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

This holds the current set of instances

m_as

AttributeStats m_as

This holds the attribute stats of the current attribute on display. It is calculated in setAttribute(int idx) when it is called to set a new attribute index.

m_attribIndex

int m_attribIndex

This holds the index of the current attribute on display and should be set through setAttribute(int idx).

m_maxValue

int m_maxValue

This holds the max value of the current attribute. In case of nominal attribute it is the highest count that a nominal value has in the attribute (given by m_as.nominalCounts[i]), otherwise in case of numeric attribute it is simply the maximum value present in the attribute (given by m_as.numericStats.max). It is used to calculate the ratio of the height of the bars with respect to the height of the display area.

m_histBarCounts

int[] m_histBarCounts

This array holds the count (or height) for the each of the bars in a barplot or a histogram. In case of barplots (and current attribute being nominal) its length (and the number of bars) is equal to the number of nominal values in the current attribute, with each field of the array being equal to the count of each nominal that it represents (the count of ith nominal value of an attribute is given by m_as.nominalCounts[i]). Whereas, in case of histograms (and current attribute being numeric) the width of its intervals is calculated by Scott's(1979) method:
intervalWidth = Max(1, 3.49*Std.Dev*numInstances^(1/3)) And the number of intervals by:
intervals = max(1, Math.round(Range/intervalWidth); Then each field of this array contains the number of values of the current attribute that fall in the histogram interval that it represents.
NOTE: The values of this array are only calculated if the class attribute is not set or if it is numeric.

m_histBarClassCounts

int[][] m_histBarClassCounts

This array holds the per class count (or per class height) of the each of the bars in a barplot or a histogram. For nominal attributes the format is:
m_histBarClassCounts[nominalValue][classValue+1]. For numeric attributes the format is:
m_histBarClassCounts[interval][classValues+1],
where the number of intervals is calculated by the Scott's method as mentioned above. The array is initialized to have 1+numClasses to accomodate for instances with missing class value. The ones with missing class value are displayed as a black sub par in a histogram or a barplot. NOTE: The values of this array are only calculated if the class attribute is set and it is nominal.

m_barRange

double m_barRange

Contains the range of each bar in a histogram. It is used to work out the range of bar the mouse pointer is on in getToolTipText().

m_classIndex

int m_classIndex

Contains the current class index.

m_hc

java.lang.Thread m_hc

This stores the BarCalc or HistCalc thread while a new barplot or histogram is being calculated.

m_threadRun

boolean m_threadRun

True if the thread m_hc above is running.

m_colorAttrib

javax.swing.JComboBox m_colorAttrib

This stores and lets the user select a class attribute. It also has an entry "No Class" if the user does not want to set a class attribute for colouring.

m_fm

java.awt.FontMetrics m_fm

Fontmetrics used to get the font size which is required for calculating displayable area size, bar height ratio and width of strings that are displayed on top of bars indicating their count.

m_locker

java.lang.Integer m_locker

Lock variable to synchronize the different threads running currently in this class. There are two to three threads in this class, AWT paint thread which is handled differently in paintComponent() which checks on m_threadRun to determine if it can perform full paint or not, the second thread is the main execution thread and the third is the one represented by m_hc which we start when we want to calculate the internal fields for a bar plot or a histogram.

m_colorList

FastVector m_colorList

Contains discrete colours for colouring of subbars of histograms and bar plots when the class attribute is set and is nominal

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 javax.swing.JFrame 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

weka.gui.HierarchyPropertyParser.TreeNode m_Root

Keep track of the root of the tree

m_Current

weka.gui.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

m_aboutPanel

javax.swing.JPanel m_aboutPanel

The panel holding global info and help, if provided by the object being editied

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

AttributeSelectionPanel m_AttPanel

Panel to let the user toggle attributes

m_RemoveButton

javax.swing.JButton m_RemoveButton

Button for removing 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_FileChooser

javax.swing.JFileChooser m_FileChooser

The file chooser for selecting arff files

m_bsiFileFilter

ExtensionFileFilter m_bsiFileFilter

File filters for various file types

m_c45FileFilter

ExtensionFileFilter m_c45FileFilter

m_csvFileFilter

ExtensionFileFilter m_csvFileFilter

m_arffFileFilter

ExtensionFileFilter m_arffFileFilter

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

weka.gui.treevisualizer.TreeVisualizer.NodeInfo[] m_nodes

An array with the Nodes sorted into it and display information about the Nodes.

m_edges

weka.gui.treevisualizer.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.

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

Serialized Fields

m_classifier

Classifier m_classifier

The classifier

m_testSet

DataSetEvent 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.BatchClustererEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_clusterer

Clusterer m_clusterer

The clusterer

m_testSet

DataSetEvent m_testSet

Training or Test Instances

m_setNumber

int m_setNumber

The set number for the test set

m_testOrTrain

int m_testOrTrain

Indicates if m_testSet is a training or a test set. 0 for test, >0 for training

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_connectedFormat

Instances m_connectedFormat

format of instances for current incoming connection (if any)

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_dataFormatListeners

java.util.Vector m_dataFormatListeners

m_visual

BeanVisual m_visual

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

Serialized Fields

m_displayColNames

boolean m_displayColNames

m_classAssigner

ClassAssigner m_classAssigner

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_caEditor

PropertySheetPanel m_caEditor

m_ClassCombo

javax.swing.JComboBox m_ClassCombo

m_holderP

javax.swing.JPanel m_holderP

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_globalInfo

java.lang.String m_globalInfo

Global info for the wrapped classifier (if it exists).

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

PropertySheetPanel 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_textListeners

java.util.Vector m_textListeners

m_thresholdListeners

java.util.Vector m_thresholdListeners

m_rocListenersConnected

boolean m_rocListenersConnected

m_predInstances

Instances m_predInstances

m_preds

FastVector m_preds

m_plotShape

FastVector m_plotShape

m_plotSize

FastVector m_plotSize

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

Serialized Fields

m_classValueIndex

int m_classValueIndex

the class value index considered to be the positive class

m_connectedFormat

Instances m_connectedFormat

format of instances for the current incoming connection (if any)

m_dataProvider

java.lang.Object m_dataProvider

m_dataListeners

java.util.Vector m_dataListeners

m_dataFormatListeners

java.util.Vector m_dataFormatListeners

m_visual

BeanVisual m_visual

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

Serialized Fields

m_displayValNames

boolean m_displayValNames

m_classValuePicker

ClassValuePicker m_classValuePicker

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_ClassValueCombo

javax.swing.JComboBox m_ClassValueCombo

m_holderP

javax.swing.JPanel m_holderP

m_messageLabel

javax.swing.JLabel m_messageLabel

Class weka.gui.beans.Clusterer 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_globalInfo

java.lang.String m_globalInfo

Global info for the wrapped classifier (if it exists).

m_listenees

java.util.Hashtable m_listenees

Objects talking to us

m_batchClustererListeners

java.util.Vector m_batchClustererListeners

Objects listening for batch clusterer 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.

m_Clusterer

Clusterer m_Clusterer

m_dummy

java.lang.Double m_dummy

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

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsClusterer

Clusterer m_dsClusterer

m_ClustererEditor

PropertySheetPanel m_ClustererEditor

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

Serialized Fields

m_textListeners

java.util.Vector m_textListeners

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.DataSetEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_dataSet

Instances m_dataSet

m_structureOnly

boolean m_structureOnly

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_globalInfo

java.lang.String m_globalInfo

Global info for the wrapped filter (if it exists).

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

m_structurePassedOn

boolean m_structurePassedOn

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

m_graphType

int m_graphType

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_structure

Instances m_structure

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_structure

Instances m_structure

m_instance

Instance m_instance

m_status

int m_status

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

Serialized Fields

m_fontM

java.awt.FontMetrics m_fontM

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

weka.gui.beans.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_globalInfo

java.lang.String m_globalInfo

Global info for the wrapped loader (if it exists).

m_ioThread

weka.gui.beans.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

m_dbSet

boolean m_dbSet

Flag indicating that a database has already been configured

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_LoaderEditor

PropertySheetPanel m_LoaderEditor

m_fileChooser

javax.swing.JFileChooser m_fileChooser

m_parentFrame

javax.swing.JFrame m_parentFrame

m_dbaseURLText

javax.swing.JTextField m_dbaseURLText

m_userNameText

javax.swing.JTextField m_userNameText

m_queryText

javax.swing.JTextField m_queryText

m_keyText

javax.swing.JTextField m_keyText

m_passwordText

javax.swing.JPasswordField m_passwordText

Class weka.gui.beans.ModelPerformanceChart 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_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport

BeanContextChild support

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_format

Instances m_format

Format of instances to be produced.

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_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.Saver extends AbstractDataSink implements Serializable

Serialized Fields

m_dataSet

Instances m_dataSet

Holds the instances to be saved

m_structure

Instances m_structure

Holds the structure

m_globalInfo

java.lang.String m_globalInfo

Global info for the wrapped loader (if it exists).

m_Saver

Saver m_Saver

Saver

m_fileName

java.lang.String m_fileName

The relation name that becomes part of the file name

m_isDBSaver

boolean m_isDBSaver

Flag indicating that instances will be saved to database. Used because structure information can only be sent after a database has been configured.

m_count

int m_count

Count for structure available messages

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

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsSaver

Saver m_dsSaver

m_SaverEditor

PropertySheetPanel m_SaverEditor

m_fileChooser

javax.swing.JFileChooser m_fileChooser

m_parentFrame

javax.swing.JFrame m_parentFrame

m_dbaseURLText

javax.swing.JTextField m_dbaseURLText

m_userNameText

javax.swing.JTextField m_userNameText

m_passwordText

javax.swing.JPasswordField m_passwordText

m_tableText

javax.swing.JTextField m_tableText

m_idBox

javax.swing.JComboBox m_idBox

m_tabBox

javax.swing.JComboBox m_tabBox

m_prefixText

javax.swing.JTextField m_prefixText

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

m_ce

ChartEvent m_ce

m_dataPoint

double[] m_dataPoint

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_structureOnly

boolean m_structureOnly

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.ThresholdDataEvent extends java.util.EventObject implements Serializable

Serialized Fields

m_dataSet

PlotData2D m_dataSet

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

Serialized Fields

m_trainingSet

Instances m_trainingSet

The training instances

m_structureOnly

boolean m_structureOnly

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

weka.gui.graphvisualizer.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_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_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_FileChooser

javax.swing.JFileChooser m_FileChooser

The file chooser for selecting result files

m_csvFileFilter

ExtensionFileFilter m_csvFileFilter

File filters for various file types

m_arffFileFilter

ExtensionFileFilter m_arffFileFilter

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

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

m_ResultsPanel

ResultsPanel m_ResultsPanel

A pointer to the results panel

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

weka.gui.visualize.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 attribute value (only the selected attributes+class attribute) that is missing, for each instance. m_missing[i][j] == true if m_selectedAttribs[j] is missing in instance i. m_missing[i][m_missing[].length-1] == true if class value is missing in instance i.

m_type

int[] m_type

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

weka.gui.visualize.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

weka.gui.boundaryvisualizer.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

weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_xAxisPanel

m_yAxisPanel

weka.gui.boundaryvisualizer.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.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

int 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

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

weka.core.Queue.QueueNode m_Head

Store a reference to the head of the queue

m_Tail

weka.core.Queue.QueueNode m_Tail

Store a reference to the tail of the queue

m_Size

int m_Size

Store the c m_Tail.m_Nexturrent number of elements in the queue

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

Serialized Fields

m_Next

weka.core.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.AbstractFileSaver extends AbstractSaver implements Serializable

Serialized Fields

m_outputFile

java.io.File m_outputFile

The destination file.

m_writer

java.io.BufferedWriter m_writer

The writer.

FILE_EXTENSION

java.lang.String FILE_EXTENSION

The file extension of the destination file.

m_prefix

java.lang.String m_prefix

The prefix for the filename (chosen in the GUI).

m_dir

java.lang.String m_dir

The directory of the file (chosen in the GUI).

m_incrementalCounter

int m_incrementalCounter

Counter. In incremental mode after reading 100 instances they will be written to a file.

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.AbstractSaver extends java.lang.Object implements Serializable

Serialized Fields

m_instances

Instances m_instances

The instances that should be stored

m_retrieval

int m_retrieval

The current retrieval mode

m_writeMode

int m_writeMode

The current write mode

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

Serialized Fields

m_File

java.lang.String m_File

Class weka.core.converters.ArffSaver extends AbstractFileSaver implements Serializable

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

Serialized Fields

m_File

java.lang.String m_File

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.C45Saver extends AbstractFileSaver implements Serializable

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

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

Serialized Fields

m_File

java.lang.String m_File

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.CSVSaver extends AbstractFileSaver implements Serializable

Class weka.core.converters.DatabaseConnection 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_checkForUpperCaseNames

boolean m_checkForUpperCaseNames

m_setAutoCommit

boolean m_setAutoCommit

m_createIndex

boolean m_createIndex

m_userName

java.lang.String m_userName

Database username

m_password

java.lang.String m_password

Database Password

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

Serialized Fields

m_structure

Instances m_structure

The header information that is retrieved in the beginning of incremental loading

m_datasetPseudoInc

Instances m_datasetPseudoInc

Used in pseudoincremental mode. The whole dataset from which instances will be read incrementally.

m_oldStructure

Instances m_oldStructure

Set of instances that equals m_structure except that the auto_generated_id column is not included as an attribute

m_DataBaseConnection

DatabaseConnection m_DataBaseConnection

The database connection

m_query

java.lang.String m_query

The user defined query to load instances. (form: SELECT *| FROM [WHERE ])

m_pseudoIncremental

boolean m_pseudoIncremental

Flag indicating that pseudo incremental mode is used (all instances load at once into main memeory and then incrementally from main memory instead of the database)

m_nominalToStringLimit

int m_nominalToStringLimit

Limit when an attribute is treated as string attribute and not as a nominal one because it has to many values.

m_rowCount

int m_rowCount

The number of rows obtained by m_query, eg the size of the ResultSet to load

m_counter

int m_counter

Indicates how many rows has already been loaded incrementally

m_choice

int m_choice

Decides which SQL statement to limit the number of rows should be used. DBMS dependent. Algorithm just tries several possibilities.

m_firstTime

boolean m_firstTime

Flag indicating that incremental process wants to read first instance

m_inc

boolean m_inc

Flag indicating that incremental mode is chosen (for command line use only)

m_orderBy

FastVector m_orderBy

Contains the name of the columns that uniquely define a row in the ResultSet. Ensures a unique ordering of instances for indremental loading.

m_nominalIndexes

java.util.Hashtable[] m_nominalIndexes

Stores the index of a nominal value

m_nominalStrings

FastVector[] m_nominalStrings

Stores the nominal value

m_idColumn

java.lang.String m_idColumn

Name of the primary key column that will allow unique ordering necessary for incremental loading. The name is specified in the DatabaseUtils file.

Class weka.core.converters.DatabaseSaver extends AbstractSaver implements Serializable

Serialized Fields

m_DataBaseConnection

DatabaseConnection m_DataBaseConnection

The database connection

m_tableName

java.lang.String m_tableName

The name of the tablein which the instances should be stored

m_inputFile

java.lang.String m_inputFile

An input arff file (for command line use)

m_createText

java.lang.String m_createText

The database specific type for a string (read in from the properties file)

m_createDouble

java.lang.String m_createDouble

The database specific type for a double (read in from the properties file)

m_createInt

java.lang.String m_createInt

The database specific type for an int (read in from the properties file)

m_idColumn

java.lang.String m_idColumn

The name of the primary key column that will be automatically generated (if enabled). The name is read from DatabaseUtils.

m_count

int m_count

counts the rowsand used as a primary key value

m_id

boolean m_id

Flag indicating if a primary key column should be added

m_tabName

boolean m_tabName

Flag indicating whether the default name of the table is the relaion name or not.

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

Serialized Fields

m_File

java.lang.String m_File

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.SerializedInstancesSaver extends AbstractFileSaver implements Serializable

Package weka.clusterers

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

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

Serialized Fields

m_trainInstances

Instances m_trainInstances

the instances to cluster

m_Clusterer

Clusterer m_Clusterer

the clusterer

m_clusteringResults

java.lang.StringBuffer m_clusteringResults

holds a string describing the results of clustering the training data

m_numClusters

int m_numClusters

holds the number of clusters found by the clusterer

m_clusterAssignments

double[] m_clusterAssignments

holds the assigments of instances to clusters for a particular testing dataset

m_logL

double m_logL

m_classToCluster

int[] m_classToCluster

will hold the mapping of classes to clusters (for class based evaluation)

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

weka.clusterers.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 the numeric attributes in each cluster

m_ClusterNominalCounts

int[][][] m_ClusterNominalCounts

For each cluster, holds the frequency counts for the values of each nominal attribute

m_ClusterSizes

int[] m_ClusterSizes

The number of instances 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

m_squaredErrors

double[] m_squaredErrors

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?

m_TransformAll

boolean m_TransformAll

Are all values transformed into new attributes?

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.ChangeDateFormat extends Filter implements Serializable

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_DateFormat

java.text.SimpleDateFormat m_DateFormat

The output date format.

m_OutputAttribute

Attribute m_OutputAttribute

The output attribute.

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_Columns

Range m_Columns

Stores which columns to act on

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.Normalize extends Filter implements Serializable

Serialized Fields

m_Norm

double m_Norm

The norm that each instance must have at the end

m_LNorm

double m_LNorm

The L-norm to use

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 requested by the 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)

m_predTargetColumn

boolean m_predTargetColumn

Flag for prediction and target columns output.

m_attID

int m_attID

Attribute index of instance identifier (default -1)

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

m_OutputFileName

java.lang.String m_OutputFileName

The name of the output file. Empty for temporary file.

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

m_data

java.lang.Object[] m_data

m_merit

double m_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.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

weka.attributeSelection.GeneticSearch.GABitSet[] m_population

the current population

m_popSize

int m_popSize

the number of individual solutions

m_best

weka.attributeSelection.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.GreedyStepwise 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

m_backward

boolean m_backward

Use a backwards search instead of a forwards one

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

m_onlyClass

Instances m_onlyClass

Only the class attribute of all Instances.

m_classIndex

int m_classIndex

The class index.

m_car

boolean m_car

Flag indicating whether class association rules are mined.

Class weka.associations.AprioriItemSet extends ItemSet implements Serializable

Class weka.associations.Associator extends java.lang.Object implements Serializable

Class weka.associations.CaRuleGeneration extends RuleGeneration 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.LabeledItemSet extends ItemSet implements Serializable

Serialized Fields

m_classLabel

int m_classLabel

The class label.

m_ruleSupCounter

int m_ruleSupCounter

The support of the rule.

Class weka.associations.PredictiveApriori extends Associator implements Serializable

Serialized Fields

m_premiseCount

int m_premiseCount

The minimum support.

m_numRules

int m_numRules

The maximum number of rules that are output.

m_Ls

FastVector m_Ls

The set of all sets of itemsets.

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_priors

java.util.Hashtable m_priors

The hashtable containing the prior probabilities.

m_midPoints

double[] m_midPoints

The mid points of the intervals used for the prior estimation.

m_expectation

double m_expectation

The expected predictive accuracy a rule needs to be a candidate for the output.

m_best

java.util.TreeSet m_best

The n best rules.

m_bestChanged

boolean m_bestChanged

Flag keeping track if the list of the n best rules has changed.

m_count

int m_count

Counter for the time of generation for an association rule.

m_priorEstimator

PriorEstimation m_priorEstimator

The prior estimator.

m_classIndex

int m_classIndex

The class index.

m_car

boolean m_car

Flag indicating whether class association rules are mined.

Class weka.associations.PriorEstimation extends java.lang.Object implements Serializable

Serialized Fields

m_numRandRules

int m_numRandRules

The number of rnadom rules.

m_numIntervals

int m_numIntervals

The number of intervals.

m_randNum

java.util.Random m_randNum

The random number generator.

m_instances

Instances m_instances

The instances for which association rules are mined.

m_CARs

boolean m_CARs

Flag indicating whether standard association rules or class association rules are mined.

m_distribution

java.util.Hashtable m_distribution

Hashtable to store the confidence values of randomly generated rules.

m_priors

java.util.Hashtable m_priors

Hashtable containing the estimated prior probabilities.

m_sum

double m_sum

Sums up the confidences of all rules with a certain length.

m_midPoints

double[] m_midPoints

The mid points of the discrete intervals in which the interval [0,1] is divided.

Class weka.associations.RuleGeneration extends java.lang.Object implements Serializable

Serialized Fields

m_items

int[] m_items

The items stored as an array of of integer.

m_counter

int m_counter

Counter for how many transactions contain this item set.

m_totalTransactions

int m_totalTransactions

The total number of transactions

m_change

boolean m_change

Flag indicating whether the list fo the best rules has changed.

m_expectation

double m_expectation

The minimum expected predictive accuracy that is needed to be a candidate for the list of the best rules.

m_minRuleCount

int m_minRuleCount

The minimum support a rule needs to be a candidate for the list of the best rules.

m_midPoints

double[] m_midPoints

Sorted array of the mied points of the intervals used for prior estimation.

m_priors

java.util.Hashtable m_priors

Hashtable conatining the estimated prior probabilities.

m_best

java.util.TreeSet m_best

The list of the actual n<\i> best rules.

m_count

int m_count

Integer indicating the generation time of a rule.

m_instances

Instances m_instances

The instances.

Class weka.associations.RuleItem extends java.lang.Object implements Serializable

Serialized Fields

m_premise

ItemSet m_premise

The premise of a rule.

m_consequence

ItemSet m_consequence

The consequence of a rule.

m_accuracy

double m_accuracy

The expected predictive accuracy of a rule.

m_genTime

int m_genTime

The generation time of a rule.

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

weka.associations.tertius.SimpleLinkedList.Entry first

last

weka.associations.tertius.SimpleLinkedList.Entry last

Class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator extends java.lang.Object implements Serializable

Serialized Fields

current

weka.associations.tertius.SimpleLinkedList.Entry current

lastReturned

weka.associations.tertius.SimpleLinkedList.Entry lastReturned

Class weka.associations.tertius.SimpleLinkedList.LinkedListIterator extends java.lang.Object implements Serializable

Serialized Fields

current

weka.associations.tertius.SimpleLinkedList.Entry current

lastReturned

weka.associations.tertius.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_NoAttribNorm

boolean m_NoAttribNorm

True if numeric attributes' values should not be normalized in distance calculation.

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

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.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.MultiClassClassifierHamming extends Classifier implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers

m_ClassFilters

Filter[] m_ClassFilters

m_Classifier

Classifier m_Classifier

m_ZeroR

ZeroR m_ZeroR

m_ClassAttribute

Attribute m_ClassAttribute

m_TwoClassDataset

Instances m_TwoClassDataset

m_Seed

int m_Seed

m_RandomWidthFactor

double m_RandomWidthFactor

m_Method

int m_Method

Class weka.classifiers.meta.MultiClassClassifierLogLoss extends Classifier implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers

m_ClassFilters

Filter[] m_ClassFilters

m_Classifier

Classifier m_Classifier

m_ZeroR

ZeroR m_ZeroR

m_ClassAttribute

Attribute m_ClassAttribute

m_TwoClassDataset

Instances m_TwoClassDataset

m_Seed

int m_Seed

m_RandomWidthFactor

double m_RandomWidthFactor

m_Method

int m_Method

Class weka.classifiers.meta.MultiClassClassifierPairwiseWithLogLoss extends Classifier implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers

m_ClassFilters

Filter[] m_ClassFilters

m_Classifier

Classifier m_Classifier

m_ZeroR

ZeroR m_ZeroR

m_ClassAttribute

Attribute m_ClassAttribute

m_TwoClassDataset

Instances m_TwoClassDataset

m_Seed

int m_Seed

m_RandomWidthFactor

double m_RandomWidthFactor

m_Method

int m_Method

Class weka.classifiers.meta.MultiClassClassifierPairwiseWithLoss extends Classifier implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers

m_ClassFilters

Filter[] m_ClassFilters

m_Classifier

Classifier m_Classifier

m_ZeroR

ZeroR m_ZeroR

m_ClassAttribute

Attribute m_ClassAttribute

m_TwoClassDataset

Instances m_TwoClassDataset

m_Seed

int m_Seed

m_RandomWidthFactor

double m_RandomWidthFactor

m_Method

int m_Method

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

weka.classifiers.meta.ND.NDTree m_ndtree

m_classifiers

java.util.Hashtable m_classifiers

m_seed

int m_seed

m_classifier

Classifier m_classifier

m_hashtablegiven

boolean m_hashtablegiven

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

weka.classifiers.meta.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

m_Filter

MakeIndicator m_Filter

m_FirstSuccessor

weka.classifiers.meta.TreeBasedMultiClassClassifier m_FirstSuccessor

m_SecondSuccessor

weka.classifiers.meta.TreeBasedMultiClassClassifier m_SecondSuccessor

m_Random

java.util.Random m_Random

m_Range

Range m_Range

m_UseRandomSelection

boolean m_UseRandomSelection

m_ClassIsOrdinal

boolean m_ClassIsOrdinal

m_Seed

int m_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

weka.classifiers.misc.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, i.e. the number of times an attribute value occurs in conjunction with another attribute value and a class value.

m_ClassCounts

double[] m_ClassCounts

The number of times each class value occurs in the dataset

m_SumForCounts

double[][] 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 (including an extra for each attribute's missing value, which are included in m_CondiCounts) for all attributes (not including class). Eg. for three atts each with two possible values, m_TotalAttValues would be 9 (6 values + 3 missing). 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 the values for each attribute

m_NumAttValues

int[] m_NumAttValues

The number of values for each attribute

m_Frequencies

double[] 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_ParentSets

ParentSet[] m_ParentSets

The parent sets.

m_Distributions

Estimator[][] m_Distributions

The attribute estimators containing CPTs.

m_DiscretizeFilter

Discretize m_DiscretizeFilter

filter used to quantize continuous variables, if any

m_nNonDiscreteAttribute

int m_nNonDiscreteAttribute

m_MissingValuesFilter

ReplaceMissingValues m_MissingValuesFilter

filter used to fill in missing values, if any

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

Datastructure containing ADTree representation of the database. This may result in more efficient access to the data.

m_otherBayesNet

BIFReader m_otherBayesNet

Bayes network to compare the structure with.

m_bUseADTree

boolean m_bUseADTree

Use the experimental ADTree datastructure for calculating contingency tables

m_SearchAlgorithm

SearchAlgorithm m_SearchAlgorithm

Search algorithm used for learning the structure of a network.

m_BayesNetEstimator

BayesNetEstimator m_BayesNetEstimator

Search algorithm used for learning the structure of a network.

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.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

Package weka.classifiers.bayes.net

Class weka.classifiers.bayes.net.ADNode extends java.lang.Object implements Serializable

Serialized Fields

m_VaryNodes

VaryNode[] m_VaryNodes

list of VaryNode children

m_Instances

Instance[] m_Instances

list of Instance children (either m_Instances or m_VaryNodes is instantiated)

m_nCount

int m_nCount

count

m_nStartNode

int m_nStartNode

first node in VaryNode array

Class weka.classifiers.bayes.net.BayesNetGenerator extends BayesNet implements Serializable

Serialized Fields

m_nSeed

int m_nSeed

random

java.util.Random random

m_bGenerateNet

boolean m_bGenerateNet

m_nNrOfNodes

int m_nNrOfNodes

m_nNrOfArcs

int m_nNrOfArcs

m_nNrOfInstances

int m_nNrOfInstances

m_nCardinality

int m_nCardinality

m_sBIFFile

java.lang.String m_sBIFFile

Class weka.classifiers.bayes.net.BIFReader extends BayesNet implements Serializable

Serialized Fields

m_nPositionX

int[] m_nPositionX

m_nPositionY

int[] m_nPositionY

m_order

int[] m_order

m_sFile

java.lang.String m_sFile

Class weka.classifiers.bayes.net.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)

Class weka.classifiers.bayes.net.VaryNode extends java.lang.Object implements Serializable

Serialized Fields

m_iNode

int m_iNode

index of the node varied

m_nMCV

int m_nMCV

most common value

m_ADNodes

ADNode[] m_ADNodes

list of ADNode children

Package weka.classifiers.bayes.net.estimate

Class weka.classifiers.bayes.net.estimate.BayesNetEstimator extends java.lang.Object implements Serializable

Serialized Fields

m_fAlpha

double m_fAlpha

Holds prior on count

Class weka.classifiers.bayes.net.estimate.BMAEstimator extends SimpleEstimator implements Serializable

Serialized Fields

m_bUseK2Prior

boolean m_bUseK2Prior

Class weka.classifiers.bayes.net.estimate.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.net.estimate.DiscreteEstimatorFullBayes extends DiscreteEstimatorBayes implements Serializable

Class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator extends BayesNetEstimator implements Serializable

Serialized Fields

m_bUseK2Prior

boolean m_bUseK2Prior

Class weka.classifiers.bayes.net.estimate.SimpleEstimator extends BayesNetEstimator implements Serializable

Package weka.classifiers.bayes.net.search

Class weka.classifiers.bayes.net.search.SearchAlgorithm extends java.lang.Object implements Serializable

Serialized Fields

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_bMarkovBlanketClassifier

boolean m_bMarkovBlanketClassifier

Determines whether after structure is found a MarkovBlanketClassifier correction should be applied If this is true, m_bInitAsNaiveBayes is overridden and interpreted as false.

Package weka.classifiers.bayes.net.search.ci

Class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_BayesNet

BayesNet m_BayesNet

m_instances

Instances m_instances

Class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm extends CISearchAlgorithm implements Serializable

Serialized Fields

m_nMaxCardinality

int m_nMaxCardinality

maximum size of separating set

Package weka.classifiers.bayes.net.search.fixed

Class weka.classifiers.bayes.net.search.fixed.FromFile extends SearchAlgorithm implements Serializable

Serialized Fields

m_sBIFFile

java.lang.String m_sBIFFile

name of file to read structure from

Class weka.classifiers.bayes.net.search.fixed.NaiveBayes extends SearchAlgorithm implements Serializable

Package weka.classifiers.bayes.net.search.local

Class weka.classifiers.bayes.net.search.local.GeneticSearch extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_nRuns

int m_nRuns

number of runs

m_nPopulationSize

int m_nPopulationSize

size of population

m_nDescendantPopulationSize

int m_nDescendantPopulationSize

size of descendant population

m_bUseCrossOver

boolean m_bUseCrossOver

use cross-over?

m_bUseMutation

boolean m_bUseMutation

use mutation?

m_bUseTournamentSelection

boolean m_bUseTournamentSelection

use tournament selection or take best sub-population

m_nSeed

int m_nSeed

random number seed

m_random

java.util.Random m_random

random number generator

Class weka.classifiers.bayes.net.search.local.HillClimber extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_Cache

HillClimber.Cache m_Cache

cache for storing score differences

m_bUseArcReversal

boolean m_bUseArcReversal

use the arc reversal operator

Class weka.classifiers.bayes.net.search.local.K2 extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_bRandomOrder

boolean m_bRandomOrder

Holds flag to indicate ordering should be random

Class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm extends SearchAlgorithm implements Serializable

Serialized Fields

m_BayesNet

BayesNet m_BayesNet

points to Bayes network for which a structure is searched for

m_fAlpha

double m_fAlpha

Holds prior on count

m_nScoreType

int m_nScoreType

Holds the score type used to measure quality of network

Class weka.classifiers.bayes.net.search.local.RepeatedHillClimber extends HillClimber implements Serializable

Serialized Fields

m_nRuns

int m_nRuns

number of runs

m_nSeed

int m_nSeed

random number seed

m_random

java.util.Random m_random

random number generator

Class weka.classifiers.bayes.net.search.local.SimulatedAnnealing extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_fTStart

double m_fTStart

start temperature

m_fDelta

double m_fDelta

change in temperature at every run

m_nRuns

int m_nRuns

number of runs

m_bUseArcReversal

boolean m_bUseArcReversal

use the arc reversal operator

m_nSeed

int m_nSeed

random number seed

m_random

java.util.Random m_random

random number generator

Class weka.classifiers.bayes.net.search.local.TabuSearch extends HillClimber implements Serializable

Serialized Fields

m_nRuns

int m_nRuns

number of runs

m_nTabuList

int m_nTabuList

size of tabu list

m_oTabuList

HillClimber.Operation[] m_oTabuList

the actual tabu list

Class weka.classifiers.bayes.net.search.local.TAN extends LocalScoreSearchAlgorithm implements Serializable

Package weka.classifiers.bayes.net.search.global

Class weka.classifiers.bayes.net.search.global.GeneticSearch extends GlobalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_nRuns

int m_nRuns

number of runs

m_nPopulationSize

int m_nPopulationSize

size of population

m_nDescendantPopulationSize

int m_nDescendantPopulationSize

size of descendant population

m_bUseCrossOver

boolean m_bUseCrossOver

use cross-over?

m_bUseMutation

boolean m_bUseMutation

use mutation?

m_bUseTournamentSelection

boolean m_bUseTournamentSelection

use tournament selection or take best sub-population

m_nSeed

int m_nSeed

random number seed

m_random

java.util.Random m_random

random number generator

Class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm extends SearchAlgorithm implements Serializable

Serialized Fields

m_BayesNet

BayesNet m_BayesNet

points to Bayes network for which a structure is searched for

m_bUseProb

boolean m_bUseProb

toggle between scoring using accuracy = 0-1 loss (when false) or class probabilities (when true)

m_nNrOfFolds

int m_nNrOfFolds

number of folds for k-fold cross validation

m_nCVType

int m_nCVType

Holds the cross validation strategy used to measure quality of network

Class weka.classifiers.bayes.net.search.global.HillClimber extends GlobalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_bUseArcReversal

boolean m_bUseArcReversal

use the arc reversal operator

Class weka.classifiers.bayes.net.search.global.K2 extends GlobalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_bRandomOrder

boolean m_bRandomOrder

Holds flag to indicate ordering should be random

Class weka.classifiers.bayes.net.search.global.RepeatedHillClimber extends HillClimber implements Serializable

Serialized Fields

m_nRuns

int m_nRuns

number of runs

m_nSeed

int m_nSeed

random number seed

m_random

java.util.Random m_random

random number generator

Class weka.classifiers.bayes.net.search.global.SimulatedAnnealing extends GlobalScoreSearchAlgorithm implements Serializable

Serialized Fields

m_fTStart

double m_fTStart

start temperature

m_fDelta

double m_fDelta

change in temperature at every run

m_nRuns

int m_nRuns

number of runs

m_bUseArcReversal

boolean m_bUseArcReversal

use the arc reversal operator

m_nSeed

int m_nSeed

random number seed

m_random

java.util.Random m_random

random number generator

Class weka.classifiers.bayes.net.search.global.TabuSearch extends HillClimber implements Serializable

Serialized Fields

m_nRuns

int m_nRuns

number of runs

m_nTabuList

int m_nTabuList

size of tabu list

m_oTabuList

HillClimber.Operation[] m_oTabuList

the actual tabu list

Class weka.classifiers.bayes.net.search.global.TAN extends GlobalScoreSearchAlgorithm implements Serializable

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

weka.classifiers.rules.NNge.Exemplar m_Exemplars

The list of Exemplars

m_ExemplarsByClass

weka.classifiers.rules.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

weka.classifiers.rules.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

weka.classifiers.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

weka.classifiers.rules.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.NBTree extends Classifier implements Serializable

Serialized Fields

m_minNumObj

int m_minNumObj

Minimum number of instances

m_root

NBTreeClassifierTree m_root

The root of the tree

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

weka.classifiers.trees.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

weka.classifiers.trees.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_top

weka.classifiers.trees.UserClassifier.TreeClass m_top

Two references to the structure of the decision tree.

m_focus

weka.classifiers.trees.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_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.NBTreeClassifierTree extends ClassifierTree implements Serializable

Class weka.classifiers.trees.j48.NBTreeModelSelection 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.NBTreeNoSplit extends ClassifierSplitModel implements Serializable

Serialized Fields

m_nb

NaiveBayesUpdateable m_nb

the naive bayes classifier

m_disc

Discretize m_disc

the discretizer used

m_errors

double m_errors

errors on the training data at this node

Class weka.classifiers.trees.j48.NBTreeSplit 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_sumOfWeights

double m_sumOfWeights

The sum of the weights of the instances.

m_errors

double m_errors

The weight of the instances incorrectly classified by the naive bayes models arising from this split

m_c45S

C45Split m_c45S

m_globalNB

NBTreeNoSplit m_globalNB

The global naive bayes model for this node

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

weka.classifiers.functions.MultilayerPerceptron.NeuralEnd[] m_outputs

The output units.(only feeds the errors, does no calcs)

m_inputs

weka.classifiers.functions.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

weka.classifiers.functions.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.Perceptron extends Classifier implements Serializable

Serialized Fields

MAX_NUM_ITERATIONS

int MAX_NUM_ITERATIONS

m_weights

double[] m_weights

m_insts

Instances m_insts

m_its

int m_its

m_seed

long m_seed

m_ntb

NominalToBinary m_ntb

m_rmv

ReplaceMissingValues m_rmv

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

weka.classifiers.functions.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