGenerator.
A panel for generating artificial data via DataGenerators.
A step that allows the user to define instances to output
Step editor dialog for the data grid
Event encapsulating a data set
This panel controls setting a list of datasets for an experiment to iterate
over.
Indicator interface to something that can store instances to some
destination
Interface to something that is capable of being a source for data -
either batch or incremental data
Interface to something that can accept DataSetEvents
Java class for DATATYPE.
Bean that encapsulates weka.gui.visualize.VisualizePanel
A step that provides a visualization based on
weka.gui.visualize.VisualizePanel
Bean info class for the data visualizer
GUI customizer for data visualizer.
Interactive viewer for the DataVisualizer step
Editor dialog for the DataVisualizer step
Stores information for date attributes.
A filter for turning date attributes into numeric ones.
A little bit extended DatabaseUtils class.
Conditional probability estimator for a discrete domain conditional upon
a discrete domain.
A helper class for debug output, logging, clocking, etc.
A little helper class for clocking and outputting times.
contains debug methods
A helper class for logging stuff.
This extended Random class enables one to print the generated random
numbers etc., before they are returned.
A little, simple helper class for logging stuff.
A class that can be used for timestamps in files, The toString() method
simply returns the associated Date object in a timestamp format.
Java class for Decision element declaration.
Java class for Decisions element declaration.
Class for building and using a decision stump.
Class for building and using a simple decision
table majority classifier.
For more information see:
Ron Kohavi: The Power of Decision Tables.
For more information see:
Ron Kohavi: The Power of Decision Tables.
Class providing hash table keys for DecisionTable
Java class for DecisionTree element declaration.
Class for storing and manipulating an association rule.
Enum for holding different metric types
Default implementation of a CallbackNotifierDelegate.
A concrete implementation of Package that uses Java properties files/classes
to manage package meta data.
A concrete implementation of PackageManager that uses Java properties
files/class to manage package meta data.
Base class for providing a set of default settings for an application.
Class encapsulating DefineFunction (used in TransformationDictionary).
Java class for DefineFunction element declaration.
Implementation of a CallbackNotifierDelegate that stores the ExecutionResult
and only notifies the callback when the notifyNow() method is called.
Java class for Delimiter element declaration.
Java class for DELIMITER.
Class for handling an instance.
Interface for clusterers that can estimate the density for a given instance.
A SplitEvaluator that produces results for a
density based clusterer.
Class that encapsulates a dependency between two packages
Java class for DerivedField element declaration.
Panel that contains the tree view of steps and the search
field.
Class for building and maintaining a dictionary of terms.
Writes a dictionary constructed from string
attributes in incoming instances to a destination.
Simple symbolic probability estimator based on symbol counts.
Symbolic probability estimator based on symbol counts and a prior.
Symbolic probability estimator based on symbol counts and a prior.
Class encapsulating a Discretize Expression.
Java class for Discretize element declaration.
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
An instance filter that discretizes a range of
numeric attributes in the dataset into nominal attributes.
Java class for DiscretizeBin element declaration.
Java class for DiscrStats element declaration.
Interface for any class that can compute and return distances between two
instances.
This panel enables an experiment to be distributed to multiple hosts;
it also allows remote host names to be specified.
Class for handling a distribution of class values.
Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
DocumentPrinting is a class that lets you print documents on
the fly for free ;) Printing in JDK 1.2 - 1.5 is hard.
Java class for DocumentTermMatrix element declaration.
This class parses input in DOT format, and builds the datastructures that are
passed to it.
A vector specialized on doubles.
Interface to something that can be drawn as a graph.
A "dummy" no-op step
Outputs a message.
This class is used in conjunction with the Node class to form a tree
structure.
Bayes Network learning using various search
algorithms and quality measures.
Base class for a Bayes Network classifier.
Base class for a Bayes Network classifier.
Eigenvalues and eigenvectors of a real matrix.
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.
EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
"Abstract" class for computing splitting criteria
based on the entropy of a class distribution.
Class for computing the entropy for a given distribution.
Helper/wrapper class for obtaining an arbitrary enum value from an arbitrary
enum type.
This class encapsulates a map of all environment and java system properties.
Deprecated.
Widget that displays a label and a combo box for selecting environment
variables.
Combo box that allows the drop-down list to be wider than the component
itself.
Combo box that allows the drop-down list to be wider than the component
itself.
Interface for something that can utilize environment
variables.
Extends Properties to allow the value of a system property (if set) to
override that which has been loaded/set.
Interface for evaluators that calculate the "merit" of attributes/subsets
as the error of a learning scheme
Interface implemented by classes loaded dynamically to
visualize classifier errors in the explorer.
Abstract class for all estimators.
Contains static utility functions for Estimators.
A better-looking table than JTable.
Java class for euclidean element declaration.
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.
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.
Class for evaluating machine learning models.
Class for evaluating machine learning models.
Helper routines for extracting metric values from built-in and plugin
evaluation metrics.
A GUI dialog for selecting classification/regression evaluation metrics to be
output.
Contains utility functions for generating lists of predictions in various
manners.
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.
Java class for EventValues element declaration.
Knowledge Flow step that can execute static system commands or commands that
are dynamically defined by the values of attributes in incoming instance or
environment connections.
Step editor dialog for the ExecuteProcess step
Client (i.e.
Callback interface for receiving notification of a flow finishing
execution
Stores the result generated by a StepTask.
Closes the Simple CLI window.
Holds all the necessary configuration information for a standard type
experiment.
The main class for the experiment environment.
This class offers get methods for the default Experimenter settings in the
props file
weka/gui/experiment/Experimenter.props.Loads the external test set and calls the
appropriate SplitEvaluator to generate some results.
The filename of the test set is constructed as follows:
<dir> + / + <prefix> + <relation-name> + <suffix>
The relation-name can be modified by using the regular expression to replace the matching sub-string with a specified replacement string.
The filename of the test set is constructed as follows:
<dir> + / + <prefix> + <relation-name> + <suffix>
The relation-name can be modified by using the regular expression to replace the matching sub-string with a specified replacement string.
The main class for the Weka explorer.
This event can be fired in case the capabilities filter got changed
Interface for classes that listen for filter changes.
A common interface for panels to be displayed in the Explorer
A common interface for panels in the explorer that can handle logs
This class offers get methods for the default Explorer settings in the props
file
weka/gui/explorer/Explorer.props.Java class for ExponentialSmoothing element declaration.
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.
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.
Java class for Extension element declaration.
Provides a file filter for FileChoosers that accepts or rejects files based
on their extension.
File filter that stores an associated class alongside name and extension(s).
Java class for FactorList element declaration.
Java class for False element declaration.
Cluster data using the FarthestFirst algorithm.
For more information see:
Hochbaum, Shmoys (1985).
For more information see:
Hochbaum, Shmoys (1985).
Deprecated.
Java class for FieldColumnPair element declaration.
Abstract superclass for various types of field meta data.
Inner class for an Interval.
Enumerated type for the closure.
Enumerated type for the Optype
Inner class for Values
Enumerated type for the property.
Class encapsulating a FieldRef Expression.
Java class for FieldRef element declaration.
Java class for FIELD-USAGE-TYPE.
Java class for FieldValue element declaration.
Java class for FieldValueCount element declaration.
A PropertyEditor for File objects that lets the user select a file.
Deprecated.
Widget that displays a label, editable combo box for selecting environment
variables and a button for brining up a file browser.
Wrapper class for File objects.
A simple file logger, that just logs to a single file.
Method annotation that can be used to provide some additional information for
handling file properties in the GUI.
Supports loading/saving of files.
Interface to a loader/saver that loads/saves from a file source.
An abstract class for instance filters: objects that take instances as input,
carry out some transformation on the instance and then output the instance.
A wrapper bean for Weka filters
Step that wraps a Weka filter.
Bean info class for the Filter bean
GUI customizer for the filter bean
Class encapsulating a list of association rules and the preprocessing filter
that was applied before they were generated.
Class for running an arbitrary associator on data
that has been passed through an arbitrary filter.
Class for running an arbitrary classifier on data
that has been passed through an arbitrary filter.
Class for running an arbitrary clusterer on data
that has been passed through an arbitrary filter.
Applies the given filter before calling the given distance function.
Applies the given filter before calling the given neighbour search method.
Locates all classes with certain capabilities.
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.
Converts String attributes into a set of attributes
representing word occurrence (depending on the tokenizer) information from
the text contained in the strings.
Class for the format of floating point numbers
Class that encapsulates the Steps involved in a Knowledge Flow process.
A bean that splits incoming instances (or instance streams) according to the
evaluation of a logical expression.
A step that splits incoming instances (or instance streams) according to the
evaluation of a logical expression.
An expression node that encloses other expression nodes in brackets
An expression node that represents a clause of an expression
Abstract base class for parts of a boolean expression.
BeanInfo class for FlowByExpression
Customizer for the FlowByExpression node
Step editor dialog for the FlowByExpression step
Interface to something that can execute a Knowledge Flow process
Interface to something that can load a Knowledge Flow
Small utility class for executing KnowledgeFlow flows outside of the
KnowledgeFlow application
A FlowExecutor that can launch start points in a flow in parallel or
sequentially.
A simple logging implementation that writes to standard out
Wrapper class for Font objects.
Class implementing the FP-growth algorithm for
finding large item sets without candidate generation.
The FromFile reads the structure of a Bayes net
from a file in BIFF format.
Abstract superclass for PMML built-in and DefineFunctions.
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).
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).
Class for computing the gain ratio for a given distribution.
Java class for GAP.
Maintains sufficient stats for a Gaussian distribution for a numeric
attribute
Java class for GaussianDistribution element declaration.
* Implements Gaussian processes for regression without hyperparameter-tuning.
Class implementing import of PMML General Regression model.
Java class for GeneralRegressionModel element declaration.
This panel controls setting a list of values for an arbitrary resultgenerator
property for an experiment to iterate over.
A PropertyEditor for arrays of objects that themselves have property editors.
A PropertyEditor for objects.
A helper class for maintaining a history of objects selected in the GOE.
Event that gets sent when a history item gets selected.
Interface for classes that listen to selections of history items.
This class can generate the properties object that is normally loaded from
the
GenericObjectEditor.props file (= PROPERTY_FILE).This Bayes Network learning algorithm uses genetic
search for finding a well scoring Bayes network structure.
This Bayes Network learning algorithm uses genetic
search for finding a well scoring Bayes network structure.
Step that outputs data stored in the job environment
Class implementing a command for getting the names of all visible
perspectives
Implements the gini splitting criterion
Generates Javadoc comments from the class's globalInfo method.
This Bayes Network learning algorithm uses cross
validation to estimate classification accuracy.
Extends BeanCustomizer.
A step editor dialog that uses the GOE mechanism to provide property editors.
Marker annotation.
GraphConstants.java
This class represents an edge in the graph
Event for graphs
Interface for graphical command handlers
Describe interface
TextListener here.This class represents a node in the Graph.
A bean encapsulating weka.gui.treevisualize.TreeVisualizer
Step for collecting and visualizing graph output from Drawable schemes.
Bean info class for the graph viewer
Interactive viewer for the GraphViewer step.
Interface implemented by classes loaded dynamically to
visualize graphs in the explorer.
This class displays the graph we want to visualize.
GreedyStepwise :
Performs a greedy forward or backward search through the space of attribute subsets.
Performs a greedy forward or backward search through the space of attribute subsets.
A helper class for Groovy.
A scripting panel for Groovy.
Represents a Groovy script.
Executes a Groovy script in a thread.
GUI interface to Bayesian Networks.
Interface to a GUIApplication that can have multiple "perspectives" and
provide application-level and perspective-level settings.
Launcher class for the Weka GUIChooser.
Interface for plugin components that can be accessed from either the
Visualization or Tools menu.
Enum listing possible menus that plugins can appear in
The main class for the Weka GUIChooser.
Specialized JFrame class.
Inner class for defaults
Java class for Header element declaration.
Interface for Knowledge Flow components that (typically) provide
an interactive graphical visualization to implement.
Outputs help for a command or for all.
This class lays out the vertices of a graph in a hierarchy of vertical
levels, with a number of nodes in each level.
Hierarchical clustering class.
This class implements a parser to read properties that have a hierarchy(i.e.
This Bayes Network learning algorithm uses a hill
climbing algorithm adding, deleting and reversing arcs.
This Bayes Network learning algorithm uses a hill
climbing algorithm adding, deleting and reversing arcs.
Prints all issued commands.
An event that is generated when a history is modified.
A listener for changes in a history.
Abstract base class for nodes in a Hoeffding tree
A Hoeffding tree (VFDT) is an incremental, anytime
decision tree induction algorithm that is capable of learning from massive
data streams, assuming that the distribution generating examples does not
change over time.
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.
This panel controls setting a list of hosts for a RemoteExperiment to
use.
Outputs the predictions in HTML.
K-nearest neighbours classifier.
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.
A macro declaration exposing the
ifelse function.Event that encapsulates an Image
Interface to something that can process an ImageEvent
Component that can accept ImageEvents and save their encapsulated
images to a file.
Step for saving static images as either png or gif.
BeanInfo class for the ImageSaver component.
Customizer for the ImageSaver component.
A KF component that can accept imageEvent connections in order to display
static images in a popup window
A step for collecting and viewing image data
BeanInfo class for the ImageViewer component
Interactive viewer for the ImageViewer step
Class for handling the impurity values when spliting the instances
Class implementing an inactive node (i.e.
Bean that evaluates incremental classifiers
Step that evaluates incremental classifiers and produces strip chart data
Bean info class for the incremental classifier evaluator bean
GUI Customizer for the incremental classifier evaluator bean
Class encapsulating an incrementally built classifier and current instance
Interface to something that can process a IncrementalClassifierEvent
Marker interface for a loader/saver that can retrieve instances incrementally
Interface for an incremental probability estimators.
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).
Evaluates the worth of an attribute by measuring the information gain with respect to the class.
InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).
Class for computing the information gain for a given distribution.
Implements the info gain splitting criterion
A simple panel for displaying information, e.g.
A specialized renderer that takes care of JLabels in a JList.
An interface for information retrieval evaluation metrics to implement.
Primarily a marker interface for information theoretic evaluation metrics to
implement.
Helper class for inheritance related operations.
Java class for InlineTable element declaration.
* Stores the predictions in memory for programmatic retrieval.
* Stores the instance, a prediction object and a map of attribute names with their associated values if an attribute was defined in a container per prediction.
* The list of predictions can get retrieved using the getPredictions() method.
* File output is disabled and buffer doesn't need to be supplied.
* Stores the instance, a prediction object and a map of attribute names with their associated values if an attribute was defined in a container per prediction.
* The list of predictions can get retrieved using the getPredictions() method.
* File output is disabled and buffer doesn't need to be supplied.
Container for storing the predictions alongside the additional attributes.
Wrapper classifier that addresses incompatible
training and test data by building a mapping between the training data that a
classifier has been built with and the incoming test instances' structure.
Bean info class for the InputMappedClassifier.
Interface representing an instance.
A comparator for the Instance class.
A bean that counts instances streamed to it.
Event that encapsulates a single instance or header information only
An event encapsulating an instance stream event.
Java class for InstanceField element declaration.
Java class for InstanceFields element declaration.
Interface for JFrames that display instance info.
Frame for displaying information on the displayed data.
A bean that joins two streams of instances into one.
Interface to something that can accept instance events
An interface for objects interested in listening to streams of instances.
A bean that produces a stream of instances from a file.
An interface for objects capable of producing streams of instances.
Convert the results of a database query into instances.
An interface implemented by InstanceQuery and any user class that is
to be passed as the first argument to
InstanceQuery.retrieveInstances(InstanceQueryAdapter, ResultSet).
Class for handling an ordered set of weighted instances.
A bean that saves a stream of instances to a file.
A helper class to expose instance values and macros for instance values to a
program
Outputs the received results in arff format to a
Writer.
This panel just displays relation name, number of instances, and number of
attributes.
Bean that converts an instance stream into a (batch) data set.
Step that converts an incoming instance stream to a batch dataset
BeanInfo class for the InstanceStreamToBatchMaker bean
A bean that takes a stream of instances and displays in a table.
This is a very simple instance viewer - just displays the dataset as
text output as it would be written to a file.
Deprecated.
Table model that automatically adds a new row to the table on pressing enter
in the last cell of a row.
Deprecated.
Use
weka.gui.InteractiveTablePanel instead.Provides a panel using an interactive table model.
Java class for INTERPOLATION-METHOD.
A filter for detecting outliers and extreme values
based on interquartile ranges.
enum for obtaining the various determined IQR values.
Java class for Interval element declaration.
Primarily a marker interface for interval-based evaluation metrics to
implement.
Interface for numeric prediction schemes that can output prediction
intervals.
Java class for INT-SparseArray element declaration.
A vector specialized on integers.
Java class for INVALID-VALUE-TREATMENT-METHOD.
Subclass of
DefaultMutableTreeNode that can hide itself in a
JTree.Subclass of
DefaultTreeModel that contains InvisibleNodes.Class that encapsulates information about an individual item.
Java class for Item element declaration.
Java class for ItemRef element declaration.
Java class for Itemset element declaration.
Class for storing a set of items.
An iterated version of the Lovins stemmer.
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from a single base learner.
Interface for classifiers that can induce models of growing complexity one
step at a time.
Chooses the best number of iterations for an IterativeClassifier such as
LogitBoost using cross-validation or a percentage split evaluation.
Class for generating a pruned or unpruned C4.5
decision tree.
Java class for jaccard element declaration.
Sets a variable.
Abstract superclass for classes that generate Javadoc comments and replace
the content between certain comment tags.
A macro declarations that exposes the java macro to a program.
This class takes any JComponent and outputs it to a file.
A helper class for JList GUI elements with DefaultListModel or
derived models.
Step that executes another flow as a "job".
Extended Environment with support for storing results and property values to
be set at a later date on the base schemes of WekaAlgorithmWrapper steps.
Editor dialog for the Job step.
Step that performs an inner join on one or more key fields from two incoming
batch or streaming datasets.
BeanInfo for the Join step
Customizer component for the Join step
Step editor dialog for the Join step
This class takes any JComponent and outputs it to a JPEG-file.
This class implements a propositional rule learner,
Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was
proposed by William W.
Flow loader that wraps the routines in JSONFlowUtils
Utilities for building and saving flows from JSON data
Class for transforming Instances objects into JSON
objects and vice versa.
Reads a source that is in the JSON format.
It automatically decompresses the data if the extension is '.json.gz'.
For more information, see JSON homepage:
http://www.json.org/
It automatically decompresses the data if the extension is '.json.gz'.
For more information, see JSON homepage:
http://www.json.org/
Container class for storing a
JSON data structure.
The type of a node.
Writes to a destination that is in JSON format.
The data can be compressed with gzip, in order to save space.
For more information, see JSON homepage:
http://www.json.org/
The data can be compressed with gzip, in order to save space.
For more information, see JSON homepage:
http://www.json.org/
A helper class for JTable, e.g.
A helper class for Jython.
An indicator interface for Jython objects.
A scripting panel for Jython.
Represents a Jython
script.
Executes a Jython script in a thread.
An indicator interface for serializable Jython objects.
This Bayes Network learning algorithm uses a hill
climbing algorithm restricted by an order on the variables.
For more information see:
G.F.
For more information see:
G.F.
This Bayes Network learning algorithm uses a hill
climbing algorithm restricted by an order on the variables.
For more information see:
G.F.
For more information see:
G.F.
Conditional probability estimator for a numeric domain conditional upon
a discrete domain (utilises separate kernel estimators for each discrete
conditioning value).
KDDataGenerator.
Class implementing the KDTree search algorithm for nearest neighbour search.
The connection to dataset is only a reference.
The connection to dataset is only a reference.
A class representing a KDTree node.
Class that splits up a KDTreeNode.
Abstract kernel.
Simple kernel density estimator.
Class for evaluating Kernels.
Converts the given set of data into
a kernel matrix.
Default settings for the Knowledge Flow
Default Knowledge Flow graphical command handler
Class that holds constants that are used within the GUI side of the
Knowledge Flow.
Marker annotation.
Optional annotation for plugin beans in the Knowledge Flow.
KFStep class annotation
Kills the running process.
Conditional probability estimator for a numeric domain conditional upon
a numeric domain.
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).
For more information see also:
Ashraf Masood Kibriya (2007).
Java class for KNNInput element declaration.
Java class for KNNInputs element declaration.
Startup class for the KnowledgeFlow.
Launcher class for the Weka Knowledge Flow.
Main GUI class for the KnowledgeFlow.
Main Knowledge Flow application class
Interface for perspectives.
General default settings for the Knowledge Flow
Java class for KohonenMap element declaration.
This class is a helper class for XML serialization using KOML .
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.
A class representing the caching system used to keep track of each attribute
value and its corresponding scale factor or stop parameter.
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.
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.
Class for storing a set of items together with a class label.
This Bayes Network learning algorithm uses a Look
Ahead Hill Climbing algorithm called LAGD Hill Climbing.
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.
This interface should be implemented by any class
which needs to receive LayoutCompleteEvents from
the LayoutEngine.
This interface class has been added to facilitate the addition of other
layout engines to this package.
Provides a panel just for laying out a Knowledge Flow graph.
Leaf node in a HoeffdingTree
Marker interface for a node that can be updated with incoming instances in a
HoeffdingTree.
Tells a sub-ResultProducer to reproduce the current
run for varying sized subsamples of the dataset.
This generator produces data for a display with 7
LEDs.
Flow loader that reads legacy .kfml files and translates them to the new
implementation.
This panel displays legends for a list of plots.
Java class for Level element declaration.
Reads a source that is in libsvm format.
For more information about libsvm see:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
For more information about libsvm see:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Writes to a destination that is in libsvm format.
For more information about libsvm see:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
For more information about libsvm see:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Java class for LiftData element declaration.
Java class for LiftGraph element declaration.
Java class for LinearKernelType element declaration.
Class implementing the brute force search algorithm for nearest neighbour search.
Java class for LinearNorm element declaration.
Class for using linear regression for prediction.
Class for performing (ridged) linear regression using Tikhonov
regularization.
This can be used by the
neuralnode to perform all it's computations (as a Linear unit).
Java class for LINK-FUNCTION.
Lists the options of an OptionHandler
A dialog to present the user with a list of items, that the user can
make a selection from, or cancel the selection.
Classifier for building 'logistic model trees',
which are classification trees with logistic regression functions at the
leaves.
Class for logistic model tree structure.
Interface to something that can load Instances from an input source in some
format.
Loads data sets using weka.core.converter classes
This class is for loading resources from a JAR archive.
Knowledge Flow step that wraps
weka.core.converters.Loaders.Exception that implementers can throw from getStructure() when they have
not been configured sufficiently in order to read the structure (or data).
Bean info class for the loader bean
GUI Customizer for the loader bean
Provides a custom editor dialog for Loaders.
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).
Java class for LocalTransformations element declaration.
Abstract superclass for all loggers.
Interface for objects that display log (permanent historical) and
status (transient) messages.
The logging level.
Enum for different logging levels
Interface to something that can output messages to a log
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.
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.
Base/helper class for building logistic regression models with the LogitBoost
algorithm.
Helper class for producing PMML for a Logistic classifier.
Class for performing additive logistic regression.
Class that wraps a
weka.gui.Logger and filters log messages according
to the set logging level.Class for displaying a status area (made up of a variable number of lines)
and a log area.
This panel allows log and status messages to be posted.
Frame that shows the output from stdout and stderr.
Interface to be implemented by classes that should be able to write their
own output to the Weka logger.
A little helper class for setting the Look and Feel of the user interface.
A stemmer based on the Lovins stemmer, described here:
Julie Beth Lovins (1968).
Julie Beth Lovins (1968).
LU Decomposition.
Locally weighted learning.
M5Base.
M5Base.
Generates a decision list for regression problems using separate-and-conquer.
Interface for compile time macros to enable meta programming
Interface to expose macros to a program.
A helper class that allows to combine several macro declarations together.
Simple probability estimator that places a single normal distribution
over the observed values.
Menu-based GUI for Weka, replacement for the GUIChooser.
DesktopPane with background image.
Specialized JInternalFrame class.
Specialized JFrame class.
Main perspective for the Knowledge flow application
Class that provides the main editing widget toolbar and menu items
Enum containing all the widgets provided by the toolbar.
Classes implementing this interface will be displayed in the "Extensions"
menu in the main GUI of Weka.
Class for handling a decision list.
Class for wrapping a Clusterer to make it return a
distribution and density.
A filter that creates a new dataset with a Boolean
attribute replacing a nominal attribute.
A Step that makes downstream steps that are directly connected to this step
resource intensive (or not).
Implements the Manhattan distance (or Taxicab geometry).
Class that maintains the mapping between incoming data set structure and that
of the mining schema.
Java class for MapValues element declaration.
Generates points illustrating the prediction margin.
Java class for MatCell element declaration.
Interface to something that can be matched with tree matching
algorithms.
Modify numeric attributes according to a given
mathematical expression.
Macro declarations for common mathematical functions.
Utility class.
Reads a Matlab file containing a single matrix in ASCII format.
Writes Matlab ASCII files, in single or double
precision format.
Deprecated.
Use
weka.core.matrix.Matrix instead - only for
backwards compatibility.Jama = Java Matrix class.
Java class for Matrix element declaration.
This panel displays a plot matrix of the user selected attributes of a given
data set.
Class that splits a BallNode of a ball tree using
Uhlmann's described method.
For information see:
Jeffrey K.
For information see:
Jeffrey K.
Class that splits a BallNode of a ball tree based
on the median value of the widest dimension of the points in the ball.
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.
For more information see also:
Jerome H.
A little helper class for Memory management.
Simple start step that stores a set of instances and outputs it in a
dataSet connection.
A panel for displaying the memory usage.
Merges all values of the specified nominal attributes that are insufficiently frequent.
Merges many values of a nominal attribute into one
value.
Merges values of all nominal attributes among the
specified attributes, excluding the class attribute, using the CHAID method,
but without considering re-splitting of merged subsets.
Merges two values of a nominal attribute into one
value.
A meta bean that encapsulates several other regular beans, useful for
grouping large KnowledgeFlows.
Interface for metastore implementations.
This class handles relationships between display names of properties (or
classes) and Methods that are associated with them.
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.
y = sin|x| / |x|
In addition to this simple function, the amplitude can be changed and gaussian noise can be added.
The class that builds a BallTree middle out.
For more information see also:
Andrew W.
For more information see also:
Andrew W.
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).
For more information see also:
Andrew Moore (1991).
Java class for MiningBuildTask element declaration.
Java class for MiningField element declaration.
Class encapsulating information about a MiningField.
Java class for MINING-FUNCTION.
Java class for MiningModel element declaration.
Java class for MiningSchema element declaration.
This class encapsulates the mining schema from
a PMML xml file.
Java class for minkowski element declaration.
Implementing Minkowski 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.
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.
Java class for MISSING-VALUE-STRATEGY.
Java class for MISSING-VALUE-TREATMENT-METHOD.
Java class for MissingValueWeights element declaration.
Java class for ModelExplanation element declaration.
Java class for ModelLiftGraph element declaration.
Bean that can be used for displaying threshold curves (e.g.
A Step that collects and displays either classifier error plots or threshold
curves
Bean info class for the model performance chart
GUI customizer for model performance chart.
Interactive viewer for the ModelPerformanceChart step
Step editor dialog for the ModelPerformanceChart step
Abstract class for model selection criteria.
Java class for ModelStats element declaration.
Java class for ModelVerification element declaration.
A metaclassifier for handling multi-class datasets with 2-class classifiers.
A metaclassifier for handling multi-class datasets with 2-class classifiers.
Applies several filters successively.
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".
A classifier that uses backpropagation to learn a multi-layer perceptron to classify instances.
Multinomial BMA Estimator.
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from multiple classifiers.
Java class for MULTIPLE-MODEL-METHOD.
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
Applies the specified stopwords algorithms one after other.
As soon as a word has been identified as stopword, the loop is exited.
As soon as a word has been identified as stopword, the loop is exited.
Interface to Multivariate Distribution Estimation
Implementation of maximum likelihood Multivariate Distribution Estimation using Normal
Distribution.
Java class for MultivariateStat element declaration.
Java class for MultivariateStats element declaration.
Class for a Naive Bayes classifier using estimator
classes.
The NaiveBayes class generates a fixed Bayes network structure with arrows from the class variable to each of the attribute variables.
Java class for NaiveBayesModel element declaration.
Class for building and using a multinomial Naive Bayes classifier.
Multinomial naive bayes for text data.
Class for building and using an updateable multinomial Naive Bayes classifier.
Class for a Naive Bayes classifier using estimator classes.
This class contains a color name and the rgb values of that color
Implements a LearningNode that uses a naive Bayes model
Implements a LearningNode that chooses between using majority class or naive
Bayes for prediction
Class for handling a naive bayes tree structure used for classification.
Class for selecting a NB tree split.
Class implementing a "no-split"-split (leaf node) for naive bayes
trees.
Class implementing a NBTree split on an attribute.
Conditional probability estimator for a numeric domain conditional upon
a discrete domain (utilises separate normal estimators for each discrete
conditioning value).
Java class for NearestNeighborModel element declaration.
Abstract class for nearest neighbour search.
Abstract unit in a NeuralNetwork.
Java class for NeuralInput element declaration.
Java class for NeuralInputs element declaration.
Java class for NeuralLayer element declaration.
This is an interface used to create classes that can be used by the
neuralnode to perform all it's computations.
Class implementing import of PMML Neural Network model.
Java class for NeuralNetwork element declaration.
This class is used to represent a node in the neuralnet.
Java class for NeuralOutput element declaration.
Java class for NeuralOutputs element declaration.
Java class for Neuron element declaration.
Splits a string into an n-gram with min and max
grams.
Conditional probability estimator for a numeric domain conditional upon a
numeric domain (using Mahalanobis distance).
Java class for NN-NORMALIZATION-METHOD.
A node of the AST (abstract syntax tree) for a program
Java class for Node element declaration.
This class records all the data about a particular node for displaying.
This is an interface for classes that wish to take a node structure and
arrange them
A macro declarations that contains no macros at all
Stores information for nominal and string attributes.
Maintains sufficient stats for the distribution of a nominal attribute
Class that encapsulates a nominal item.
Encapsulates an evaluatable nominal prediction: the predicted probability
distribution plus the actual class value.
Converts all nominal attributes into binary numeric
attributes.
Converts all nominal attributes into binary numeric
attributes.
Converts a nominal attribute (i.e.
An instance filter that converts all incoming
instances into sparse format.
Simple probability estimator that places a single normal distribution over
the observed values.
Represents the abstract ancestor for normalizable distance functions, like
Euclidean or Manhattan distance.
Normalizes all numeric values in the given dataset
(apart from the class attribute, if set).
The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
Java class for NormContinuous element declaration.
Class encapsulating a NormContinuous Expression.
Java class for NormDiscrete element declaration.
Class encapsulating a NormDiscrete Expression.
Class implementing a "no-split"-split.
Exception that is raised by an object that is unable to process
data with missing values.
Simple bean for displaying a textual note on the layout.
A Knowledge Flow "step" that implements a note on the GUI layout
Bean info class for the Note bean.
Customizer for the note component.
Editor dialog for Notes
Visual representation for the Note "step".
Annotation for properties that should not be persisted
Java class for NO-TRUE-CHILD-STRATEGY.
A variable declarations that contains no variables
Suppresses all output.
Dummy stopwords scheme, always returns false.
A dummy stemmer that performs no stemming at all.
Interface to a clusterer that can generate a requested number of
clusters
A filter that 'cleanses' the numeric data from
values that are too small, too big or very close to a certain value,
and sets these values to a pre-defined default.
Java class for NumericInfo element declaration.
Class that encapsulates a numeric item.
Encapsulates an evaluatable numeric prediction: the predicted class value
plus the actual class value.
Java class for NumericPredictor element declaration.
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.
A filter for turning numeric attributes into date attributes.
A filter for turning numeric attributes into
nominal ones.
Transforms numeric attributes using a given
transformation method.
A simple instance filter that renames the relation,
all attribute names and all nominal attribute values.
This object contains factory methods for each
Java content interface and Java element interface
generated in the weka.core.pmml.jaxbbindings package.
Interface to something that can render certain types of charts
in headless mode.
Class for building and using a 1R classifier; in
other words, uses the minimum-error attribute for prediction, discretizing
numeric attributes.
OneRAttributeEval :
Evaluates the worth of an attribute by using the OneR classifier.
Evaluates the worth of an attribute by using the OneR classifier.
A class to specify the semantics of operators in the expressionlanguage
Implementation of Active-sets method with BFGS update to solve optimization
problem with only bounds constraints in multi-dimensions.
Java class for OptimumLiftGraph element declaration.
Class to store information about an option.
Interface to something that understands options.
Generates Javadoc comments from the OptionHandler's options.
Method annotation that can be used with scheme parameters to provide a nice
display-ready name for the parameter, help information and command-line
option details.
Java class for OPTYPE.
An attribute filter that converts ordinal nominal attributes into numeric ones
Valid options are:
Valid options are:
Java class for OUTLIER-TREATMENT-METHOD.
Java class for Output element declaration.
Java class for OutputField element declaration.
A dialog for setting various output format parameters.
A logger that logs all output on stdout and stderr to a file.
A print stream class to capture all data from stdout and stderr.
OutputZipper writes output to either gzipped files or to a
multi entry zip file.
Abstract base class for Packages.
Abstract base class for package constraints.
Abstract base class for package managers.
A GUI interface the the package management system.
Java class for PairCounts element declaration.
Behaves the same as PairedTTester, only it uses the corrected resampled
t-test statistic.
A helper class that Step implementations can use when processing paired data
(e.g.
Interface for processors of paired data to implement.
A class for storing stats on a paired comparison (t-test and correlation)
A class for storing stats on a paired comparison.
Calculates T-Test statistics on data stored in a set of instances.
Abstract utility class for handling settings common to meta classifiers that
build an ensemble in parallel from a single base learner.
Abstract utility class for handling settings common to
meta classifiers that build an ensemble in parallel using multiple
classifiers.
Java class for Parameter element declaration.
Java class for ParameterField element declaration.
Java class for ParameterList element declaration.
Java class for ParamMatrix element declaration.
Helper class for Bayes Network classifiers.
CUP v0.11b 20160615 (GIT 4ac7450) generated parser.
CUP v0.11b 20160615 (GIT 4ac7450) generated parser.
Class for generating a PART decision list.
Java class for Partition element declaration.
A filter that applies filters on subsets of
attributes and assembles the output into a new dataset.
Java class for PartitionFieldStats element declaration.
This interface can be implemented by algorithms that generate
a partition of the instance space (e.g., decision trees).
* A filter that uses a PartitionGenerator to generate partition membership values; filtered instances are composed of these values plus the class attribute (if set in the input data) and rendered as sparse instances.
Property editor widget that wraps and displays a JPasswordField.
Method annotation that can be used to indicate that a property is a
password.
Java class for PCell element declaration.
Java class for PCovCell element declaration.
Java class for PCovMatrix element declaration.
The class that measures the performance of a nearest
neighbour search (NNS) algorithm.
Interface for GUI elements that can appear as a perspective in a
GUIApplication.Manages perspectives and the main menu bar (if visible), holds the currently
selected perspective, and implements the perspective button bar.
Class to manage user preferences with respect to visible perspectives and
whether the perspectives toolbar is always hidden or is visible on
application startup
Discretizes numeric attributes using equal
frequency binning and forces the number of bins to be equal to the square root of
the number of values of the numeric attribute.
For more information, see:
Ying Yang, Geoffrey I.
For more information, see:
Ying Yang, Geoffrey I.
This class will place the Nodes of a tree.
This class will place the Nodes of a tree.
Outputs the predictions in plain text.
This class plots datasets in two dimensions.
Interface for classes that need to draw to the Plot2D panel *before*
Plot2D renders anything (eg.
This class is a container for plottable data.
Class that manages a global map of plugins.
Deprecated.
Use weka.core.PluginManager instead
Java class for PMML element declaration.
Abstract base class for all PMML classifiers.
This class is a factory class for reading/writing PMML models
Interface for all PMML models
Interface to something that can produce a PMML representation of itself.
Utility routines.
This class takes any JComponent and outputs it to a PNG-file.
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.
For more information please see section 2 of the 1st and 3.2.3 of the 2nd:
Andrew W.
Java class for PoissonDistribution element declaration.
Simple probability estimator that places a single Poisson distribution
over the observed values.
The polynomial kernel : K(x, y) = <x, y>^p or
K(x, y) = (<x, y>+1)^p
Java class for PolynomialKernelType element declaration.
The PostscriptGraphics class extends the Graphics2D class to produce an
encapsulated postscript file rather than on-screen display.
This class takes any Component and outputs it to a Postscript file.
This filter should be extended by other unsupervised attribute filters to
allow processing of the class attribute if that's required.
Java class for PPCell element declaration.
Java class for PPMatrix element declaration.
This kernel is based on a static kernel matrix that
is read from a file.
This class encapsulates a linear regression function.
Encapsulates a single evaluatable prediction: the predicted value plus the
actual class value.
Bean that can can accept batch or incremental classifier events and produce
dataset or instance events which contain instances with predictions appended.
Step that can produce data with predictions appended from batch or
incremental classifiers and clusterers
Bean info class for PredictionAppender.
GUI Customizer for the prediction appender bean
Java class for PredictiveModelQuality element declaration.
Java class for Predictor element declaration.
Java class for PredictorTerm element declaration.
This panel controls simple preprocessing of instances.
A class providing AST (abstract syntax tree) nodes to support primitive types.
An AST node representing a boolean constant
An AST node for an expression of boolean type
An AST node representing a boolean variable
An AST node representing a double constant
An AST node for an expression of double type
An AST node representing a double variable
An AST node representing a string constant
An AST node for an expression of String type
An AST node representing a string variable
Performs a principal components analysis and
transformation of the data.
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.
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.
This class extends the component which is handed over in the constructor by a
print dialog.
This interface is for all JComponent classes that provide the ability to
print itself to a file.
This Panel enables the user to print the panel to various file formats.
Method annotation that can be used with bean properties that are to be considered as
programmatic only (i.e.
Support for PropertyEditors with custom editors: puts the editor into
a separate frame.
This class stores information about properties to ignore or properties that
are allowed for a certain class.
Stores information on a property of an object: the class of the object with
the property; the property descriptor, and the current value.
Support for drawing a property value in a component.
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.
Contains a (property) path structure
Represents a single element of a property path
Allows the user to select any (supported) property of an object, including
properties that any of it's property values may have.
Displays a property sheet where (supported) properties of the target object
may be edited.
Simple class that extends the Properties class so that the properties are
unable to be modified.
Class for handling a tree structure that can
be pruned using a pruning set.
Class for handling a partial tree structure that can be pruned using a
pruning set.
The Pearson VII function-based universal kernel.
For more information see:
B.
For more information see:
B.
QR Decomposition.
Java class for Quantile element declaration.
An event that is generated when a query is executed.
A listener for executing queries.
Represents a panel for entering an SQL query.
Class representing a FIFO queue.
Java class for RadialBasisKernelType element declaration.
Stopwords list based on Rainbow:
http://www.cs.cmu.edu/~mccallum/bow/rainbow/
http://www.cs.cmu.edu/~mccallum/bow/rainbow/
Class for building an ensemble of randomizable base classifiers.
Class for constructing a forest of random trees.
For more information see:
Leo Breiman (2001).
For more information see:
Leo Breiman (2001).
Interface to something that has random behaviour that is able to be
seeded with an integer.
Abstract utility class for handling settings common to randomizable
classifiers.
Abstract utility class for handling settings common to randomizable
clusterers.
Abstract utility class for handling settings common to randomizable
clusterers.
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
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.
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble in parallel from a single base
learner.
Abstract utility class for handling settings common to
meta classifiers that build an ensemble in parallel using multiple
classifiers based on a given random number seed.
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
Abstract utility class for handling settings common to randomizable
clusterers.
Randomly shuffles the order of instances passed
through it.
Java class for RandomLiftGraph element declaration.
Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
RandomRBF data is generated by first creating a
random set of centers for each class.
Class holding static utility methods for drawing random samples.
Generates a single train/test split and calls the
appropriate SplitEvaluator to generate some results.
Chooses a random subset of non-class attributes, either an absolute number or a percentage.
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.
Class for constructing a tree that considers K
randomly chosen attributes at each node.
Class implementing some simple random variates generator.
Class representing a range of cardinal numbers.
A PropertyEditor that can be used to edit Range objects (really, just appropriately formatted strings).
Interface for search methods capable of producing a
ranked list of attributes.
Ranker :
Ranks attributes by their individual evaluations.
Ranks attributes by their individual evaluations.
The RBF kernel : K(x, y) = exp(-gamma*(x-y)^2)
Valid options are:
Valid options are:
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.
The decision list consists of rules.
Instances are generated randomly one by one.
A class that sends all lines from a reader to a JTextPane component.
Java class for REAL-SparseArray element declaration.
Uses the regular expressions stored in the file for determining whether a word is a stopword (ignored if pointing to a directory).
Base class implementation for learning algorithm of SMOreg
Valid options are:
Class implementing import of PMML Regression model.
Java class for Regression element declaration.
Analyzes linear regression model by using the Student's t-test on each
coefficient.
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
Abstract class for data generators for regression classifiers.
Java class for RegressionModel element declaration.
Java class for REGRESSIONNORMALIZATIONMETHOD.
A SplitEvaluator that produces results for a
classification scheme on a numeric class attribute.
Java class for RegressionTable element declaration.
Implementation of SMO for support vector regression
as described in :
A.J.
A.J.
Learn SVM for regression using SMO with Shevade,
Keerthi, et al.
Stores information for relational attributes.
This class locates and records the indices of relational attributes,
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.
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.
Class that encapsulates a sub task for distributed boundary visualization.
A general purpose server for executing Task objects sent via RMI.
Holds all the necessary configuration information for a distributed
experiment.
Class encapsulating information on progress of a remote experiment
Interface for classes that want to listen for updates on RemoteExperiment
progress
Class to encapsulate an experiment as a task that can be executed on a remote
host.
Class that encapsulates a result (and progress info) for part of a
distributed boundary visualization.
An filter that removes a range of attributes from
the dataset.
Removes attributes based on a regular expression
matched against their names.
Removes all duplicate instances from the first batch of data it receives.
This filter takes a dataset and outputs a specified
fold for cross validation.
Determines which values (frequent or infrequent
ones) of an (nominal) attribute are retained and filters the instances
accordingly.
A filter that removes instances which are
incorrectly classified.
A filter that removes a given percentage of a
dataset.
A filter that removes a given range of instances of
a dataset.
Removes attributes of a given type.
This filter removes attributes that do not vary at
all or that vary too much.
Filters instances according to the value of an
attribute.
This filter is used for renaming attributes.
Regular expressions can be used in the matching and replacing.
See Javadoc of java.util.regex.Pattern class for more information:
http://java.sun.com/javase/6/docs/api/java/util/regex/Pattern.html
Regular expressions can be used in the matching and replacing.
See Javadoc of java.util.regex.Pattern class for more information:
http://java.sun.com/javase/6/docs/api/java/util/regex/Pattern.html
Renames the values of nominal attributes.
A simple filter that allows the relation name of a set of instances to be
altered in various ways.
A filter that generates output with a new order of
the attributes.
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.
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.
Replaces all missing values for nominal and numeric
attributes in a dataset with the modes and means from the training data.
Replaces all missing values for nominal, string,
numeric and date attributes in the dataset with user-supplied constant
values.
A filter that can be used to introduce missing values in a dataset.
Class for generating html index files and supporting text files for a Weka
package meta data repository.
Fast decision tree learner.
Produces a random subsample of a dataset using
either sampling with replacement or without replacement.
The original dataset must fit entirely in memory.
The original dataset must fit entirely in memory.
Produces a random subsample of a dataset using
either sampling with replacement or without replacement.
Helper class for resampling.
Produces a random subsample of a dataset using the
reservoir sampling Algorithm "R" by Vitter.
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the
splitting criterion based on residuals.
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the
splitting criterion based on residuals of the LogitBoost algorithm.
Helper for resources.
An event that is generated when a different Result is activated in the
ResultPanel.
A listener that is notified if another Result is activated in the
ResultPanel.
Java class for RESULT-FEATURE.
Java class for ResultField element declaration.
A component that accepts named stringbuffers and displays the name in a list
box.
Interface for something to be notified when an entry in the list is deleted
Extension of KeyAdapter that implements Serializable.
Extension of MouseAdapter that implements Serializable.
Interface for objects able to listen for results obtained
by a ResultProducer
This matrix is a container for the datasets and classifier setups and their
statistics.
Generates the matrix in CSV ('comma-separated values') format.
Generates output for a data and script file for GnuPlot.
Generates the matrix output as HTML.
Generates the matrix output in LaTeX-syntax.
Generates the output as plain text (for fixed width
fonts).
Only outputs the significance indicators.
Represents a panel for displaying the results of a query in table format.
This interface defines the methods required for an object
that produces results for different randomizations of a dataset.
Represents an extended JTable, containing a table model based on a ResultSet
and the corresponding query.
Represents an extended JTable, containing a table model based on a ResultSet
and the corresponding query.
Handles the background colors for missing values differently than the
DefaultTableCellRenderer.
The model for an SQL ResultSet.
This panel controls simple analysis of experimental results.
For classes that should return their source control revision.
Contains utility functions for handling revisions.
Enumeration of source control types.
Java class for ROC element declaration.
Java class for ROCGraph element declaration.
Java class for row element declaration.
Abstract class of generic rule
Generates a single m5 tree or rule
Java class for RULE-FEATURE.
Constructs a node for use in an m5 tree or rule
Java class for RuleSelectionMethod element declaration.
Java class for RuleSet element declaration.
Class implementing import of PMML RuleSetModel.
Java class for RuleSetModel element declaration.
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.
Helper class that executes Weka schemes from the command line.
This panel controls configuration of lower and upper run numbers
in an experiment.
This panel controls the running of an experiment.
This class handles the saving of StringBuffers to files.
Interface to something that can save Instances to an output destination in some
format.
Saves data sets using weka.core.converter classes
Step that wraps weka.core.converters.Saver classes
Bean info class for the saver bean
GUI Customizer for the saver bean
Editor dialog for the saver step
A lexical scanner for an expression language.
A scanner for JSON data files.
Bean that encapsulates weka.gui.visualize.MatrixPanel for displaying a
scatter plot matrix.
Step that collects data for display in a scatter plot matrix.
Bean info class for the scatter plot matrix bean
Interactive viewer for the ScatterPlotMatrix step
Knowledge Flow perspective for the scatter plot matrix
Interface for allowing to score a classifier
Java class for Scorecard element declaration.
Java class for ScoreDistribution element declaration.
A simple helper class for loading, saving scripts.
Executes commands from a script file.
The Thread for running a script.
Event that gets sent when a script is executed.
Defines the type of event.
For classes that want to be notified about changes in the script execution.
Abstract ancestor for scripting panels.
A helper class for Script related stuff.
This is the base class for all search algorithms for learning Bayes networks.
Java class for Seasonality_ExpoSmooth element declaration.
Java class for Segment element declaration.
Java class for Segmentation element declaration.
Represents a selected value from a finite set of values, where each
value is a Tag (i.e.
A PropertyEditor that uses tags, where the tags are obtained from a
weka.core.SelectedTag object.
An exception that should be used if a program doesn't have valid semantics
Step that can send incoming instances to a perspective.
Class implementing sending a set of Instances to a named perspective
Dialog for the SendToPerspective step
Java class for Sequence element declaration.
Java class for SequenceModel element declaration.
Java class for SequenceReference element declaration.
Java class for SequenceRule element declaration.
Defines an interface for objects able to produce two output streams of
instances.
A helper class for determining serialVersionUIDs and checking whether classes
contain one and/or need one.
A wrapper around a serialized classifier model.
Reads a source that contains serialized Instances.
Serializes the instances to a file with extension bsi.
A bean that saves serialized models
Step that can save models encapsulated in incoming
Data objects to
the filesystem.Bean info class for the serialized model saver bean
GUI Customizer for the SerializedModelSaver bean
Class for storing an object in serialized form in memory.
This class enables one to change the UID of a serialized object and therefore
not losing the data stored in the binary format.
Sets a variable.
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.
Java class for SetPredicate element declaration.
Step that accesses property values stored in the flow environment and
attempts to set them on the algorithm-based step that it is connected to.
Java class for SetReference element declaration.
Maintains a collection of settings.
Class implementing a key for a setting.
Provides a panel for editing application and perspective settings
This panel switches between simple and advanced experiment setup panels.
This panel controls the configuration of an experiment.
Step that can be used to set the values of environment variables for the flow
being executed.
Editor dialog for the
SetVariables stepImplements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression).
Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
Border implementation that provides a drop shadow
Java class for SigmoidKernelType element declaration.
This can be used by the
neuralnode to perform all it's computations (as a sigmoid unit).
This filter is a superclass for simple batch filters.
Creates a very simple command line for invoking the main method of
classes.
Creates a very simple command line for invoking the main method of classes.
A class that handles running the main method of the class in a separate
thread.
A class for commandline completion of classnames.
Class for editing SimpleDateFormat strings.
SimpleEstimator is used for estimating the
conditional probability tables of a Bayes network once the structure has been
learned.
This filter contains common behavior of the SimpleBatchFilter and the
SimpleStreamFilter.
Cluster data using the k means algorithm.
Learns a simple linear regression model.
Stripped down version of SimpleLinearRegression.
Classifier for building linear logistic regression
models.
Java class for simpleMatching element declaration.
Java class for SimplePredicate element declaration.
Java class for SimpleRule element declaration.
Java class for SimpleSetPredicate element declaration.
This panel controls the configuration of an experiment.
This filter is a superclass for simple stream filters.
A set of customizable variable declarations for primitive types.
A class to initialize variables that have been declared by a
SimpleVariableDeclarations class and used inside a programThis 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.
For more information see:
R.R.
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.
For more information see:
R.R.
Abstract utility class for handling settings common to meta associators that
use a single base associator.
Abstract utility class for handling settings common to meta
classifiers that use a single base learner.
Meta-clusterer for enhancing a base clusterer.
Class representing a single cardinal number.
Class annotation that can be used to indicate that something should
be executed in a non-parallel manner - i.e.
Singular Value Decomposition.
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
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.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
SMOreg implements the support vector machine for regression.
Stores a set of integer of a given size.
A wrapper class for the Snowball stemmers.
Represents a TableModel with sorting functionality.
Helper class for sorting the columns.
Sorts incoming instances in ascending or descending
order according to the values of user specified attributes.
Step for sorting instances according to one or more attributes.
Implements a sorting rule based on a single attribute
BeanInfo class for the Sorter step
Customizer for the Sorter step
Step editor dialog for the Sorter step
A simple filter for sorting the labels of nominal
attributes.
Represents a case-insensitive comparator for two strings.
Represents a case-sensitive comparator for two strings.
Interface for classifiers that can be converted to Java source.
Interface for filters that can be converted to Java source.
Implementation of a sparse array.
Class for storing an instance as a sparse vector.
An instance filter that converts all incoming sparse instances into non-sparse format.
Class implementing some mathematical functions.
A Splash window.
Base class for different split types
Encapsulates a candidate split
Abstract class for computing splitting criteria
with respect to distributions of class values.
Interface for objects that determine a split point on an attribute
Interface to objects able to generate a fixed set of results for
a particular split of a dataset.
Base class for split metrics
Class for a node that splits the data in a Hoeffding tree
Produces a random subsample of a dataset.
Represents a little tool for querying SQL databases.
A little dialog containing the SqlViewer.
Simple Knowledge Flow perspective that wraps the SqlViewer class
Perspective that wraps the {@code SQLViewer) component
@author Mark Hall (mhall{[at]}pentaho{[dot]}com)
@version $Revision: $
Java class for squaredEuclidean element declaration.
Class implementing a stack.
Combines several classifiers using the stacking method.
Primarily a marker interface for a "standard" evaluation metric - i.e.
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
Interface to something that is a start point for a flow and
can be launched programatically.
Interface for search methods capable of doing something sensible
given a starting set of attributes.
Interface to something that can be notified of a successful startup
Class implementing some distributions, tests, etc.
Class implementing a statistical routine needed by J48 to
compute its error estimate.
A class to store simple statistics.
A helper class to expose a Stats object to a program
Interface for all stemming algorithms.
A helper class for using the stemmers.
Client API for Knowledge Flow steps.
Base class for step editor dialogs.
Interface for those that want to be notified when this dialog closes
A flow runner that runs a flow by injecting data into a target step
Interface for GUI interactive viewer components that can be popped up from
the contextual menu in the Knowledge Flow that appears when you right-click
over a step on the layout.
Client public interface for the StepManager.
Concrete implementation of the StepManager interface.
Inteface to something that listens to the output from a
StepA task that can be executed by the ExecutionEnvironment's submitTask()
service.
Callback that Steps can use when executing StepTasks via
EnvironmentManager.submitTask().
Subclass of JTree for displaying available steps.
Marker annotation.
Maintains information about a step in the
StepTree - e.g.Class for managing the appearance of a step in the GUI Knowledge Flow
environment.
Class that can test whether a given string is a stop word.
Interface for classes that support stopword handling.
Stores property values specified in incoming instances in the flow
environment.
Editor dialog for the StorePropertiesInEnvironment step.
This filter takes a dataset and outputs a specified
fold for cross validation.
Interface for filters can work with a stream of instances.
Class for measuring throughput of an incremental Knowledge Flow step.
Helper class for using stream tokenizers
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.
For more information, see
Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.
This class locates and records the indices of String attributes, recursively
in case of Relational attributes.
Converts a range of string attributes (unspecified
number of values) to nominal (set number of values).
Converts string attributes into a set of numeric attributes representing word occurrence
information from the text contained in the strings.
Bean that can display a horizontally scrolling strip chart.
A step that can display a viewer showing a right-to-left scrolling chart
for streaming data
StripChartInteractiveView implements this in order to receive data points.
Bean info class for the strip chart bean
GUI Customizer for the strip chart bean
Implements the actual strip chart view
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.
Filters instances according to a user-specified expression.
Examples:
- extracting only mammals and birds from the 'zoo' UCI dataset:
(CLASS is 'mammal') or (CLASS is 'bird')
- extracting only animals with at least 2 legs from the 'zoo' UCI dataset:
(ATT14 >= 2)
- extracting only instances with non-missing 'wage-increase-second-year'
from the 'labor' UCI dataset:
not ismissing(ATT3)
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)
Interface for attribute subset evaluators.
A data generator that produces data points in
hyperrectangular subspace clusters.
A single cluster for the SubspaceCluster data generator.
A bean that finds matches in string attribute values (using either substring
or regular expression matches) and labels the instance (sets the value of a
new attribute) according to the supplied label for the matching rule.
Step that appends a label to incoming instances according to substring
matches in string attributes.
Bean info class for the substring labeler bean
Customizer class for the Substring labeler step
Manages a list of match rules for labeling strings.
Inner class encapsulating the logic for matching
Step editor dialog for the SubstringLabeler step
A bean that can replace substrings in the values of string attributes.
A step that can replace sub-strings in the values of string attributes.
Bean info class for the substring replacer
Customizer for the SubstringReplacer
Manages a list of match and replace rules for replacing values in string
attributes
Inner class encapsulating the logic for matching and replacing.
Step editor dialog for the SubstringReplacer step
Interface to something that provides a short textual summary (as opposed
to toString() which is usually a fairly complete description) of itself.
Interface for filters that make use of a class attribute.
Java class for SupportVector element declaration.
Java class for SupportVectorMachine element declaration.
Implements a PMML SupportVectorMachineModel
Java class for SupportVectorMachineModel element declaration.
Java class for SupportVectors element declaration.
Java class for SVM-CLASSIFICATION-METHOD.
Reads a source that is in svm light format.
For more information about svm light see:
http://svmlight.joachims.org/
For more information about svm light see:
http://svmlight.joachims.org/
Writes to a destination that is in svm light
format.
For more information about svm light see:
http://svmlight.joachims.org/
For more information about svm light see:
http://svmlight.joachims.org/
Java class for SVM-REPRESENTATION.
Swaps two values of a nominal attribute.
CUP generated interface containing symbol constants.
CUP generated interface containing symbol constants.
SymmetricalUncertAttributeEval :
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
Highlights syntax in a DefaultStyledDocument.
The attribute type.
An exception to represent an invalid syntax of a program
This Logger just sends messages to System.err.
This class prints some information about the system setup, like Java version,
JVM settings etc.
Java class for TableLocator element declaration.
This Bayes Network learning algorithm uses tabu
search for finding a well scoring Bayes network structure.
This Bayes Network learning algorithm uses tabu
search for finding a well scoring Bayes network structure.
A
Tag simply associates a numeric ID with a String description.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.
For more information see:
N.
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.
For more information see:
N.
Java class for tanimoto element declaration.
Java class for Target element declaration.
Class to encapsulate information about a Target.
Java class for Targets element declaration.
Java class for TargetValue element declaration.
Java class for TargetValueCount element declaration.
Java class for TargetValueCounts element declaration.
Interface to something that can be remotely executed as a task.
Interface for objects that display log and display information on
running tasks.
A class holding information for tasks being executed
on RemoteEngines.
Java class for Taxonomy element declaration.
Used for paper references in the Javadoc and for BibTex generation.
the possible fields
the different types of information
For classes that are based on some kind of publications.
Generates Javadoc comments from the TechnicalInformationHandler's data.
This class pipelines print/println's to several PrintStreams.
Manages all things template-related
Class to represent a test.
Java class for TestDistributions element declaration.
Interface for different kinds of Testers in the Experimenter.
Generates artificial datasets for testing.
Event encapsulating a test set
Interface to something that can accpet test set events
Bean that accepts data sets and produces test sets
A step that makes an incoming dataSet or trainingSet into a testSet.
Bean info class for the test set maker bean.
Interface to something that can produce test sets
Java class for TextCorpus element declaration.
Java class for TextDictionary element declaration.
Loads all text files in a directory and uses the
subdirectory names as class labels.
Java class for TextDocument element declaration.
Event that encapsulates some textual information
Interface to something that can process a TextEvent
Java class for TextModel element declaration.
Java class for TextModelNormalization element declaration.
Java class for TextModelSimiliarity element declaration.
Simple component to save the text carried in text events out to a file
Step for saving textual data to a file.
Defaults for the step
Bean info class for the serialized model saver bean
Customizer for the TextSaver component.
Bean that collects and displays pieces of text
A step for collecting and viewing textual data
Interface for listeners of textual results
Bean info class for the text viewer
Interactive viewer for the TextViewer step
Interface to something that is thread safe
Generates points illustrating prediction tradeoffs that can be obtained by
varying the threshold value between classes.
Event encapsulating classifier performance data based on
varying a threshold over the classifier's predicted probabilities
Interface to something that can accept ThresholdDataEvents
Some evaluation measures may optimize thresholds on the
class probabilities.
This panel is a VisualizePanel, with the added ablility to display the area
under the ROC curve if an ROC curve is chosen.
Java class for Time element declaration.
Java class for TimeAnchor element declaration.
Java class for TIME-ANCHOR.
Java class for TimeCycle element declaration.
Java class for TimeException element declaration.
Java class for TIME-EXCEPTION-TYPE.
Java class for TimeSeries element declaration.
Java class for TIMESERIES-ALGORITHM.
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.
Java class for TimeSeriesModel element declaration.
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.
Java class for TIMESERIES-USAGE.
Java class for Timestamp element declaration.
Java class for TimeValue element declaration.
Event that gets send in case a scripting panel updates the title.
Interface for frames/dialogs that listen to changes of the title.
A superclass for all tokenizer algorithms.
The class implementing the TopDown construction
method of ball trees.
Java class for TrainingInstances element declaration.
Event encapsulating a training set
Interface to something that can accept and process training set events
Bean that accepts a data sets and produces a training set
Step that converts an incoming dataSet or testSet into a trainingSet.
Bean info class for the training set maker bean
Interface to something that can produce a training set
Bean that accepts data sets, training sets, test sets and produces both a
training and test set by randomly spliting the data
A step that creates a random train/test split from an incoming data set.
Bean info class for the train test split maker bean
GUI customizer for the train test split maker bean
Java class for TransformationDictionary element declaration.
Transposes the data: instances become attributes and attributes become instances.
This class will parse a dotty file and construct a tree structure from it
with Edge's and Node's
An event containing the user selection from the tree display
Interface implemented by classes that wish to recieve user selection events
from a tree displayer.
Class implementing import of PMML TreeModel.
Java class for TreeModel element declaration.
The class that measures the performance of a tree based
nearest neighbour search algorithm.
Interface implemented by classes loaded dynamically to
visualize classifier results in the explorer.
Class for displaying a Node structure in Swing.
Java class for Trend_ExpoSmooth element declaration.
A class representing a Trie data structure for strings.
Represents an iterator over a trie
Represents a node in the trie.
Java class for True element declaration.
Encapsulates performance functions for two-class problems.
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.
Exception that is raised when trying to use something that has no
reference to a dataset, when one is required.
Interface implemented by classes that support undo.
Java class for UniformDistribution element declaration.
Interface that can be implemented by simple weighted univariate
density estimators.
Simple histogram density estimator.
Interface that can be implemented by simple weighted univariate
interval estimators.
Simple weighted kernel density estimator.
Simple weighted mixture density estimator.
A multiway split based on a single nominal attribute
Simple weighted normal density estimator.
A binary split based on a single numeric attribute
Interface that can be implemented by simple weighted univariate
quantile estimators.
Java class for UnivariateStats element declaration.
Removes a variable.
Abstract unsupervised attribute evaluator.
Interface for filters that do not need a class attribute.
Abstract unsupervised attribute subset evaluator.
Exception that is raised by an object that is unable to process some of the
attribute types it has been passed.
Exception that is raised by an object that is unable to process the
class type of the data it has been passed.
Updateable classifiers can implement this if they wish to be informed at the
end of the training stream.
Interface to incremental classification models that can learn using
one instance at a time.
Interface to incremental cluster models that can learn using one instance
at a time.
Interface to a loader that can load from a http url
Interface to something that can accept requests from a user to perform some
action
Class implementing some simple utility methods.
Java class for VALID-TIME-SPEC.
Java class for Value element declaration.
Stores some statistics.
Interface to expose variables to a program.
A helper class that allows to combine several variable declarations together.
Part of ADTree implementation.
Java class for VectorDictionary element declaration.
Class encapsulating the PMML VectorDictionary construct.
Java class for VectorFields element declaration.
Java class for VectorInstance element declaration.
Class encapsulating a PMML VectorInstance construct
Java class for VerificationField element declaration.
Java class for VerificationFields element declaration.
This class contains the version number of the current WEKA release and some
methods for comparing another version string.
Concrete implementation of PackageConstraint that encapsulates constraints
related to version numbers.
Enumeration encapsulating version comparison operations
A concrete implementation of PackgageConstraint that encapsulates ranged
version number constraints.
A downsized version of the ArffViewer, displaying only one Instances-Object.
Interface to something that has a visible (via BeanVisual) reprentation
Panel that wraps a flow and makes it visible in the KnowledgeFlow, along with
it's associated log panel
Event encapsulating error information for a learning scheme
that can be visualized in the DataVisualizer
Interface to something that can accept VisualizableErrorEvents
A slightly extended MatrixPanel for better support in the Explorer.
This panel allows the user to visualize a dataset (and if provided) a
classifier's/clusterer's predictions in two dimensions.
Default settings specific to the
MatrixPanel that provides the
scatter plot matrixThis event Is fired to a listeners 'userDataEvent' function when The user on
the VisualizePanel clicks submit.
Interface implemented by a class that is interested in receiving
submited shapes from a visualize panel.
Interface implemented by classes loaded dynamically to visualize classifier
results in the explorer.
This class contains utility routines for visualization
Defaults for the 2D scatter plot and attribute bars
Class for combining classifiers.
Implementation of the voted perceptron algorithm by Freund and Schapire.
Interface to something that makes use of the information provided
by attribute weights.
Interface to something that makes use of the information provided
by instance weights.
Generic wrapper around any classifier to enable weighted instances support.
Uses resampling with weights if the base classifier is not implementing the weka.core.WeightedInstancesHandler interface and there are instance weights other 1.0 present.
Uses resampling with weights if the base classifier is not implementing the weka.core.WeightedInstancesHandler interface and there are instance weights other 1.0 present.
Simple container for a weight
A step that wraps a class of standard Weka algorithm (e.g.
Class for enumerating an array list's elements.
Class for Weka-specific exceptions.
Customized WekaFileChooser with support for bookmarks.
Default OffscreenChartRenderer that uses Weka's built-in chart and graph
classes.
Class that manages classloaders from individual Weka plugin packages.
A ClassLoader that loads/finds classes from one Weka plugin package.
Class providing package management and manipulation routines.
This panel records the number of weka tasks running and displays a
simple bird animation while their are active tasks
Interface to something that can wrap around a class of Weka
algorithms (classifiers, filters etc).
Uses the stopwords located in the specified file (ignored _if pointing to a directory).
A simple tokenizer that is using the
java.util.StringTokenizer class to tokenize the strings.
Launcher class for the Weka workbench.
One app to rule them all, one app to find them, one app to bring them all and
with perspectives bind them.
Default settings for the Workbench app.
FlowLayout subclass that fully supports wrapping of components.
WrapperSubsetEval:
Evaluates attribute sets by using a learning scheme.
Evaluates attribute sets by using a learning scheme.
Step that stores incoming non-incremental data in the job environment
Step that takes incoming data and writes it to the Weka log
Java class for XCoordinates element declaration.
Outputs the predictions in XML.
The following DTD is used:
<!DOCTYPE predictions
[
<!ELEMENT predictions (prediction*)>
<!ATTLIST predictions version CDATA "3.5.8">
<!ATTLIST predictions name CDATA #REQUIRED>
<!ELEMENT prediction ((actual_label,predicted_label,error,(prediction|distribution),attributes?)|(actual_value,predicted_value,error,attributes?))>
<!ATTLIST prediction index CDATA #REQUIRED>
<!ELEMENT actual_label ANY>
<!ATTLIST actual_label index CDATA #REQUIRED>
<!ELEMENT predicted_label ANY>
<!ATTLIST predicted_label index CDATA #REQUIRED>
<!ELEMENT error ANY>
<!ELEMENT prediction ANY>
<!ELEMENT distribution (class_label+)>
<!ELEMENT class_label ANY>
<!ATTLIST class_label index CDATA #REQUIRED>
<!ATTLIST class_label predicted (yes|no) "no">
<!ELEMENT actual_value ANY>
<!ELEMENT predicted_value ANY>
<!ELEMENT attributes (attribute+)>
<!ELEMENT attribute ANY>
<!ATTLIST attribute index CDATA #REQUIRED>
<!ATTLIST attribute name CDATA #REQUIRED>
<!ATTLIST attribute type (numeric|date|nominal|string|relational) #REQUIRED>
]
>
The following DTD is used:
<!DOCTYPE predictions
[
<!ELEMENT predictions (prediction*)>
<!ATTLIST predictions version CDATA "3.5.8">
<!ATTLIST predictions name CDATA #REQUIRED>
<!ELEMENT prediction ((actual_label,predicted_label,error,(prediction|distribution),attributes?)|(actual_value,predicted_value,error,attributes?))>
<!ATTLIST prediction index CDATA #REQUIRED>
<!ELEMENT actual_label ANY>
<!ATTLIST actual_label index CDATA #REQUIRED>
<!ELEMENT predicted_label ANY>
<!ATTLIST predicted_label index CDATA #REQUIRED>
<!ELEMENT error ANY>
<!ELEMENT prediction ANY>
<!ELEMENT distribution (class_label+)>
<!ELEMENT class_label ANY>
<!ATTLIST class_label index CDATA #REQUIRED>
<!ATTLIST class_label predicted (yes|no) "no">
<!ELEMENT actual_value ANY>
<!ELEMENT predicted_value ANY>
<!ELEMENT attributes (attribute+)>
<!ELEMENT attribute ANY>
<!ATTLIST attribute index CDATA #REQUIRED>
<!ATTLIST attribute name CDATA #REQUIRED>
<!ATTLIST attribute type (numeric|date|nominal|string|relational) #REQUIRED>
]
>
This serializer contains some read/write methods for common classes that are
not beans-conform.
This class serializes and deserializes a KnowledgeFlow setup to and fro XML.
This class serializes and deserializes a Classifier instance to and
fro XML.
This class offers some methods for generating, reading and writing
XML documents.
It can only handle UTF-8.
It can only handle UTF-8.
This class serializes and deserializes an Experiment instance to and fro XML.
It omits the
It omits the
options from the Experiment, since these are
handled by the get/set-methods.A simple default implementation of MetaStore that uses Weka's XML
serialization mechanism to persist entries as XML files in
${WEKA_HOME}/wekaMetaStore
XML representation of the Instances class.
A class for transforming options listed in XML to a regular WEKA command line
string.
With this class objects can be serialized to XML instead into a binary
format.
This class handles relationships between display names of properties (or
classes) and Methods that are associated with them.
Reads a source that is in the XML version of the ARFF format.
Writes to a destination that is in the XML version
of the ARFF format.
This class is a helper class for XML serialization using XStream .
Java class for YCoordinates element declaration.
Stores split information.
Class for building and using a 0-R classifier.
weka.gui.InteractiveTableModelinstead.