Serialized Form


Package weka.gui

Class weka.gui.AttributeListPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Table

javax.swing.JTable m_Table
The table displaying attribute names


m_Model

AttributeListPanel.AttributeTableModel m_Model
The table model containing attribute names

Class weka.gui.AttributeSelectionPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_IncludeAll

javax.swing.JButton m_IncludeAll
Press to select all attributes


m_RemoveAll

javax.swing.JButton m_RemoveAll
Press to deselect all attributes


m_Invert

javax.swing.JButton m_Invert
Press to invert the current selection


m_Table

javax.swing.JTable m_Table
The table displaying attribute names and selection status


m_Model

AttributeSelectionPanel.AttributeTableModel m_Model
The table model containingn attribute names and selection status

Class weka.gui.AttributeSummaryPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_AttributeNameLab

javax.swing.JLabel m_AttributeNameLab
Displays the name of the relation


m_AttributeTypeLab

javax.swing.JLabel m_AttributeTypeLab
Displays the type of attribute


m_MissingLab

javax.swing.JLabel m_MissingLab
Displays the number of missing values


m_UniqueLab

javax.swing.JLabel m_UniqueLab
Displays the number of unique values


m_DistinctLab

javax.swing.JLabel m_DistinctLab
Displays the number of distinct values


m_StatsTable

javax.swing.JTable m_StatsTable
Displays other stats in a table


m_Instances

Instances m_Instances
The instances we're playing with


m_AttributeStats

AttributeStats[] m_AttributeStats
Cached stats on the attributes we've summarized so far

Class weka.gui.AttributeVisualizationPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_data

Instances m_data

as

AttributeStats as

attribIndex

int attribIndex

maxValue

int maxValue

histBarCounts

int[] histBarCounts

histBarClassCounts

int[][] histBarClassCounts

m_barRange

double m_barRange

classIndex

int classIndex

hc

java.lang.Thread hc

threadRun

boolean threadRun

m_colorAttrib

javax.swing.JComboBox m_colorAttrib

fm

java.awt.FontMetrics fm

m_locker

java.lang.Integer m_locker

m_colorList

FastVector m_colorList
Contains discrete colours for colouring for nominal attributes

Class weka.gui.DatabaseConnectionDialog extends javax.swing.JDialog implements Serializable

Serialized Fields

m_DbaseURLText

javax.swing.JTextField m_DbaseURLText

m_DbaseURLLab

javax.swing.JLabel m_DbaseURLLab

m_UserNameText

javax.swing.JTextField m_UserNameText

m_UserNameLab

javax.swing.JLabel m_UserNameLab

m_PasswordText

javax.swing.JPasswordField m_PasswordText

m_PasswordLab

javax.swing.JLabel m_PasswordLab

m_returnValue

int m_returnValue

Class weka.gui.GenericArrayEditor extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Support

java.beans.PropertyChangeSupport m_Support
Handles property change notification


m_Label

javax.swing.JLabel m_Label
The label for when we can't edit that type


m_ElementList

javax.swing.JList m_ElementList
The list component displaying current values


m_ElementClass

java.lang.Class m_ElementClass
The class of objects allowed in the array


m_ListModel

javax.swing.DefaultListModel m_ListModel
The defaultlistmodel holding our data


m_ElementEditor

java.beans.PropertyEditor m_ElementEditor
The property editor for the class we are editing


m_DeleteBut

javax.swing.JButton m_DeleteBut
Click this to delete the selected array values


m_AddBut

javax.swing.JButton m_AddBut
Click to add the current object configuration to the array


m_InnerActionListener

java.awt.event.ActionListener m_InnerActionListener
Listens to buttons being pressed and taking the appropriate action


m_InnerSelectionListener

javax.swing.event.ListSelectionListener m_InnerSelectionListener
Listens to list items being selected and takes appropriate action

Class weka.gui.GenericObjectEditor.GOEPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_ChildPropertySheet

PropertySheetPanel m_ChildPropertySheet
The component that performs classifier customization


m_ClassNameLabel

javax.swing.JLabel m_ClassNameLabel
The name of the current class


m_OpenBut

javax.swing.JButton m_OpenBut
Open object from disk


m_SaveBut

javax.swing.JButton m_SaveBut
Save object to disk


m_okBut

javax.swing.JButton m_okBut
ok button


m_cancelBut

javax.swing.JButton m_cancelBut
cancel button


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The filechooser for opening and saving object files

Class weka.gui.GenericObjectEditor.JTreePopupMenu extends javax.swing.JPopupMenu implements Serializable

Serialized Fields

m_tree

javax.swing.JTree m_tree
The tree


m_scroller

javax.swing.JScrollPane m_scroller
The scroller

Class weka.gui.GUIChooser extends java.awt.Frame implements Serializable

Serialized Fields

m_SimpleBut

java.awt.Button m_SimpleBut
Click to open the simplecli


m_ExplorerBut

java.awt.Button m_ExplorerBut
Click to open the Explorer


m_ExperimenterBut

java.awt.Button m_ExperimenterBut
Click to open the Explorer


m_KnowledgeFlowBut

java.awt.Button m_KnowledgeFlowBut
Click to open the KnowledgeFlow


m_SimpleCLI

SimpleCLI m_SimpleCLI
The SimpleCLI


m_ExplorerFrame

javax.swing.JFrame m_ExplorerFrame
The frame containing the explorer interface


m_ExperimenterFrame

javax.swing.JFrame m_ExperimenterFrame
The frame containing the experiment interface


m_KnowledgeFlowFrame

javax.swing.JFrame m_KnowledgeFlowFrame
The frame containing the knowledge flow interface


m_weka

java.awt.Image m_weka
The weka image

Class weka.gui.HierarchyPropertyParser extends java.lang.Object implements Serializable

Serialized Fields

m_Root

HierarchyPropertyParser.TreeNode m_Root
Keep track of the root of the tree


m_Current

HierarchyPropertyParser.TreeNode m_Current
Keep track of the current node when traversing the tree


m_Seperator

java.lang.String m_Seperator
The level separate in the path


m_Depth

int m_Depth
The depth of the tree

Class weka.gui.InstancesSummaryPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_RelationNameLab

javax.swing.JLabel m_RelationNameLab
Displays the name of the relation


m_NumInstancesLab

javax.swing.JLabel m_NumInstancesLab
Displays the number of instances


m_NumAttributesLab

javax.swing.JLabel m_NumAttributesLab
Displays the number of attributes


m_Instances

Instances m_Instances
The instances we're playing with

Class weka.gui.ListSelectorDialog extends javax.swing.JDialog implements Serializable

Serialized Fields

m_SelectBut

javax.swing.JButton m_SelectBut
Click to choose the currently selected property


m_CancelBut

javax.swing.JButton m_CancelBut
Click to cancel the property selection


m_List

javax.swing.JList m_List
The list component


m_Result

int m_Result
Whether the selection was made or cancelled

Class weka.gui.LogPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_StatusLab

javax.swing.JLabel m_StatusLab
Displays the current status


m_LogText

javax.swing.JTextArea m_LogText
Displays the log messages


m_logButton

javax.swing.JButton m_logButton
The button for viewing the log


m_First

boolean m_First
An indicator for whether text has been output yet


m_TaskMonitor

WekaTaskMonitor m_TaskMonitor
The panel for monitoring the number of running tasks (if supplied)

Class weka.gui.PropertyDialog extends javax.swing.JFrame implements Serializable

Serialized Fields

m_Editor

java.beans.PropertyEditor m_Editor
The property editor


m_EditorComponent

java.awt.Component m_EditorComponent
The custom editor component

Class weka.gui.PropertyPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Editor

java.beans.PropertyEditor m_Editor
The property editor


m_PD

PropertyDialog m_PD
The currently displayed property dialog, if any


m_HasCustomPanel

boolean m_HasCustomPanel
Whether the editor has provided its own panel

Class weka.gui.PropertySelectorDialog extends javax.swing.JDialog implements Serializable

Serialized Fields

m_SelectBut

javax.swing.JButton m_SelectBut
Click to choose the currently selected property


m_CancelBut

javax.swing.JButton m_CancelBut
Click to cancel the property selection


m_Root

javax.swing.tree.DefaultMutableTreeNode m_Root
The root of the property tree


m_RootObject

java.lang.Object m_RootObject
The object at the root of the tree


m_Result

int m_Result
Whether the selection was made or cancelled


m_ResultPath

java.lang.Object[] m_ResultPath
Stores the path to the selected property


m_Tree

javax.swing.JTree m_Tree
The component displaying the property tree

Class weka.gui.PropertySheetPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Target

java.lang.Object m_Target
The target object being edited


m_Properties

java.beans.PropertyDescriptor[] m_Properties
Holds properties of the target


m_Methods

java.beans.MethodDescriptor[] m_Methods
Holds the methods of the target


m_Editors

java.beans.PropertyEditor[] m_Editors
Holds property editors of the object


m_Values

java.lang.Object[] m_Values
Holds current object values for each property


m_Views

javax.swing.JComponent[] m_Views
Stores GUI components containing each editing component


m_Labels

javax.swing.JLabel[] m_Labels
The labels for each property


m_TipTexts

java.lang.String[] m_TipTexts
The tool tip text for each property


m_HelpText

java.lang.StringBuffer m_HelpText
StringBuffer containing help text for the object being edited


m_HelpFrame

javax.swing.JFrame m_HelpFrame
Help frame


m_HelpBut

javax.swing.JButton m_HelpBut
Button to pop up the full help text in a separate frame


m_NumEditable

int m_NumEditable
A count of the number of properties we have an editor for


support

java.beans.PropertyChangeSupport support
A support object for handling property change listeners

Class weka.gui.ResultHistoryPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_SingleText

javax.swing.text.JTextComponent m_SingleText
An optional component for single-click display


m_SingleName

java.lang.String m_SingleName
The named result being viewed in the single-click display


m_Model

javax.swing.DefaultListModel m_Model
The list model


m_List

javax.swing.JList m_List
The list component


m_Results

java.util.Hashtable m_Results
A Hashtable mapping names to result buffers


m_FramedOutput

java.util.Hashtable m_FramedOutput
A Hashtable mapping names to output text components


m_Objs

java.util.Hashtable m_Objs
A hashtable mapping names to arbitrary objects


m_HandleRightClicks

boolean m_HandleRightClicks
Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer

Class weka.gui.ResultHistoryPanel.RKeyAdapter extends java.awt.event.KeyAdapter implements Serializable

Class weka.gui.ResultHistoryPanel.RMouseAdapter extends java.awt.event.MouseAdapter implements Serializable

Class weka.gui.SetInstancesPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_OpenFileBut

javax.swing.JButton m_OpenFileBut
Click to open instances from a file


m_OpenURLBut

javax.swing.JButton m_OpenURLBut
Click to open instances from a URL


m_Summary

InstancesSummaryPanel m_Summary
The instance summary component


m_ArffFilter

javax.swing.filechooser.FileFilter m_ArffFilter
Filter to ensure only arff files are selected


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting arff files


m_LastURL

java.lang.String m_LastURL
Stores the last URL that instances were loaded from


m_IOThread

java.lang.Thread m_IOThread
The thread we do loading in


m_Support

java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the set of working instances.


m_Instances

Instances m_Instances
The current set of instances loaded

Class weka.gui.SimpleCLI extends java.awt.Frame implements Serializable

Serialized Fields

m_OutputArea

java.awt.TextArea m_OutputArea
The output area canvas added to the frame


m_Input

java.awt.TextField m_Input
The command input area


m_CommandHistory

java.util.Vector m_CommandHistory
The history of commands entered interactively


m_HistoryPos

int m_HistoryPos
The current position in the command history


m_POO

java.io.PipedOutputStream m_POO
The new output stream for System.out


m_POE

java.io.PipedOutputStream m_POE
The new output stream for System.err


m_OutRedirector

java.lang.Thread m_OutRedirector
The thread that sends output from m_POO to the output box


m_ErrRedirector

java.lang.Thread m_ErrRedirector
The thread that sends output from m_POE to the output box


m_RunThread

java.lang.Thread m_RunThread
The thread currently running a class main method

Class weka.gui.WekaTaskMonitor extends javax.swing.JPanel implements Serializable

Serialized Fields

m_ActiveTasks

int m_ActiveTasks
The number of running weka threads


m_MonitorLabel

javax.swing.JLabel m_MonitorLabel
The label for displaying info


m_iconStationary

javax.swing.ImageIcon m_iconStationary
The icon for the stationary bird


m_iconAnimated

javax.swing.ImageIcon m_iconAnimated
The icon for the animated bird


m_animating

boolean m_animating
True if their are active tasks


Package weka.gui.explorer

Class weka.gui.explorer.AssociationsPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_AssociatorEditor

GenericObjectEditor m_AssociatorEditor
Lets the user configure the associator


m_CEPanel

PropertyPanel m_CEPanel
The panel showing the current associator selection


m_OutText

javax.swing.JTextArea m_OutText
The output area for associations


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_StartBut

javax.swing.JButton m_StartBut
Click to start running the associator


m_StopBut

javax.swing.JButton m_StopBut
Click to stop a running associator


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestInstances

Instances m_TestInstances
The user-supplied test set (if any)


m_RunThread

java.lang.Thread m_RunThread
A thread that associator runs in

Class weka.gui.explorer.AttributeSelectionPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_AttributeEvaluatorEditor

GenericObjectEditor m_AttributeEvaluatorEditor
Lets the user configure the attribute evaluator


m_AttributeSearchEditor

GenericObjectEditor m_AttributeSearchEditor
Lets the user configure the search method


m_AEEPanel

PropertyPanel m_AEEPanel
The panel showing the current attribute evaluation method


m_ASEPanel

PropertyPanel m_ASEPanel
The panel showing the current search method


m_OutText

javax.swing.JTextArea m_OutText
The output area for attribute selection results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_ClassCombo

javax.swing.JComboBox m_ClassCombo
Lets the user select the class column


m_CVBut

javax.swing.JRadioButton m_CVBut
Click to set evaluation mode to cross-validation


m_TrainBut

javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data


m_CVLab

javax.swing.JLabel m_CVLab
Label by where the cv folds are entered


m_CVText

javax.swing.JTextField m_CVText
The field where the cv folds are entered


m_SeedLab

javax.swing.JLabel m_SeedLab
Label by where cv random seed is entered


m_SeedText

javax.swing.JTextField m_SeedText
The field where the seed value is entered


m_RadioListener

java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_StartBut

javax.swing.JButton m_StartBut
Click to start running the attribute selector


m_StopBut

javax.swing.JButton m_StopBut
Click to stop a running classifier


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_RunThread

java.lang.Thread m_RunThread
A thread that attribute selection runs in

Class weka.gui.explorer.ClassifierPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_ClassifierEditor

GenericObjectEditor m_ClassifierEditor
Lets the user configure the classifier


m_CEPanel

PropertyPanel m_CEPanel
The panel showing the current classifier selection


m_OutText

javax.swing.JTextArea m_OutText
The output area for classification results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_ClassCombo

javax.swing.JComboBox m_ClassCombo
Lets the user select the class column


m_CVBut

javax.swing.JRadioButton m_CVBut
Click to set test mode to cross-validation


m_PercentBut

javax.swing.JRadioButton m_PercentBut
Click to set test mode to generate a % split


m_TrainBut

javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data


m_TestSplitBut

javax.swing.JRadioButton m_TestSplitBut
Click to set test mode to a user-specified test set


m_StorePredictionsBut

javax.swing.JCheckBox m_StorePredictionsBut
Check to save the predictions in the results list for visualizing later on


m_OutputModelBut

javax.swing.JCheckBox m_OutputModelBut
Check to output the model built from the training data


m_OutputPerClassBut

javax.swing.JCheckBox m_OutputPerClassBut
Check to output true/false positives, precision/recall for each class


m_OutputConfusionBut

javax.swing.JCheckBox m_OutputConfusionBut
Check to output a confusion matrix


m_OutputEntropyBut

javax.swing.JCheckBox m_OutputEntropyBut
Check to output entropy statistics


m_OutputPredictionsTextBut

javax.swing.JCheckBox m_OutputPredictionsTextBut
Check to output text predictions


m_EvalWRTCostsBut

javax.swing.JCheckBox m_EvalWRTCostsBut
Check to evaluate w.r.t a cost matrix


m_SetCostsBut

javax.swing.JButton m_SetCostsBut

m_CVLab

javax.swing.JLabel m_CVLab
Label by where the cv folds are entered


m_CVText

javax.swing.JTextField m_CVText
The field where the cv folds are entered


m_PercentLab

javax.swing.JLabel m_PercentLab
Label by where the % split is entered


m_PercentText

javax.swing.JTextField m_PercentText
The field where the % split is entered


m_SetTestBut

javax.swing.JButton m_SetTestBut
The button used to open a separate test dataset


m_SetTestFrame

javax.swing.JFrame m_SetTestFrame
The frame used to show the test set selection panel


m_SetCostsFrame

PropertyDialog m_SetCostsFrame
The frame used to show the cost matrix editing panel


m_RadioListener

java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_MoreOptions

javax.swing.JButton m_MoreOptions
Button for further output/visualize options


m_RandomSeedText

javax.swing.JTextField m_RandomSeedText
User specified random seed for cross validation or % split


m_RandomLab

javax.swing.JLabel m_RandomLab

m_StartBut

javax.swing.JButton m_StartBut
Click to start running the classifier


m_StopBut

javax.swing.JButton m_StopBut
Click to stop a running classifier


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_CostMatrixEditor

CostMatrixEditor m_CostMatrixEditor
The cost matrix editor for evaluation costs


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestInstances

Instances m_TestInstances
The user-supplied test set (if any)


m_RunThread

java.lang.Thread m_RunThread
A thread that classification runs in


m_visXIndex

int m_visXIndex
default x index for visualizing


m_visYIndex

int m_visYIndex
default y index for visualizing


m_CurrentVis

VisualizePanel m_CurrentVis
The current visualization object


m_Summary

InstancesSummaryPanel m_Summary
The instances summary panel displayed by m_SetTestFrame


m_ModelFilter

javax.swing.filechooser.FileFilter m_ModelFilter
Filter to ensure only model files are selected


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting model files

Class weka.gui.explorer.ClustererPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_ClustererEditor

GenericObjectEditor m_ClustererEditor
Lets the user configure the clusterer


m_CLPanel

PropertyPanel m_CLPanel
The panel showing the current clusterer selection


m_OutText

javax.swing.JTextArea m_OutText
The output area for classification results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_PercentBut

javax.swing.JRadioButton m_PercentBut
Click to set test mode to generate a % split


m_TrainBut

javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data


m_TestSplitBut

javax.swing.JRadioButton m_TestSplitBut
Click to set test mode to a user-specified test set


m_ClassesToClustersBut

javax.swing.JRadioButton m_ClassesToClustersBut
Click to set test mode to classes to clusters based evaluation


m_ClassCombo

javax.swing.JComboBox m_ClassCombo
Lets the user select the class column for classes to clusters based evaluation


m_PercentLab

javax.swing.JLabel m_PercentLab
Label by where the % split is entered


m_PercentText

javax.swing.JTextField m_PercentText
The field where the % split is entered


m_SetTestBut

javax.swing.JButton m_SetTestBut
The button used to open a separate test dataset


m_SetTestFrame

javax.swing.JFrame m_SetTestFrame
The frame used to show the test set selection panel


m_ignoreBut

javax.swing.JButton m_ignoreBut
The button used to popup a list for choosing attributes to ignore while clustering


m_ignoreKeyModel

javax.swing.DefaultListModel m_ignoreKeyModel

m_ignoreKeyList

javax.swing.JList m_ignoreKeyList

m_RadioListener

java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_StartBut

javax.swing.JButton m_StartBut
Click to start running the clusterer


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_StopBut

javax.swing.JButton m_StopBut
Click to stop a running clusterer


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestInstances

Instances m_TestInstances
The user-supplied test set (if any)


m_TestInstancesCopy

Instances m_TestInstancesCopy
The user supplied test set after preprocess filters have been applied


m_CurrentVis

VisualizePanel m_CurrentVis
The current visualization object


m_visXIndex

int m_visXIndex
default x index for visualizing


m_visYIndex

int m_visYIndex
default y index for visualizing


m_StorePredictionsBut

javax.swing.JCheckBox m_StorePredictionsBut
Check to save the predictions in the results list for visualizing later on


m_RunThread

java.lang.Thread m_RunThread
A thread that clustering runs in


m_Summary

InstancesSummaryPanel m_Summary
The instances summary panel displayed by m_SetTestFrame


m_ModelFilter

javax.swing.filechooser.FileFilter m_ModelFilter
Filter to ensure only model files are selected


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting model files

Class weka.gui.explorer.Explorer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_PreprocessPanel

PreprocessPanel m_PreprocessPanel
The panel for preprocessing instances


m_ClassifierPanel

ClassifierPanel m_ClassifierPanel
The panel for running classifiers


m_ClustererPanel

ClustererPanel m_ClustererPanel
Label for a panel that still need to be implemented


m_AssociationPanel

AssociationsPanel m_AssociationPanel
Label for a panel that still need to be implemented


m_AttributeSelectionPanel

AttributeSelectionPanel m_AttributeSelectionPanel
Label for a panel that still need to be implemented


m_VisualizePanel

MatrixPanel m_VisualizePanel
Label for a panel that still need to be implemented


m_TabbedPane

javax.swing.JTabbedPane m_TabbedPane
The tabbed pane that controls which sub-pane we are working with


m_LogPanel

LogPanel m_LogPanel
The panel for log and status messages

Class weka.gui.explorer.PreprocessPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_InstSummaryPanel

InstancesSummaryPanel m_InstSummaryPanel
Displays simple stats on the working instances


m_OpenFileBut

javax.swing.JButton m_OpenFileBut
Click to load base instances from a file


m_OpenURLBut

javax.swing.JButton m_OpenURLBut
Click to load base instances from a URL


m_OpenDBBut

javax.swing.JButton m_OpenDBBut
Click to load base instances from a Database


m_DatabaseQueryEditor

GenericObjectEditor m_DatabaseQueryEditor
Lets the user enter a DB query


m_UndoBut

javax.swing.JButton m_UndoBut
Click to revert back to the last saved point


m_SaveBut

javax.swing.JButton m_SaveBut
Click to apply filters and save the results


m_AttPanel

AttributeListPanel m_AttPanel
Panel to let the user toggle attributes


m_AttSummaryPanel

AttributeSummaryPanel m_AttSummaryPanel
Displays summary stats on the selected attribute


m_FilterEditor

GenericObjectEditor m_FilterEditor
Lets the user configure the filter


m_FilterPanel

PropertyPanel m_FilterPanel
Filter configuration


m_ApplyFilterBut

javax.swing.JButton m_ApplyFilterBut
Click to apply filters and save the results


m_ArffFilter

javax.swing.filechooser.FileFilter m_ArffFilter
Filter to ensure only arff files are selected


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting arff files


m_LastURL

java.lang.String m_LastURL
Stores the last URL that instances were loaded from


m_SQLQ

java.lang.String m_SQLQ
Stores the last sql query executed


m_Instances

Instances m_Instances
The working instances


m_AttVisualizePanel

AttributeVisualizationPanel m_AttVisualizePanel
The visualization of the attribute values


m_tempUndoFiles

java.io.File[] m_tempUndoFiles
Keeps track of undo points


m_tempUndoIndex

int m_tempUndoIndex
The next available slot for an undo point


m_Support

java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the set of working instances.


m_IOThread

java.lang.Thread m_IOThread
A thread for loading/saving instances from a file or URL


m_Log

Logger m_Log
The message logger


Package weka.gui.treevisualizer

Class weka.gui.treevisualizer.TreeVisualizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_placer

NodePlace m_placer
The placement algorithm for the Node structure.


m_topNode

Node m_topNode
The top Node.


m_viewPos

java.awt.Dimension m_viewPos
The postion of the view relative to the tree.


m_viewSize

java.awt.Dimension m_viewSize
The size of the tree in pixels.


m_currentFont

java.awt.Font m_currentFont
The font used to display the tree.


m_fontSize

java.awt.FontMetrics m_fontSize
The size information for the current font.


m_numNodes

int m_numNodes
The number of Nodes in the tree.


m_numLevels

int m_numLevels
The number of levels in the tree.


m_nodes

TreeVisualizer.NodeInfo[] m_nodes
An array with the Nodes sorted into it and display information about the Nodes.


m_edges

TreeVisualizer.EdgeInfo[] m_edges
An array with the Edges sorted into it and display information about the Edges.


m_frameLimiter

javax.swing.Timer m_frameLimiter
A timer to keep the frame rate constant.


m_mouseState

int m_mouseState
Describes the action the user is performing.


m_oldMousePos

java.awt.Dimension m_oldMousePos
A variable used to tag the start pos of a user action.


m_newMousePos

java.awt.Dimension m_newMousePos
A variable used to tag the most current point of a user action.


m_clickAvailable

boolean m_clickAvailable
A variable used to determine for the clicked method if any other mouse state has already taken place.


m_nViewPos

java.awt.Dimension m_nViewPos
A variable used to remember the desired view pos.


m_nViewSize

java.awt.Dimension m_nViewSize
A variable used to remember the desired tree size.


m_scaling

int m_scaling
The number of frames left to calculate.


m_winMenu

javax.swing.JPopupMenu m_winMenu
A right (or middle) click popup menu.


m_topN

javax.swing.JMenuItem m_topN
An option on the win_menu


m_fitToScreen

javax.swing.JMenuItem m_fitToScreen
An option on the win_menu


m_autoScale

javax.swing.JMenuItem m_autoScale
An option on the win_menu


m_selectFont

javax.swing.JMenu m_selectFont
A ub group on the win_menu


m_selectFontGroup

javax.swing.ButtonGroup m_selectFontGroup
A grouping for the font choices


m_size24

javax.swing.JRadioButtonMenuItem m_size24
A font choice.


m_size22

javax.swing.JRadioButtonMenuItem m_size22
A font choice.


m_size20

javax.swing.JRadioButtonMenuItem m_size20
A font choice.


m_size18

javax.swing.JRadioButtonMenuItem m_size18
A font choice.


m_size16

javax.swing.JRadioButtonMenuItem m_size16
A font choice.


m_size14

javax.swing.JRadioButtonMenuItem m_size14
A font choice.


m_size12

javax.swing.JRadioButtonMenuItem m_size12
A font choice.


m_size10

javax.swing.JRadioButtonMenuItem m_size10
A font choice.


m_size8

javax.swing.JRadioButtonMenuItem m_size8
A font choice.


m_size6

javax.swing.JRadioButtonMenuItem m_size6
A font choice.


m_size4

javax.swing.JRadioButtonMenuItem m_size4
A font choice.


m_size2

javax.swing.JRadioButtonMenuItem m_size2
A font choice.


m_size1

javax.swing.JRadioButtonMenuItem m_size1
A font choice.


m_accept

javax.swing.JMenuItem m_accept
An option on the win menu.


m_nodeMenu

javax.swing.JPopupMenu m_nodeMenu
A right or middle click popup menu for nodes.


m_visualise

javax.swing.JMenuItem m_visualise
A visualize choice for the node, may not be available.


m_addChildren

javax.swing.JMenuItem m_addChildren
An add children to Node choice, This is only available if the tree display has a treedisplay listerner added to it.


m_remChildren

javax.swing.JMenuItem m_remChildren
Similar to add children but now it removes children.


m_classifyChild

javax.swing.JMenuItem m_classifyChild
Use this to have J48 classify this node.


m_sendInstances

javax.swing.JMenuItem m_sendInstances
Use this to dump the instances from this node to the vis panel.


m_focusNode

int m_focusNode
The subscript for the currently selected node (this is an internal thing, so the user is unaware of this).


m_highlightNode

int m_highlightNode
The Node the user is currently focused on , this is similar to focus node except that it is used by other classes rather than this one.


m_listener

TreeDisplayListener m_listener

m_searchString

javax.swing.JTextField m_searchString

m_searchWin

javax.swing.JDialog m_searchWin

m_caseSen

javax.swing.JRadioButton m_caseSen


Package weka.gui.beans

Class weka.gui.beans.AbstractDataSink extends javax.swing.JPanel implements Serializable

Serialized Fields

m_visual

BeanVisual m_visual
Default visual for data sources


m_listenee

java.lang.Object m_listenee
Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed. Subclasses can overide the appropriate BeanCommon methods to change this behaviour and allow multiple connections if desired

Class weka.gui.beans.AbstractDataSource extends javax.swing.JPanel implements Serializable

Serialized Fields

m_design

boolean m_design
True if this bean's appearance is the design mode appearance


m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support


m_visual

BeanVisual m_visual
Default visual for data sources


m_listeners

java.util.Vector m_listeners
Objects listening for events from data sources

Class weka.gui.beans.AbstractEvaluator extends javax.swing.JPanel implements Serializable

Serialized Fields

m_visual

BeanVisual m_visual
Default visual for evaluators


m_listenee

java.lang.Object m_listenee

Class weka.gui.beans.AbstractTestSetProducer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_listeners

java.util.Vector m_listeners
Objects listening to us


m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee
non null if this object is a target for any events.

Class weka.gui.beans.AbstractTrainAndTestSetProducer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_trainingListeners

java.util.Vector m_trainingListeners
Objects listening for trainin set events


m_testListeners

java.util.Vector m_testListeners
Objects listening for test set events


m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee
non null if this object is a target for any events.

Class weka.gui.beans.AbstractTrainingSetProducer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_listeners

java.util.Vector m_listeners
Objects listening for training set events


m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee
non null if this object is a target for any events.

Class weka.gui.beans.AttributeSummarizer extends DataVisualizer implements Serializable

Serialized Fields

m_gridWidth

int m_gridWidth
The number of plots horizontally in the display


m_maxPlots

int m_maxPlots
The maximum number of plots to show


m_coloringIndex

int m_coloringIndex
Index on which to color the plots. -1 indicates that we let the attribute visualize panels set this on the basis of the class index in the data.

Class weka.gui.beans.BatchClassifierEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_classifier

Classifier m_classifier
The classifier


m_testSet

Instances m_testSet
Instances that can be used for testing the classifier


m_setNumber

int m_setNumber
The set number for the test set


m_maxSetNumber

int m_maxSetNumber
The last set number for this series

Class weka.gui.beans.BeanConnection extends java.lang.Object implements Serializable

Serialized Fields

m_source

BeanInstance m_source

m_target

BeanInstance m_target

m_eventName

java.lang.String m_eventName
The name of the event for this connection

Class weka.gui.beans.BeanInstance extends java.lang.Object implements Serializable

Serialized Fields

m_bean

java.lang.Object m_bean
Holds the bean encapsulated in this instance


m_x

int m_x

m_y

int m_y

Class weka.gui.beans.BeanVisual extends javax.swing.JPanel implements Serializable

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream ois)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Overides default read object in order to reload icons. This is necessary because for some strange reason animated gifs stop being animated after being serialized/deserialized.

Throws:
java.io.IOException - if an error occurs
java.lang.ClassNotFoundException - if an error occurs
Serialized Fields

m_iconPath

java.lang.String m_iconPath
Holds name (including path) of the static icon


m_animatedIconPath

java.lang.String m_animatedIconPath
Holds name (including path) of the animated icon


m_visualName

java.lang.String m_visualName
Name for the bean


m_visualLabel

javax.swing.JLabel m_visualLabel

m_stationary

boolean m_stationary
Container for the icon


m_pcs

java.beans.PropertyChangeSupport m_pcs

m_displayConnectors

boolean m_displayConnectors

Class weka.gui.beans.ChartEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_legendText

java.util.Vector m_legendText

m_max

double m_max

m_min

double m_min

m_reset

boolean m_reset

m_dataPoint

double[] m_dataPoint
Y values of the data points

Class weka.gui.beans.ClassAssigner extends javax.swing.JPanel implements Serializable

Serialized Fields

m_classColumn

java.lang.String m_classColumn

m_trainingProvider

java.lang.Object m_trainingProvider

m_testProvider

java.lang.Object m_testProvider

m_dataProvider

java.lang.Object m_dataProvider

m_instanceProvider

java.lang.Object m_instanceProvider

m_trainingListeners

java.util.Vector m_trainingListeners

m_testListeners

java.util.Vector m_testListeners

m_dataListeners

java.util.Vector m_dataListeners

m_instanceListeners

java.util.Vector m_instanceListeners

m_visual

BeanVisual m_visual

Class weka.gui.beans.ClassAssignerCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_caEditor

PropertySheetPanel m_caEditor

Class weka.gui.beans.Classifier extends javax.swing.JPanel implements Serializable

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_buildThread

java.lang.Thread m_buildThread

m_listenees

java.util.Hashtable m_listenees
Objects talking to us


m_batchClassifierListeners

java.util.Vector m_batchClassifierListeners
Objects listening for batch classifier events


m_incrementalClassifierListeners

java.util.Vector m_incrementalClassifierListeners
Objects listening for incremental classifier events


m_graphListeners

java.util.Vector m_graphListeners
Objects listening for graph events


m_textListeners

java.util.Vector m_textListeners
Objects listening for text events


m_trainingSet

Instances m_trainingSet
Holds training instances for batch training. Not transient because header is retained for validating any instance events that this classifier might be asked to predict in the future.


m_Classifier

Classifier m_Classifier

m_ie

IncrementalClassifierEvent m_ie

m_updateIncrementalClassifier

boolean m_updateIncrementalClassifier
If the classifier is an incremental classifier, should we update it (ie train it on incoming instances). This makes it possible incrementally test on a separate stream of instances without updating the classifier, or mix batch training/testing with incremental training/testing


m_incrementalEvent

InstanceEvent m_incrementalEvent
Event to handle when processing incremental updates


m_dummy

java.lang.Double m_dummy

Class weka.gui.beans.ClassifierCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsClassifier

Classifier m_dsClassifier

m_ClassifierEditor

GenericObjectEditor m_ClassifierEditor

m_incrementalPanel

javax.swing.JPanel m_incrementalPanel

m_updateIncrementalClassifier

javax.swing.JCheckBox m_updateIncrementalClassifier

m_panelVisible

boolean m_panelVisible

Class weka.gui.beans.ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable

Serialized Fields

m_listeners

java.util.Vector m_listeners

Class weka.gui.beans.CrossValidationFoldMaker extends AbstractTrainAndTestSetProducer implements Serializable

Serialized Fields

m_numFolds

int m_numFolds

m_randomSeed

int m_randomSeed

m_foldThread

java.lang.Thread m_foldThread

Class weka.gui.beans.CrossValidationFoldMakerCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_cvEditor

PropertySheetPanel m_cvEditor

Class weka.gui.beans.CSVDataSink extends AbstractDataSink implements Serializable

Serialized Fields

m_fileStem

java.lang.String m_fileStem

Class weka.gui.beans.DataSetEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_dataSet

Instances m_dataSet

Class weka.gui.beans.DataVisualizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_visual

BeanVisual m_visual

m_framePoppedUp

boolean m_framePoppedUp

m_design

boolean m_design
True if this bean's appearance is the design mode appearance


m_visPanel

VisualizePanel m_visPanel

m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support

Class weka.gui.beans.Filter extends javax.swing.JPanel implements Serializable

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_filterThread

java.lang.Thread m_filterThread

m_listenees

java.util.Hashtable m_listenees
Objects talking to us


m_trainingListeners

java.util.Vector m_trainingListeners
Objects listening for training set events


m_testListeners

java.util.Vector m_testListeners
Objects listening for test set events


m_instanceListeners

java.util.Vector m_instanceListeners
Objects listening for instance events


m_dataListeners

java.util.Vector m_dataListeners
Objects listening for data set events


m_Filter

Filter m_Filter
The filter to use.


m_ie

InstanceEvent m_ie
Instance event object for passing on filtered instance streams

Class weka.gui.beans.FilterCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_filter

Filter m_filter

m_filterEditor

GenericObjectEditor m_filterEditor

Class weka.gui.beans.GraphEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_graphString

java.lang.String m_graphString

m_graphTitle

java.lang.String m_graphTitle

Class weka.gui.beans.GraphViewer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_visual

BeanVisual m_visual

Class weka.gui.beans.IncrementalClassifierEvaluator extends AbstractEvaluator implements Serializable

Serialized Fields

m_listeners

java.util.Vector m_listeners

m_textListeners

java.util.Vector m_textListeners

m_dataLegend

java.util.Vector m_dataLegend

m_ce

ChartEvent m_ce

m_dataPoint

double[] m_dataPoint

m_reset

boolean m_reset

m_min

double m_min

m_max

double m_max

Class weka.gui.beans.IncrementalClassifierEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_status

int m_status

m_classifier

Classifier m_classifier

m_currentInstance

Instance m_currentInstance

Class weka.gui.beans.InstanceEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_instance

Instance m_instance

m_status

int m_status

Class weka.gui.beans.KnowledgeFlow extends javax.swing.JPanel implements Serializable

Serialized Fields

m_mode

int m_mode

m_toolBarGroup

javax.swing.ButtonGroup m_toolBarGroup
Button group to manage all toolbar buttons


m_toolBarBean

java.lang.Object m_toolBarBean
Holds the selected toolbar bean


m_beanLayout

KnowledgeFlow.BeanLayout m_beanLayout
The layout area


m_toolBars

javax.swing.JTabbedPane m_toolBars
Tabbed pane to hold tool bars


m_pointerB

javax.swing.JToggleButton m_pointerB

m_saveB

javax.swing.JButton m_saveB

m_loadB

javax.swing.JButton m_loadB

m_stopB

javax.swing.JButton m_stopB

m_helpB

javax.swing.JButton m_helpB

m_editElement

BeanInstance m_editElement
Reference to bean being manipulated


m_sourceEventSetDescriptor

java.beans.EventSetDescriptor m_sourceEventSetDescriptor
Event set descriptor for the bean being manipulated


m_oldX

int m_oldX
Used to record screen coordinates during move and connect operations


m_oldY

int m_oldY
Used to record screen coordinates during move and connect operations


m_startX

int m_startX

m_startY

int m_startY

m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting layout files


m_logPanel

LogPanel m_logPanel

m_bcSupport

java.beans.beancontext.BeanContextSupport m_bcSupport

Class weka.gui.beans.KnowledgeFlow.BeanLayout extends javax.swing.JPanel implements Serializable

Class weka.gui.beans.Loader extends AbstractDataSource implements Serializable

Serialized Fields

m_ioThread

Loader.LoadThread m_ioThread
Thread for doing IO in


m_Loader

Loader m_Loader
Loader


m_ie

InstanceEvent m_ie

m_instanceEventTargets

int m_instanceEventTargets
Keep track of how many listeners for different types of events there are.


m_dataSetEventTargets

int m_dataSetEventTargets

Class weka.gui.beans.LoaderCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsLoader

Loader m_dsLoader

m_fileEditor

FileEditor m_fileEditor

m_LoaderEditor

GenericObjectEditor m_LoaderEditor

Class weka.gui.beans.PredictionAppender extends javax.swing.JPanel implements Serializable

Serialized Fields

m_dataSourceListeners

java.util.Vector m_dataSourceListeners
Objects listenening for dataset events


m_instanceListeners

java.util.Vector m_instanceListeners
Objects listening for instances events


m_listenee

java.lang.Object m_listenee
Non null if this object is a target for any events.


m_visual

BeanVisual m_visual

m_appendProbabilities

boolean m_appendProbabilities
Append classifier's predicted probabilities (if the class is discrete and the classifier is a distribution classifier)


m_logger

Logger m_logger

m_incrementalStructure

Instances m_incrementalStructure

m_instanceEvent

InstanceEvent m_instanceEvent

m_instanceVals

double[] m_instanceVals

Class weka.gui.beans.PredictionAppenderCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_paEditor

PropertySheetPanel m_paEditor

Class weka.gui.beans.ScatterPlotMatrix extends DataVisualizer implements Serializable

Serialized Fields

m_matrixPanel

MatrixPanel m_matrixPanel

Class weka.gui.beans.StripChart extends javax.swing.JPanel implements Serializable

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream ois)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Provide some necessary initialization after object has been deserialized.

Throws:
java.io.IOException - if an error occurs
java.lang.ClassNotFoundException - if an error occurs
Serialized Fields

m_colorList

java.awt.Color[] m_colorList
default colours for colouring lines


m_iheight

int m_iheight
Width and height of the off screen image


m_iwidth

int m_iwidth

m_max

double m_max
Max value for the y axis


m_min

double m_min
Min value for the y axis


m_yScaleUpdate

boolean m_yScaleUpdate
Scale update requested


m_oldMax

double m_oldMax

m_oldMin

double m_oldMin

m_labelFont

java.awt.Font m_labelFont

m_labelMetrics

java.awt.FontMetrics m_labelMetrics

m_legendText

java.util.Vector m_legendText

m_scalePanel

javax.swing.JPanel m_scalePanel
Class providing a panel for displaying the y axis


m_legendPanel

javax.swing.JPanel m_legendPanel
Class providing a panel for the legend


m_dataList

java.util.LinkedList m_dataList
Holds the data to be plotted. Entries in the list are arrays of y points.


m_previousY

double[] m_previousY

m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee

m_xValFreq

int m_xValFreq
Print x axis labels every m_xValFreq points


m_xCount

int m_xCount

m_refreshWidth

int m_refreshWidth
Shift the plot by this many pixels every time a point is plotted


m_refreshFrequency

int m_refreshFrequency
Plot every m_refreshFrequency'th point

Class weka.gui.beans.StripChartCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_cvEditor

PropertySheetPanel m_cvEditor

Class weka.gui.beans.TestSetEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_testSet

Instances m_testSet
The test set instances


m_setNumber

int m_setNumber
what number is this test set (ie fold 2 of 10 folds)


m_maxSetNumber

int m_maxSetNumber
Maximum number of sets (ie 10 in a 10 fold)

Class weka.gui.beans.TestSetMaker extends AbstractTestSetProducer implements Serializable

Class weka.gui.beans.TextEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_text

java.lang.String m_text
The text


m_textTitle

java.lang.String m_textTitle
The title for the text. Could be used in a list component

Class weka.gui.beans.TextViewer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_visual

BeanVisual m_visual

m_design

boolean m_design
True if this bean's appearance is the design mode appearance


m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support

Class weka.gui.beans.TrainingSetEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_trainingSet

Instances m_trainingSet
The training instances


m_setNumber

int m_setNumber
what number is this training set (ie fold 2 of 10 folds)


m_maxSetNumber

int m_maxSetNumber
Maximum number of sets (ie 10 in a 10 fold)

Class weka.gui.beans.TrainingSetMaker extends AbstractTrainingSetProducer implements Serializable

Class weka.gui.beans.TrainTestSplitMaker extends AbstractTrainAndTestSetProducer implements Serializable

Serialized Fields

m_trainPercentage

int m_trainPercentage

m_randomSeed

int m_randomSeed

m_splitThread

java.lang.Thread m_splitThread

Class weka.gui.beans.TrainTestSplitMakerCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_splitEditor

PropertySheetPanel m_splitEditor


Package weka.gui.graphvisualizer

Class weka.gui.graphvisualizer.BIFFormatException extends java.lang.Exception implements Serializable

Class weka.gui.graphvisualizer.GraphVisualizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_nodes

FastVector m_nodes
Vector containing nodes


m_edges

FastVector m_edges
Vector containing edges


m_le

LayoutEngine m_le
The current LayoutEngine


m_gp

GraphVisualizer.GraphPanel m_gp
Panel actually displaying the graph


graphID

java.lang.String graphID
String containing graph's name


m_jBtSave

javax.swing.JButton m_jBtSave
Save button to save the current graph in DOT or XMLBIF format. The graph should be layed out again to get the original form if reloaded from command line, as the formats do not allow saving specific information for a properly layed out graph.


ICONPATH

java.lang.String ICONPATH
path for icons

See Also:
Constant Field Values

fm

java.awt.FontMetrics fm

scale

double scale

nodeHeight

int nodeHeight

nodeWidth

int nodeWidth

paddedNodeWidth

int paddedNodeWidth

jTfNodeWidth

javax.swing.JTextField jTfNodeWidth
TextField for node's width


jTfNodeHeight

javax.swing.JTextField jTfNodeHeight
TextField for nodes height


jBtLayout

javax.swing.JButton jBtLayout
Button for laying out the graph again, necessary after changing node's size or some other property of the layout engine


maxStringWidth

int maxStringWidth
used for setting appropriate node size


zoomPercents

int[] zoomPercents
used when using zoomIn and zoomOut buttons


m_js

javax.swing.JScrollPane m_js
this contains the m_gp GraphPanel

Class weka.gui.graphvisualizer.LayoutCompleteEvent extends java.util.EventObject implements Serializable


Package weka.gui.experiment

Class weka.gui.experiment.AlgorithmListPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Exp

Experiment m_Exp
The experiment to set the algorithm list of


m_List

javax.swing.JList m_List
The component displaying the algorithm list


m_AddBut

javax.swing.JButton m_AddBut
Click to add an algorithm


m_DeleteBut

javax.swing.JButton m_DeleteBut
Click to remove the selected dataset from the list


m_ClassifierEditor

GenericObjectEditor m_ClassifierEditor
Lets the user configure the classifier


m_PD

PropertyDialog m_PD
The currently displayed property dialog, if any


m_AlgorithmListModel

javax.swing.DefaultListModel m_AlgorithmListModel
The list model used

Class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer extends javax.swing.DefaultListCellRenderer implements Serializable

Class weka.gui.experiment.DatasetListPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Exp

Experiment m_Exp
The experiment to set the dataset list of


m_List

javax.swing.JList m_List
The component displaying the dataset list


m_AddBut

javax.swing.JButton m_AddBut
Click to add a dataset


m_DeleteBut

javax.swing.JButton m_DeleteBut
Click to remove the selected dataset from the list


m_relativeCheck

javax.swing.JCheckBox m_relativeCheck
Make file paths relative to the user (start) directory


m_ArffFilter

javax.swing.filechooser.FileFilter m_ArffFilter
A filter to ensure only arff files get selected


m_UserDir

java.io.File m_UserDir
The user (start) directory


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser component

Class weka.gui.experiment.DistributeExperimentPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Exp

RemoteExperiment m_Exp
The experiment to configure.


m_enableDistributedExperiment

javax.swing.JCheckBox m_enableDistributedExperiment
Distribute the current experiment to remote hosts


m_configureHostNames

javax.swing.JButton m_configureHostNames
Popup the HostListPanel


m_hostList

HostListPanel m_hostList
The host list panel


m_splitByDataSet

javax.swing.JRadioButton m_splitByDataSet
Split experiment up by data set.


m_splitByRun

javax.swing.JRadioButton m_splitByRun
Split experiment up by run number.


m_radioListener

java.awt.event.ActionListener m_radioListener
Handle radio buttons

Class weka.gui.experiment.Experimenter extends javax.swing.JPanel implements Serializable

Serialized Fields

m_SetupPanel

SetupModePanel m_SetupPanel
The panel for configuring the experiment


m_RunPanel

RunPanel m_RunPanel
The panel for running the experiment


m_ResultsPanel

ResultsPanel m_ResultsPanel
The panel for analysing experimental results


m_TabbedPane

javax.swing.JTabbedPane m_TabbedPane
The tabbed pane that controls which sub-pane we are working with


m_ClassFirst

boolean m_ClassFirst
True if the class attribute is the first attribute for all datasets involved in this experiment.

Class weka.gui.experiment.GeneratorPropertyIteratorPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_ConfigureBut

javax.swing.JButton m_ConfigureBut
Click to select the property to iterate over


m_StatusBox

javax.swing.JComboBox m_StatusBox
Controls whether the custom iterator is used or not


m_ArrayEditor

GenericArrayEditor m_ArrayEditor
Allows editing of the custom property values


m_Exp

Experiment m_Exp
The experiment this all applies to


m_Listeners

FastVector m_Listeners
Listeners who want to be notified about editing status of this panel

Class weka.gui.experiment.HostListPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Exp

RemoteExperiment m_Exp
The remote experiment to set the host list of


m_List

javax.swing.JList m_List
The component displaying the host list


m_DeleteBut

javax.swing.JButton m_DeleteBut
Click to remove the selected host from the list


m_HostField

javax.swing.JTextField m_HostField
The field with which to enter host names

Class weka.gui.experiment.ResultsPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_FromFileBut

javax.swing.JButton m_FromFileBut
Click to load results from a file


m_FromDBaseBut

javax.swing.JButton m_FromDBaseBut
Click to load results from a database


m_FromExpBut

javax.swing.JButton m_FromExpBut
Click to get results from the destination given in the experiment


m_FromLab

javax.swing.JLabel m_FromLab
Displays a message about the current result set


m_DatasetModel

javax.swing.DefaultComboBoxModel m_DatasetModel
The model embedded in m_DatasetCombo


m_RunModel

javax.swing.DefaultComboBoxModel m_RunModel
The model embedded in m_RunCombo


m_CompareModel

javax.swing.DefaultComboBoxModel m_CompareModel
The model embedded in m_CompareCombo


m_TestsModel

javax.swing.DefaultListModel m_TestsModel
The model embedded in m_TestsList


m_DatasetKeyLabel

javax.swing.JLabel m_DatasetKeyLabel
Displays the currently selected column names for the scheme & options


m_DatasetKeyBut

javax.swing.JButton m_DatasetKeyBut
Click to edit the columns used to determine the scheme


m_DatasetKeyModel

javax.swing.DefaultListModel m_DatasetKeyModel
Stores the list of attributes for selecting the scheme columns


m_DatasetKeyList

javax.swing.JList m_DatasetKeyList
Displays the list of selected columns determining the scheme


m_RunCombo

javax.swing.JComboBox m_RunCombo
Lets the user select which column contains the run number


m_ResultKeyLabel

javax.swing.JLabel m_ResultKeyLabel
Displays the currently selected column names for the scheme & options


m_ResultKeyBut

javax.swing.JButton m_ResultKeyBut
Click to edit the columns used to determine the scheme


m_ResultKeyModel

javax.swing.DefaultListModel m_ResultKeyModel
Stores the list of attributes for selecting the scheme columns


m_ResultKeyList

javax.swing.JList m_ResultKeyList
Displays the list of selected columns determining the scheme


m_TestsButton

javax.swing.JButton m_TestsButton
Lets the user select which scheme to base comparisons against


m_TestsList

javax.swing.JList m_TestsList
Holds the list of schemes to base the test against


m_CompareCombo

javax.swing.JComboBox m_CompareCombo
Lets the user select which performance measure to analyze


m_SigTex

javax.swing.JTextField m_SigTex
Lets the user edit the test significance


m_ShowStdDevs

javax.swing.JCheckBox m_ShowStdDevs
Lets the user select whether standard deviations are to be output or not


m_PerformBut

javax.swing.JButton m_PerformBut
Click to start the test


m_SaveOutBut

javax.swing.JButton m_SaveOutBut
Click to save test output to a file


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_OutText

javax.swing.JTextArea m_OutText
Displays the output of tests


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_ArffFilter

javax.swing.filechooser.FileFilter m_ArffFilter
Filter to ensure only arff files are selected for result files


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting result files


m_TTester

PairedTTester m_TTester
The PairedTTester object


m_Instances

Instances m_Instances
The instances we're extracting results from


m_InstanceQuery

InstanceQuery m_InstanceQuery
Does any database querying for us


m_LoadThread

java.lang.Thread m_LoadThread
A thread to load results instances from a file or database


m_Exp

Experiment m_Exp
An experiment (used for identifying a result source) -- optional


m_ConfigureListener

java.awt.event.ActionListener m_ConfigureListener
An actionlisteners that updates ttest settings


COMBO_SIZE

java.awt.Dimension COMBO_SIZE

Class weka.gui.experiment.RunNumberPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_LowerText

javax.swing.JTextField m_LowerText
Configures the lower run number


m_UpperText

javax.swing.JTextField m_UpperText
Configures the upper run number


m_Exp

Experiment m_Exp
The experiment being configured

Class weka.gui.experiment.RunPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_StartBut

javax.swing.JButton m_StartBut
Click to start running the experiment


m_StopBut

javax.swing.JButton m_StopBut
Click to signal the running experiment to halt


m_Log

LogPanel m_Log

m_Exp

Experiment m_Exp
The experiment to run


m_RunThread

java.lang.Thread m_RunThread
The thread running the experiment

Class weka.gui.experiment.SetupModePanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_SimpleSetupRBut

javax.swing.JRadioButton m_SimpleSetupRBut
The button for choosing simple setup mode


m_AdvancedSetupRBut

javax.swing.JRadioButton m_AdvancedSetupRBut
The button for choosing advanced setup mode


m_simplePanel

SimpleSetupPanel m_simplePanel
The simple setup panel


m_advancedPanel

SetupPanel m_advancedPanel
The advanced setup panel

Class weka.gui.experiment.SetupPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Exp

Experiment m_Exp
The experiment being configured


m_OpenBut

javax.swing.JButton m_OpenBut
Click to load an experiment


m_SaveBut

javax.swing.JButton m_SaveBut
Click to save an experiment


m_NewBut

javax.swing.JButton m_NewBut
Click to create a new experiment with default settings


m_ExpFilter

javax.swing.filechooser.FileFilter m_ExpFilter
A filter to ensure only experiment files get shown in the chooser


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting experiments


m_RPEditor

GenericObjectEditor m_RPEditor
The ResultProducer editor


m_RPEditorPanel

PropertyPanel m_RPEditorPanel
The panel to contain the ResultProducer editor


m_RLEditor

GenericObjectEditor m_RLEditor
The ResultListener editor


m_RLEditorPanel

PropertyPanel m_RLEditorPanel
The panel to contain the ResultListener editor


m_GeneratorPropertyPanel

GeneratorPropertyIteratorPanel m_GeneratorPropertyPanel
The panel that configures iteration on custom resultproducer property


m_RunNumberPanel

RunNumberPanel m_RunNumberPanel
The panel for configuring run numbers


m_DistributeExperimentPanel

DistributeExperimentPanel m_DistributeExperimentPanel
The panel for enabling a distributed experiment


m_DatasetListPanel

DatasetListPanel m_DatasetListPanel
The panel for configuring selected datasets


m_NotesText

javax.swing.JTextArea m_NotesText
Area for user notes Default of 5 rows


m_Support

java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.


m_advanceDataSetFirst

javax.swing.JRadioButton m_advanceDataSetFirst
Click to advacne data set before custom generator


m_advanceIteratorFirst

javax.swing.JRadioButton m_advanceIteratorFirst
Click to advance custom generator before data set


m_RadioListener

java.awt.event.ActionListener m_RadioListener
Handle radio buttons

Class weka.gui.experiment.SimpleSetupPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Exp

Experiment m_Exp
The experiment being configured


m_modePanel

SetupModePanel m_modePanel
The panel which switched between simple and advanced setup modes


m_destinationDatabaseURL

java.lang.String m_destinationDatabaseURL
The database destination URL to store results into


m_destinationFilename

java.lang.String m_destinationFilename
The filename to store results into


m_numFolds

int m_numFolds
The number of folds for a cross-validation experiment


m_trainPercent

double m_trainPercent
The training percentage for a train/test split experiment


m_numRepetitions

int m_numRepetitions
The number of times to repeat the sub-experiment


m_userHasBeenAskedAboutConversion

boolean m_userHasBeenAskedAboutConversion
Whether or not the user has consented for the experiment to be simplified


m_csvFileFilter

ExtensionFileFilter m_csvFileFilter
Filter for choosing CSV files


m_arffFileFilter

ExtensionFileFilter m_arffFileFilter
FIlter for choosing ARFF files


m_OpenBut

javax.swing.JButton m_OpenBut
Click to load an experiment


m_SaveBut

javax.swing.JButton m_SaveBut
Click to save an experiment


m_NewBut

javax.swing.JButton m_NewBut
Click to create a new experiment with default settings


m_ExpFilter

javax.swing.filechooser.FileFilter m_ExpFilter
A filter to ensure only experiment files get shown in the chooser


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting experiments


m_DestFileChooser

javax.swing.JFileChooser m_DestFileChooser
The file chooser for selecting result destinations


m_ResultsDestinationCBox

javax.swing.JComboBox m_ResultsDestinationCBox
Combo box for choosing experiment destination type


m_ResultsDestinationPathLabel

javax.swing.JLabel m_ResultsDestinationPathLabel
Label for destination field


m_ResultsDestinationPathTField

javax.swing.JTextField m_ResultsDestinationPathTField
Input field for result destination path


m_BrowseDestinationButton

javax.swing.JButton m_BrowseDestinationButton
Button for browsing destination files


m_ExperimentTypeCBox

javax.swing.JComboBox m_ExperimentTypeCBox
Combo box for choosing experiment type


m_ExperimentParameterLabel

javax.swing.JLabel m_ExperimentParameterLabel
Label for parameter field


m_ExperimentParameterTField

javax.swing.JTextField m_ExperimentParameterTField
Input field for experiment parameter


m_ExpClassificationRBut

javax.swing.JRadioButton m_ExpClassificationRBut
Radio button for choosing classification experiment


m_ExpRegressionRBut

javax.swing.JRadioButton m_ExpRegressionRBut
Radio button for choosing regression experiment


m_NumberOfRepetitionsTField

javax.swing.JTextField m_NumberOfRepetitionsTField
Input field for number of repetitions


m_OrderDatasetsFirstRBut

javax.swing.JRadioButton m_OrderDatasetsFirstRBut
Radio button for choosing datasets first in order of execution


m_OrderAlgorithmsFirstRBut

javax.swing.JRadioButton m_OrderAlgorithmsFirstRBut
Radio button for choosing algorithms first in order of execution


m_DatasetListPanel

DatasetListPanel m_DatasetListPanel
The panel for configuring selected datasets


m_AlgorithmListPanel

AlgorithmListPanel m_AlgorithmListPanel
The panel for configuring selected algorithms


m_NotesText

javax.swing.JTextArea m_NotesText
Area for user notes Default of 5 rows


m_Support

java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.


Package weka.gui.streams

Class weka.gui.streams.InstanceCounter extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Count_Lab

javax.swing.JLabel m_Count_Lab

m_Count

int m_Count

m_Debug

boolean m_Debug

Class weka.gui.streams.InstanceEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_ID

int m_ID

Class weka.gui.streams.InstanceJoiner extends java.lang.Object implements Serializable

Serialized Fields

listeners

java.util.Vector listeners
The listeners


b_Debug

boolean b_Debug
Debugging mode


m_InputFormat

Instances m_InputFormat
The input format for instances


m_OutputInstance

Instance m_OutputInstance
The current output instance


b_FirstInputFinished

boolean b_FirstInputFinished
Whether the first input batch has finished


b_SecondInputFinished

boolean b_SecondInputFinished

Class weka.gui.streams.InstanceLoader extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Listeners

java.util.Vector m_Listeners

m_LoaderThread

java.lang.Thread m_LoaderThread

m_OutputInstance

Instance m_OutputInstance

m_OutputInstances

Instances m_OutputInstances

m_Debug

boolean m_Debug

m_StartBut

javax.swing.JButton m_StartBut

m_FileNameTex

javax.swing.JTextField m_FileNameTex

Class weka.gui.streams.InstanceSavePanel extends java.awt.Panel implements Serializable

Serialized Fields

count_Lab

java.awt.Label count_Lab

m_Count

int m_Count

arffFile_Tex

java.awt.TextField arffFile_Tex

b_Debug

boolean b_Debug

outputWriter

java.io.PrintWriter outputWriter

Class weka.gui.streams.InstanceTable extends javax.swing.JPanel implements Serializable

Serialized Fields

m_InstanceTable

javax.swing.JTable m_InstanceTable

m_Debug

boolean m_Debug

m_Clear

boolean m_Clear

m_UpdateString

java.lang.String m_UpdateString

m_Instances

Instances m_Instances

Class weka.gui.streams.InstanceViewer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_OutputTex

javax.swing.JTextArea m_OutputTex

m_Debug

boolean m_Debug

m_Clear

boolean m_Clear

m_UpdateString

java.lang.String m_UpdateString


Package weka.gui.visualize

Class weka.gui.visualize.AttributePanel extends javax.swing.JScrollPane implements Serializable

Serialized Fields

m_plotInstances

Instances m_plotInstances
The instances to be plotted


m_maxC

double m_maxC
Holds the min and max values of the colouring attributes


m_minC

double m_minC

m_cIndex

int m_cIndex

m_xIndex

int m_xIndex

m_yIndex

int m_yIndex

m_colorList

FastVector m_colorList
The colour map to use for colouring points


m_DefaultColors

java.awt.Color[] m_DefaultColors
default colours for colouring discrete class


m_Listeners

FastVector m_Listeners
The list of things listening to this panel


m_heights

int[] m_heights
Holds the random height for each instance.


m_span

javax.swing.JPanel m_span
The container window for the attribute bars, and also where the X,Y or B get printed.


m_barColour

java.awt.Color m_barColour
The default colour to use for the background of the bars if a colour is not defined in Visualize.props

Class weka.gui.visualize.AttributePanel.AttributeSpacing extends javax.swing.JPanel implements Serializable

Serialized Fields

m_maxVal

double m_maxVal
The min and max values for this attribute.


m_minVal

double m_minVal

m_attrib

Attribute m_attrib
The attribute itself.


m_attribIndex

int m_attribIndex
The index for this attribute.


m_cached

int[] m_cached
The x position of each point.


m_pointDrawn

boolean[][] m_pointDrawn
A temporary array used to strike any instances that would be drawn redundantly.


m_oldWidth

int m_oldWidth
Used to determine if the positions need to be recalculated.

Class weka.gui.visualize.ClassPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_isEnabled

boolean m_isEnabled
True when the panel has been enabled (ie after setNumeric or setNominal has been called


m_isNumeric

boolean m_isNumeric
True if the colouring attribute is numeric


m_spectrumHeight

int m_spectrumHeight
The height of the spectrum for numeric class

See Also:
Constant Field Values

m_maxC

double m_maxC
The maximum value for the colouring attribute


m_minC

double m_minC
The minimum value for the colouring attribute


m_tickSize

int m_tickSize
The size of the ticks

See Also:
Constant Field Values

m_labelMetrics

java.awt.FontMetrics m_labelMetrics
Font metrics


m_labelFont

java.awt.Font m_labelFont
The font used in labeling


m_HorizontalPad

int m_HorizontalPad
The amount of space to leave either side of the legend


m_precisionC

int m_precisionC
The precision with which to display real values


m_fieldWidthC

int m_fieldWidthC
Field width for numeric values


m_oldWidth

int m_oldWidth
The old width.


m_Instances

Instances m_Instances
Instances being plotted


m_cIndex

int m_cIndex
Index of the colouring attribute


m_colorList

FastVector m_colorList
the list of colours to use for colouring nominal attribute labels


m_Repainters

FastVector m_Repainters
An optional list of Components that use the colour list maintained by this class. If the user changes a colour using the colour chooser, then these components need to be repainted in order to display the change


m_ColourChangeListeners

FastVector m_ColourChangeListeners
An optional list of listeners who want to know when a colour changes. Listeners are notified via an ActionEvent


m_DefaultColors

java.awt.Color[] m_DefaultColors
default colours for colouring discrete class

Class weka.gui.visualize.LegendPanel extends javax.swing.JScrollPane implements Serializable

Serialized Fields

m_plots

FastVector m_plots
the list of plot elements


m_span

javax.swing.JPanel m_span
the panel that contains the legend entries


m_Repainters

FastVector m_Repainters
a list of components that need to be repainted when a colour is changed

Class weka.gui.visualize.LegendPanel.LegendEntry extends javax.swing.JPanel implements Serializable

Serialized Fields

m_plotData

PlotData2D m_plotData
the data for this legend entry


m_dataIndex

int m_dataIndex
the index (in the list of plots) of the data for this legend--- used to draw the correct shape for this data


m_legendText

javax.swing.JLabel m_legendText
the text part of this legend


m_pointShape

javax.swing.JPanel m_pointShape
displays the point shape associated with this legend entry

Class weka.gui.visualize.MatrixPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_plotsPanel

MatrixPanel.Plot m_plotsPanel
The that panel contains the actual matrix


m_cp

ClassPanel m_cp
The panel that displays the legend of the colouring attribute


optionsPanel

javax.swing.JPanel optionsPanel
The panel that contains all the buttons and tools, i.e. resize, jitter bars and sub-sampling buttons etc on the bottom of the panel


jp

javax.swing.JSplitPane jp
Split pane for splitting the matrix and the buttons and bars


m_updateBt

javax.swing.JButton m_updateBt
The button that updates the display to reflect the changes made by the user. E.g. changed attribute set for the matrix


m_selAttrib

javax.swing.JButton m_selAttrib
The button to display a window to select attributes


m_data

Instances m_data
The dataset for which this panel will display the plot matrix for


m_attribList

javax.swing.JList m_attribList
The list for selecting the attributes to display the plot matrix


m_js

javax.swing.JScrollPane m_js
The scroll pane to scrolling the matrix


m_classAttrib

javax.swing.JComboBox m_classAttrib
The combo box to allow user to select the colouring attribute


m_plotSize

javax.swing.JSlider m_plotSize
The slider to adjust the size of the cells in the matrix


m_pointSize

javax.swing.JSlider m_pointSize
The slider to adjust the size of the datapoints


m_jitter

javax.swing.JSlider m_jitter
The slider to add jitter to the plots


rnd

java.util.Random rnd
For adding random jitter


jitterVals

int[][] jitterVals
Array containing precalculated jitter values


datapointSize

int datapointSize
This stores the size of the datapoint


m_resamplePercent

javax.swing.JTextField m_resamplePercent
The text area for percentage to resample data


m_resampleBt

javax.swing.JButton m_resampleBt
The label for resample percentage


m_rseed

javax.swing.JTextField m_rseed
Random seed for random subsample


origDist

javax.swing.JRadioButton origDist
For selecting same class distribution in the subsample as in the input


unifDist

javax.swing.JRadioButton unifDist
For selecting uniform class distribution in the subsample


distGroup

javax.swing.ButtonGroup distGroup
Button group for subsampling radio buttons


m_plotSizeLb

javax.swing.JLabel m_plotSizeLb
Displays the current size beside the slider bar for cell size


m_pointSizeLb

javax.swing.JLabel m_pointSizeLb
Displays the current size beside the slider bar for point size


m_selectedAttribs

int[] m_selectedAttribs
This array contains the indices of the attributes currently selected


m_classIndex

int m_classIndex
This contains the index of the currently selected colouring attribute


m_points

int[][] m_points
This is a local array cache for all the instance values for faster rendering


m_pointColors

int[] m_pointColors
This is an array cache for the colour of each of the instances depending on the colouring attribute. If the colouring attribute is nominal then it contains the index of the colour in our colour list. Otherwise, for numeric colouring attribute, it contains the precalculated red component for each instance's colour


m_missing

boolean[][] m_missing
Contains true for each value that is missing, for each instance


m_type

int[][] m_type
This array contains:
m_type[0][i] = [type of attribute, nominal, string or numeric]
m_type[1][i] = [number of discrete values of nominal or string attribute
or same as m_type[0][i] for numeric attribute]


m_plotLBSizeD

java.awt.Dimension m_plotLBSizeD
Stores the maximum size for PlotSize label to keep it's size constant


m_pointLBSizeD

java.awt.Dimension m_pointLBSizeD
Stores the maximum size for PointSize label to keep it's size constant


m_colorList

FastVector m_colorList
Contains discrete colours for colouring for nominal attributes


fontColor

java.awt.Color fontColor
color for the font used in column and row names


f

java.awt.Font f
font used in column and row names

Class weka.gui.visualize.Plot2D extends javax.swing.JPanel implements Serializable

Serialized Fields

m_axisColour

java.awt.Color m_axisColour
Default colour for the axis


m_backgroundColour

java.awt.Color m_backgroundColour
Default colour for the plot background


m_plots

FastVector m_plots
The plots to display


m_masterPlot

PlotData2D m_masterPlot
The master plot


m_masterName

java.lang.String m_masterName
The name of the master plot


m_plotInstances

Instances m_plotInstances
The instances to be plotted


m_plotCompanion

Plot2DCompanion m_plotCompanion
An optional "compainion" of the panel. If specified, this class will get to do its thing with our graphics context before we do any drawing. Eg. the visualize panel may need to draw polygons etc. before we draw plot axis and data points


m_InstanceInfo

javax.swing.JFrame m_InstanceInfo
For popping up text info on data points


m_InstanceInfoText

javax.swing.JTextArea m_InstanceInfoText

m_colorList

FastVector m_colorList
The list of the colors used


m_DefaultColors

java.awt.Color[] m_DefaultColors
default colours for colouring discrete class


m_xIndex

int m_xIndex
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing


m_yIndex

int m_yIndex

m_cIndex

int m_cIndex

m_sIndex

int m_sIndex

m_maxX

double m_maxX
Holds the min and max values of the x, y and colouring attributes over all plots


m_minX

double m_minX

m_maxY

double m_maxY

m_minY

double m_minY

m_maxC

double m_maxC

m_minC

double m_minC

m_axisPad

int m_axisPad
Axis padding

See Also:
Constant Field Values

m_tickSize

int m_tickSize
Tick size

See Also:
Constant Field Values

m_XaxisStart

int m_XaxisStart
the offsets of the axes once label metrics are calculated


m_YaxisStart

int m_YaxisStart

m_XaxisEnd

int m_XaxisEnd

m_YaxisEnd

int m_YaxisEnd

m_plotResize

boolean m_plotResize
if the user resizes the window, or the attributes selected for the attributes change, then the lookup table for points needs to be recalculated


m_axisChanged

boolean m_axisChanged
if the user changes attribute assigned to an axis


m_drawnPoints

int[][] m_drawnPoints
An array used to show if a point is hidden or not. This is used for speeding up the drawing of the plot panel although I am not sure how much performance this grants over not having it.


m_labelFont

java.awt.Font m_labelFont
Font for labels


m_labelMetrics

java.awt.FontMetrics m_labelMetrics

m_JitterVal

int m_JitterVal
the level of jitter


m_JRand

java.util.Random m_JRand
random values for perterbing the data points


m_pointLookup

double[][] m_pointLookup
lookup table for plotted points

Class weka.gui.visualize.ThresholdVisualizePanel extends VisualizePanel implements Serializable

Serialized Fields

m_ROCString

java.lang.String m_ROCString
The string to add to the Plot Border.


m_savePanelBorderText

java.lang.String m_savePanelBorderText
Original border text

Class weka.gui.visualize.VisualizePanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_DefaultColors

java.awt.Color[] m_DefaultColors
default colours for colouring discrete class


m_XCombo

javax.swing.JComboBox m_XCombo
Lets the user select the attribute for the x axis


m_YCombo

javax.swing.JComboBox m_YCombo
Lets the user select the attribute for the y axis


m_ColourCombo

javax.swing.JComboBox m_ColourCombo
Lets the user select the attribute to use for colouring


m_ShapeCombo

javax.swing.JComboBox m_ShapeCombo
Lets the user select the shape they want to create for instance selection.


m_submit

javax.swing.JButton m_submit
Button for the user to enter the splits.


m_cancel

javax.swing.JButton m_cancel
Button for the user to remove all splits.


m_saveBut

javax.swing.JButton m_saveBut
Button for the user to save the visualized set of instances


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
Stop the combos from growing out of control


m_FileChooser

javax.swing.JFileChooser m_FileChooser
file chooser for saving instances


m_ArffFilter

javax.swing.filechooser.FileFilter m_ArffFilter
Filter to ensure only arff files are selected


m_JitterLab

javax.swing.JLabel m_JitterLab
Label for the jitter slider


m_Jitter

javax.swing.JSlider m_Jitter
The jitter slider


m_plot

VisualizePanel.PlotPanel m_plot
The panel that displays the plot


m_attrib

AttributePanel m_attrib
The panel that displays the attributes , using color to represent another attribute.


m_legendPanel

LegendPanel m_legendPanel
The panel that displays legend info if there is more than one plot


m_plotSurround

javax.swing.JPanel m_plotSurround
Panel that surrounds the plot panel with a titled border


m_classSurround

javax.swing.JPanel m_classSurround
Panel that surrounds the class panel with a titled border


listener

java.awt.event.ActionListener listener
An optional listener that we will inform when ComboBox selections change


m_splitListener

VisualizePanelListener m_splitListener
An optional listener that we will inform when the user creates a split to seperate instances.


m_plotName

java.lang.String m_plotName
The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)


m_classPanel

ClassPanel m_classPanel
The panel that displays the legend for the colouring attribute


m_colorList

FastVector m_colorList
The list of the colors used


m_preferredXDimension

java.lang.String m_preferredXDimension
These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file


m_preferredYDimension

java.lang.String m_preferredYDimension

m_preferredColourDimension

java.lang.String m_preferredColourDimension

m_showAttBars

boolean m_showAttBars
Show the attribute bar panel


m_Log

Logger m_Log
the logger

Class weka.gui.visualize.VisualizePanel.PlotPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_plot2D

Plot2D m_plot2D
The actual generic plotting panel


m_plotInstances

Instances m_plotInstances
The instances from the master plot


m_originalPlot

PlotData2D m_originalPlot
The master plot


m_xIndex

int m_xIndex
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing


m_yIndex

int m_yIndex

m_cIndex

int m_cIndex

m_sIndex

int m_sIndex

m_axisPad

int m_axisPad
Axis padding

See Also:
Constant Field Values

m_tickSize

int m_tickSize
Tick size

See Also:
Constant Field Values

m_XaxisStart

int m_XaxisStart
the offsets of the axes once label metrics are calculated


m_YaxisStart

int m_YaxisStart

m_XaxisEnd

int m_XaxisEnd

m_YaxisEnd

int m_YaxisEnd

m_createShape

boolean m_createShape
True if the user is currently dragging a box.


m_shapes

FastVector m_shapes
contains all the shapes that have been drawn for these attribs


m_shapePoints

FastVector m_shapePoints
contains the points of the shape currently being drawn.


m_newMousePos

java.awt.Dimension m_newMousePos
contains the position of the mouse (used for rubberbanding).


Package weka.gui.boundaryvisualizer

Class weka.gui.boundaryvisualizer.BoundaryPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_Colors

FastVector m_Colors

m_trainingData

Instances m_trainingData

m_classifier

Classifier m_classifier

m_dataGenerator

DataGenerator m_dataGenerator

m_classIndex

int m_classIndex

m_xAttribute

int m_xAttribute

m_yAttribute

int m_yAttribute

m_minX

double m_minX

m_minY

double m_minY

m_maxX

double m_maxX

m_maxY

double m_maxY

m_rangeX

double m_rangeX

m_rangeY

double m_rangeY

m_pixHeight

double m_pixHeight

m_pixWidth

double m_pixWidth

m_osi

java.awt.Image m_osi

m_panelWidth

int m_panelWidth

m_panelHeight

int m_panelHeight

m_numOfSamplesPerRegion

int m_numOfSamplesPerRegion

m_numOfSamplesPerGenerator

int m_numOfSamplesPerGenerator

m_samplesBase

double m_samplesBase

m_listeners

java.util.Vector m_listeners

m_plotPanel

BoundaryPanel.PlotPanel m_plotPanel

m_plotThread

java.lang.Thread m_plotThread

m_stopPlotting

boolean m_stopPlotting

m_stopReplotting

boolean m_stopReplotting

m_dummy

java.lang.Double m_dummy

m_pausePlotting

boolean m_pausePlotting

m_size

int m_size

m_initialTiling

boolean m_initialTiling

m_random

java.util.Random m_random

m_probabilityCache

double[][][] m_probabilityCache

m_plotTrainingData

boolean m_plotTrainingData

Class weka.gui.boundaryvisualizer.BoundaryPanelDistributed extends BoundaryPanel implements Serializable

Serialized Fields

m_listeners

java.util.Vector m_listeners
a list of RemoteExperimentListeners


m_remoteHosts

java.util.Vector m_remoteHosts
Holds the names of machines with remoteEngine servers running


m_remoteHostsQueue

Queue m_remoteHostsQueue
The queue of available hosts


m_remoteHostsStatus

int[] m_remoteHostsStatus
The status of each of the remote hosts


m_remoteHostFailureCounts

int[] m_remoteHostFailureCounts
The number of times tasks have failed on each remote host


m_plottingAborted

boolean m_plottingAborted
Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts plotting


m_removedHosts

int m_removedHosts
The number of hosts removed due to exceeding max failures


m_failedCount

int m_failedCount
The count of failed sub-tasks


m_finishedCount

int m_finishedCount
The count of successfully completed sub-tasks


m_subExpQueue

Queue m_subExpQueue
The queue of sub-tasks waiting to be processed


m_minTaskPollTime

int m_minTaskPollTime

m_hostPollingTime

int[] m_hostPollingTime

Class weka.gui.boundaryvisualizer.BoundaryVisualizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_trainingInstances

Instances m_trainingInstances

m_classifier

Classifier m_classifier

m_plotAreaWidth

int m_plotAreaWidth

m_plotAreaHeight

int m_plotAreaHeight

m_boundaryPanel

BoundaryPanel m_boundaryPanel

m_classAttBox

javax.swing.JComboBox m_classAttBox

m_xAttBox

javax.swing.JComboBox m_xAttBox

m_yAttBox

javax.swing.JComboBox m_yAttBox

COMBO_SIZE

java.awt.Dimension COMBO_SIZE

m_startBut

javax.swing.JButton m_startBut

m_plotTrainingData

javax.swing.JCheckBox m_plotTrainingData

m_controlPanel

javax.swing.JPanel m_controlPanel

m_classPanel

ClassPanel m_classPanel

m_xAxisPanel

BoundaryVisualizer.AxisPanel m_xAxisPanel

m_yAxisPanel

BoundaryVisualizer.AxisPanel m_yAxisPanel

m_maxX

double m_maxX

m_maxY

double m_maxY

m_minX

double m_minX

m_minY

double m_minY

m_xIndex

int m_xIndex

m_yIndex

int m_yIndex

m_dataGenerator

KDDataGenerator m_dataGenerator

m_numberOfSamplesFromEachRegion

int m_numberOfSamplesFromEachRegion

m_generatorSamplesBase

int m_generatorSamplesBase

m_kernelBandwidth

int m_kernelBandwidth

m_regionSamplesText

javax.swing.JTextField m_regionSamplesText

m_generatorSamplesText

javax.swing.JTextField m_generatorSamplesText

m_kernelBandwidthText

javax.swing.JTextField m_kernelBandwidthText

Class weka.gui.boundaryvisualizer.KDDataGenerator extends java.lang.Object implements Serializable

Serialized Fields

m_instances

Instances m_instances

m_standardDeviations

double[] m_standardDeviations

m_globalMeansOrModes

double[] m_globalMeansOrModes

m_minStdDev

double m_minStdDev

m_laplaceConst

double m_laplaceConst

m_seed

int m_seed

m_random

java.util.Random m_random

m_weightingDimensions

boolean[] m_weightingDimensions

m_weightingValues

double[] m_weightingValues

m_kernelBandwidth

int m_kernelBandwidth

m_kernelParams

double[][] m_kernelParams

m_Min

double[] m_Min
The minimum values for numeric attributes.


m_Max

double[] m_Max
The maximum values for numeric attributes.

Class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask extends java.lang.Object implements Serializable

Serialized Fields

m_status

TaskStatusInfo m_status

m_result

RemoteResult m_result

m_rowNumber

int m_rowNumber

m_panelHeight

int m_panelHeight

m_panelWidth

int m_panelWidth

m_classifier

Classifier m_classifier

m_dataGenerator

DataGenerator m_dataGenerator

m_trainingData

Instances m_trainingData

m_xAttribute

int m_xAttribute

m_yAttribute

int m_yAttribute

m_pixHeight

double m_pixHeight

m_pixWidth

double m_pixWidth

m_minX

double m_minX

m_minY

double m_minY

m_maxX

double m_maxX

m_maxY

double m_maxY

m_numOfSamplesPerRegion

int m_numOfSamplesPerRegion

m_numOfSamplesPerGenerator

int m_numOfSamplesPerGenerator

m_samplesBase

double m_samplesBase

m_random

java.util.Random m_random

m_weightingAttsValues

double[] m_weightingAttsValues

m_attsToWeightOn

boolean[] m_attsToWeightOn

m_vals

double[] m_vals

m_dist

double[] m_dist

m_predInst

Instance m_predInst

Class weka.gui.boundaryvisualizer.RemoteResult extends java.lang.Object implements Serializable

Serialized Fields

m_rowNumber

int m_rowNumber

m_rowLength

int m_rowLength

m_probabilities

double[][] m_probabilities

m_percentCompleted

int m_percentCompleted


Package weka.core

Class weka.core.Attribute extends java.lang.Object implements Serializable

Serialized Fields

m_Name

java.lang.String m_Name
The attribute's name.


m_Type

int m_Type
The attribute's type.


m_Values

FastVector m_Values
The attribute's values (if nominal or string).


m_Hashtable

java.util.Hashtable m_Hashtable
Mapping of values to indices (if nominal or string).


m_DateFormat

java.text.SimpleDateFormat m_DateFormat
Date format specification for date attributes


m_Index

int m_Index
The attribute's index.


m_Metadata

ProtectedProperties m_Metadata
The attribute's metadata.


m_Ordering

int m_Ordering
The attribute's ordering.


m_IsRegular

boolean m_IsRegular
Whether the attribute is regular.


m_IsAveragable

boolean m_IsAveragable
Whether the attribute is averagable.


m_HasZeropoint

boolean m_HasZeropoint
Whether the attribute has a zeropoint.


m_Weight

double m_Weight
The attribute's weight.


m_LowerBound

double m_LowerBound
The attribute's lower numeric bound.


m_LowerBoundIsOpen

boolean m_LowerBoundIsOpen
Whether the lower bound is open.


m_UpperBound

double m_UpperBound
The attribute's upper numeric bound.


m_UpperBoundIsOpen

boolean m_UpperBoundIsOpen
Whether the upper bound is open

Class weka.core.AttributeStats extends java.lang.Object implements Serializable

Serialized Fields

intCount

int intCount
The number of int-like values


realCount

int realCount
The number of real-like values (i.e. have a fractional part)


missingCount

int missingCount
The number of missing values


distinctCount

int distinctCount
The number of distinct values


uniqueCount

int uniqueCount
The number of values that only appear once


totalCount

int totalCount
The total number of values (i.e. number of instances)


numericStats

Stats numericStats
Stats on numeric value distributions


nominalCounts

int[] nominalCounts
Counts of each nominal value

Class weka.core.BinarySparseInstance extends SparseInstance implements Serializable

Class weka.core.ClassRemoveableInstances extends Instances implements Serializable

Serialized Fields

lastRemoved

int lastRemoved

classRemoved

boolean classRemoved

Class weka.core.ClassTree extends java.lang.Object implements Serializable

Serialized Fields

children

ClassTree[] children
The subtrees which are children of this node of the hierarchy. Should remain null, if leaves is not null.


leaves

java.lang.String[] leaves
The class-names directly coverd by this node of the hierarchy. Should remain null, if children is not null.

Class weka.core.FastVector extends java.lang.Object implements Serializable

Serialized Fields

m_Objects

java.lang.Object[] m_Objects
The array of objects.


m_Size

int m_Size
The current size;


m_CapacityIncrement

int m_CapacityIncrement
The capacity increment


m_CapacityMultiplier

double m_CapacityMultiplier
The capacity multiplier.

Class weka.core.Instance extends java.lang.Object implements Serializable

Serialized Fields

m_Dataset

Instances m_Dataset
The dataset the instance has access to. Null if the instance doesn't have access to any dataset. Only if an instance has access to a dataset, it knows about the actual attribute types.


m_AttValues

double[] m_AttValues
The instance's attribute values.


m_Weight

double m_Weight
The instance's weight.

Class weka.core.Instances extends java.lang.Object implements Serializable

Serialized Fields

lastRemoved

int lastRemoved
Keeps the index of the last removed attribute position.


classRemoved

boolean classRemoved
Should be set to true, if the class-attribute was removed, and to false, if the class-attribute was added again.


m_RelationName

java.lang.String m_RelationName
The dataset's name.


m_Attributes

FastVector m_Attributes
The attribute information.


m_Instances

FastVector m_Instances
The instances.


m_ClassIndex

int m_ClassIndex
The class attribute's index


m_ValueBuffer

double[] m_ValueBuffer
Buffer of values for sparse instance


m_IndicesBuffer

int[] m_IndicesBuffer
Buffer of indices for sparse instance

Class weka.core.Matrix extends java.lang.Object implements Serializable

Serialized Fields

m_Elements

double[][] m_Elements
The values of the matrix

Class weka.core.NoSupportForMissingValuesException extends WekaException implements Serializable

Class weka.core.ProtectedProperties extends java.util.Properties implements Serializable

Serialized Fields

closed

boolean closed

Class weka.core.Queue extends java.lang.Object implements Serializable

Serialized Fields

m_Head

Queue.QueueNode m_Head
Store a reference to the head of the queue


m_Tail

Queue.QueueNode m_Tail
Store a reference to the tail of the queue


m_Size

int m_Size
Store the current number of elements in the queue

Class weka.core.Queue.QueueNode extends java.lang.Object implements Serializable

Serialized Fields

m_Next

Queue.QueueNode m_Next
The next node in the queue


m_Contents

java.lang.Object m_Contents
The nodes contents

Class weka.core.RandomVariates extends java.util.Random implements Serializable

Class weka.core.Range extends java.lang.Object implements Serializable

Serialized Fields

m_RangeStrings

java.util.Vector m_RangeStrings
Record the string representations of the columns to delete


m_Invert

boolean m_Invert
Whether matching should be inverted


m_SelectFlags

boolean[] m_SelectFlags
The array of flags for whether an column is selected


m_Upper

int m_Upper
Store the maximum value permitted in the range. -1 indicates that no upper value has been set

Class weka.core.SerializedObject extends java.lang.Object implements Serializable

Serialized Fields

m_storedObjectArray

byte[] m_storedObjectArray
The array storing the object.


m_isCompressed

boolean m_isCompressed
Whether or not the object is compressed.

Class weka.core.SingleIndex extends java.lang.Object implements Serializable

Serialized Fields

m_IndexString

java.lang.String m_IndexString
Record the string representation of the number


m_SelectedIndex

int m_SelectedIndex
The selected index


m_Upper

int m_Upper
Store the maximum value permitted. -1 indicates that no upper value has been set

Class weka.core.SparseInstance extends Instance implements Serializable

Serialized Fields

m_Indices

int[] m_Indices
The index of the attribute associated with each stored value.


m_NumAttributes

int m_NumAttributes
The maximum number of values that can be stored.

Class weka.core.UnassignedClassException extends java.lang.RuntimeException implements Serializable

Class weka.core.UnassignedDatasetException extends java.lang.RuntimeException implements Serializable

Class weka.core.UnsupportedAttributeTypeException extends WekaException implements Serializable

Class weka.core.UnsupportedClassTypeException extends WekaException implements Serializable

Class weka.core.WekaException extends java.lang.Exception implements Serializable


Package weka.core.converters

Class weka.core.converters.AbstractClassHierarchyParser extends java.lang.Object implements Serializable

Class weka.core.converters.AbstractLoader extends java.lang.Object implements Serializable

Serialized Fields

m_retrieval

int m_retrieval
The current retrieval mode

Class weka.core.converters.ArffLoader extends AbstractLoader implements Serializable

Serialized Fields

m_File

java.lang.String m_File

Class weka.core.converters.C45Loader extends AbstractLoader implements Serializable

Serialized Fields

m_structure

Instances m_structure
Holds the determined structure (header) of the data set.


m_sourceFile

java.io.File m_sourceFile
Holds the source of the data set. In this case the names file of the data set. m_sourceFileData is the data file.


m_sourceFileData

java.io.File m_sourceFileData
Describe variable m_sourceFileData here.


m_fileStem

java.lang.String m_fileStem
Holds the filestem.


m_numAttribs

int m_numAttribs
Number of attributes in the data (including ignore and label attributes).


m_ignore

boolean[] m_ignore
Which attributes are ignore or label. These are *not* included in the arff representation.

Class weka.core.converters.ClassTreeArffFileParser extends ClassTreeFileParser implements Serializable

Class weka.core.converters.ClassTreeFileParser extends ClassTreeParser implements Serializable

Serialized Fields

encodingFile

java.lang.String encodingFile
Keeps the name of file providing the hierarchy String.

Class weka.core.converters.ClassTreeParser extends AbstractClassHierarchyParser implements Serializable

Serialized Fields

classNames

java.util.List classNames
Provides the class-names of a nominal class-attribute after initialization with the init(Instances)-method.


encodedHierarchy

java.lang.String encodedHierarchy
The encoded hierarchy currently to be parsed by this ClassTreeParser.

Class weka.core.converters.ConverterUtils extends java.lang.Object implements Serializable

Class weka.core.converters.CSVLoader extends AbstractLoader implements Serializable

Serialized Fields

m_structure

Instances m_structure
Holds the determined structure (header) of the data set.


m_sourceFile

java.io.File m_sourceFile
Holds the source of the data set.


m_cumulativeStructure

FastVector m_cumulativeStructure
A list of hash tables for accumulating nominal values during parsing.


m_cumulativeInstances

FastVector m_cumulativeInstances
Holds instances accumulated so far

Class weka.core.converters.SerializedInstancesLoader extends AbstractLoader implements Serializable

Serialized Fields

m_Dataset

Instances m_Dataset
Holds the structure (header) of the data set.


m_IncrementalIndex

int m_IncrementalIndex
The current index position for incremental reading

Class weka.core.converters.TreeLoader extends AbstractLoader implements Serializable


Package weka.clusterers

Class weka.clusterers.Clusterer extends java.lang.Object implements Serializable

Class weka.clusterers.Cobweb extends Clusterer implements Serializable

Serialized Fields

m_acuity

double m_acuity
Acuity (minimum standard deviation).


m_cutoff

double m_cutoff
Cutoff (minimum category utility).


m_cobwebTree

Cobweb.CNode m_cobwebTree
Holds the root of the Cobweb tree.


m_numberOfClusters

int m_numberOfClusters
Number of clusters (nodes in the tree).


m_numberSplits

int m_numberSplits

m_numberMerges

int m_numberMerges

m_saveInstances

boolean m_saveInstances
Output instances in graph representation of Cobweb tree (Allows instances at nodes in the tree to be visualized in the Explorer).

Class weka.clusterers.DensityBasedClusterer extends Clusterer implements Serializable

Class weka.clusterers.EM extends DensityBasedClusterer implements Serializable

Serialized Fields

m_model

Estimator[][] m_model
hold the discrete estimators for each cluster


m_modelNormal

double[][][] m_modelNormal
hold the normal estimators for each cluster


m_minStdDev

double m_minStdDev
default minimum standard deviation


m_weights

double[][] m_weights
hold the weights of each instance for each cluster


m_priors

double[] m_priors
the prior probabilities for clusters


m_loglikely

double m_loglikely
the loglikelihood of the data


m_theInstances

Instances m_theInstances
training instances


m_num_clusters

int m_num_clusters
number of clusters selected by the user or cross validation


m_initialNumClusters

int m_initialNumClusters
the initial number of clusters requested by the user--- -1 if xval is to be used to find the number of clusters


m_num_attribs

int m_num_attribs
number of attributes


m_num_instances

int m_num_instances
number of training instances


m_max_iterations

int m_max_iterations
maximum iterations to perform


m_minValues

double[] m_minValues
attribute min values


m_maxValues

double[] m_maxValues
attribute max values


m_rr

java.util.Random m_rr
random numbers and seed


m_rseed

int m_rseed

m_verbose

boolean m_verbose
Verbose?

Class weka.clusterers.FarthestFirst extends Clusterer implements Serializable

Serialized Fields

m_instances

Instances m_instances
training instances, not necessary to keep, could be replaced by m_ClusterCentroids where needed for header info


m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter
replace missing values in training instances


m_NumClusters

int m_NumClusters
number of clusters to generate


m_ClusterCentroids

Instances m_ClusterCentroids
holds the cluster centroids


m_Min

double[] m_Min
attribute min values


m_Max

double[] m_Max
attribute max values


m_Seed

int m_Seed
random seed

Class weka.clusterers.MakeDensityBasedClusterer extends DensityBasedClusterer implements Serializable

Serialized Fields

m_theInstances

Instances m_theInstances
holds training instances header information


m_priors

double[] m_priors
prior probabilities for the fitted clusters


m_modelNormal

double[][][] m_modelNormal
normal distributions fitted to each numeric attribute in each cluster


m_model

DiscreteEstimator[][] m_model
discrete distributions fitted to each discrete attribute in each cluster


m_minStdDev

double m_minStdDev
default minimum standard deviation


m_wrappedClusterer

Clusterer m_wrappedClusterer
The clusterer being wrapped

Class weka.clusterers.SimpleKMeans extends Clusterer implements Serializable

Serialized Fields

m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter
replace missing values in training instances


m_NumClusters

int m_NumClusters
number of clusters to generate


m_ClusterCentroids

Instances m_ClusterCentroids
holds the cluster centroids


m_ClusterStdDevs

Instances m_ClusterStdDevs
Holds the standard deviations of attributes in each cluster


m_Seed

int m_Seed
random seed


m_Min

double[] m_Min
attribute min values


m_Max

double[] m_Max
attribute max values


m_Iterations

int m_Iterations
Keep track of the number of iterations completed before convergence


Package weka.datagenerators

Class weka.datagenerators.BIRCHCluster extends ClusterGenerator implements Serializable

Serialized Fields

m_MinInstNum

int m_MinInstNum
minimal number of instances per cluster (option N)

m_MaxInstNum

int m_MaxInstNum
maximal number of instances per cluster (option N)

m_MinRadius

double m_MinRadius
minimum radius (option R)

m_MaxRadius

double m_MaxRadius
maximum radius (option R)

m_Pattern

int m_Pattern
pattern (changed with options G or S)

m_DistMult

double m_DistMult
distance multiplier (option M)

m_NumCycles

int m_NumCycles
number of cycles (option C)

m_InputOrder

int m_InputOrder
input order (changed with option O)

m_NoiseRate

double m_NoiseRate
noise rate in percent (option P, between 0 and 30)

m_Seed

int m_Seed
random number generator seed (option S)

m_DatasetFormat

Instances m_DatasetFormat
dataset format

m_Random

java.util.Random m_Random
random number generator

m_Debug

int m_Debug
debug flag

m_ClusterList

FastVector m_ClusterList
cluster list

m_GridSize

int m_GridSize
grid size

m_GridWidth

double m_GridWidth
grid width

Class weka.datagenerators.ClusterGenerator extends java.lang.Object implements Serializable

Serialized Fields

m_Debug

boolean m_Debug
Debugging mode

m_Format

Instances m_Format
The format for the generated dataset

m_RelationName

java.lang.String m_RelationName
Relation name the dataset should have

m_NumAttributes

int m_NumAttributes
Number of attribute the dataset should have

m_NumClusters

int m_NumClusters
Number of Clusters the dataset should have

m_ClassFlag

boolean m_ClassFlag
class flag

m_NumExamplesAct

int m_NumExamplesAct
Number of instances that should be produced into the dataset this number is by default m_NumExamples, but can be reset by the generator

m_Output

java.io.PrintWriter m_Output
PrintWriter

Class weka.datagenerators.Generator extends java.lang.Object implements Serializable

Serialized Fields

m_Debug

boolean m_Debug
Debugging mode

m_Format

Instances m_Format
The format for the generated dataset

m_RelationName

java.lang.String m_RelationName
Relation name the dataset should have

m_NumAttributes

int m_NumAttributes
Number of attribute the dataset should have

m_NumClasses

int m_NumClasses
Number of Classes the dataset should have

m_NumExamples

int m_NumExamples
Number of instances

m_NumExamplesAct

int m_NumExamplesAct
Number of instances that should be produced into the dataset this number is by default m_NumExamples, but can be reset by the generator

m_Output

java.io.PrintWriter m_Output
PrintWriter

Class weka.datagenerators.RDG1 extends Generator implements Serializable

Serialized Fields

m_MaxRuleSize

int m_MaxRuleSize
maximum rule size

m_MinRuleSize

int m_MinRuleSize
minimum rule size

m_NumIrrelevant

int m_NumIrrelevant
number of irrelevant attributes.

m_NumNumeric

int m_NumNumeric
number of numeric attribute

m_Seed

int m_Seed
random number generator seed

m_VoteFlag

boolean m_VoteFlag
flag that stores if voting is wished

m_DatasetFormat

Instances m_DatasetFormat
dataset format

m_Random

java.util.Random m_Random
random number generator

m_DecisionList

FastVector m_DecisionList
decision list

m_AttList_Irr

boolean[] m_AttList_Irr
array defines which attributes are irrelevant, with:

m_Debug

int m_Debug
debug flag

Class weka.datagenerators.Test extends java.lang.Object implements Serializable

Serialized Fields

m_AttIndex

int m_AttIndex

m_Split

double m_Split

m_Not

boolean m_Not

m_Dataset

Instances m_Dataset


Package weka.filters

Class weka.filters.AllFilter extends Filter implements Serializable

Class weka.filters.Filter extends java.lang.Object implements Serializable

Serialized Fields

m_Debug

boolean m_Debug
Debugging mode


m_OutputFormat

Instances m_OutputFormat
The output format for instances


m_OutputQueue

Queue m_OutputQueue
The output instance queue


m_OutputStringAtts

int[] m_OutputStringAtts
Indices of string attributes in the output format


m_InputStringAtts

int[] m_InputStringAtts
Indices of string attributes in the input format


m_InputFormat

Instances m_InputFormat
The input format for instances


m_NewBatch

boolean m_NewBatch
Record whether the filter is at the start of a batch

Class weka.filters.NullFilter extends Filter implements Serializable


Package weka.filters.supervised.attribute

Class weka.filters.supervised.attribute.AttributeSelection extends Filter implements Serializable

Serialized Fields

m_trainSelector

AttributeSelection m_trainSelector
the attribute selection evaluation object


m_ASEvaluator

ASEvaluation m_ASEvaluator
the attribute evaluator to use


m_ASSearch

ASSearch m_ASSearch
the search method if any


m_FilterOptions

java.lang.String[] m_FilterOptions
holds a copy of the full set of valid options passed to the filter


m_SelectedAttributes

int[] m_SelectedAttributes
holds the selected attributes

Class weka.filters.supervised.attribute.ClassOrder extends Filter implements Serializable

Serialized Fields

m_Seed

long m_Seed
The seed of randomization


m_Random

java.util.Random m_Random
The random object


m_Converter

int[] m_Converter
The 1-1 converting table from the original class values to the new values


m_ClassAttribute

Attribute m_ClassAttribute
Class attribute of the data


m_ClassOrder

int m_ClassOrder
The class order to be sorted


m_ClassCounts

double[] m_ClassCounts
This class can provide the class distribution in the sorted order as side effect

Class weka.filters.supervised.attribute.Discretize extends Filter implements Serializable

Serialized Fields

m_DiscretizeCols

Range m_DiscretizeCols
Stores which columns to Discretize


m_CutPoints

double[][] m_CutPoints
Store the current cutpoints


m_MakeBinary

boolean m_MakeBinary
Output binary attributes for discretized attributes.


m_UseBetterEncoding

boolean m_UseBetterEncoding
Use better encoding of split point for MDL.


m_UseKononenko

boolean m_UseKononenko
Use Kononenko's MDL criterion instead of Fayyad et al.'s

Class weka.filters.supervised.attribute.NominalToBinary extends Filter implements Serializable

Serialized Fields

m_Indices

int[][] m_Indices
The sorted indices of the attribute values.


m_Numeric

boolean m_Numeric
Are the new attributes going to be nominal or numeric ones?


Package weka.filters.supervised.instance

Class weka.filters.supervised.instance.Resample extends Filter implements Serializable

Serialized Fields

m_SampleSizePercent

double m_SampleSizePercent
The subsample size, percent of original set, default 100%


m_RandomSeed

int m_RandomSeed
The random number generator seed


m_BiasToUniformClass

double m_BiasToUniformClass
The degree of bias towards uniform (nominal) class distribution


m_FirstBatchDone

boolean m_FirstBatchDone
True if the first batch has been done

Class weka.filters.supervised.instance.SpreadSubsample extends Filter implements Serializable

Serialized Fields

m_RandomSeed

int m_RandomSeed
The random number generator seed


m_MaxCount

int m_MaxCount
The maximum count of any class


m_FirstBatchDone

boolean m_FirstBatchDone
True if the first batch has been done


m_DistributionSpread

double m_DistributionSpread
True if the first batch has been done


m_AdjustWeights

boolean m_AdjustWeights
True if instance weights will be adjusted to maintain total weight per class.

Class weka.filters.supervised.instance.StratifiedRemoveFolds extends Filter implements Serializable

Serialized Fields

m_Inverse

boolean m_Inverse
Indicates if inverse of selection is to be output.


m_NumFolds

int m_NumFolds
Number of folds to split dataset into


m_Fold

int m_Fold
Fold to output


m_Seed

long m_Seed
Random number seed.


Package weka.filters.unsupervised.attribute

Class weka.filters.unsupervised.attribute.AbstractTimeSeries extends Filter implements Serializable

Serialized Fields

m_SelectedCols

Range m_SelectedCols
Stores which columns to copy


m_FillWithMissing

boolean m_FillWithMissing
True if missing values should be used rather than removing instances where the translated value is not known (due to border effects).


m_InstanceRange

int m_InstanceRange
The number of instances forward to translate values between. A negative number indicates taking values from a past instance.


m_History

Queue m_History
Stores the historical instances to copy values between

Class weka.filters.unsupervised.attribute.Add extends Filter implements Serializable

Serialized Fields

m_AttributeType

int m_AttributeType
Record the type of attribute to insert


m_Name

java.lang.String m_Name
The name for the new attribute


m_Insert

SingleIndex m_Insert
The location to insert the new attribute


m_Labels

FastVector m_Labels
The list of labels for nominal attribute

Class weka.filters.unsupervised.attribute.AddCluster extends Filter implements Serializable

Serialized Fields

m_Clusterer

Clusterer m_Clusterer
The clusterer used to do the cleansing


m_IgnoreAttributesRange

Range m_IgnoreAttributesRange
Range of attributes to ignore


m_removeAttributes

Filter m_removeAttributes
Filter for removing attributes

Class weka.filters.unsupervised.attribute.AddExpression extends Filter implements Serializable

Serialized Fields

m_infixExpression

java.lang.String m_infixExpression
The infix expression


m_operatorStack

java.util.Stack m_operatorStack
Operator stack


m_postFixExpVector

java.util.Vector m_postFixExpVector
Holds the expression in postfix form


m_signMod

boolean m_signMod
True if the next numeric constant or attribute index is negative


m_previousTok

java.lang.String m_previousTok
Holds the previous token


m_attributeName

java.lang.String m_attributeName
Name of the new attribute. "expression" length string will use the provided expression as the new attribute name


m_Debug

boolean m_Debug
If true, makes the attribute name equal to the postfix parse of the expression

Class weka.filters.unsupervised.attribute.AddNoise extends Filter implements Serializable

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_UseMissing

boolean m_UseMissing
Flag if missing values are taken as value.


m_Percent

int m_Percent
The subsample size, percent of original set, default 10%


m_RandomSeed

int m_RandomSeed
The random number generator seed

Class weka.filters.unsupervised.attribute.ClusterMembership extends Filter implements Serializable

Serialized Fields

m_clusterer

DensityBasedClusterer m_clusterer
The clusterer


m_ignoreAttributesRange

Range m_ignoreAttributesRange
Range of attributes to ignore


m_removeAttributes

Filter m_removeAttributes
Filter for removing attributes

Class weka.filters.unsupervised.attribute.Copy extends Filter implements Serializable

Serialized Fields

m_CopyCols

Range m_CopyCols
Stores which columns to copy


m_SelectedAttributes

int[] m_SelectedAttributes
Stores the indexes of the selected attributes in order, once the dataset is seen


m_InputStringIndex

int[] m_InputStringIndex
Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated

Class weka.filters.unsupervised.attribute.Discretize extends PotentialClassIgnorer implements Serializable

Serialized Fields

m_DiscretizeCols

Range m_DiscretizeCols
Stores which columns to Discretize


m_NumBins

int m_NumBins
The number of bins to divide the attribute into


m_DesiredWeightOfInstancesPerInterval

double m_DesiredWeightOfInstancesPerInterval
The desired weight of instances per bin


m_CutPoints

double[][] m_CutPoints
Store the current cutpoints


m_MakeBinary

boolean m_MakeBinary
Output binary attributes for discretized attributes.


m_FindNumBins

boolean m_FindNumBins
Find the number of bins using cross-validated entropy.


m_UseEqualFrequency

boolean m_UseEqualFrequency
Use equal-frequency binning if unsupervised discretization turned on


m_DefaultCols

java.lang.String m_DefaultCols
The default columns to discretize

Class weka.filters.unsupervised.attribute.FirstOrder extends Filter implements Serializable

Serialized Fields

m_DeltaCols

Range m_DeltaCols
Stores which columns to take differences between

Class weka.filters.unsupervised.attribute.MakeIndicator extends Filter implements Serializable

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_ValIndex

Range m_ValIndex
The value's index


m_Numeric

boolean m_Numeric
Make boolean attribute numeric.

Class weka.filters.unsupervised.attribute.MergeTwoValues extends Filter implements Serializable

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_FirstIndex

SingleIndex m_FirstIndex
The first value's index setting.


m_SecondIndex

SingleIndex m_SecondIndex
The second value's index setting.

Class weka.filters.unsupervised.attribute.NominalToBinary extends Filter implements Serializable

Serialized Fields

m_Indices

int[][] m_Indices
The sorted indices of the attribute values.


m_Numeric

boolean m_Numeric
Are the new attributes going to be nominal or numeric ones?

Class weka.filters.unsupervised.attribute.Normalize extends PotentialClassIgnorer implements Serializable

Serialized Fields

m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.

Class weka.filters.unsupervised.attribute.NumericToBinary extends PotentialClassIgnorer implements Serializable

Class weka.filters.unsupervised.attribute.NumericTransform extends Filter implements Serializable

Serialized Fields

m_Cols

Range m_Cols
Stores which columns to transform.


m_Class

java.lang.Class m_Class
Class containing transformation method.


m_Method

java.lang.reflect.Method m_Method
Transformation method.

Class weka.filters.unsupervised.attribute.Obfuscate extends Filter implements Serializable

Class weka.filters.unsupervised.attribute.PKIDiscretize extends Discretize implements Serializable

Class weka.filters.unsupervised.attribute.PotentialClassIgnorer extends Filter implements Serializable

Serialized Fields

m_IgnoreClass

boolean m_IgnoreClass
True if the class is to be unset


m_ClassIndex

int m_ClassIndex
Storing the class index

Class weka.filters.unsupervised.attribute.RandomProjection extends Filter implements Serializable

Serialized Fields

m_k

int m_k
Stores the number of dimensions to reduce the data to


m_percent

double m_percent
Stores the dimensionality the data should be reduced to as percentage of the original dimension


m_useGaussian

boolean m_useGaussian
Is the random matrix will be computed using Gaussian distribution or not


m_distribution

int m_distribution
Stores the distribution to use for calculating the random matrix


m_replaceMissing

boolean m_replaceMissing
Should the missing values be replaced using unsupervised.ReplaceMissingValues filter


m_OutputFormatDefined

boolean m_OutputFormatDefined
Keeps track of output format if it is defined or not


ntob

Filter ntob
The NominalToBinary filter applied to the data before this filter


replaceMissing

Filter replaceMissing
The ReplaceMissingValues filter


m_rndmSeed

long m_rndmSeed
Stores the random seed used to generate the random matrix


rmatrix

double[][] rmatrix
The random matrix


r

java.util.Random r
The random number generator used for generating the random matrix

Class weka.filters.unsupervised.attribute.Remove extends Filter implements Serializable

Serialized Fields

m_SelectCols

Range m_SelectCols
Stores which columns to select as a funky range


m_SelectedAttributes

int[] m_SelectedAttributes
Stores the indexes of the selected attributes in order, once the dataset is seen


m_InputStringIndex

int[] m_InputStringIndex
Contains an index of string attributes in the input format that will survive the filtering process

Class weka.filters.unsupervised.attribute.RemoveType extends Filter implements Serializable

Serialized Fields

m_attributeFilter

Remove m_attributeFilter
The attribute filter used to do the filtering


m_attTypeToDelete

int m_attTypeToDelete
The type of attribute to delete


m_invert

boolean m_invert
Whether to invert selection

Class weka.filters.unsupervised.attribute.RemoveUseless extends Filter implements Serializable

Serialized Fields

m_removeFilter

Remove m_removeFilter
The filter used to remove attributes


m_maxVariancePercentage

double m_maxVariancePercentage
The type of attribute to delete

Class weka.filters.unsupervised.attribute.ReplaceMissingValues extends PotentialClassIgnorer implements Serializable

Serialized Fields

m_ModesAndMeans

double[] m_ModesAndMeans
The modes and means

Class weka.filters.unsupervised.attribute.Standardize extends PotentialClassIgnorer implements Serializable

Serialized Fields

m_Means

double[] m_Means
The means


m_StdDevs

double[] m_StdDevs
The variances

Class weka.filters.unsupervised.attribute.StringToNominal extends Filter implements Serializable

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.

Class weka.filters.unsupervised.attribute.StringToWordVector extends Filter implements Serializable

Serialized Fields

delimiters

java.lang.String delimiters
Delimiters used in tokenization


m_SelectedRange

Range m_SelectedRange
Range of columns to convert to word vectors


m_Dictionary

java.util.TreeMap m_Dictionary
Contains a mapping of valid words to attribute indexes


m_FirstBatchDone

boolean m_FirstBatchDone
True if the first batch has been done


m_OutputCounts

boolean m_OutputCounts
True if output instances should contain word frequency rather than boolean 0 or 1.


m_Prefix

java.lang.String m_Prefix
A String prefix for the attribute names


docsCounts

int[] docsCounts
Contains the number of documents (instances) a particular word appears in. The counts are stored with the same indexing as given by m_Dictionary.


numInstances

int numInstances
Contains the number of documents (instances) in the input format from which the dictionary is created. It is used in IDF transform.


avgDocLength

double avgDocLength
Contains the average length of documents (among the first batch of instances aka training data). This is used in length normalization of documents which will be normalized to average document length.


m_WordsToKeep

int m_WordsToKeep
The default number of words (per class if there is a class attribute assigned) to attempt to keep.


m_TFTransform

boolean m_TFTransform
True if word frequencies should be transformed into log(1+fi) where fi is the frequency of word i


m_normalizeDocLength

boolean m_normalizeDocLength
True if document's (instance's) word frequencies are to be normalized. The are normalized to average length of documents specified as input format.


m_IDFTransform

boolean m_IDFTransform
True if word frequencies should be transformed into fij*log(numOfDocs/numOfDocsWithWordi)


m_onlyAlphabeticTokens

boolean m_onlyAlphabeticTokens
True if tokens are to be formed only from alphabetic sequences of characters. (The delimiters string property is ignored if this is true).


m_lowerCaseTokens

boolean m_lowerCaseTokens
True if all tokens should be downcased


m_useStoplist

boolean m_useStoplist
True if tokens that are on a stoplist are to be ignored.

Class weka.filters.unsupervised.attribute.SwapValues extends Filter implements Serializable

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_FirstIndex

SingleIndex m_FirstIndex
The first value's index setting.


m_SecondIndex

SingleIndex m_SecondIndex
The second value's index setting.

Class weka.filters.unsupervised.attribute.TimeSeriesDelta extends TimeSeriesTranslate implements Serializable

Class weka.filters.unsupervised.attribute.TimeSeriesTranslate extends AbstractTimeSeries implements Serializable


Package weka.filters.unsupervised.instance

Class weka.filters.unsupervised.instance.NonSparseToSparse extends Filter implements Serializable

Class weka.filters.unsupervised.instance.Randomize extends Filter implements Serializable

Serialized Fields

m_Seed

int m_Seed
The random number seed


m_Random

java.util.Random m_Random
The current random number generator

Class weka.filters.unsupervised.instance.RemoveFolds extends Filter implements Serializable

Serialized Fields

m_Inverse

boolean m_Inverse
Indicates if inverse of selection is to be output.


m_NumFolds

int m_NumFolds
Number of folds to split dataset into


m_Fold

int m_Fold
Fold to output


m_Seed

long m_Seed
Random number seed.

Class weka.filters.unsupervised.instance.RemoveMisclassified extends Filter implements Serializable

Serialized Fields

m_cleansingClassifier

Classifier m_cleansingClassifier
The classifier used to do the cleansing


m_classIndex

int m_classIndex
The attribute to treat as the class for purposes of cleansing.


m_numOfCrossValidationFolds

int m_numOfCrossValidationFolds
The number of cross validation folds to perform (<2 = no cross validation)


m_numOfCleansingIterations

int m_numOfCleansingIterations
The maximum number of cleansing iterations to perform (<1 = until fully cleansed)


m_numericClassifyThreshold

double m_numericClassifyThreshold
The threshold for deciding when a numeric value is correctly classified


m_invertMatching

boolean m_invertMatching
Whether to invert the match so the correctly classified instances are discarded


m_firstBatchFinished

boolean m_firstBatchFinished
Have we processed the first batch (i.e. training data)?

Class weka.filters.unsupervised.instance.RemovePercentage extends Filter implements Serializable

Serialized Fields

m_Percentage

int m_Percentage
Percentage of instances to select.


m_Inverse

boolean m_Inverse
Indicates if inverse of selection is to be output.

Class weka.filters.unsupervised.instance.RemoveRange extends Filter implements Serializable

Serialized Fields

m_Range

Range m_Range
Range of instances provided by user.

Class weka.filters.unsupervised.instance.RemoveWithValues extends Filter implements Serializable

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_Values

Range m_Values
Stores which values of nominal attribute are to be used for filtering.


m_Value

double m_Value
Stores which value of a numeric attribute is to be used for filtering.


m_MatchMissingValues

boolean m_MatchMissingValues
True if missing values should count as a match


m_ModifyHeader

boolean m_ModifyHeader
Modify header for nominal attributes?


m_NominalMapping

int[] m_NominalMapping
If m_ModifyHeader, stores a mapping from old to new indexes

Class weka.filters.unsupervised.instance.Resample extends Filter implements Serializable

Serialized Fields

m_SampleSizePercent

double m_SampleSizePercent
The subsample size, percent of original set, default 100%


m_RandomSeed

int m_RandomSeed
The random number generator seed


m_FirstBatchDone

boolean m_FirstBatchDone
True if the first batch has been done

Class weka.filters.unsupervised.instance.SparseToNonSparse extends Filter implements Serializable


Package weka.experiment

Class weka.experiment.AveragingResultProducer extends java.lang.Object implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_ExpectedResultsPerAverage

int m_ExpectedResultsPerAverage
The number of results expected to average over for each run


m_CalculateStdDevs

boolean m_CalculateStdDevs
True if standard deviation fields should be produced


m_CountFieldName

java.lang.String m_CountFieldName
The name of the field that will contain the number of results averaged over.


m_KeyFieldName

java.lang.String m_KeyFieldName
The name of the key field to average over


m_KeyIndex

int m_KeyIndex
The index of the field to average over in the resultproducers key


m_Keys

FastVector m_Keys
Collects the keys from a single run


m_Results

FastVector m_Results
Collects the results from a single run

Class weka.experiment.ClassifierSplitEvaluator extends java.lang.Object implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier used for evaluation


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_doesProduce

boolean[] m_doesProduce
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce


m_numberAdditionalMeasures

int m_numberAdditionalMeasures
The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results


m_result

java.lang.String m_result
Holds the statistics for the most recent application of the classifier


m_ClassifierOptions

java.lang.String m_ClassifierOptions
The classifier options (if any)


m_ClassifierVersion

java.lang.String m_ClassifierVersion
The classifier version


m_IRclass

int m_IRclass
Class index for information retrieval statistics (default 0)

Class weka.experiment.CostSensitiveClassifierSplitEvaluator extends ClassifierSplitEvaluator implements Serializable

Serialized Fields

m_OnDemandDirectory

java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory

Class weka.experiment.CrossValidationResultProducer extends java.lang.Object implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_NumFolds

int m_NumFolds
The number of folds in the cross-validation


m_debugOutput

boolean m_debugOutput
Save raw output of split evaluators --- for debugging purposes


m_ZipDest

OutputZipper m_ZipDest
The output zipper to use for saving raw splitEvaluator output


m_OutputFile

java.io.File m_OutputFile
The destination output file/directory for raw output


m_SplitEvaluator

SplitEvaluator m_SplitEvaluator
The SplitEvaluator used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.CSVResultListener extends java.lang.Object implements Serializable

Serialized Fields

m_RP

ResultProducer m_RP
The ResultProducer sending us results


m_OutputFile

java.io.File m_OutputFile
The destination output file, null sends to System.out

Class weka.experiment.DatabaseResultListener extends DatabaseUtils implements Serializable

Serialized Fields

m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer to listen to


m_ResultsTableName

java.lang.String m_ResultsTableName
The name of the current results table


m_Debug

boolean m_Debug
True if debugging output should be printed


m_CacheKeyName

java.lang.String m_CacheKeyName
Holds the name of the key field to cache upon, or null if no caching


m_CacheKeyIndex

int m_CacheKeyIndex
Stores the index of the key column holding the cache key data


m_CacheKey

java.lang.Object[] m_CacheKey
Stores the key for which the cache is valid


m_Cache

FastVector m_Cache
Stores the cached values

Class weka.experiment.DatabaseResultProducer extends DatabaseResultListener implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.DatabaseUtils extends java.lang.Object implements Serializable

Serialized Fields

m_DatabaseURL

java.lang.String m_DatabaseURL
Database URL


m_PreparedStatement

java.sql.PreparedStatement m_PreparedStatement
The prepared statement used for database queries.


m_Connection

java.sql.Connection m_Connection
The database connection


m_Debug

boolean m_Debug
True if debugging output should be printed


m_userName

java.lang.String m_userName
Database username


m_password

java.lang.String m_password
Database Password


m_stringType

java.lang.String m_stringType
mappings used for creating Tables. Can be overridden in DatabaseUtils.props


m_intType

java.lang.String m_intType

m_doubleType

java.lang.String m_doubleType

m_checkForUpperCaseNames

boolean m_checkForUpperCaseNames

m_setAutoCommit

boolean m_setAutoCommit

m_createIndex

boolean m_createIndex

Class weka.experiment.Experiment extends java.lang.Object implements Serializable

Serialized Fields

m_ResultListener

ResultListener m_ResultListener
Where results will be sent


m_ResultProducer

ResultProducer m_ResultProducer
The result producer


m_RunLower

int m_RunLower
Lower run number


m_RunUpper

int m_RunUpper
Upper run number


m_Datasets

javax.swing.DefaultListModel m_Datasets
An array of dataset files


m_UsePropertyIterator

boolean m_UsePropertyIterator
True if the exp should also iterate over a property of the RP


m_PropertyPath

PropertyNode[] m_PropertyPath
The path to the iterator property


m_PropertyArray

java.lang.Object m_PropertyArray
The array of values to set the property to


m_Notes

java.lang.String m_Notes
User notes about the experiment


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
Method names of additional measures of objects contained in the custom property iterator. Only methods names beginning with "measure" and returning doubles are recognised


m_ClassFirst

boolean m_ClassFirst
True if the class attribute is the first attribute for all datasets involved in this experiment.


m_AdvanceDataSetFirst

boolean m_AdvanceDataSetFirst
If true an experiment will advance the current data set befor any custom itererator


m_m_AdvanceRunFirst

boolean m_m_AdvanceRunFirst

Class weka.experiment.InstanceQuery extends DatabaseUtils implements Serializable

Serialized Fields

m_CreateSparseData

boolean m_CreateSparseData
Determines whether sparse data is created


m_Query

java.lang.String m_Query
Query to execute

Class weka.experiment.InstancesResultListener extends CSVResultListener implements Serializable

Class weka.experiment.LearningRateResultProducer extends java.lang.Object implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_LowerSize

int m_LowerSize
The minimum number of instances to use. If this is zero, the first step will contain m_StepSize instances


m_UpperSize

int m_UpperSize
The maximum number of instances to use. -1 indicates no maximum (other than the total number of instances)


m_StepSize

int m_StepSize
The number of instances to add at each step


m_CurrentSize

int m_CurrentSize
The current dataset size during stepping

Class weka.experiment.PropertyNode extends java.lang.Object implements Serializable

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream in)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException

writeObject

private void writeObject(java.io.ObjectOutputStream out)
                  throws java.io.IOException
Serialized Fields

value

java.lang.Object value
The current property value


parentClass

java.lang.Class parentClass
The class of the object with this property


property

java.beans.PropertyDescriptor property
Other info about the property

Class weka.experiment.RandomSplitResultProducer extends java.lang.Object implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_TrainPercent

double m_TrainPercent
The percentage of instances to use for training


m_randomize

boolean m_randomize
Whether dataset is to be randomized


m_SplitEvaluator

SplitEvaluator m_SplitEvaluator
The SplitEvaluator used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_debugOutput

boolean m_debugOutput
Save raw output of split evaluators --- for debugging purposes


m_ZipDest

OutputZipper m_ZipDest
The output zipper to use for saving raw splitEvaluator output


m_OutputFile

java.io.File m_OutputFile
The destination output file/directory for raw output

Class weka.experiment.RegressionSplitEvaluator extends java.lang.Object implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier used for evaluation


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_doesProduce

boolean[] m_doesProduce
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce


m_result

java.lang.String m_result
Holds the statistics for the most recent application of the classifier


m_ClassifierOptions

java.lang.String m_ClassifierOptions
The classifier options (if any)


m_ClassifierVersion

java.lang.String m_ClassifierVersion
The classifier version

Class weka.experiment.RemoteEngine extends java.rmi.server.UnicastRemoteObject implements Serializable

Serialized Fields

m_HostName

java.lang.String m_HostName
The name of the host that this engine is started on


m_TaskQueue

Queue m_TaskQueue
A queue of waiting tasks


m_TaskIdQueue

Queue m_TaskIdQueue
A queue of corresponding ID's for tasks


m_TaskStatus

java.util.Hashtable m_TaskStatus
A hashtable of experiment status


m_TaskRunning

boolean m_TaskRunning
Is there a task running

Class weka.experiment.RemoteExperiment extends Experiment implements Serializable

Serialized Fields

m_listeners

FastVector m_listeners
The list of objects listening for remote experiment events


m_remoteHosts

javax.swing.DefaultListModel m_remoteHosts
Holds the names of machines with remoteEngine servers running


m_remoteHostsQueue

Queue m_remoteHostsQueue
The queue of available hosts


m_remoteHostsStatus

int[] m_remoteHostsStatus
The status of each of the remote hosts


m_remoteHostFailureCounts

int[] m_remoteHostFailureCounts
The number of times tasks have failed on each remote host


m_experimentAborted

boolean m_experimentAborted
Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts experiment (via gui)


m_removedHosts

int m_removedHosts
The number of hosts removed due to exceeding max failures


m_failedCount

int m_failedCount
The count of failed sub-experiments


m_finishedCount

int m_finishedCount
The count of successfully completed sub-experiments


m_baseExperiment

Experiment m_baseExperiment
The base experiment to split up into sub experiments for remote execution


m_subExperiments

Experiment[] m_subExperiments
The sub experiments


m_subExpQueue

Queue m_subExpQueue
The queue of sub experiments waiting to be processed


m_subExpComplete

int[] m_subExpComplete
The status of each of the sub-experiments


m_splitByDataSet

boolean m_splitByDataSet
If true, then sub experiments are created on the basis of data sets rather than run number.

Class weka.experiment.RemoteExperimentEvent extends java.lang.Object implements Serializable

Serialized Fields

m_statusMessage

boolean m_statusMessage
A status type message


m_logMessage

boolean m_logMessage
A log type message


m_messageString

java.lang.String m_messageString
The message


m_experimentFinished

boolean m_experimentFinished
True if a remote experiment has finished

Class weka.experiment.RemoteExperimentSubTask extends java.lang.Object implements Serializable

Serialized Fields

m_result

TaskStatusInfo m_result

m_experiment

Experiment m_experiment

Class weka.experiment.Stats extends java.lang.Object implements Serializable

Serialized Fields

count

double count
The number of values seen


sum

double sum
The sum of values seen


sumSq

double sumSq
The sum of values squared seen


stdDev

double stdDev
The std deviation of values at the last calculateDerived() call


mean

double mean
The mean of values at the last calculateDerived() call


min

double min
The minimum value seen, or Double.NaN if no values seen


max

double max
The maximum value seen, or Double.NaN if no values seen

Class weka.experiment.TaskStatusInfo extends java.lang.Object implements Serializable

Serialized Fields

m_ExecutionStatus

int m_ExecutionStatus
Holds current execution status.


m_StatusMessage

java.lang.String m_StatusMessage
Holds current status message.


m_TaskResult

java.lang.Object m_TaskResult
Holds task result. Set to null for no returnable result.


Package weka.estimators

Class weka.estimators.DiscreteEstimator extends java.lang.Object implements Serializable

Serialized Fields

m_Counts

double[] m_Counts
Hold the counts


m_SumOfCounts

double m_SumOfCounts
Hold the sum of counts

Class weka.estimators.KernelEstimator extends java.lang.Object implements Serializable

Serialized Fields

m_Values

double[] m_Values
Vector containing all of the values seen


m_Weights

double[] m_Weights
Vector containing the associated weights


m_NumValues

int m_NumValues
Number of values stored in m_Weights and m_Values so far


m_SumOfWeights

double m_SumOfWeights
The sum of the weights so far


m_StandardDev

double m_StandardDev
The standard deviation


m_Precision

double m_Precision
The precision of data values


m_AllWeightsOne

boolean m_AllWeightsOne
Whether we can optimise the kernel summation

Class weka.estimators.MahalanobisEstimator extends java.lang.Object implements Serializable

Serialized Fields

m_CovarianceInverse

Matrix m_CovarianceInverse
The inverse of the covariance matrix


m_Determinant

double m_Determinant
The determinant of the covariance matrix


m_ConstDelta

double m_ConstDelta
The difference between the conditioning value and the conditioning mean


m_ValueMean

double m_ValueMean
The mean of the values

Class weka.estimators.NormalEstimator extends java.lang.Object implements Serializable

Serialized Fields

m_SumOfWeights

double m_SumOfWeights
The sum of the weights


m_SumOfValues

double m_SumOfValues
The sum of the values seen


m_SumOfValuesSq

double m_SumOfValuesSq
The sum of the values squared


m_Mean

double m_Mean
The current mean


m_StandardDev

double m_StandardDev
The current standard deviation


m_Precision

double m_Precision
The precision of numeric values ( = minimum std dev permitted)

Class weka.estimators.PoissonEstimator extends java.lang.Object implements Serializable

Serialized Fields

m_NumValues

double m_NumValues
The number of values seen


m_SumOfValues

double m_SumOfValues
The sum of the values seen


m_Lambda

double m_Lambda
The average number of times an event occurs in an interval.


Package weka.attributeSelection

Class weka.attributeSelection.ASEvaluation extends java.lang.Object implements Serializable

Class weka.attributeSelection.ASSearch extends java.lang.Object implements Serializable

Class weka.attributeSelection.AttributeEvaluator extends ASEvaluation implements Serializable

Class weka.attributeSelection.AttributeSelection extends java.lang.Object implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
the instances to select attributes from


m_ASEvaluator

ASEvaluation m_ASEvaluator
the attribute/subset evaluator


m_searchMethod

ASSearch m_searchMethod
the search method


m_numFolds

int m_numFolds
the number of folds to use for cross validation


m_selectionResults

java.lang.StringBuffer m_selectionResults
holds a string describing the results of the attribute selection


m_doRank

boolean m_doRank
rank features (if allowed by the search method)


m_doXval

boolean m_doXval
do cross validation


m_seed

int m_seed
seed used to randomly shuffle instances for cross validation


m_threshold

double m_threshold
cutoff value by which to select attributes for ranked results


m_numToSelect

int m_numToSelect
number of attributes requested from ranked results


m_selectedAttributeSet

int[] m_selectedAttributeSet
the selected attributes


m_attributeRanking

double[][] m_attributeRanking
the attribute indexes and associated merits if a ranking is produced


m_transformer

AttributeTransformer m_transformer
if a feature selection run involves an attribute transformer


m_attributeFilter

Remove m_attributeFilter
the attribute filter for processing instances with respect to the most recent feature selection run


m_rankResults

double[][] m_rankResults
hold statistics for repeated feature selection, such as under cross validation


m_subsetResults

double[] m_subsetResults

m_trials

int m_trials

Class weka.attributeSelection.BestFirst extends ASSearch implements Serializable

Serialized Fields

m_maxStale

int m_maxStale
maximum number of stale nodes before terminating search


m_searchDirection

int m_searchDirection
0 == backward search, 1 == forward search, 2 == bidirectional


m_starting

int[] m_starting
holds an array of starting attributes


m_startRange

Range m_startRange
holds the start set for the search as a Range


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_totalEvals

int m_totalEvals
total number of subsets evaluated during a search


m_debug

boolean m_debug
for debugging


m_bestMerit

double m_bestMerit
holds the merit of the best subset found


m_cacheSize

int m_cacheSize
holds the maximum size of the lookup cache for evaluated subsets

Class weka.attributeSelection.BestFirst.Link2 extends java.lang.Object implements Serializable

Serialized Fields

group

java.util.BitSet group

merit

double merit

Class weka.attributeSelection.BestFirst.LinkedList2 extends FastVector implements Serializable

Serialized Fields

m_MaxSize

int m_MaxSize

Class weka.attributeSelection.CfsSubsetEval extends SubsetEvaluator implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_disTransform

Discretize m_disTransform
Discretise attributes when class in nominal


m_classIndex

int m_classIndex
The class index


m_isNumeric

boolean m_isNumeric
Is the class numeric


m_numAttribs

int m_numAttribs
Number of attributes in the training data


m_numInstances

int m_numInstances
Number of instances in the training data


m_missingSeperate

boolean m_missingSeperate
Treat missing values as seperate values


m_locallyPredictive

boolean m_locallyPredictive
Include locally predicitive attributes


m_corr_matrix

float[][] m_corr_matrix
Holds the matrix of attribute correlations


m_std_devs

double[] m_std_devs
Standard deviations of attributes (when using pearsons correlation)


m_c_Threshold

double m_c_Threshold
Threshold for admitting locally predictive features

Class weka.attributeSelection.ChiSquaredAttributeEval extends AttributeEvaluator implements Serializable

Serialized Fields

m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value


m_Binarize

boolean m_Binarize
Just binarize numeric attributes


m_ChiSquareds

double[] m_ChiSquareds
The chi-squared value for each attribute

Class weka.attributeSelection.ClassifierSubsetEval extends HoldOutSubsetEvaluator implements Serializable

Serialized Fields

m_trainingInstances

Instances m_trainingInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of training instances


m_Classifier

Classifier m_Classifier
holds the classifier to use for error estimates


m_Evaluation

Evaluation m_Evaluation
holds the evaluation object to use for evaluating the classifier


m_holdOutFile

java.io.File m_holdOutFile
the file that containts hold out/test instances


m_holdOutInstances

Instances m_holdOutInstances
the instances to test on


m_useTraining

boolean m_useTraining
evaluate on training data rather than seperate hold out/test set

Class weka.attributeSelection.ConsistencySubsetEval extends SubsetEvaluator implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of instances in the training data


m_disTransform

Discretize m_disTransform
Discretise numeric attributes


m_table

java.util.Hashtable m_table
Hash table for evaluating feature subsets

Class weka.attributeSelection.ConsistencySubsetEval.hashKey extends java.lang.Object implements Serializable

Serialized Fields

attributes

double[] attributes
Array of attribute values for an instance


missing

boolean[] missing
True for an index if the corresponding attribute value is missing.


values

java.lang.String[] values
The values


key

int key
The key

Class weka.attributeSelection.ExhaustiveSearch extends ASSearch implements Serializable

Serialized Fields

m_starting

int[] m_starting
holds a starting set as an array of attributes.


m_startRange

Range m_startRange
the start set as a Range


m_bestGroup

java.util.BitSet m_bestGroup
the best feature set found during the search


m_bestMerit

double m_bestMerit
the merit of the best subset found


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_verbose

boolean m_verbose
if true, then ouput new best subsets as the search progresses


m_stopAfterFirst

boolean m_stopAfterFirst
stop after finding the first subset equal to or better than the supplied start set (set to true if start set is supplied).


m_evaluations

int m_evaluations
the number of subsets evaluated during the search

Class weka.attributeSelection.ForwardSelection extends ASSearch implements Serializable

Serialized Fields

m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_rankingRequested

boolean m_rankingRequested
true if the user has requested a ranked list of attributes


m_doRank

boolean m_doRank
go from one side of the search space to the other in order to generate a ranking


m_doneRanking

boolean m_doneRanking
used to indicate whether or not ranking has been performed


m_threshold

double m_threshold
A threshold by which to discard attributes---used by the AttributeSelection module


m_numToSelect

int m_numToSelect
The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold


m_calculatedNumToSelect

int m_calculatedNumToSelect

m_bestMerit

double m_bestMerit
the merit of the best subset found


m_rankedAtts

double[][] m_rankedAtts
a ranked list of attribute indexes


m_rankedSoFar

int m_rankedSoFar

m_best_group

java.util.BitSet m_best_group
the best subset found


m_ASEval

ASEvaluation m_ASEval

m_Instances

Instances m_Instances

m_startRange

Range m_startRange
holds the start set for the search as a Range


m_starting

int[] m_starting
holds an array of starting attributes

Class weka.attributeSelection.GainRatioAttributeEval extends AttributeEvaluator implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numClasses

int m_numClasses
The number of classes


m_missing_merge

boolean m_missing_merge
Merge missing values

Class weka.attributeSelection.GeneticSearch extends ASSearch implements Serializable

Serialized Fields

m_starting

int[] m_starting
holds a starting set as an array of attributes. Becomes one member of the initial random population


m_startRange

Range m_startRange
holds the start set for the search as a Range


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_population

GeneticSearch.GABitSet[] m_population
the current population


m_popSize

int m_popSize
the number of individual solutions


m_best

GeneticSearch.GABitSet m_best
the best population member found during the search


m_bestFeatureCount

int m_bestFeatureCount
the number of features in the best population member


m_lookupTableSize

int m_lookupTableSize
the number of entries to cache for lookup


m_lookupTable

java.util.Hashtable m_lookupTable
the lookup table


m_random

java.util.Random m_random
random number generation


m_seed

int m_seed
seed for random number generation


m_pCrossover

double m_pCrossover
the probability of crossover occuring


m_pMutation

double m_pMutation
the probability of mutation occuring


m_sumFitness

double m_sumFitness
sum of the current population fitness


m_maxFitness

double m_maxFitness

m_minFitness

double m_minFitness

m_avgFitness

double m_avgFitness

m_maxGenerations

int m_maxGenerations
the maximum number of generations to evaluate


m_reportFrequency

int m_reportFrequency
how often reports are generated


m_generationReports

java.lang.StringBuffer m_generationReports
holds the generation reports

Class weka.attributeSelection.GeneticSearch.GABitSet extends java.lang.Object implements Serializable

Serialized Fields

m_chromosome

java.util.BitSet m_chromosome

m_objective

double m_objective
holds raw merit


m_fitness

double m_fitness

Class weka.attributeSelection.HoldOutSubsetEvaluator extends SubsetEvaluator implements Serializable

Class weka.attributeSelection.InfoGainAttributeEval extends AttributeEvaluator implements Serializable

Serialized Fields

m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value


m_Binarize

boolean m_Binarize
Just binarize numeric attributes


m_InfoGains

double[] m_InfoGains
The info gain for each attribute

Class weka.attributeSelection.OneRAttributeEval extends AttributeEvaluator implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_randomSeed

int m_randomSeed
Random number seed


m_folds

int m_folds
Number of folds for cross validation


m_evalUsingTrainingData

boolean m_evalUsingTrainingData
Use training data to evaluate merit rather than x-val


m_minBucketSize

int m_minBucketSize
Passed on to OneR

Class weka.attributeSelection.PrincipalComponents extends UnsupervisedAttributeEvaluator implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The data to transform analyse/transform


m_trainCopy

Instances m_trainCopy
Keep a copy for the class attribute (if set)


m_transformedFormat

Instances m_transformedFormat
The header for the transformed data format


m_originalSpaceFormat

Instances m_originalSpaceFormat
The header for data transformed back to the original space


m_hasClass

boolean m_hasClass
Data has a class set


m_classIndex

int m_classIndex
Class index


m_numAttribs

int m_numAttribs
Number of attributes


m_numInstances

int m_numInstances
Number of instances


m_correlation

double[][] m_correlation
Correlation matrix for the original data


m_eigenvectors

double[][] m_eigenvectors
Will hold the unordered linear transformations of the (normalized) original data


m_eigenvalues

double[] m_eigenvalues
Eigenvalues for the corresponding eigenvectors


m_sortedEigens

int[] m_sortedEigens
Sorted eigenvalues


m_sumOfEigenValues

double m_sumOfEigenValues
sum of the eigenvalues


m_replaceMissingFilter

ReplaceMissingValues m_replaceMissingFilter
Filters for original data


m_normalizeFilter

Normalize m_normalizeFilter

m_nominalToBinFilter

NominalToBinary m_nominalToBinFilter

m_attributeFilter

Remove m_attributeFilter

m_attribFilter

Remove m_attribFilter
used to remove the class column if a class column is set


m_outputNumAtts

int m_outputNumAtts
The number of attributes in the pc transformed data


m_normalize

boolean m_normalize
normalize the input data?


m_coverVariance

double m_coverVariance
the amount of varaince to cover in the original data when retaining the best n PC's


m_transBackToOriginal

boolean m_transBackToOriginal
transform the data through the pc space and back to the original space ?


m_eTranspose

double[][] m_eTranspose
holds the transposed eigenvectors for converting back to the original space

Class weka.attributeSelection.RaceSearch extends ASSearch implements Serializable

Serialized Fields

m_Instances

Instances m_Instances

m_raceType

int m_raceType
the selected search type


m_xvalType

int m_xvalType
the selected xval type


m_classIndex

int m_classIndex
the class index


m_numAttribs

int m_numAttribs
the number of attributes in the data


m_totalEvals

int m_totalEvals
the total number of partially/fully evaluated subsets


m_bestMerit

double m_bestMerit
holds the merit of the best subset found


m_theEvaluator

HoldOutSubsetEvaluator m_theEvaluator
the subset evaluator to use


m_sigLevel

double m_sigLevel
the significance level for comparisons


m_delta

double m_delta
threshold for comparisons


m_samples

int m_samples
the number of samples above which to begin testing for similarity between competing subsets


m_numFolds

int m_numFolds
number of cross validation folds---equal to the number of instances for leave-one-out cv


m_ASEval

ASEvaluation m_ASEval
the attribute evaluator to generate the initial ranking when doing a rank race


m_Ranking

int[] m_Ranking
will hold the attribute ranking produced by the above attribute evaluator if doing a rank search


m_debug

boolean m_debug
verbose output for monitoring the search and debugging


m_rankingRequested

boolean m_rankingRequested
If true then produce a ranked list of attributes by fully traversing a forward hillclimb race


m_rankedAtts

double[][] m_rankedAtts
The ranked list of attributes produced if m_rankingRequested is true


m_rankedSoFar

int m_rankedSoFar
The number of attributes ranked so far (if ranking is requested)


m_numToSelect

int m_numToSelect
The number of attributes to retain if a ranking is requested. -1 indicates that all attributes are to be retained. Has precedence over m_threshold


m_calculatedNumToSelect

int m_calculatedNumToSelect

m_threshold

double m_threshold
the threshold for removing attributes if ranking is requested

Class weka.attributeSelection.RandomSearch extends ASSearch implements Serializable

Serialized Fields

m_starting

int[] m_starting
holds a starting set as an array of attributes.


m_startRange

Range m_startRange
holds the start set as a range


m_bestGroup

java.util.BitSet m_bestGroup
the best feature set found during the search


m_bestMerit

double m_bestMerit
the merit of the best subset found


m_onlyConsiderBetterAndSmaller

boolean m_onlyConsiderBetterAndSmaller
only accept a feature set as being "better" than the best if its merit is better or equal to the best, and it contains fewer features than the best (this allows LVF to be implimented).


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_seed

int m_seed
seed for random number generation


m_searchSize

double m_searchSize
percentage of the search space to consider


m_iterations

int m_iterations
the number of iterations performed


m_random

java.util.Random m_random
random number object


m_verbose

boolean m_verbose
output new best subsets as the search progresses

Class weka.attributeSelection.Ranker extends ASSearch implements Serializable

Serialized Fields

m_starting

int[] m_starting
Holds the starting set as an array of attributes


m_startRange

Range m_startRange
Holds the start set for the search as a range


m_attributeList

int[] m_attributeList
Holds the ordered list of attributes


m_attributeMerit

double[] m_attributeMerit
Holds the list of attribute merit scores


m_hasClass

boolean m_hasClass
Data has class attribute---if unsupervised evaluator then no class


m_classIndex

int m_classIndex
Class index of the data if supervised evaluator


m_numAttribs

int m_numAttribs
The number of attribtes


m_threshold

double m_threshold
A threshold by which to discard attributes---used by the AttributeSelection module


m_numToSelect

int m_numToSelect
The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold


m_calculatedNumToSelect

int m_calculatedNumToSelect
Used to compute the number to select

Class weka.attributeSelection.RankSearch extends ASSearch implements Serializable

Serialized Fields

m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_best_group

java.util.BitSet m_best_group
the best subset found


m_ASEval

ASEvaluation m_ASEval
the attribute evaluator to use for generating the ranking


m_SubsetEval

ASEvaluation m_SubsetEval
the subset evaluator with which to evaluate the ranking


m_Instances

Instances m_Instances
the training instances


m_bestMerit

double m_bestMerit
the merit of the best subset found


m_Ranking

int[] m_Ranking
will hold the attribute ranking

Class weka.attributeSelection.ReliefFAttributeEval extends AttributeEvaluator implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numericClass

boolean m_numericClass
Numeric class


m_numClasses

int m_numClasses
The number of classes if class is nominal


m_ndc

double m_ndc
Used to hold the probability of a different class val given nearest instances (numeric class)


m_nda

double[] m_nda
Used to hold the prob of different value of an attribute given nearest instances (numeric class case)


m_ndcda

double[] m_ndcda
Used to hold the prob of a different class val and different att val given nearest instances (numeric class case)


m_weights

double[] m_weights
Holds the weights that relief assigns to attributes


m_classProbs

double[] m_classProbs
Prior class probabilities (discrete class case)


m_sampleM

int m_sampleM
The number of instances to sample when estimating attributes default == -1, use all instances


m_Knn

int m_Knn
The number of nearest hits/misses


m_karray

double[][][] m_karray
k nearest scores + instance indexes for n classes


m_maxArray

double[] m_maxArray
Upper bound for numeric attributes


m_minArray

double[] m_minArray
Lower bound for numeric attributes


m_worst

double[] m_worst
Keep track of the farthest instance for each class


m_index

int[] m_index
Index in the m_karray of the farthest instance for each class


m_stored

int[] m_stored
Number of nearest neighbours stored of each class


m_seed

int m_seed
Random number seed used for sampling instances


m_weightsByRank

double[] m_weightsByRank
used to (optionally) weight nearest neighbours by their distance from the instance in question. Each entry holds exp(-((rank(r_i, i_j)/sigma)^2)) where rank(r_i,i_j) is the rank of instance i_j in a sequence of instances ordered by the distance from r_i. sigma is a user defined parameter, default=20


m_sigma

int m_sigma

m_weightByDistance

boolean m_weightByDistance
Weight by distance rather than equal weights

Class weka.attributeSelection.SubsetEvaluator extends ASEvaluation implements Serializable

Class weka.attributeSelection.SVMAttributeEval extends AttributeEvaluator implements Serializable

Serialized Fields

m_attScores

double[] m_attScores
The attribute scores


m_numToEliminate

int m_numToEliminate
Constant rate of attribute elimination per iteration


m_percentToEliminate

int m_percentToEliminate
Percentage rate of attribute elimination, trumps constant rate (above threshold), ignored if = 0


m_percentThreshold

int m_percentThreshold
Threshold below which percent elimination switches to constant elimination


m_smoCParameter

double m_smoCParameter
Complexity parameter to pass on to SMO


m_smoTParameter

double m_smoTParameter
Tolerance parameter to pass on to SMO


m_smoPParameter

double m_smoPParameter
Epsilon parameter to pass on to SMO


m_smoFilterType

int m_smoFilterType
Filter parameter to pass on to SMO

Class weka.attributeSelection.SymmetricalUncertAttributeEval extends AttributeEvaluator implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numClasses

int m_numClasses
The number of classes


m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value

Class weka.attributeSelection.UnsupervisedAttributeEvaluator extends AttributeEvaluator implements Serializable

Class weka.attributeSelection.UnsupervisedSubsetEvaluator extends SubsetEvaluator implements Serializable

Class weka.attributeSelection.WrapperSubsetEval extends SubsetEvaluator implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of instances in the training data


m_Evaluation

Evaluation m_Evaluation
holds an evaluation object


m_BaseClassifier

Classifier m_BaseClassifier
holds the base classifier object


m_folds

int m_folds
number of folds to use for cross validation


m_seed

int m_seed
random number seed


m_threshold

double m_threshold
the threshold by which to do further cross validations when estimating the accuracy of a subset


Package weka.associations

Class weka.associations.Apriori extends Associator implements Serializable

Serialized Fields

m_minSupport

double m_minSupport
The minimum support.


m_upperBoundMinSupport

double m_upperBoundMinSupport
The upper bound on the support


m_lowerBoundMinSupport

double m_lowerBoundMinSupport
The lower bound for the minimum support.


m_metricType

int m_metricType
The selected metric type.


m_minMetric

double m_minMetric
The minimum metric score.


m_numRules

int m_numRules
The maximum number of rules that are output.


m_delta

double m_delta
Delta by which m_minSupport is decreased in each iteration.


m_significanceLevel

double m_significanceLevel
Significance level for optional significance test.


m_cycles

int m_cycles
Number of cycles used before required number of rules was one.


m_Ls

FastVector m_Ls
The set of all sets of itemsets L.


m_hashtables

FastVector m_hashtables
The same information stored in hash tables.


m_allTheRules

FastVector[] m_allTheRules
The list of all generated rules.


m_instances

Instances m_instances
The instances (transactions) to be used for generating the association rules.


m_outputItemSets

boolean m_outputItemSets
Output itemsets found?


m_removeMissingCols

boolean m_removeMissingCols

m_verbose

boolean m_verbose
Report progress iteratively

Class weka.associations.Associator extends java.lang.Object implements Serializable

Class weka.associations.ItemSet extends java.lang.Object implements Serializable

Serialized Fields

m_items

int[] m_items
The items stored as an array of of ints.


m_counter

int m_counter
Counter for how many transactions contain this item set.


m_totalTransactions

int m_totalTransactions
The total number of transactions

Class weka.associations.Tertius extends Associator implements Serializable

Serialized Fields

m_results

SimpleLinkedList m_results
The results.


m_hypotheses

int m_hypotheses
Number of hypotheses considered.


m_explored

int m_explored
Number of hypotheses explored.


m_time

java.util.Date m_time
Time needed for the search.


m_valuesText

java.awt.TextField m_valuesText
Field to output the current values.


m_instances

Instances m_instances
Instances used for the search.


m_predicates

java.util.ArrayList m_predicates
Predicates used in the rules.


m_status

int m_status
Status of the search.


m_best

int m_best
Number of best confirmation values to search.


m_frequencyThreshold

double m_frequencyThreshold
Frequency threshold for the body and the negation of the head.


m_confirmationThreshold

double m_confirmationThreshold
Confirmation threshold for the rules.


m_noiseThreshold

double m_noiseThreshold
Maximal number of counter-instances.


m_repeat

boolean m_repeat
Repeat attributes ?


m_numLiterals

int m_numLiterals
Number of literals in a rule.


m_negation

int m_negation
Type of negation used in the rules.


m_classification

boolean m_classification
Classification bias.


m_classIndex

int m_classIndex
Index of class attribute.


m_horn

boolean m_horn
Horn clauses bias.


m_equivalent

boolean m_equivalent
Perform test on equivalent rules ?


m_sameClause

boolean m_sameClause
Perform test on same clauses ?


m_subsumption

boolean m_subsumption
Perform subsumption test ?


m_missing

int m_missing
Way of handling missing values in the search.


m_roc

boolean m_roc
Perform ROC analysis ?


m_partsString

java.lang.String m_partsString
Name of the file containing the parts for individual-based learning.


m_parts

Instances m_parts
Part instances for individual-based learning.


m_printValues

int m_printValues
Type of values output.


Package weka.associations.tertius

Class weka.associations.tertius.AttributeValueLiteral extends Literal implements Serializable

Serialized Fields

m_value

java.lang.String m_value

m_index

int m_index

Class weka.associations.tertius.Body extends LiteralSet implements Serializable

Class weka.associations.tertius.Head extends LiteralSet implements Serializable

Class weka.associations.tertius.IndividualInstance extends Instance implements Serializable

Serialized Fields

m_parts

Instances m_parts

Class weka.associations.tertius.IndividualInstances extends Instances implements Serializable

Class weka.associations.tertius.IndividualLiteral extends AttributeValueLiteral implements Serializable

Serialized Fields

m_type

int m_type

Class weka.associations.tertius.Literal extends java.lang.Object implements Serializable

Serialized Fields

m_predicate

Predicate m_predicate

m_sign

int m_sign

m_negation

Literal m_negation

m_missing

int m_missing

Class weka.associations.tertius.LiteralSet extends java.lang.Object implements Serializable

Serialized Fields

m_literals

java.util.ArrayList m_literals
Literals contained in this set.


m_lastLiteral

Literal m_lastLiteral
Last literal added to this set.


m_numInstances

int m_numInstances
Number of instances in the data the set deals with.


m_counterInstances

java.util.ArrayList m_counterInstances
Set of counter-instances of this part of the rule.


m_counter

int m_counter
Counter for the number of counter-instances.


m_type

int m_type
Type of properties expressed in this set (individual or parts properties).

Class weka.associations.tertius.Predicate extends java.lang.Object implements Serializable

Serialized Fields

m_literals

java.util.ArrayList m_literals

m_name

java.lang.String m_name

m_index

int m_index

m_isClass

boolean m_isClass

Class weka.associations.tertius.Rule extends java.lang.Object implements Serializable

Serialized Fields

m_body

Body m_body
The body of the rule.


m_head

Head m_head
The head of the rule.


m_repeatPredicate

boolean m_repeatPredicate
Can repeat predicates in the rule ?


m_maxLiterals

int m_maxLiterals
Maximal number of literals in the rule.


m_negBody

boolean m_negBody
Can there be negations in the body ?


m_negHead

boolean m_negHead
Can there be negations in the head ?


m_classRule

boolean m_classRule
Is this rule a classification rule ?


m_singleHead

boolean m_singleHead
Can there be only one literal in the head ?


m_numInstances

int m_numInstances
Number of instances in the data this rule deals with.


m_counterInstances

java.util.ArrayList m_counterInstances
Set of counter-instances of this rule.


m_counter

int m_counter
Counter for the counter-instances of this rule.


m_confirmation

double m_confirmation
Confirmation of this rule.


m_optimistic

double m_optimistic
Optimistic estimate of this rule.

Class weka.associations.tertius.SimpleLinkedList extends java.lang.Object implements Serializable

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream s)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Reconstitute this LinkedList instance from a stream (that is deserialize it).


writeObject

private void writeObject(java.io.ObjectOutputStream s)
                  throws java.io.IOException
Save the state of this LinkedList instance to a stream (that is, serialize it).

Serial Data:
The size of the list (the number of elements it contains) is emitted (int), followed by all of its elements (each an Object) in the proper order.
Throws:
java.io.IOException
Serialized Fields

first

SimpleLinkedList.Entry first

last

SimpleLinkedList.Entry last

Class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator extends java.lang.Object implements Serializable

Serialized Fields

current

SimpleLinkedList.Entry current

lastReturned

SimpleLinkedList.Entry lastReturned

Class weka.associations.tertius.SimpleLinkedList.LinkedListIterator extends java.lang.Object implements Serializable

Serialized Fields

current

SimpleLinkedList.Entry current

lastReturned

SimpleLinkedList.Entry lastReturned


Package weka.classifiers

Class weka.classifiers.Classifier extends java.lang.Object implements Serializable

Serialized Fields

m_Debug

boolean m_Debug
Whether the classifier is run in debug mode.

Class weka.classifiers.CostMatrix extends Matrix implements Serializable

Class weka.classifiers.IteratedSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
Array for storing the generated base classifiers.


m_NumIterations

int m_NumIterations
The number of iterations.

Class weka.classifiers.MultipleClassifiersCombiner extends Classifier implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
Array for storing the generated base classifiers.

Class weka.classifiers.RandomizableClassifier extends Classifier implements Serializable

Serialized Fields

m_Seed

int m_Seed
The random number seed.

Class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

m_Seed

int m_Seed
The random number seed.

Class weka.classifiers.RandomizableMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable

Serialized Fields

m_Seed

int m_Seed
The random number seed.

Class weka.classifiers.RandomizableSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

Serialized Fields

m_Seed

int m_Seed
The random number seed.

Class weka.classifiers.SingleClassifierEnhancer extends Classifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The base classifier to use


Package weka.classifiers.lazy

Class weka.classifiers.lazy.IB1 extends Classifier implements Serializable

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.

Class weka.classifiers.lazy.IBk extends Classifier implements Serializable

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_NumClasses

int m_NumClasses
The number of class values (or 1 if predicting numeric)


m_ClassType

int m_ClassType
The class attribute type


m_Min

double[] m_Min
The minimum values for numeric attributes.


m_Max

double[] m_Max
The maximum values for numeric attributes.


m_kNN

int m_kNN
The number of neighbours to use for classification (currently)


m_kNNUpper

int m_kNNUpper
The value of kNN provided by the user. This may differ from m_kNN if cross-validation is being used


m_kNNValid

boolean m_kNNValid
Whether the value of k selected by cross validation has been invalidated by a change in the training instances


m_WindowSize

int m_WindowSize
The maximum number of training instances allowed. When this limit is reached, old training instances are removed, so the training data is "windowed". Set to 0 for unlimited numbers of instances.


m_DistanceWeighting

int m_DistanceWeighting
Whether the neighbours should be distance-weighted


m_CrossValidate

boolean m_CrossValidate
Whether to select k by cross validation


m_MeanSquared

boolean m_MeanSquared
Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks


m_DontNormalize

boolean m_DontNormalize
True if normalization is turned off


m_NumAttributesUsed

double m_NumAttributesUsed
The number of attributes the contribute to a prediction

Class weka.classifiers.lazy.KStar extends Classifier implements Serializable

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_NumInstances

int m_NumInstances
The number of instances in the dataset


m_NumClasses

int m_NumClasses
The number of class values


m_NumAttributes

int m_NumAttributes
The number of attributes


m_ClassType

int m_ClassType
The class attribute type


m_RandClassCols

int[][] m_RandClassCols
Table of random class value colomns


m_ComputeRandomCols

int m_ComputeRandomCols
Flag turning on and off the computation of random class colomns


m_InitFlag

int m_InitFlag
Flag turning on and off the initialisation of config variables


m_Cache

KStarCache[] m_Cache
A custom data structure for caching distinct attribute values and their scale factor or stop parameter.


m_MissingMode

int m_MissingMode
missing value treatment


m_BlendMethod

int m_BlendMethod
0 = use specified blend, 1 = entropic blend setting


m_GlobalBlend

int m_GlobalBlend
default sphere of influence blend setting

Class weka.classifiers.lazy.LBR extends Classifier implements Serializable

Serialized Fields

m_Counts

int[][][] m_Counts
All the counts for nominal attributes.


m_tCounts

int[][][] m_tCounts
All the counts for nominal attributes.


m_Priors

int[] m_Priors
The prior probabilities of the classes.


m_tPriors

int[] m_tPriors
The prior probabilities of the classes.


m_numAtts

int m_numAtts
number of attributes for the dataset


m_numClasses

int m_numClasses
number of classes for dataset


m_numInsts

int m_numInsts
number of instances in dataset


m_Instances

Instances m_Instances
The set of instances used for current training.


m_Errors

int m_Errors

m_ErrorFlags

boolean[] m_ErrorFlags

leftHand

java.util.ArrayList leftHand

m_subOldErrorFlags

boolean[] m_subOldErrorFlags

m_RemainderErrors

int m_RemainderErrors

m_Number

int m_Number

m_NumberOfInstances

int m_NumberOfInstances

m_NCV

boolean m_NCV

m_subInstances

LBR.Indexes m_subInstances

tempSubInstances

LBR.Indexes tempSubInstances

posteriorsArray

double[] posteriorsArray

bestCnt

int bestCnt

tempCnt

int tempCnt

forCnt

int forCnt

whileCnt

int whileCnt

Class weka.classifiers.lazy.LBR.Indexes extends java.lang.Object implements Serializable

Serialized Fields

m_InstIndexes

boolean[] m_InstIndexes
the array instance indexes


m_AttIndexes

boolean[] m_AttIndexes
the array attribute indexes


m_NumInstances

int m_NumInstances
the number of instances indexed


m_NumAtts

int m_NumAtts
the number of attributes indexed


m_SequentialInstIndexes

int[] m_SequentialInstIndexes
the array of instance indexes that are set to a either true or false


m_SequentialAttIndexes

int[] m_SequentialAttIndexes
an array of attribute indexes that are set to either true or false


m_SequentialInstanceIndex_valid

boolean m_SequentialInstanceIndex_valid
flag to check if sequential array must be rebuilt due to changes to the instance index


m_SequentialAttIndex_valid

boolean m_SequentialAttIndex_valid
flag to check if sequential array must be rebuilt due to changes to the attribute index


m_NumInstsSet

int m_NumInstsSet
the number of instances "in use" or set to a the original value (true or false)


m_NumAttsSet

int m_NumAttsSet
the number of attributes "in use" or set to a the original value (true or false)


m_NumSeqInstsSet

int m_NumSeqInstsSet
the number of sequential instances "in use" or set to a the original value (true or false)


m_NumSeqAttsSet

int m_NumSeqAttsSet
the number of sequential attributes "in use" or set to a the original value (true or false)


m_ClassIndex

int m_ClassIndex
the Class Index for the data set

Class weka.classifiers.lazy.LWL extends SingleClassifierEnhancer implements Serializable

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_Min

double[] m_Min
The minimum values for numeric attributes.


m_Max

double[] m_Max
The maximum values for numeric attributes.


m_kNN

int m_kNN
The number of neighbours used to select the kernel bandwidth


m_WeightKernel

int m_WeightKernel
The weighting kernel method currently selected


m_UseAllK

boolean m_UseAllK
True if m_kNN should be set to all instances


Package weka.classifiers.lazy.kstar

Class weka.classifiers.lazy.kstar.KStarCache extends java.lang.Object implements Serializable

Serialized Fields

m_Cache

KStarCache.CacheTable m_Cache
cache table

Class weka.classifiers.lazy.kstar.KStarCache.CacheTable extends java.lang.Object implements Serializable

Serialized Fields

m_Table

KStarCache.TableEntry[] m_Table
The hash table data.


m_Count

int m_Count
The total number of entries in the hash table.


m_Threshold

int m_Threshold
Rehashes the table when count exceeds this threshold.


m_LoadFactor

float m_LoadFactor
The load factor for the hashtable.


DEFAULT_TABLE_SIZE

int DEFAULT_TABLE_SIZE
The default size of the hashtable

See Also:
Constant Field Values

DEFAULT_LOAD_FACTOR

float DEFAULT_LOAD_FACTOR
The default load factor for the hashtable

See Also:
Constant Field Values

EPSILON

double EPSILON
Accuracy value for equality

See Also:
Constant Field Values

Class weka.classifiers.lazy.kstar.KStarCache.TableEntry extends java.lang.Object implements Serializable

Serialized Fields

hash

int hash
attribute value hash code


key

double key
attribute value


value

double value
scale factor or stop parameter


pmiss

double pmiss
transformation probability to missing value


next

KStarCache.TableEntry next
next table entry (separate chaining)


Package weka.classifiers.meta

Class weka.classifiers.meta.AdaBoostM1 extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

m_Betas

double[] m_Betas
Array for storing the weights for the votes.


m_NumIterationsPerformed

int m_NumIterationsPerformed
The number of successfully generated base classifiers.


m_WeightThreshold

int m_WeightThreshold
Weight Threshold. The percentage of weight mass used in training


m_UseResampling

boolean m_UseResampling
Use boosting with reweighting?


m_NumClasses

int m_NumClasses
The number of classes

Class weka.classifiers.meta.AdditiveRegression extends Classifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
Base classifier.


m_classIndex

int m_classIndex
Class index.


m_shrinkage

double m_shrinkage
Shrinkage (Learning rate). Default = no shrinkage.


m_additiveModels

FastVector m_additiveModels
The list of iteratively generated models.


m_debug

boolean m_debug
Produce debugging output.


m_maxModels

int m_maxModels
Maximum number of models to produce. -1 indicates keep going until the error threshold is met.

Class weka.classifiers.meta.AttributeSelectedClassifier extends Classifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier


m_AttributeSelection

AttributeSelection m_AttributeSelection
The attribute selection object


m_Evaluator

ASEvaluation m_Evaluator
The attribute evaluator to use


m_Search

ASSearch m_Search
The search method to use


m_ReducedHeader

Instances m_ReducedHeader
The header of the dimensionally reduced data


m_numClasses

int m_numClasses
The number of class vals in the training data (1 if class is numeric)


m_numAttributesSelected

double m_numAttributesSelected
The number of attributes selected by the attribute selection phase


m_selectionTime

double m_selectionTime
The time taken to select attributes in milliseconds


m_totalTime

double m_totalTime
The time taken to select attributes AND build the classifier

Class weka.classifiers.meta.Bagging extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

m_BagSizePercent

int m_BagSizePercent
The size of each bag sample, as a percentage of the training size


m_CalcOutOfBag

boolean m_CalcOutOfBag
Whether to calculate the out of bag error


m_OutOfBagError

double m_OutOfBagError
The out of bag error that has been calculated

Class weka.classifiers.meta.ClassificationViaRegression extends SingleClassifierEnhancer implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
The classifiers. (One for each class.)


m_ClassFilters

MakeIndicator[] m_ClassFilters
The filters used to transform the class.

Class weka.classifiers.meta.CostSensitiveClassifier extends Classifier implements Serializable

Serialized Fields

m_MatrixSource

int m_MatrixSource
Indicates the current cost matrix source


m_OnDemandDirectory

java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory


m_CostFile

java.lang.String m_CostFile
The name of the cost file, for command line options


m_CostMatrix

CostMatrix m_CostMatrix
The cost matrix


m_Classifier

Classifier m_Classifier
The classifier


m_Seed

int m_Seed
Seed for reweighting using resampling.


m_MinimizeExpectedCost

boolean m_MinimizeExpectedCost
True if the costs should be used by selecting the minimum expected cost (false means weight training data by the costs)

Class weka.classifiers.meta.CVParameterSelection extends RandomizableSingleClassifierEnhancer implements Serializable

Serialized Fields

m_ClassifierOptions

java.lang.String[] m_ClassifierOptions
The base classifier options (not including those being set by cross-validation)


m_BestClassifierOptions

java.lang.String[] m_BestClassifierOptions
The set of all classifier options as determined by cross-validation


m_InitOptions

java.lang.String[] m_InitOptions
The set of all options at initialization time. So that getOptions can return this.


m_BestPerformance

double m_BestPerformance
The cross-validated performance of the best options


m_CVParams

FastVector m_CVParams
The set of parameters to cross-validate over


m_NumAttributes

int m_NumAttributes
The number of attributes in the data


m_TrainFoldSize

int m_TrainFoldSize
The number of instances in a training fold


m_NumFolds

int m_NumFolds
The number of folds used in cross-validation

Class weka.classifiers.meta.CVParameterSelection.CVParameter extends java.lang.Object implements Serializable

Serialized Fields

m_ParamChar

char m_ParamChar
Char used to identify the option of interest


m_Lower

double m_Lower
Lower bound for the CV search


m_Upper

double m_Upper
Upper bound for the CV search


m_Steps

double m_Steps
Increment during the search


m_ParamValue

double m_ParamValue
The parameter value with the best performance


m_AddAtEnd

boolean m_AddAtEnd
True if the parameter should be added at the end of the argument list


m_RoundParam

boolean m_RoundParam
True if the parameter should be rounded to an integer

Class weka.classifiers.meta.Decorate extends Classifier implements Serializable

Serialized Fields

m_Debug

boolean m_Debug
Set to true to get debugging output.


m_Classifier

Classifier m_Classifier
The model base classifier to use.


m_Committee

java.util.Vector m_Committee
Vector of classifiers that make up the committee/ensemble.


m_DesiredSize

int m_DesiredSize
The desired ensemble size.


m_NumIterations

int m_NumIterations
The maximum number of Decorate iterations to run.


m_Seed

int m_Seed
The seed for random number generation.


m_ArtSize

double m_ArtSize
Amount of artificial/random instances to use - specified as a fraction of the training data size.


m_Random

java.util.Random m_Random
The random number generator.


m_AttributeStats

java.util.Vector m_AttributeStats
Attribute statistics - used for generating artificial examples.

Class weka.classifiers.meta.END extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

m_hashtable

java.util.Hashtable m_hashtable
The hashtable containing the classifiers for the END.

Class weka.classifiers.meta.FilteredClassifier extends Classifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier


m_Filter

Filter m_Filter
The filter


m_FilteredInstances

Instances m_FilteredInstances
The instance structure of the filtered instances

Class weka.classifiers.meta.Grading extends Stacking implements Serializable

Serialized Fields

m_MetaClassifiers

Classifier[] m_MetaClassifiers
The meta classifiers, one for each base classifier.


m_InstPerClass

double[] m_InstPerClass
InstPerClass

Class weka.classifiers.meta.HND extends Classifier implements Serializable

Serialized Fields

nd

ND nd
The ND to classify the instances at the currentlevel


properties

java.util.Properties properties
The properties for levelwise settings of the respective ND.


propertyFilename

java.lang.String propertyFilename
The filename of the property-file as set via setOptions.


LEVEL

int LEVEL
The level of this HND in the hierarchy (root is 0).


ndOptions

java.lang.String[] ndOptions
The options to be set for each ND.


childrenHNDs

java.util.Map childrenHNDs
The HNDs covering the instances at deeper levels, if there are such. Remains null for a HND covering the base-classes (leaves of the hierarchy). Is set during the method buildLevelwiseCLassifier. A superclasses-String is key for the according HND.


hierarchy

ClassHierarchy hierarchy
The hierarchy of classes for the current and deeper levels.


classHierarchyParser

ClassHierarchyParser classHierarchyParser
The default parser is a ClassTreeArffFileParser, suitable for a hierarchy encoded within a single String.

Class weka.classifiers.meta.LogitBoost extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

m_Classifiers

Classifier[][] m_Classifiers
Array for storing the generated base classifiers. Note: we are hiding the variable from IteratedSingleClassifierEnhancer


m_NumClasses

int m_NumClasses
The number of classes


m_NumGenerated

int m_NumGenerated
The number of successfully generated base classifiers.


m_NumFolds

int m_NumFolds
The number of folds for the internal cross-validation.


m_NumRuns

int m_NumRuns
The number of runs for the internal cross-validation.


m_WeightThreshold

int m_WeightThreshold
Weight thresholding. The percentage of weight mass used in training


m_NumericClassData

Instances m_NumericClassData
Dummy dataset with a numeric class


m_ClassAttribute

Attribute m_ClassAttribute
The actual class attribute (for getting class names)


m_UseResampling

boolean m_UseResampling
Use boosting with reweighting?


m_Precision

double m_Precision
The threshold on the improvement of the likelihood


m_Shrinkage

double m_Shrinkage
The value of the shrinkage parameter


m_RandomInstance

java.util.Random m_RandomInstance
The random number generator used


m_Offset

double m_Offset
The value by which the actual target value for the true class is offset.

Class weka.classifiers.meta.MetaCost extends RandomizableSingleClassifierEnhancer implements Serializable

Serialized Fields

m_MatrixSource

int m_MatrixSource
Indicates the current cost matrix source


m_OnDemandDirectory

java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory


m_CostFile

java.lang.String m_CostFile
The name of the cost file, for command line options


m_CostMatrix

CostMatrix m_CostMatrix
The cost matrix


m_NumIterations

int m_NumIterations
The number of iterations.


m_BagSizePercent

int m_BagSizePercent
The size of each bag sample, as a percentage of the training size

Class weka.classifiers.meta.MultiBoostAB extends AdaBoostM1 implements Serializable

Serialized Fields

m_NumSubCmtys

int m_NumSubCmtys
The number of sub-committees to use


m_Random

java.util.Random m_Random
Random number generator

Class weka.classifiers.meta.MultiClassClassifier extends Classifier implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
The classifiers.


m_ClassFilters

Filter[] m_ClassFilters
The filters used to transform the class.


m_Classifier

Classifier m_Classifier
The class name of the base classifier.


m_ZeroR

ZeroR m_ZeroR
ZeroR classifier for when all base classifier return zero probability.


m_ClassAttribute

Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes


m_TwoClassDataset

Instances m_TwoClassDataset
A transformed dataset header used by the 1-against-1 method


m_Seed

int m_Seed
Random number seed


m_RandomWidthFactor

double m_RandomWidthFactor
The multiplier when generating random codes. Will generate numClasses * m_RandomWidthFactor codes


m_Method

int m_Method
The multiclass method to use

Class weka.classifiers.meta.MultiScheme extends RandomizableMultipleClassifiersCombiner implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier that had the best performance on training data.


m_ClassifierIndex

int m_ClassifierIndex
The index into the vector for the selected scheme


m_NumXValFolds

int m_NumXValFolds
Number of folds to use for cross validation (0 means use training error for selection)

Class weka.classifiers.meta.ND extends Classifier implements Serializable

Serialized Fields

m_ndtree

ND.NDTree m_ndtree
The tree of classes


m_classifiers

java.util.Hashtable m_classifiers
The hashtable containing all the classifiers


m_seed

int m_seed
The random number seed used


m_classifier

Classifier m_classifier
The base classifier .


m_hashtablegiven

boolean m_hashtablegiven
Is Hashtable given from END?

Class weka.classifiers.meta.OrdinalClassClassifier extends Classifier implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
The classifiers. (One for each class.)


m_ClassFilters

MakeIndicator[] m_ClassFilters
The filters used to transform the class.


m_Classifier

Classifier m_Classifier
The class name of the base classifier.


m_ClassAttribute

Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes


m_ZeroR

ZeroR m_ZeroR
ZeroR classifier for when all base classifier return zero probability.

Class weka.classifiers.meta.RacedIncrementalLogitBoost extends Classifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The model base classifier to use


m_committees

FastVector m_committees
The committees


m_PruningType

int m_PruningType
The pruning type used


m_UseResampling

boolean m_UseResampling
Whether to use resampling


m_Seed

int m_Seed
Seed for boosting with resampling.


m_NumClasses

int m_NumClasses
The number of classes


m_NumericClassData

Instances m_NumericClassData
Dummy dataset with a numeric class


m_ClassAttribute

Attribute m_ClassAttribute
The actual class attribute (for getting class names)


m_minChunkSize

int m_minChunkSize
The minimum chunk size used for training


m_maxChunkSize

int m_maxChunkSize
The maimum chunk size used for training


m_validationChunkSize

int m_validationChunkSize
The size of the validation set


m_numInstancesConsumed

int m_numInstancesConsumed
The number of instances consumed


m_validationSet

Instances m_validationSet
The instances used for validation


m_currentSet

Instances m_currentSet
The instances currently in memory for training


m_bestCommittee

RacedIncrementalLogitBoost.Committee m_bestCommittee
The current best committee


m_zeroR

ZeroR m_zeroR
The default scheme used when committees aren't ready


m_validationSetChanged

boolean m_validationSetChanged
Whether the validation set has recently been changed


m_maxBatchSizeRequired

int m_maxBatchSizeRequired
The maximum number of instances required for processing


m_Debug

boolean m_Debug
Whether to output debug messages


m_RandomInstance

java.util.Random m_RandomInstance
The random number generator used

Class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee extends java.lang.Object implements Serializable

Serialized Fields

m_chunkSize

int m_chunkSize

m_instancesConsumed

int m_instancesConsumed

m_models

FastVector m_models

m_lastValidationError

double m_lastValidationError

m_lastLogLikelihood

double m_lastLogLikelihood

m_modelHasChanged

boolean m_modelHasChanged

m_modelHasChangedLL

boolean m_modelHasChangedLL

m_validationFs

double[][] m_validationFs

m_newValidationFs

double[][] m_newValidationFs

Class weka.classifiers.meta.RandomCommittee extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Class weka.classifiers.meta.RegressionByDiscretization extends SingleClassifierEnhancer implements Serializable

Serialized Fields

m_Discretizer

Discretize m_Discretizer
The discretization filter.


m_NumBins

int m_NumBins
The number of discretization intervals.


m_ClassMeans

double[] m_ClassMeans
The mean values for each Discretized class interval.

Class weka.classifiers.meta.Stacking extends RandomizableMultipleClassifiersCombiner implements Serializable

Serialized Fields

m_MetaClassifier

Classifier m_MetaClassifier
The meta classifier


m_MetaFormat

Instances m_MetaFormat
Format for meta data


m_BaseFormat

Instances m_BaseFormat
Format for base data


m_NumFolds

int m_NumFolds
Set the number of folds for the cross-validation

Class weka.classifiers.meta.StackingC extends Stacking implements Serializable

Serialized Fields

m_MetaClassifiers

Classifier[] m_MetaClassifiers
The meta classifiers (one for each class, like in ClassificationViaRegression)


m_attrFilter

Remove m_attrFilter
Filters to transform metaData


m_makeIndicatorFilter

MakeIndicator m_makeIndicatorFilter

Class weka.classifiers.meta.ThresholdSelector extends Classifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The generated base classifier


m_HighThreshold

double m_HighThreshold
The upper threshold used as the basis of correction


m_LowThreshold

double m_LowThreshold
The lower threshold used as the basis of correction


m_BestThreshold

double m_BestThreshold
The threshold that lead to the best performance


m_BestValue

double m_BestValue
The best value that has been observed


m_NumXValFolds

int m_NumXValFolds
The number of folds used in cross-validation


m_Seed

int m_Seed
Random number seed


m_DesignatedClass

int m_DesignatedClass
Designated class value, determined during building


m_ClassMode

int m_ClassMode
Method to determine which class to optimize for


m_EvalMode

int m_EvalMode
The evaluation mode


m_RangeMode

int m_RangeMode
The range correction mode

Class weka.classifiers.meta.TreeBasedMultiClassClassifier extends Classifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier at this node.


m_Filter

MakeIndicator m_Filter
The filter used at this node


m_FirstSuccessor

TreeBasedMultiClassClassifier m_FirstSuccessor
The first successor


m_SecondSuccessor

TreeBasedMultiClassClassifier m_SecondSuccessor
The second successor


m_Random

java.util.Random m_Random
The random number generator.


m_Range

Range m_Range
The classes that are grouped together at the current node


m_UseRandomSelection

boolean m_UseRandomSelection
Whether to split classes randomly


m_ClassIsOrdinal

boolean m_ClassIsOrdinal
Whether class is ordinal


m_Seed

int m_Seed
Random number seed


m_Data

Instances m_Data

Class weka.classifiers.meta.Vote extends MultipleClassifiersCombiner implements Serializable


Package weka.classifiers.misc

Class weka.classifiers.misc.FLR extends Classifier implements Serializable

Serialized Fields

learnedCode

java.util.Vector learnedCode

m_Rhoa

double m_Rhoa

bounds

FLR.FuzzyLattice bounds

m_BoundsFile

java.io.File m_BoundsFile

m_showRules

boolean m_showRules

index

int[] index

classNames

java.lang.String[] classNames

Class weka.classifiers.misc.HyperPipes extends Classifier implements Serializable

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Instances

Instances m_Instances
The structure of the training data


m_HyperPipes

HyperPipes.HyperPipe[] m_HyperPipes
Stores the HyperPipe for each class

Class weka.classifiers.misc.VFI extends Classifier implements Serializable

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_NumClasses

int m_NumClasses
The number of classes


m_Instances

Instances m_Instances
The training data


m_counts

double[][][] m_counts
The class counts for each interval of each attribute


m_globalCounts

double[] m_globalCounts
The global class counts


m_intervalBounds

double[][] m_intervalBounds
The lower bounds for each attribute


m_maxEntrop

double m_maxEntrop
The maximum entropy for the class


m_weightByConfidence

boolean m_weightByConfidence
Exponentially bias more confident intervals


m_bias

double m_bias
Bias towards more confident intervals


TINY

double TINY


Package weka.classifiers.bayes

Class weka.classifiers.bayes.AODE extends Classifier implements Serializable

Serialized Fields

m_CondiCounts

double[][][] m_CondiCounts
3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts


m_ClassCounts

double[] m_ClassCounts
The number of times each class value occurs in the dataset


m_SumForCounts

int[][] m_SumForCounts
The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]


m_NumClasses

int m_NumClasses
The number of classes


m_NumAttributes

int m_NumAttributes
The number of attributes in dataset, including class


m_NumInstances

int m_NumInstances
The number of instances in the dataset


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Instances

Instances m_Instances
The dataset


m_TotalAttValues

int m_TotalAttValues
The total number of values for all attributes (not including class). Eg. for three atts each with two possible values, m_TotalAttValues would be 6. This variable is used when allocating space for m_CondiCounts matrix.


m_StartAttIndex

int[] m_StartAttIndex
The starting index (in the m_CondiCounts matrix) of each attribute


m_NumAttValues

int[] m_NumAttValues
The number of values for each attribute


m_Frequencies

int[] m_Frequencies
The frequency of each attribute value for the dataset


m_SumInstances

double m_SumInstances
The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.


m_Limit

int m_Limit
An att's frequency must be this value or more to be a superParent


m_Debug

boolean m_Debug
If true, outputs debugging info

Class weka.classifiers.bayes.BayesNet extends Classifier implements Serializable

Serialized Fields

m_nOrder

int[] m_nOrder
topological ordering of the network


m_ParentSets

ParentSet[] m_ParentSets
The parent sets.


m_Distributions

Estimator[][] m_Distributions
The attribute estimators containing CPTs.


m_NumClasses

int m_NumClasses
The number of classes


m_Instances

Instances m_Instances
The dataset header for the purposes of printing out a semi-intelligible model


m_ADTree

ADNode m_ADTree

m_nScoreType

int m_nScoreType
Holds the score type used to measure quality of network


m_fAlpha

double m_fAlpha
Holds prior on count


m_nMaxNrOfParents

int m_nMaxNrOfParents
Holds upper bound on number of parents


m_bInitAsNaiveBayes

boolean m_bInitAsNaiveBayes
determines whether initial structure is an empty graph or a Naive Bayes network


m_bUseADTree

boolean m_bUseADTree
Use the experimental ADTree datastructure for calculating contingency tables

Class weka.classifiers.bayes.BayesNetB extends BayesNet implements Serializable

Class weka.classifiers.bayes.BayesNetB2 extends BayesNetB implements Serializable

Class weka.classifiers.bayes.BayesNetK2 extends BayesNet implements Serializable

Serialized Fields

m_bRandomOrder

boolean m_bRandomOrder
Holds flag to indicate ordering should be random

Class weka.classifiers.bayes.ComplementNaiveBayes extends Classifier implements Serializable

Serialized Fields

wordWeights

double[][] wordWeights
Weight of words for each class. The weight is actually the log of the probability of a word (w) given a class (c) (i.e. log(Pr[w|c])). The format of the matrix is: wordWeights[class][wordAttribute]


smoothingParameter

double smoothingParameter
Holds the smoothing value to avoid word probabilities of zero.
P.S.: According to the paper this is the Alpha i parameter


m_normalizeWordWeights

boolean m_normalizeWordWeights
True if the words weights are to be normalized


numClasses

int numClasses
Holds the number of Class values present in the set of specified instances


header

Instances header
The instances header that'll be used in toString

Class weka.classifiers.bayes.DiscreteEstimatorBayes extends java.lang.Object implements Serializable

Serialized Fields

m_Counts

double[] m_Counts
Hold the counts


m_SumOfCounts

double m_SumOfCounts
Hold the sum of counts


m_nSymbols

int m_nSymbols
Holds number of symbols in distribution


m_fPrior

double m_fPrior
Holds the prior probability

Class weka.classifiers.bayes.NaiveBayes extends Classifier implements Serializable

Serialized Fields

m_Distributions

Estimator[][] m_Distributions
The attribute estimators.


m_ClassDistribution

Estimator m_ClassDistribution
The class estimator.


m_UseKernelEstimator

boolean m_UseKernelEstimator
Whether to use kernel density estimator rather than normal distribution for numeric attributes


m_UseDiscretization

boolean m_UseDiscretization
Whether to use discretization than normal distribution for numeric attributes


m_NumClasses

int m_NumClasses
The number of classes (or 1 for numeric class)


m_Instances

Instances m_Instances
The dataset header for the purposes of printing out a semi-intelligible model


m_Disc

Discretize m_Disc
The discretization filter.

Class weka.classifiers.bayes.NaiveBayesMultinomial extends Classifier implements Serializable

Serialized Fields

probOfWordGivenClass

double[][] probOfWordGivenClass

probOfClass

double[] probOfClass

numAttributes

int numAttributes

numClasses

int numClasses

lnFactorialCache

double[] lnFactorialCache

headerInfo

Instances headerInfo

Class weka.classifiers.bayes.NaiveBayesSimple extends Classifier implements Serializable

Serialized Fields

m_Counts

double[][][] m_Counts
All the counts for nominal attributes.


m_Means

double[][] m_Means
The means for numeric attributes.


m_Devs

double[][] m_Devs
The standard deviations for numeric attributes.


m_Priors

double[] m_Priors
The prior probabilities of the classes.


m_Instances

Instances m_Instances
The instances used for training.

Class weka.classifiers.bayes.NaiveBayesUpdateable extends NaiveBayes implements Serializable

Class weka.classifiers.bayes.ParentSet extends java.lang.Object implements Serializable

Serialized Fields

m_nParents

int[] m_nParents
Holds indexes of parents


m_nNrOfParents

int m_nNrOfParents
Holds number of parents


m_nCardinalityOfParents

int m_nCardinalityOfParents
Holds cardinality of parents (= number of instantiations the parents can take)


Package weka.classifiers.rules

Class weka.classifiers.rules.ConjunctiveRule extends Classifier implements Serializable

Serialized Fields

m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for REP


m_ClassAttribute

Attribute m_ClassAttribute
The class attribute of the data


m_Antds

FastVector m_Antds
The vector of antecedents of this rule


m_DefDstr

double[] m_DefDstr
The default rule distribution of the data not covered


m_Cnsqt

double[] m_Cnsqt
The consequent of this rule


m_NumClasses

int m_NumClasses
Number of classes in the training data


m_Seed

long m_Seed
The seed to perform randomization


m_Random

java.util.Random m_Random
The Random object used for randomization


m_Targets

FastVector m_Targets
The predicted classes recorded for each antecedent in the growing data


m_IsExclude

boolean m_IsExclude
Whether to use exlusive expressions for nominal attributes


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_NumAntds

int m_NumAntds
The number of antecedents in pre-pruning

Class weka.classifiers.rules.DecisionTable extends Classifier implements Serializable

Serialized Fields

m_entries

java.util.Hashtable m_entries
The hashtable used to hold training instances


m_decisionFeatures

int[] m_decisionFeatures
Holds the final feature set


m_disTransform

Filter m_disTransform
Discretization filter


m_delTransform

Remove m_delTransform
Filter used to remove columns discarded by feature selection


m_ibk

IBk m_ibk
IB1 used to classify non matching instances rather than majority class


m_theInstances

Instances m_theInstances
Holds the training instances


m_numAttributes

int m_numAttributes
The number of attributes in the dataset


m_numInstances

int m_numInstances
The number of instances in the dataset


m_classIsNominal

boolean m_classIsNominal
Class is nominal


m_debug

boolean m_debug
Output debug info


m_useIBk

boolean m_useIBk
Use the IBk classifier rather than majority class


m_displayRules

boolean m_displayRules
Display Rules


m_maxStale

int m_maxStale
Maximum number of fully expanded non improving subsets for a best first search.


m_CVFolds

int m_CVFolds
Number of folds for cross validating feature sets


m_rr

java.util.Random m_rr
Random numbers for use in cross validation


m_majority

double m_majority
Holds the majority class

Class weka.classifiers.rules.DecisionTable.hashKey extends java.lang.Object implements Serializable

Serialized Fields

attributes

double[] attributes
Array of attribute values for an instance


missing

boolean[] missing
True for an index if the corresponding attribute value is missing.


values

java.lang.String[] values
The values


key

int key
The key

Class weka.classifiers.rules.DecisionTable.LinkedList extends FastVector implements Serializable

Class weka.classifiers.rules.JRip extends Classifier implements Serializable

Serialized Fields

m_Class

Attribute m_Class
The class attribute of the data


m_Ruleset

FastVector m_Ruleset
The ruleset


m_Distributions

FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


m_UsePruning

boolean m_UsePruning
Whether use pruning, i.e. the data is clean or not


m_Filter

Filter m_Filter
The filter used to randomize the class order


m_RulesetStats

FastVector m_RulesetStats
The RuleStats for the ruleset of each class value

Class weka.classifiers.rules.JRip.RipperRule extends Rule implements Serializable

Serialized Fields

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

FastVector m_Antds
The vector of antecedents of this rule

Class weka.classifiers.rules.M5Rules extends M5Base implements Serializable

Class weka.classifiers.rules.NNge extends Classifier implements Serializable

Serialized Fields

m_Train

Instances m_Train
An empty instances to keep the headers, the classIndex, etc...


m_Exemplars

NNge.Exemplar m_Exemplars
The list of Exemplars


m_ExemplarsByClass

NNge.Exemplar[] m_ExemplarsByClass
The lists of Exemplars by class


m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.


m_NumAttemptsOfGene

int m_NumAttemptsOfGene
The number of try for generalisation


m_NumFoldersMI

int m_NumFoldersMI
The number of folder for the Mutual Information


m_MissingVector

double[] m_MissingVector
Values to use for missing value


m_MI_NumAttrClassInter

int[][][] m_MI_NumAttrClassInter
MUTUAL INFORMATION'S DATAS


m_MI_NumAttrInter

int[][] m_MI_NumAttrInter

m_MI_MaxArray

double[] m_MI_MaxArray

m_MI_MinArray

double[] m_MI_MinArray

m_MI_NumAttrClassValue

int[][][] m_MI_NumAttrClassValue

m_MI_NumAttrValue

int[][] m_MI_NumAttrValue

m_MI_NumClass

int[] m_MI_NumClass

m_MI_NumInst

int m_MI_NumInst

m_MI

double[] m_MI

Class weka.classifiers.rules.OneR extends Classifier implements Serializable

Serialized Fields

m_rule

OneR.OneRRule m_rule
A 1-R rule


m_minBucketSize

int m_minBucketSize
The minimum bucket size

Class weka.classifiers.rules.PART extends Classifier implements Serializable

Serialized Fields

m_root

MakeDecList m_root
The decision list


m_CF

float m_CF
Confidence level


m_minNumObj

int m_minNumObj
Minimum number of objects


m_reducedErrorPruning

boolean m_reducedErrorPruning
Use reduced error pruning?


m_numFolds

int m_numFolds
Number of folds for reduced error pruning.


m_binarySplits

boolean m_binarySplits
Binary splits on nominal attributes?


m_unpruned

boolean m_unpruned
Generate unpruned list?


m_Seed

int m_Seed
The seed for random number generation.

Class weka.classifiers.rules.Prism extends Classifier implements Serializable

Serialized Fields

m_rules

Prism.PrismRule m_rules
The first rule in the list of rules

Class weka.classifiers.rules.Ridor extends Classifier implements Serializable

Serialized Fields

m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_Shuffle

int m_Shuffle
The number of shuffles performed on the data for randomization


m_Random

java.util.Random m_Random
Random object for randomization


m_Seed

int m_Seed
The seed to perform randomization


m_IsAllErr

boolean m_IsAllErr
Whether use error rate on all the data


m_IsMajority

boolean m_IsMajority
Whether use majority class as default class


m_Root

Ridor.Ridor_node m_Root
The root of Ridor


m_Class

Attribute m_Class
The class attribute of the data


m_Cover

double m_Cover
Statistics of the data


m_Err

double m_Err
Statistics of the data


m_MinNo

double m_MinNo
The minimal number of instance weights within a split

Class weka.classifiers.rules.Rule extends java.lang.Object implements Serializable

Class weka.classifiers.rules.RuleStats extends java.lang.Object implements Serializable

Serialized Fields

m_Data

Instances m_Data
The data on which the stats calculation is based


m_Ruleset

FastVector m_Ruleset
The specific ruleset in question


m_SimpleStats

FastVector m_SimpleStats
The simple stats of each rule


m_Filtered

FastVector m_Filtered
The set of instances filtered by the ruleset


m_Total

double m_Total
The total number of possible conditions that could appear in a rule


MDL_THEORY_WEIGHT

double MDL_THEORY_WEIGHT
The theory weight in the MDL calculation


m_Distributions

FastVector m_Distributions
The class distributions predicted by each rule

Class weka.classifiers.rules.ZeroR extends Classifier implements Serializable

Serialized Fields

m_ClassValue

double m_ClassValue
The class value 0R predicts.


m_Counts

double[] m_Counts
The number of instances in each class (null if class numeric).


m_Class

Attribute m_Class
The class attribute.


Package weka.classifiers.rules.part

Class weka.classifiers.rules.part.C45PruneableDecList extends ClassifierDecList implements Serializable

Serialized Fields

CF

double CF
CF

Class weka.classifiers.rules.part.ClassifierDecList extends java.lang.Object implements Serializable

Serialized Fields

m_minNumObj

int m_minNumObj
Minimum number of objects


m_toSelectModel

ModelSelection m_toSelectModel
The model selection method.


m_localModel

ClassifierSplitModel m_localModel
Local model at node.


m_sons

ClassifierDecList[] m_sons
References to sons.


m_isLeaf

boolean m_isLeaf
True if node is leaf.


m_isEmpty

boolean m_isEmpty
True if node is empty.


m_train

Instances m_train
The training instances.


m_test

Distribution m_test
The pruning instances.


indeX

int indeX
Which son to expand?

Class weka.classifiers.rules.part.MakeDecList extends java.lang.Object implements Serializable

Serialized Fields

theRules

java.util.Vector theRules
Vector storing the rules.


CF

double CF
The confidence for C45-type pruning.


minNumObj

int minNumObj
Minimum number of objects


toSelectModeL

ModelSelection toSelectModeL
The model selection method.


numSetS

int numSetS
How many subsets of equal size? One used for pruning, the rest for training.


reducedErrorPruning

boolean reducedErrorPruning
Use reduced error pruning?


unpruned

boolean unpruned
Generated unpruned list?


m_seed

int m_seed
The seed for random number generation.

Class weka.classifiers.rules.part.PruneableDecList extends ClassifierDecList implements Serializable


Package weka.classifiers.trees

Class weka.classifiers.trees.ADTree extends Classifier implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The instances used to train the tree


m_root

PredictionNode m_root
The root of the tree


m_random

java.util.Random m_random
The random number generator - used for the random search heuristic


m_lastAddedSplitNum

int m_lastAddedSplitNum
The number of the last splitter added to the tree


m_numericAttIndices

int[] m_numericAttIndices
An array containing the inidices to the numeric attributes in the data


m_nominalAttIndices

int[] m_nominalAttIndices
An array containing the inidices to the nominal attributes in the data


m_trainTotalWeight

double m_trainTotalWeight
The total weight of the instances - used to speed Z calculations


m_posTrainInstances

ReferenceInstances m_posTrainInstances
The training instances with positive class - referencing the training dataset


m_negTrainInstances

ReferenceInstances m_negTrainInstances
The training instances with negative class - referencing the training dataset


m_search_bestInsertionNode

PredictionNode m_search_bestInsertionNode
The best node to insert under, as found so far by the latest search


m_search_bestSplitter

Splitter m_search_bestSplitter
The best splitter to insert, as found so far by the latest search


m_search_smallestZ

double m_search_smallestZ
The smallest Z value found so far by the latest search


m_search_bestPathPosInstances

Instances m_search_bestPathPosInstances
The positive instances that apply to the best path found so far


m_search_bestPathNegInstances

Instances m_search_bestPathNegInstances
The negative instances that apply to the best path found so far


m_nodesExpanded

int m_nodesExpanded
Statistics - the number of prediction nodes investigated during search


m_examplesCounted

int m_examplesCounted
Statistics - the number of instances processed during search


m_boostingIterations

int m_boostingIterations
Option - the number of boosting iterations o perform


m_searchPath

int m_searchPath
Option - the search mode


m_randomSeed

int m_randomSeed
Option - the seed to use for a random search


m_saveInstanceData

boolean m_saveInstanceData
Option - whether the tree should remember the instance data

Class weka.classifiers.trees.DecisionStump extends Classifier implements Serializable

Serialized Fields

m_AttIndex

int m_AttIndex
The attribute used for classification.


m_SplitPoint

double m_SplitPoint
The split point (index respectively).


m_Distribution

double[][] m_Distribution
The distribution of class values or the means in each subset.


m_Instances

Instances m_Instances
The instances used for training.

Class weka.classifiers.trees.Id3 extends Classifier implements Serializable

Serialized Fields

m_Successors

Id3[] m_Successors
The node's successors.


m_Attribute

Attribute m_Attribute
Attribute used for splitting.


m_ClassValue

double m_ClassValue
Class value if node is leaf.


m_Distribution

double[] m_Distribution
Class distribution if node is leaf.


m_ClassAttribute

Attribute m_ClassAttribute
Class attribute of dataset.

Class weka.classifiers.trees.J48 extends Classifier implements Serializable

serialVersionUID: -217733168393644444l

Serialized Fields

m_root

ClassifierTree m_root
The decision tree


m_unpruned

boolean m_unpruned
Unpruned tree?


m_CF

float m_CF
Confidence level


m_minNumObj

int m_minNumObj
Minimum number of instances


m_useLaplace

boolean m_useLaplace
Determines whether probabilities are smoothed using Laplace correction when predictions are generated


m_reducedErrorPruning

boolean m_reducedErrorPruning
Use reduced error pruning?


m_numFolds

int m_numFolds
Number of folds for reduced error pruning.


m_binarySplits

boolean m_binarySplits
Binary splits on nominal attributes?


m_subtreeRaising

boolean m_subtreeRaising
Subtree raising to be performed?


m_noCleanup

boolean m_noCleanup
Cleanup after the tree has been built.


m_Seed

int m_Seed
Random number seed for reduced-error pruning.

Class weka.classifiers.trees.LMT extends Classifier implements Serializable

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing
Filter to replace missing values


m_nominalToBinary

NominalToBinary m_nominalToBinary
Filter to replace nominal attributes


m_tree

LMTNode m_tree
root of the logistic model tree


m_fastRegression

boolean m_fastRegression
use heuristic that determines the number of LogitBoost iterations only once in the beginning?


m_convertNominal

boolean m_convertNominal
convert nominal attributes to binary ?


m_splitOnResiduals

boolean m_splitOnResiduals
split on residuals?


m_errorOnProbabilities

boolean m_errorOnProbabilities
use error on probabilties instead of misclassification for stopping criterion of LogitBoost?


m_minNumInstances

int m_minNumInstances
minimum number of instances at which a node is considered for splitting


m_numBoostingIterations

int m_numBoostingIterations
if non-zero, use fixed number of iterations for LogitBoost

Class weka.classifiers.trees.M5P extends M5Base implements Serializable

Class weka.classifiers.trees.RandomForest extends Classifier implements Serializable

Serialized Fields

m_numTrees

int m_numTrees
Number of trees in forest.


m_numFeatures

int m_numFeatures
Number of features to consider in random feature selection. If less than 1 will use int(logM+1) )


m_randomSeed

int m_randomSeed
The random seed.


m_KValue

int m_KValue
Final number of features that were considered in last build.


m_bagger

Bagging m_bagger
The bagger.

Class weka.classifiers.trees.RandomTree extends Classifier implements Serializable

Serialized Fields

m_Successors

RandomTree[] m_Successors
The subtrees appended to this tree.


m_Attribute

int m_Attribute
The attribute to split on.


m_SplitPoint

double m_SplitPoint
The split point.


m_Distribution

double[][] m_Distribution
The class distribution from the training data.


m_Info

Instances m_Info
The header information.


m_Prop

double[] m_Prop
The proportions of training instances going down each branch.


m_ClassProbs

double[] m_ClassProbs
Class probabilities from the training data.


m_MinNum

double m_MinNum
Minimum number of instances for leaf.


m_Debug

boolean m_Debug
Debug info


m_KValue

int m_KValue
The number of attributes considered for a split.


m_randomSeed

int m_randomSeed
The random seed to use.

Class weka.classifiers.trees.REPTree extends Classifier implements Serializable

Serialized Fields

m_Tree

REPTree.Tree m_Tree
The Tree object


m_NumFolds

int m_NumFolds
Number of folds for reduced error pruning.


m_Seed

int m_Seed
Seed for random data shuffling.


m_NoPruning

boolean m_NoPruning
Don't prune


m_MinNum

double m_MinNum
The minimum number of instances per leaf.


m_MinVarianceProp

double m_MinVarianceProp
The minimum proportion of the total variance (over all the data) required for split.


m_MaxDepth

int m_MaxDepth
Upper bound on the tree depth

Class weka.classifiers.trees.REPTree.Tree extends java.lang.Object implements Serializable

Serialized Fields

m_Info

Instances m_Info
The header information (for printing the tree).


m_Successors

REPTree.Tree[] m_Successors
The subtrees of this tree.


m_Attribute

int m_Attribute
The attribute to split on.


m_SplitPoint

double m_SplitPoint
The split point.


m_Prop

double[] m_Prop
The proportions of training instances going down each branch.


m_ClassProbs

double[] m_ClassProbs
Class probabilities from the training data in the nominal case. Holds the mean in the numeric case.


m_Distribution

double[] m_Distribution
The (unnormalized) class distribution in the nominal case. Holds the sum of squared errors and the weight in the numeric case.


m_HoldOutDist

double[] m_HoldOutDist
Class distribution of hold-out set at node in the nominal case. Straight sum of weights in the numeric case (i.e. array has only one element.


m_HoldOutError

double m_HoldOutError
The hold-out error of the node. The number of miss-classified instances in the nominal case, the sum of squared errors in the numeric case.

Class weka.classifiers.trees.UserClassifier extends Classifier implements Serializable

Serialized Fields

m_tView

TreeVisualizer m_tView
The tree display panel.


m_iView

VisualizePanel m_iView
The instances display.


m_top

UserClassifier.TreeClass m_top
Two references to the structure of the decision tree.


m_focus

UserClassifier.TreeClass m_focus
Two references to the structure of the decision tree.


m_nextId

int m_nextId
The next number that can be used as a unique id for a node.


m_treeFrame

javax.swing.JFrame m_treeFrame
These two frames aren't used anymore.


m_visFrame

javax.swing.JFrame m_visFrame

m_reps

javax.swing.JTabbedPane m_reps
The tabbed window for the tree and instances view.


m_mainWin

javax.swing.JFrame m_mainWin
The window.


m_built

boolean m_built
The status of whether there is a decision tree ready or not.


m_classifiers

GenericObjectEditor m_classifiers
A list of other m_classifiers.


m_propertyDialog

PropertyDialog m_propertyDialog
A window for selecting other classifiers.


Package weka.classifiers.trees.m5

Class weka.classifiers.trees.m5.CorrelationSplitInfo extends java.lang.Object implements Serializable

Serialized Fields

m_first

int m_first
the first instance


m_last

int m_last
the last instance


m_position

int m_position

m_maxImpurity

double m_maxImpurity
the maximum impurity reduction


m_splitAttr

int m_splitAttr
the attribute being tested


m_splitValue

double m_splitValue
the best value on which to split


m_number

int m_number
the number of instances

Class weka.classifiers.trees.m5.M5Base extends Classifier implements Serializable

Serialized Fields

m_instances

Instances m_instances
the instances covered by the tree/rules


m_classIndex

int m_classIndex
the class index


m_numAttributes

int m_numAttributes
the number of attributes


m_numInstances

int m_numInstances
the number of instances in the dataset


m_ruleSet

FastVector m_ruleSet
the rule set


m_generateRules

boolean m_generateRules
generate a decision list instead of a single tree.


m_unsmoothedPredictions

boolean m_unsmoothedPredictions
use unsmoothed predictions


m_replaceMissing

ReplaceMissingValues m_replaceMissing
filter to fill in missing values


m_nominalToBinary

NominalToBinary m_nominalToBinary
filter to convert nominal attributes to binary


m_saveInstances

boolean m_saveInstances
Save instances at each node in an M5 tree for visualization purposes.


m_regressionTree

boolean m_regressionTree
Make a regression tree/rule instead of a model tree/rule


m_useUnpruned

boolean m_useUnpruned
Do not prune tree/rules


m_minNumInstances

double m_minNumInstances
The minimum number of instances to allow at a leaf node

Class weka.classifiers.trees.m5.PreConstructedLinearModel extends Classifier implements Serializable

Serialized Fields

m_coefficients

double[] m_coefficients

m_intercept

double m_intercept

m_instancesHeader

Instances m_instancesHeader

m_numParameters

int m_numParameters

Class weka.classifiers.trees.m5.Rule extends java.lang.Object implements Serializable

Serialized Fields

m_instances

Instances m_instances
the instances covered by this rule


m_classIndex

int m_classIndex
the class index


m_numAttributes

int m_numAttributes
the number of attributes


m_numInstances

int m_numInstances
the number of instances in the dataset


m_splitAtts

int[] m_splitAtts
the indexes of the attributes used to split on for this rule


m_splitVals

double[] m_splitVals
the corresponding values of the split points


m_internalNodes

RuleNode[] m_internalNodes
the corresponding internal nodes. Used for smoothing rules.


m_relOps

int[] m_relOps
the corresponding relational operators (0 = "<=", 1 = ">")


m_ruleModel

RuleNode m_ruleModel
the leaf encapsulating the linear model for this rule


m_topOfTree

RuleNode m_topOfTree
the top of the m5 tree for this rule


m_globalStdDev

double m_globalStdDev
the standard deviation of the class for all the instances


m_globalAbsDev

double m_globalAbsDev
the absolute deviation of the class for all the instances


m_covered

Instances m_covered
the instances covered by this rule


m_numCovered

int m_numCovered
the number of instances covered by this rule


m_notCovered

Instances m_notCovered
the instances not covered by this rule


m_useTree

boolean m_useTree
use a pruned m5 tree rather than make a rule


m_smoothPredictions

boolean m_smoothPredictions
use the original m5 smoothing procedure


m_saveInstances

boolean m_saveInstances
Save instances at each node in an M5 tree for visualization purposes.


m_regressionTree

boolean m_regressionTree
Make a regression tree instead of a model tree


m_useUnpruned

boolean m_useUnpruned
Build unpruned tree/rule


m_minNumInstances

double m_minNumInstances
The minimum number of instances to allow at a leaf node

Class weka.classifiers.trees.m5.RuleNode extends Classifier implements Serializable

Serialized Fields

m_instances

Instances m_instances
instances reaching this node


m_classIndex

int m_classIndex
the class index


m_numInstances

int m_numInstances
the number of instances reaching this node


m_numAttributes

int m_numAttributes
the number of attributes


m_isLeaf

boolean m_isLeaf
Node is a leaf


m_splitAtt

int m_splitAtt
attribute this node splits on


m_splitValue

double m_splitValue
the value of the split attribute


m_nodeModel

PreConstructedLinearModel m_nodeModel
the linear model at this node


m_numParameters

int m_numParameters
the number of paramters in the chosen model for this node---either the subtree model or the linear model. The constant term is counted as a paramter---this is for pruning purposes


m_rootMeanSquaredError

double m_rootMeanSquaredError
the mean squared error of the model at this node (either linear or subtree)


m_left

RuleNode m_left
child nodes


m_right

RuleNode m_right

m_parent

RuleNode m_parent
the parent of this node


m_splitNum

double m_splitNum
a node will not be split if it contains less then m_splitNum instances


m_devFraction

double m_devFraction
a node will not be split if its class standard deviation is less than 5% of the class standard deviation of all the instances


m_pruningMultiplier

double m_pruningMultiplier

m_leafModelNum

int m_leafModelNum
the number assigned to the linear model if this node is a leaf. = 0 if this node is not a leaf


m_globalDeviation

double m_globalDeviation
a node will not be split if the class deviation of its instances is less than m_devFraction of the deviation of the global class


m_globalAbsDeviation

double m_globalAbsDeviation
the absolute deviation of the global class


m_indices

int[] m_indices
Indices of the attributes to be used in generating a linear model at this node


m_id

int m_id
Node id.


m_saveInstances

boolean m_saveInstances
Save the instances at each node (for visualizing in the Explorer's treevisualizer.


m_regressionTree

boolean m_regressionTree
Make a regression tree instead of a model tree

Class weka.classifiers.trees.m5.YongSplitInfo extends java.lang.Object implements Serializable

Serialized Fields

number

int number

first

int first

last

int last

position

int position

maxImpurity

double maxImpurity

leftAve

double leftAve

rightAve

double rightAve

splitAttr

int splitAttr

splitValue

double splitValue


Package weka.classifiers.trees.j48

Class weka.classifiers.trees.j48.BinC45ModelSelection extends ModelSelection implements Serializable

Serialized Fields

m_minNoObj

int m_minNoObj
Minimum number of instances in interval.


m_allData

Instances m_allData
The FULL training dataset.

Class weka.classifiers.trees.j48.BinC45Split extends ClassifierSplitModel implements Serializable

Serialized Fields

m_attIndex

int m_attIndex
Attribute to split on.


m_minNoObj

int m_minNoObj
Minimum number of objects in a split.


m_splitPoint

double m_splitPoint
Value of split point.


m_infoGain

double m_infoGain
InfoGain of split.


m_gainRatio

double m_gainRatio
GainRatio of split.


m_sumOfWeights

double m_sumOfWeights
The sum of the weights of the instances.

Class weka.classifiers.trees.j48.C45ModelSelection extends ModelSelection implements Serializable

Serialized Fields

m_minNoObj

int m_minNoObj
Minimum number of objects in interval.


m_allData

Instances m_allData
All the training data

Class weka.classifiers.trees.j48.C45PruneableClassifierTree extends ClassifierTree implements Serializable

Serialized Fields

m_pruneTheTree

boolean m_pruneTheTree
True if the tree is to be pruned.


m_CF

float m_CF
The confidence factor for pruning.


m_subtreeRaising

boolean m_subtreeRaising
Is subtree raising to be performed?


m_cleanup

boolean m_cleanup
Cleanup after the tree has been built.

Class weka.classifiers.trees.j48.C45Split extends ClassifierSplitModel implements Serializable

Serialized Fields

m_complexityIndex

int m_complexityIndex
Desired number of branches.


m_attIndex

int m_attIndex
Attribute to split on.


m_minNoObj

int m_minNoObj
Minimum number of objects in a split.


m_splitPoint

double m_splitPoint
Value of split point.


m_infoGain

double m_infoGain
InfoGain of split.


m_gainRatio

double m_gainRatio
GainRatio of split.


m_sumOfWeights

double m_sumOfWeights
The sum of the weights of the instances.


m_index

int m_index
Number of split points.

Class weka.classifiers.trees.j48.ClassifierSplitModel extends java.lang.Object implements Serializable

Serialized Fields

m_distribution

Distribution m_distribution
Distribution of class values.


m_numSubsets

int m_numSubsets
Number of created subsets.

Class weka.classifiers.trees.j48.ClassifierTree extends java.lang.Object implements Serializable

Serialized Fields

m_toSelectModel

ModelSelection m_toSelectModel
The model selection method.


m_localModel

ClassifierSplitModel m_localModel
Local model at node.


m_sons

ClassifierTree[] m_sons
References to sons.


m_isLeaf

boolean m_isLeaf
True if node is leaf.


m_isEmpty

boolean m_isEmpty
True if node is empty.


m_train

Instances m_train
The training instances.


m_test

Distribution m_test
The pruning instances.


m_id

int m_id
The id for the node.

Class weka.classifiers.trees.j48.Distribution extends java.lang.Object implements Serializable

Serialized Fields

m_perClassPerBag

double[][] m_perClassPerBag
Weight of instances per class per bag.


m_perBag

double[] m_perBag
Weight of instances per bag.


m_perClass

double[] m_perClass
Weight of instances per class.


totaL

double totaL
Total weight of instances.

Class weka.classifiers.trees.j48.EntropyBasedSplitCrit extends SplitCriterion implements Serializable

Class weka.classifiers.trees.j48.EntropySplitCrit extends EntropyBasedSplitCrit implements Serializable

Class weka.classifiers.trees.j48.GainRatioSplitCrit extends EntropyBasedSplitCrit implements Serializable

Class weka.classifiers.trees.j48.InfoGainSplitCrit extends EntropyBasedSplitCrit implements Serializable

Class weka.classifiers.trees.j48.ModelSelection extends java.lang.Object implements Serializable

Class weka.classifiers.trees.j48.NoSplit extends ClassifierSplitModel implements Serializable

Class weka.classifiers.trees.j48.PruneableClassifierTree extends ClassifierTree implements Serializable

Serialized Fields

pruneTheTree

boolean pruneTheTree
True if the tree is to be pruned.


numSets

int numSets
How many subsets of equal size? One used for pruning, the rest for training.


m_cleanup

boolean m_cleanup
Cleanup after the tree has been built.


m_seed

int m_seed
The random number seed.

Class weka.classifiers.trees.j48.SplitCriterion extends java.lang.Object implements Serializable


Package weka.classifiers.trees.lmt

Class weka.classifiers.trees.lmt.LMTNode extends LogisticBase implements Serializable

Serialized Fields

m_totalInstanceWeight

double m_totalInstanceWeight
Total number of training instances.


m_id

int m_id
Node id


m_leafModelNum

int m_leafModelNum
ID of logistic model at leaf


m_alpha

double m_alpha
Alpha-value (for pruning) at the node


m_numIncorrectModel

double m_numIncorrectModel
Weighted number of training examples currently misclassified by the logistic model at the node


m_numIncorrectTree

double m_numIncorrectTree
Weighted number of training examples currently misclassified by the subtree rooted at the node


m_minNumInstances

int m_minNumInstances
minimum number of instances at which a node is considered for splitting


m_modelSelection

ModelSelection m_modelSelection
ModelSelection object (for splitting)


m_nominalToBinary

NominalToBinary m_nominalToBinary
Filter to convert nominal attributes to binary


m_higherRegressions

SimpleLinearRegression[][] m_higherRegressions
Simple regression functions fit by LogitBoost at higher levels in the tree


m_numHigherRegressions

int m_numHigherRegressions
Number of simple regression functions fit by LogitBoost at higher levels in the tree


m_fastRegression

boolean m_fastRegression
Use heuristic that determines the number of LogitBoost iterations only once in the beginning?


m_numInstances

int m_numInstances
Number of instances at the node


m_localModel

ClassifierSplitModel m_localModel
The ClassifierSplitModel (for splitting)


m_sons

LMTNode[] m_sons
Array of children of the node


m_isLeaf

boolean m_isLeaf
True if node is leaf

Class weka.classifiers.trees.lmt.LogisticBase extends Classifier implements Serializable

Serialized Fields

m_numericDataHeader

Instances m_numericDataHeader
Header-only version of the numeric version of the training data


m_numericData

Instances m_numericData
Numeric version of the training data. Original class is replaced by a numeric pseudo-class.


m_train

Instances m_train
Training data


m_useCrossValidation

boolean m_useCrossValidation
Use cross-validation to determine best number of LogitBoost iterations ?


m_errorOnProbabilities

boolean m_errorOnProbabilities
Use error on probabilities for stopping criterion of LogitBoost?


m_fixedNumIterations

int m_fixedNumIterations
Use fixed number of iterations for LogitBoost? (if negative, cross-validate number of iterations)


m_heuristicStop

int m_heuristicStop
Use heuristic to stop performing LogitBoost iterations earlier? If enabled, LogitBoost is stopped if the current (local) minimum of the error on a test set as a function of the number of iterations has not changed for m_heuristicStop iterations.


m_numRegressions

int m_numRegressions
The number of LogitBoost iterations performed.


m_maxIterations

int m_maxIterations
The maximum number of LogitBoost iterations


m_numClasses

int m_numClasses
The number of different classes


m_regressions

SimpleLinearRegression[][] m_regressions
Array holding the simple regression functions fit by LogitBoost

Class weka.classifiers.trees.lmt.ResidualModelSelection extends ModelSelection implements Serializable

Serialized Fields

m_minNumInstances

int m_minNumInstances
Minimum number of instances for leaves


m_minInfoGain

double m_minInfoGain
Minimum information gain for split

Class weka.classifiers.trees.lmt.ResidualSplit extends ClassifierSplitModel implements Serializable

Serialized Fields

m_attribute

Attribute m_attribute
The attribute selected for the split


m_attIndex

int m_attIndex
The index of the attribute selected for the split


m_numInstances

int m_numInstances
Number of instances in the set


m_numClasses

int m_numClasses
Number of classed


m_data

Instances m_data
The set of instances


m_dataZs

double[][] m_dataZs
The Z-values (LogitBoost response) for the set of instances


m_dataWs

double[][] m_dataWs
The LogitBoost-weights for the set of instances


m_splitPoint

double m_splitPoint
The split point (for numeric attributes)


Package weka.classifiers.trees.adtree

Class weka.classifiers.trees.adtree.PredictionNode extends java.lang.Object implements Serializable

Serialized Fields

value

double value
The prediction value stored in this node


children

FastVector children
The children of this node - any number of splitter nodes

Class weka.classifiers.trees.adtree.ReferenceInstances extends Instances implements Serializable

Class weka.classifiers.trees.adtree.Splitter extends java.lang.Object implements Serializable

Serialized Fields

orderAdded

int orderAdded
The number this node was in the order of nodes added to the tree

Class weka.classifiers.trees.adtree.TwoWayNominalSplit extends Splitter implements Serializable

Serialized Fields

attIndex

int attIndex
The index of the attribute the split depends on


trueSplitValue

int trueSplitValue
The attribute value that is compared against


children

PredictionNode[] children
The children of this split

Class weka.classifiers.trees.adtree.TwoWayNumericSplit extends Splitter implements Serializable

Serialized Fields

attIndex

int attIndex
The index of the attribute the split depends on


splitPoint

double splitPoint
The attribute value that is compared against


children

PredictionNode[] children
The children of this split


Package weka.classifiers.evaluation

Class weka.classifiers.evaluation.ConfusionMatrix extends Matrix implements Serializable

Serialized Fields

m_ClassNames

java.lang.String[] m_ClassNames
Stores the names of the classes

Class weka.classifiers.evaluation.NominalPrediction extends java.lang.Object implements Serializable

serialVersionUID: -8871333992740492788l

Serialized Fields

m_Distribution

double[] m_Distribution
The predicted probabilities


m_Actual

double m_Actual
The actual class value


m_Predicted

double m_Predicted
The predicted class value


m_Weight

double m_Weight
The weight assigned to this prediction

Class weka.classifiers.evaluation.NumericPrediction extends java.lang.Object implements Serializable

Serialized Fields

m_Actual

double m_Actual
The actual class value


m_Predicted

double m_Predicted
The predicted class value


m_Weight

double m_Weight
The weight assigned to this prediction


Package weka.classifiers.functions

Class weka.classifiers.functions.LeastMedSq extends Classifier implements Serializable

Serialized Fields

m_Residuals

double[] m_Residuals

m_weight

double[] m_weight

m_SSR

double m_SSR

m_scalefactor

double m_scalefactor

m_bestMedian

double m_bestMedian

m_currentRegression

LinearRegression m_currentRegression

m_bestRegression

LinearRegression m_bestRegression

m_ls

LinearRegression m_ls

m_Data

Instances m_Data

m_RLSData

Instances m_RLSData

m_SubSample

Instances m_SubSample

m_MissingFilter

ReplaceMissingValues m_MissingFilter

m_TransformFilter

NominalToBinary m_TransformFilter

m_SplitFilter

RemoveRange m_SplitFilter

m_samplesize

int m_samplesize

m_samples

int m_samples

m_israndom

boolean m_israndom

m_debug

boolean m_debug

m_random

java.util.Random m_random

m_randomseed

long m_randomseed

Class weka.classifiers.functions.LinearRegression extends Classifier implements Serializable

Serialized Fields

m_Coefficients

double[] m_Coefficients
Array for storing coefficients of linear regression.


m_SelectedAttributes

boolean[] m_SelectedAttributes
Which attributes are relevant?


m_TransformedData

Instances m_TransformedData
Variable for storing transformed training data.


m_MissingFilter

ReplaceMissingValues m_MissingFilter
The filter for removing missing values.


m_TransformFilter

NominalToBinary m_TransformFilter
The filter storing the transformation from nominal to binary attributes.


m_ClassStdDev

double m_ClassStdDev
The standard deviations of the class attribute


m_ClassMean

double m_ClassMean
The mean of the class attribute


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Means

double[] m_Means
The attributes means


m_StdDevs

double[] m_StdDevs
The attribute standard deviations


b_Debug

boolean b_Debug
True if debug output will be printed


m_AttributeSelection

int m_AttributeSelection
The current attribute selection method


m_EliminateColinearAttributes

boolean m_EliminateColinearAttributes
Try to eliminate correlated attributes?


m_checksTurnedOff

boolean m_checksTurnedOff
Turn off all checks and conversions?


m_Ridge

double m_Ridge
The ridge parameter

Class weka.classifiers.functions.Logistic extends Classifier implements Serializable

Serialized Fields

m_Par

double[][] m_Par
The coefficients (optimized parameters) of the model


m_Data

double[][] m_Data
The data saved as a matrix


m_NumPredictors

int m_NumPredictors
The number of attributes in the model


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_NumClasses

int m_NumClasses
The number of the class labels


m_Ridge

double m_Ridge
The ridge parameter.


m_AttFilter

RemoveUseless m_AttFilter

m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values.


m_Debug

boolean m_Debug
Debugging output


m_LL

double m_LL
Log-likelihood of the searched model


m_MaxIts

int m_MaxIts
The maximum number of iterations.

Class weka.classifiers.functions.MultilayerPerceptron extends Classifier implements Serializable

Serialized Fields

m_instances

Instances m_instances
The training instances.


m_currentInstance

Instance m_currentInstance
The current instance running through the network.


m_numeric

boolean m_numeric
A flag to say that it's a numeric class.


m_attributeRanges

double[] m_attributeRanges
The ranges for all the attributes.


m_attributeBases

double[] m_attributeBases
The base values for all the attributes.


m_outputs

MultilayerPerceptron.NeuralEnd[] m_outputs
The output units.(only feeds the errors, does no calcs)


m_inputs

MultilayerPerceptron.NeuralEnd[] m_inputs
The input units.(only feeds the inputs does no calcs)


m_neuralNodes

NeuralConnection[] m_neuralNodes
All the nodes that actually comprise the logical neural net.


m_numClasses

int m_numClasses
The number of classes.


m_numAttributes

int m_numAttributes
The number of attributes.


m_nodePanel

MultilayerPerceptron.NodePanel m_nodePanel
The panel the nodes are displayed on.


m_controlPanel

MultilayerPerceptron.ControlPanel m_controlPanel
The control panel.


m_nextId

int m_nextId
The next id number available for default naming.


m_selected

FastVector m_selected
A Vector list of the units currently selected.


m_graphers

FastVector m_graphers
A Vector list of the graphers.


m_numEpochs

int m_numEpochs
The number of epochs to train through.


m_stopIt

boolean m_stopIt
a flag to state if the network should be running, or stopped.


m_stopped

boolean m_stopped
a flag to state that the network has in fact stopped.


m_accepted

boolean m_accepted
a flag to state that the network should be accepted the way it is.


m_win

javax.swing.JFrame m_win
The window for the network.


m_autoBuild

boolean m_autoBuild
A flag to tell the build classifier to automatically build a neural net.


m_gui

boolean m_gui
A flag to state that the gui for the network should be brought up. To allow interaction while training.


m_valSize

int m_valSize
An int to say how big the validation set should be.


m_driftThreshold

int m_driftThreshold
The number to to use to quit on validation testing.


m_randomSeed

long m_randomSeed
The number used to seed the random number generator.


m_random

java.util.Random m_random
The actual random number generator.


m_useNomToBin

boolean m_useNomToBin
A flag to state that a nominal to binary filter should be used.


m_nominalToBinaryFilter

NominalToBinary m_nominalToBinaryFilter
The actual filter.


m_hiddenLayers

java.lang.String m_hiddenLayers
The string that defines the hidden layers


m_normalizeAttributes

boolean m_normalizeAttributes
This flag states that the user wants the input values normalized.


m_decay

boolean m_decay
This flag states that the user wants the learning rate to decay.


m_learningRate

double m_learningRate
This is the learning rate for the network.


m_momentum

double m_momentum
This is the momentum for the network.


m_epoch

int m_epoch
Shows the number of the epoch that the network just finished.


m_error

double m_error
Shows the error of the epoch that the network just finished.


m_reset

boolean m_reset
This flag states that the user wants the network to restart if it is found to be generating infinity or NaN for the error value. This would restart the network with the current options except that the learning rate would be smaller than before, (perhaps half of its current value). This option will not be available if the gui is chosen (if the gui is open the user can fix the network themselves, it is an architectural minefield for the network to be reset with the gui open).


m_normalizeClass

boolean m_normalizeClass
This flag states that the user wants the class to be normalized while processing in the network is done. (the final answer will be in the original range regardless). This option will only be used when the class is numeric.


m_sigmoidUnit

SigmoidUnit m_sigmoidUnit
this is a sigmoid unit.


m_linearUnit

LinearUnit m_linearUnit
This is a linear unit.

Class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd extends NeuralConnection implements Serializable

Serialized Fields

m_link

int m_link
the value that represents the instance value this node represents. For an input it is the attribute number, for an output, if nominal it is the class value.


m_input

boolean m_input
True if node is an input, False if it's an output.

Class weka.classifiers.functions.PaceRegression extends Classifier implements Serializable

Serialized Fields

m_Model

Instances m_Model
The model used


m_Coefficients

double[] m_Coefficients
Array for storing coefficients of linear regression.


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Debug

boolean m_Debug
True if debug output will be printed


paceEstimator

int paceEstimator

olscThreshold

double olscThreshold

Class weka.classifiers.functions.RBFNetwork extends Classifier implements Serializable

Serialized Fields

m_logistic

Logistic m_logistic
The logistic regression for classification problems


m_linear

LinearRegression m_linear
The linear regression for numeric problems


m_basisFilter

ClusterMembership m_basisFilter
The filter for producing the meta data


m_numClusters

int m_numClusters
The number of clusters (basis functions to generate)


m_ridge

double m_ridge
The ridge parameter for the logistic regression.


m_maxIts

int m_maxIts
The maximum number of iterations for logistic regression.


m_clusteringSeed

int m_clusteringSeed
The seed to pass on to K-means

Class weka.classifiers.functions.SimpleLinearRegression extends Classifier implements Serializable

Serialized Fields

m_attribute

Attribute m_attribute
The chosen attribute


m_attributeIndex

int m_attributeIndex
The index of the chosen attribute


m_slope

double m_slope
The slope


m_intercept

double m_intercept
The intercept


m_suppressErrorMessage

boolean m_suppressErrorMessage
If true, suppress error message if no useful attribute was found

Class weka.classifiers.functions.SimpleLogistic extends Classifier implements Serializable

Serialized Fields

m_boostedModel

LogisticBase m_boostedModel
The actual logistic regression model


m_NominalToBinary

NominalToBinary m_NominalToBinary
Filter for converting nominal attributes to binary ones


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
Filter for replacing missing values


m_numBoostingIterations

int m_numBoostingIterations
If non-negative, use this as fixed number of LogitBoost iterations


m_maxBoostingIterations

int m_maxBoostingIterations
Maximum number of iterations for LogitBoost


m_heuristicStop

int m_heuristicStop
Parameter for the heuristic for early stopping of LogitBoost


m_useCrossValidation

boolean m_useCrossValidation
If true, cross-validate number of LogitBoost iterations


m_errorOnProbabilities

boolean m_errorOnProbabilities
If true, use minimize error on probabilities instead of misclassification error

Class weka.classifiers.functions.SMO extends Classifier implements Serializable

Serialized Fields

m_classifiers

SMO.BinarySMO[][] m_classifiers
The binary classifier(s)


m_exponent

double m_exponent
The exponent for the polynomial kernel.


m_lowerOrder

boolean m_lowerOrder
Use lower-order terms?


m_gamma

double m_gamma
Gamma for the RBF kernel.


m_C

double m_C
The complexity parameter.


m_eps

double m_eps
Epsilon for rounding.


m_tol

double m_tol
Tolerance for accuracy of result.


m_filterType

int m_filterType
Whether to normalize/standardize/neither


m_featureSpaceNormalization

boolean m_featureSpaceNormalization
Feature-space normalization?


m_useRBF

boolean m_useRBF
Use RBF kernel? (default: poly)


m_cacheSize

int m_cacheSize
The size of the cache (a prime number)


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_Filter

Filter m_Filter
The filter used to standardize/normalize all values.


m_Missing

ReplaceMissingValues m_Missing
The filter used to get rid of missing values.


m_onlyNumeric

boolean m_onlyNumeric
Only numeric attributes in the dataset?


m_classIndex

int m_classIndex
The class index from the training data


m_classAttribute

Attribute m_classAttribute
The class attribute


m_checksTurnedOff

boolean m_checksTurnedOff
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0.


m_fitLogisticModels

boolean m_fitLogisticModels
Whether logistic models are to be fit


m_numFolds

int m_numFolds
The number of folds for the internal cross-validation


m_randomSeed

int m_randomSeed
The random number seed

Class weka.classifiers.functions.SMOreg extends Classifier implements Serializable

Serialized Fields

m_featureSpaceNormalization

boolean m_featureSpaceNormalization
Feature-space normalization?


m_useRBF

boolean m_useRBF
Use RBF kernel? (default: poly)


m_cacheSize

int m_cacheSize
The size of the cache (a prime number)


m_kernel

Kernel m_kernel
Kernel to use


m_lowerOrder

boolean m_lowerOrder
Use lower-order terms?


m_exponent

double m_exponent
The exponent for the polynomial kernel.


m_gamma

double m_gamma
Gamma for the RBF kernel.


m_classIndex

int m_classIndex
The class index from the training data


m_onlyNumeric

boolean m_onlyNumeric
Only numeric attributes in the dataset?


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_Filter

Filter m_Filter
The filter used to standardize/normalize all values.


m_filterType

int m_filterType
Whether to normalize/standardize/neither


m_Missing

ReplaceMissingValues m_Missing
The filter used to get rid of missing values.


m_checksTurnedOff

boolean m_checksTurnedOff
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0.


m_data

Instances m_data
The training data.


m_C

double m_C
The complexity parameter


m_alpha

double[] m_alpha
The Lagrange multipliers


m_alpha_

double[] m_alpha_

m_b

double m_b
The thresholds.


m_bLow

double m_bLow
The thresholds.


m_bUp

double m_bUp
The thresholds.


m_iLow

int m_iLow
The indices for m_bLow and m_bUp


m_iUp

int m_iUp
The indices for m_bLow and m_bUp


m_weights

double[] m_weights
Weight vector for linear machine.


m_fcache

double[] m_fcache
The current set of errors for all non-bound examples.


m_I0

SMOset m_I0
The five different sets used by the algorithm.


m_I1

SMOset m_I1

m_I2

SMOset m_I2

m_I3

SMOset m_I3

m_epsilon

double m_epsilon
The parameter epsilon


m_tol

double m_tol
The parameter tol


m_eps

double m_eps
The parameter eps


m_Alin

double m_Alin
The parameters of the linear transforamtion realized by the filter on the class attribute


m_Blin

double m_Blin

m_sparseWeights

double[] m_sparseWeights
Variables to hold weight vector in sparse form. (To reduce storage requirements.)


m_sparseIndices

int[] m_sparseIndices

Class weka.classifiers.functions.VotedPerceptron extends Classifier implements Serializable

Serialized Fields

m_MaxK

int m_MaxK
The maximum number of alterations to the perceptron


m_NumIterations

int m_NumIterations
The number of iterations


m_Exponent

double m_Exponent
The exponent


m_K

int m_K
The actual number of alterations


m_Additions

int[] m_Additions
The training instances added to the perceptron


m_IsAddition

boolean[] m_IsAddition
Addition or subtraction?


m_Weights

int[] m_Weights
The weights for each perceptron


m_Train

Instances m_Train
The training instances


m_Seed

int m_Seed
Seed used for shuffling the dataset


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values.

Class weka.classifiers.functions.Winnow extends Classifier implements Serializable

Serialized Fields

m_Balanced

boolean m_Balanced
Use the balanced variant?


m_numIterations

int m_numIterations
The number of iterations


m_Alpha

double m_Alpha
The promotion coefficient


m_Beta

double m_Beta
The demotion coefficient


m_Threshold

double m_Threshold
Prediction threshold, <0 == numAttributes


m_Seed

int m_Seed
Random seed used for shuffling the dataset, -1 == disable


m_Mistakes

int m_Mistakes
Accumulated mistake count (for statistics)


m_defaultWeight

double m_defaultWeight
Starting weights for the prediction vector(s)


m_predPosVector

double[] m_predPosVector
The weight vectors for prediction


m_predNegVector

double[] m_predNegVector

m_actualThreshold

double m_actualThreshold
The true threshold used for prediction


m_Train

Instances m_Train
The training instances


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values.


Package weka.classifiers.functions.pace

Class weka.classifiers.functions.pace.ExponentialFormat extends java.text.DecimalFormat implements Serializable

Serialized Fields

nf

java.text.DecimalFormat nf

sign

boolean sign

digits

int digits

exp

int exp

trailing

boolean trailing

Class weka.classifiers.functions.pace.FlexibleDecimalFormat extends java.text.DecimalFormat implements Serializable

Serialized Fields

nf

java.text.DecimalFormat nf

digits

int digits

exp

boolean exp

intDigits

int intDigits

decimalDigits

int decimalDigits

expIntDigits

int expIntDigits

expDecimalDigits

int expDecimalDigits

power

int power

trailing

boolean trailing

grouping

boolean grouping

sign

boolean sign

Class weka.classifiers.functions.pace.FloatingPointFormat extends java.text.DecimalFormat implements Serializable

Serialized Fields

nf

java.text.DecimalFormat nf

width

int width

decimal

int decimal

trailing

boolean trailing

Class weka.classifiers.functions.pace.Matrix extends java.lang.Object implements Serializable

Serialized Fields

A

double[][] A
Array for internal storage of elements.

internal array storage.

m

int m
Row and column dimensions.

row dimension.

n

int n
Row and column dimensions.

row dimension.

Class weka.classifiers.functions.pace.PaceMatrix extends Matrix implements Serializable


Package weka.classifiers.functions.neural

Class weka.classifiers.functions.neural.LinearUnit extends java.lang.Object implements Serializable

Class weka.classifiers.functions.neural.NeuralConnection extends java.lang.Object implements Serializable

Serialized Fields

m_inputList

NeuralConnection[] m_inputList
The list of inputs to this unit.


m_outputList

NeuralConnection[] m_outputList
The list of outputs from this unit.


m_inputNums

int[] m_inputNums
The numbering for the connections at the other end of the input lines.


m_outputNums

int[] m_outputNums
The numbering for the connections at the other end of the out lines.


m_numInputs

int m_numInputs
The number of inputs.


m_numOutputs

int m_numOutputs
The number of outputs.


m_unitValue

double m_unitValue
The output value for this unit, NaN if not calculated.


m_unitError

double m_unitError
The error value for this unit, NaN if not calculated.


m_weightsUpdated

boolean m_weightsUpdated
True if the weights have already been updated.


m_id

java.lang.String m_id
The string that uniquely (provided naming is done properly) identifies this unit.


m_type

int m_type
The type of unit this is.


m_x

double m_x
The x coord of this unit purely for displaying purposes.


m_y

double m_y
The y coord of this unit purely for displaying purposes.

Class weka.classifiers.functions.neural.NeuralNode extends NeuralConnection implements Serializable

Serialized Fields

m_weights

double[] m_weights
The weights for each of the input connections, and the threshold.


m_changeInWeights

double[] m_changeInWeights
The change in the weights.


m_random

java.util.Random m_random

m_methods

NeuralMethod m_methods
Performs the operations for this node. Currently this defines that the node is either a sigmoid or a linear unit.

Class weka.classifiers.functions.neural.SigmoidUnit extends java.lang.Object implements Serializable


Package weka.classifiers.functions.supportVector

Class weka.classifiers.functions.supportVector.Kernel extends java.lang.Object implements Serializable

Serialized Fields

m_data

Instances m_data
The dataset

Class weka.classifiers.functions.supportVector.NormalizedPolyKernel extends PolyKernel implements Serializable

Class weka.classifiers.functions.supportVector.PolyKernel extends Kernel implements Serializable

Serialized Fields

m_cacheSize

int m_cacheSize
The size of the cache (a prime number)


m_lowerOrder

boolean m_lowerOrder
Use lower-order terms?


m_exponent

double m_exponent
The exponent for the polynomial kernel.


m_storage

double[] m_storage
Kernel cache


m_keys

long[] m_keys

m_kernelEvals

int m_kernelEvals
Counts the number of kernel evaluations.


m_numInsts

int m_numInsts
The number of instance in the dataset

Class weka.classifiers.functions.supportVector.RBFKernel extends Kernel implements Serializable

Serialized Fields

m_kernelPrecalc

double[] m_kernelPrecalc
The precalculated dotproducts of


m_kernelEvals

int m_kernelEvals
Counts the number of kernel evaluations.


m_cacheSize

int m_cacheSize
The size of the cache (a prime number)


m_storage

double[] m_storage
Kernel cache


m_keys

long[] m_keys

m_gamma

double m_gamma
Gamma for the RBF kernel.


m_numInsts

int m_numInsts
The number of instance in the dataset

Class weka.classifiers.functions.supportVector.SMOset extends java.lang.Object implements Serializable

Serialized Fields

m_number

int m_number
The current number of elements in the set


m_first

int m_first
The first element in the set


m_indicators

boolean[] m_indicators
Indicators


m_next

int[] m_next
The next element for each element


m_previous

int[] m_previous
The previous element for each element