Serialized Form
m_Table
javax.swing.JTable m_Table
- The table displaying attribute names
m_Model
AttributeListPanel.AttributeTableModel m_Model
- The table model containing attribute names
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
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
m_data
Instances m_data
as
AttributeStats as
attribIndex
int attribIndex
maxValue
int maxValue
histBarCounts
int[] histBarCounts
histBarClassCounts
int[][] histBarClassCounts
m_barRange
double m_barRange
classIndex
int classIndex
hc
java.lang.Thread hc
threadRun
boolean threadRun
m_colorAttrib
javax.swing.JComboBox m_colorAttrib
fm
java.awt.FontMetrics fm
m_locker
java.lang.Integer m_locker
m_colorList
FastVector m_colorList
- Contains discrete colours for colouring for nominal attributes
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
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
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
m_tree
javax.swing.JTree m_tree
- The tree
m_scroller
javax.swing.JScrollPane m_scroller
- The scroller
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
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
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
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
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)
m_Editor
java.beans.PropertyEditor m_Editor
- The property editor
m_EditorComponent
java.awt.Component m_EditorComponent
- The custom editor component
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
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
m_Target
java.lang.Object m_Target
- The target object being edited
m_Properties
java.beans.PropertyDescriptor[] m_Properties
- Holds properties of the target
m_Methods
java.beans.MethodDescriptor[] m_Methods
- Holds the methods of the target
m_Editors
java.beans.PropertyEditor[] m_Editors
- Holds property editors of the object
m_Values
java.lang.Object[] m_Values
- Holds current object values for each property
m_Views
javax.swing.JComponent[] m_Views
- Stores GUI components containing each editing component
m_Labels
javax.swing.JLabel[] m_Labels
- The labels for each property
m_TipTexts
java.lang.String[] m_TipTexts
- The tool tip text for each property
m_HelpText
java.lang.StringBuffer m_HelpText
- StringBuffer containing help text for the object being edited
m_HelpFrame
javax.swing.JFrame m_HelpFrame
- Help frame
m_HelpBut
javax.swing.JButton m_HelpBut
- Button to pop up the full help text in a separate frame
m_NumEditable
int m_NumEditable
- A count of the number of properties we have an editor for
support
java.beans.PropertyChangeSupport support
- A support object for handling property change listeners
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
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
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
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 |
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
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
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
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
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
m_InstSummaryPanel
InstancesSummaryPanel m_InstSummaryPanel
- Displays simple stats on the working instances
m_OpenFileBut
javax.swing.JButton m_OpenFileBut
- Click to load base instances from a file
m_OpenURLBut
javax.swing.JButton m_OpenURLBut
- Click to load base instances from a URL
m_OpenDBBut
javax.swing.JButton m_OpenDBBut
- Click to load base instances from a Database
m_DatabaseQueryEditor
GenericObjectEditor m_DatabaseQueryEditor
- Lets the user enter a DB query
m_UndoBut
javax.swing.JButton m_UndoBut
- Click to revert back to the last saved point
m_SaveBut
javax.swing.JButton m_SaveBut
- Click to apply filters and save the results
m_AttPanel
AttributeListPanel m_AttPanel
- Panel to let the user toggle attributes
m_AttSummaryPanel
AttributeSummaryPanel m_AttSummaryPanel
- Displays summary stats on the selected attribute
m_FilterEditor
GenericObjectEditor m_FilterEditor
- Lets the user configure the filter
m_FilterPanel
PropertyPanel m_FilterPanel
- Filter configuration
m_ApplyFilterBut
javax.swing.JButton m_ApplyFilterBut
- Click to apply filters and save the results
m_ArffFilter
javax.swing.filechooser.FileFilter m_ArffFilter
- Filter to ensure only arff files are selected
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- The file chooser for selecting arff files
m_LastURL
java.lang.String m_LastURL
- Stores the last URL that instances were loaded from
m_SQLQ
java.lang.String m_SQLQ
- Stores the last sql query executed
m_Instances
Instances m_Instances
- The working instances
m_AttVisualizePanel
AttributeVisualizationPanel m_AttVisualizePanel
- The visualization of the attribute values
m_tempUndoFiles
java.io.File[] m_tempUndoFiles
- Keeps track of undo points
m_tempUndoIndex
int m_tempUndoIndex
- The next available slot for an undo point
m_Support
java.beans.PropertyChangeSupport m_Support
- Manages sending notifications to people when we change the set of
working instances.
m_IOThread
java.lang.Thread m_IOThread
- A thread for loading/saving instances from a file or URL
m_Log
Logger m_Log
- The message logger
|
Package weka.gui.treevisualizer |
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
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
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
m_visual
BeanVisual m_visual
- Default visual for evaluators
m_listenee
java.lang.Object m_listenee
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.
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.
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.
m_gridWidth
int m_gridWidth
- The number of plots horizontally in the display
m_maxPlots
int m_maxPlots
- The maximum number of plots to show
m_coloringIndex
int m_coloringIndex
- Index on which to color the plots. -1 indicates that we let the
attribute visualize panels set this on the basis of the class index
in the data.
m_classifier
Classifier m_classifier
- The classifier
m_testSet
Instances m_testSet
- Instances that can be used for testing the classifier
m_setNumber
int m_setNumber
- The set number for the test set
m_maxSetNumber
int m_maxSetNumber
- The last set number for this series
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
m_bean
java.lang.Object m_bean
- Holds the bean encapsulated in this instance
m_x
int m_x
m_y
int m_y
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
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
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
m_classColumn
java.lang.String m_classColumn
m_trainingProvider
java.lang.Object m_trainingProvider
m_testProvider
java.lang.Object m_testProvider
m_dataProvider
java.lang.Object m_dataProvider
m_instanceProvider
java.lang.Object m_instanceProvider
m_trainingListeners
java.util.Vector m_trainingListeners
m_testListeners
java.util.Vector m_testListeners
m_dataListeners
java.util.Vector m_dataListeners
m_instanceListeners
java.util.Vector m_instanceListeners
m_visual
BeanVisual m_visual
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
m_caEditor
PropertySheetPanel m_caEditor
m_visual
BeanVisual m_visual
m_state
int m_state
m_buildThread
java.lang.Thread m_buildThread
m_listenees
java.util.Hashtable m_listenees
- Objects talking to us
m_batchClassifierListeners
java.util.Vector m_batchClassifierListeners
- Objects listening for batch classifier events
m_incrementalClassifierListeners
java.util.Vector m_incrementalClassifierListeners
- Objects listening for incremental classifier events
m_graphListeners
java.util.Vector m_graphListeners
- Objects listening for graph events
m_textListeners
java.util.Vector m_textListeners
- Objects listening for text events
m_trainingSet
Instances m_trainingSet
- Holds training instances for batch training. Not transient because
header is retained for validating any instance events that this
classifier might be asked to predict in the future.
m_Classifier
Classifier m_Classifier
m_ie
IncrementalClassifierEvent m_ie
m_updateIncrementalClassifier
boolean m_updateIncrementalClassifier
- If the classifier is an incremental classifier, should we
update it (ie train it on incoming instances). This makes it
possible incrementally test on a separate stream of instances
without updating the classifier, or mix batch training/testing
with incremental training/testing
m_incrementalEvent
InstanceEvent m_incrementalEvent
- Event to handle when processing incremental updates
m_dummy
java.lang.Double m_dummy
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
m_dsClassifier
Classifier m_dsClassifier
m_ClassifierEditor
GenericObjectEditor m_ClassifierEditor
m_incrementalPanel
javax.swing.JPanel m_incrementalPanel
m_updateIncrementalClassifier
javax.swing.JCheckBox m_updateIncrementalClassifier
m_panelVisible
boolean m_panelVisible
m_listeners
java.util.Vector m_listeners
m_numFolds
int m_numFolds
m_randomSeed
int m_randomSeed
m_foldThread
java.lang.Thread m_foldThread
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
m_cvEditor
PropertySheetPanel m_cvEditor
m_fileStem
java.lang.String m_fileStem
m_dataSet
Instances m_dataSet
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
m_visual
BeanVisual m_visual
m_state
int m_state
m_filterThread
java.lang.Thread m_filterThread
m_listenees
java.util.Hashtable m_listenees
- Objects talking to us
m_trainingListeners
java.util.Vector m_trainingListeners
- Objects listening for training set events
m_testListeners
java.util.Vector m_testListeners
- Objects listening for test set events
m_instanceListeners
java.util.Vector m_instanceListeners
- Objects listening for instance events
m_dataListeners
java.util.Vector m_dataListeners
- Objects listening for data set events
m_Filter
Filter m_Filter
- The filter to use.
m_ie
InstanceEvent m_ie
- Instance event object for passing on filtered instance streams
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
m_filter
Filter m_filter
m_filterEditor
GenericObjectEditor m_filterEditor
m_graphString
java.lang.String m_graphString
m_graphTitle
java.lang.String m_graphTitle
m_visual
BeanVisual m_visual
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
m_status
int m_status
m_classifier
Classifier m_classifier
m_currentInstance
Instance m_currentInstance
m_instance
Instance m_instance
m_status
int m_status
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 implements Serializable |
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_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
m_dsLoader
Loader m_dsLoader
m_fileEditor
FileEditor m_fileEditor
m_LoaderEditor
GenericObjectEditor m_LoaderEditor
m_dataSourceListeners
java.util.Vector m_dataSourceListeners
- Objects listenening for dataset events
m_instanceListeners
java.util.Vector m_instanceListeners
- Objects listening for instances events
m_listenee
java.lang.Object m_listenee
- Non null if this object is a target for any events.
m_visual
BeanVisual m_visual
m_appendProbabilities
boolean m_appendProbabilities
- Append classifier's predicted probabilities (if the class is discrete
and the classifier is a distribution classifier)
m_logger
Logger m_logger
m_incrementalStructure
Instances m_incrementalStructure
m_instanceEvent
InstanceEvent m_instanceEvent
m_instanceVals
double[] m_instanceVals
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
m_paEditor
PropertySheetPanel m_paEditor
m_matrixPanel
MatrixPanel m_matrixPanel
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
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_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
m_cvEditor
PropertySheetPanel m_cvEditor
m_testSet
Instances m_testSet
- The test set instances
m_setNumber
int m_setNumber
- what number is this test set (ie fold 2 of 10 folds)
m_maxSetNumber
int m_maxSetNumber
- Maximum number of sets (ie 10 in a 10 fold)
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
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
m_trainingSet
Instances m_trainingSet
- The training instances
m_setNumber
int m_setNumber
- what number is this training set (ie fold 2 of 10 folds)
m_maxSetNumber
int m_maxSetNumber
- Maximum number of sets (ie 10 in a 10 fold)
m_trainPercentage
int m_trainPercentage
m_randomSeed
int m_randomSeed
m_splitThread
java.lang.Thread m_splitThread
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
m_splitEditor
PropertySheetPanel m_splitEditor
|
Package weka.gui.experiment |
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
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
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
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.
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
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
m_FromFileBut
javax.swing.JButton m_FromFileBut
- Click to load results from a file
m_FromDBaseBut
javax.swing.JButton m_FromDBaseBut
- Click to load results from a database
m_FromExpBut
javax.swing.JButton m_FromExpBut
- Click to get results from the destination given in the experiment
m_FromLab
javax.swing.JLabel m_FromLab
- Displays a message about the current result set
m_DatasetModel
javax.swing.DefaultComboBoxModel m_DatasetModel
- The model embedded in m_DatasetCombo
m_RunModel
javax.swing.DefaultComboBoxModel m_RunModel
- The model embedded in m_RunCombo
m_CompareModel
javax.swing.DefaultComboBoxModel m_CompareModel
- The model embedded in m_CompareCombo
m_TestsModel
javax.swing.DefaultListModel m_TestsModel
- The model embedded in m_TestsList
m_DatasetKeyLabel
javax.swing.JLabel m_DatasetKeyLabel
- Displays the currently selected column names for the scheme & options
m_DatasetKeyBut
javax.swing.JButton m_DatasetKeyBut
- Click to edit the columns used to determine the scheme
m_DatasetKeyModel
javax.swing.DefaultListModel m_DatasetKeyModel
- Stores the list of attributes for selecting the scheme columns
m_DatasetKeyList
javax.swing.JList m_DatasetKeyList
- Displays the list of selected columns determining the scheme
m_RunCombo
javax.swing.JComboBox m_RunCombo
- Lets the user select which column contains the run number
m_ResultKeyLabel
javax.swing.JLabel m_ResultKeyLabel
- Displays the currently selected column names for the scheme & options
m_ResultKeyBut
javax.swing.JButton m_ResultKeyBut
- Click to edit the columns used to determine the scheme
m_ResultKeyModel
javax.swing.DefaultListModel m_ResultKeyModel
- Stores the list of attributes for selecting the scheme columns
m_ResultKeyList
javax.swing.JList m_ResultKeyList
- Displays the list of selected columns determining the scheme
m_TestsButton
javax.swing.JButton m_TestsButton
- Lets the user select which scheme to base comparisons against
m_TestsList
javax.swing.JList m_TestsList
- Holds the list of schemes to base the test against
m_CompareCombo
javax.swing.JComboBox m_CompareCombo
- Lets the user select which performance measure to analyze
m_SigTex
javax.swing.JTextField m_SigTex
- Lets the user edit the test significance
m_ShowStdDevs
javax.swing.JCheckBox m_ShowStdDevs
- Lets the user select whether standard deviations are to be output
or not
m_PerformBut
javax.swing.JButton m_PerformBut
- Click to start the test
m_SaveOutBut
javax.swing.JButton m_SaveOutBut
- Click to save test output to a file
m_SaveOut
SaveBuffer m_SaveOut
- The buffer saving object for saving output
m_OutText
javax.swing.JTextArea m_OutText
- Displays the output of tests
m_History
ResultHistoryPanel m_History
- A panel controlling results viewing
m_ArffFilter
javax.swing.filechooser.FileFilter m_ArffFilter
- Filter to ensure only arff files are selected for result files
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- The file chooser for selecting result files
m_TTester
PairedTTester m_TTester
- The PairedTTester object
m_Instances
Instances m_Instances
- The instances we're extracting results from
m_InstanceQuery
InstanceQuery m_InstanceQuery
- Does any database querying for us
m_LoadThread
java.lang.Thread m_LoadThread
- A thread to load results instances from a file or database
m_Exp
Experiment m_Exp
- An experiment (used for identifying a result source) -- optional
m_ConfigureListener
java.awt.event.ActionListener m_ConfigureListener
- An actionlisteners that updates ttest settings
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
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
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_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
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
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.graphvisualizer |
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
m_Count_Lab
javax.swing.JLabel m_Count_Lab
m_Count
int m_Count
m_Debug
boolean m_Debug
m_ID
int m_ID
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
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
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
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
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 |
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 implements Serializable |
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.
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
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 implements Serializable |
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
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 value that is missing, for each instance
m_type
int[][] m_type
- This array contains:
m_type[0][i] = [type of attribute, nominal, string or numeric]
m_type[1][i] = [number of discrete values of nominal or string attribute
or same as m_type[0][i] for numeric attribute]
m_plotLBSizeD
java.awt.Dimension m_plotLBSizeD
- Stores the maximum size for PlotSize label to keep it's size constant
m_pointLBSizeD
java.awt.Dimension m_pointLBSizeD
- Stores the maximum size for PointSize label to keep it's size constant
m_colorList
FastVector m_colorList
- Contains discrete colours for colouring for nominal attributes
fontColor
java.awt.Color fontColor
- color for the font used in column and row names
f
java.awt.Font f
- font used in column and row names
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
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
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 implements Serializable |
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 |
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
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
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
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.
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
m_rowNumber
int m_rowNumber
m_rowLength
int m_rowLength
m_probabilities
double[][] m_probabilities
m_percentCompleted
int m_percentCompleted
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
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
m_Objects
java.lang.Object[] m_Objects
- The array of objects.
m_Size
int m_Size
- The current size;
m_CapacityIncrement
int m_CapacityIncrement
- The capacity increment
m_CapacityMultiplier
double m_CapacityMultiplier
- The capacity multiplier.
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.
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
m_Elements
double[][] m_Elements
- The values of the matrix
closed
boolean closed
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 current number of elements in the queue
|
Class weka.core.Queue.QueueNode implements Serializable |
m_Next
weka.core.Queue.QueueNode m_Next
- The next node in the queue
m_Contents
java.lang.Object m_Contents
- The nodes contents
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
m_storedObjectArray
byte[] m_storedObjectArray
- The array storing the object.
m_isCompressed
boolean m_isCompressed
- Whether or not the object is compressed.
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
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.
|
Package weka.core.converters |
m_retrieval
int m_retrieval
- The current retrieval mode
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.
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
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
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).
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?
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
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
m_ReplaceMissingFilter
ReplaceMissingValues m_ReplaceMissingFilter
- replace missing values in training instances
m_NumClusters
int m_NumClusters
- number of clusters to generate
m_ClusterCentroids
Instances m_ClusterCentroids
- holds the cluster centroids
m_ClusterStdDevs
Instances m_ClusterStdDevs
- Holds the standard deviations of attributes in each cluster
m_Seed
int m_Seed
- random seed
m_Min
double[] m_Min
- attribute min values
m_Max
double[] m_Max
- attribute max values
m_Iterations
int m_Iterations
- Keep track of the number of iterations completed before convergence
|
Package weka.datagenerators |
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
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
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
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
m_AttIndex
int m_AttIndex
m_Split
double m_Split
m_Not
boolean m_Not
m_Dataset
Instances m_Dataset
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
|
Package weka.filters.supervised.attribute |
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
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
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
m_Indices
int[][] m_Indices
- The sorted indices of the attribute values.
m_Numeric
boolean m_Numeric
- Are the new attributes going to be nominal or numeric ones?
|
Package weka.filters.supervised.instance |
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
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.
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 |
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
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
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
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
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
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
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
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
m_DeltaCols
Range m_DeltaCols
- Stores which columns to take differences between
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.
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.
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_MinArray
double[] m_MinArray
- The minimum values for numeric attributes.
m_MaxArray
double[] m_MaxArray
- The maximum values for numeric attributes.
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.
m_IgnoreClass
boolean m_IgnoreClass
- True if the class is to be unset
m_ClassIndex
int m_ClassIndex
- Storing the class index
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
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
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
m_removeFilter
Remove m_removeFilter
- The filter used to remove attributes
m_maxVariancePercentage
double m_maxVariancePercentage
- The type of attribute to delete
m_ModesAndMeans
double[] m_ModesAndMeans
- The modes and means
m_Means
double[] m_Means
- The means
m_StdDevs
double[] m_StdDevs
- The variances
m_AttIndex
SingleIndex m_AttIndex
- The attribute's index setting.
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.
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.
|
Package weka.filters.unsupervised.instance |
m_Seed
int m_Seed
- The random number seed
m_Random
java.util.Random m_Random
- The current random number generator
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.
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)?
m_Percentage
int m_Percentage
- Percentage of instances to select.
m_Inverse
boolean m_Inverse
- Indicates if inverse of selection is to be output.
m_Range
Range m_Range
- Range of instances provided by user.
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
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
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
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_OnDemandDirectory
java.io.File m_OnDemandDirectory
- The directory used when loading cost files on demand, null indicates
current directory
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
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_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
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_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
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
m_CreateSparseData
boolean m_CreateSparseData
- Determines whether sparse data is created
m_Query
java.lang.String m_Query
- Query to execute
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
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
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
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
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
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
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.
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
m_result
TaskStatusInfo m_result
m_experiment
Experiment m_experiment
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
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.
m_Counts
double[] m_Counts
- Hold the counts
m_SumOfCounts
double m_SumOfCounts
- Hold the sum of counts
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
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
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)
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 |
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
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
group
java.util.BitSet group
merit
double merit
m_MaxSize
int m_MaxSize
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
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
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
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
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
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
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_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
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 implements Serializable |
m_chromosome
java.util.BitSet m_chromosome
m_objective
double m_objective
- holds raw merit
m_fitness
double m_fitness
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
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
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
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
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
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
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
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
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
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
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 |
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_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
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 |
m_value
java.lang.String m_value
m_index
int m_index
m_parts
Instances m_parts
m_type
int m_type
m_predicate
Predicate m_predicate
m_sign
int m_sign
m_negation
Literal m_negation
m_missing
int m_missing
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).
m_literals
java.util.ArrayList m_literals
m_name
java.lang.String m_name
m_index
int m_index
m_isClass
boolean m_isClass
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.
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.
java.io.IOException
first
weka.associations.tertius.SimpleLinkedList.Entry first
last
weka.associations.tertius.SimpleLinkedList.Entry last
current
weka.associations.tertius.SimpleLinkedList.Entry current
lastReturned
weka.associations.tertius.SimpleLinkedList.Entry lastReturned
current
weka.associations.tertius.SimpleLinkedList.Entry current
lastReturned
weka.associations.tertius.SimpleLinkedList.Entry lastReturned
m_Debug
boolean m_Debug
- Whether the classifier is run in debug mode.
m_Classifiers
Classifier[] m_Classifiers
- Array for storing the generated base classifiers.
m_NumIterations
int m_NumIterations
- The number of iterations.
m_Classifiers
Classifier[] m_Classifiers
- Array for storing the generated base classifiers.
m_Seed
int m_Seed
- The random number seed.
m_Seed
int m_Seed
- The random number seed.
m_Seed
int m_Seed
- The random number seed.
m_Seed
int m_Seed
- The random number seed.
m_Classifier
Classifier m_Classifier
- The base classifier to use
|
Package weka.classifiers.lazy |
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.
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
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
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
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
m_Train
Instances m_Train
- The training instances used for classification.
m_Min
double[] m_Min
- The minimum values for numeric attributes.
m_Max
double[] m_Max
- The maximum values for numeric attributes.
m_kNN
int m_kNN
- The number of neighbours used to select the kernel bandwidth
m_WeightKernel
int m_WeightKernel
- The weighting kernel method currently selected
m_UseAllK
boolean m_UseAllK
- True if m_kNN should be set to all instances
|
Package weka.classifiers.lazy.kstar |
m_Cache
KStarCache.CacheTable m_Cache
- cache table
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
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 |
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
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.
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
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
m_Classifiers
Classifier[] m_Classifiers
- The classifiers. (One for each class.)
m_ClassFilters
MakeIndicator[] m_ClassFilters
- The filters used to transform the class.
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)
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 implements Serializable |
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
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.
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
m_MetaClassifiers
Classifier[] m_MetaClassifiers
- The meta classifiers, one for each base classifier.
m_InstPerClass
double[] m_InstPerClass
- InstPerClass
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.
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
m_NumSubCmtys
int m_NumSubCmtys
- The number of sub-committees to use
m_Random
java.util.Random m_Random
- Random number generator
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
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)
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.
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 implements Serializable |
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
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.
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
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
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
|
Package weka.classifiers.misc |
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
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
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 |
m_CondiCounts
double[][][] m_CondiCounts
- 3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues)
of attribute counts
m_ClassCounts
double[] m_ClassCounts
- The number of times each class value occurs in the dataset
m_SumForCounts
int[][] m_SumForCounts
- The sums of attribute-class counts
-- if there are no missing values for att, then m_SumForCounts[classVal][att] will
be the same as m_ClassCounts[classVal]
m_NumClasses
int m_NumClasses
- The number of classes
m_NumAttributes
int m_NumAttributes
- The number of attributes in dataset, including class
m_NumInstances
int m_NumInstances
- The number of instances in the dataset
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Instances
Instances m_Instances
- The dataset
m_TotalAttValues
int m_TotalAttValues
- The total number of values for all attributes (not including
class). Eg. for three atts each with two possible values,
m_TotalAttValues would be 6.
This variable is used when allocating space for m_CondiCounts matrix.
m_StartAttIndex
int[] m_StartAttIndex
- The starting index (in the m_CondiCounts matrix) of each attribute
m_NumAttValues
int[] m_NumAttValues
- The number of values for each attribute
m_Frequencies
int[] m_Frequencies
- The frequency of each attribute value for the dataset
m_SumInstances
double m_SumInstances
- The number of valid class values observed in dataset
-- with no missing classes, this number is the same as m_NumInstances.
m_Limit
int m_Limit
- An att's frequency must be this value or more to be a superParent
m_Debug
boolean m_Debug
- If true, outputs debugging info
m_nOrder
int[] m_nOrder
- topological ordering of the network
m_ParentSets
ParentSet[] m_ParentSets
- The parent sets.
m_Distributions
Estimator[][] m_Distributions
- The attribute estimators containing CPTs.
m_NumClasses
int m_NumClasses
- The number of classes
m_Instances
Instances m_Instances
- The dataset header for the purposes of printing out a semi-intelligible
model
m_ADTree
ADNode m_ADTree
m_nScoreType
int m_nScoreType
- Holds the score type used to measure quality of network
m_fAlpha
double m_fAlpha
- Holds prior on count
m_nMaxNrOfParents
int m_nMaxNrOfParents
- Holds upper bound on number of parents
m_bInitAsNaiveBayes
boolean m_bInitAsNaiveBayes
- determines whether initial structure is an empty graph or a Naive Bayes network
m_bUseADTree
boolean m_bUseADTree
- Use the experimental ADTree datastructure for calculating contingency tables
m_bRandomOrder
boolean m_bRandomOrder
- Holds flag to indicate ordering should be random
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
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
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.
probOfWordGivenClass
double[][] probOfWordGivenClass
probOfClass
double[] probOfClass
numAttributes
int numAttributes
numClasses
int numClasses
lnFactorialCache
double[] lnFactorialCache
headerInfo
Instances headerInfo
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.
m_nParents
int[] m_nParents
- Holds indexes of parents
m_nNrOfParents
int m_nNrOfParents
- Holds number of parents
m_nCardinalityOfParents
int m_nCardinalityOfParents
- Holds cardinality of parents (= number of instantiations the parents can take)
|
Package weka.classifiers.rules |
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
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
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
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 implements Serializable |
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
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
m_rule
weka.classifiers.rules.OneR.OneRRule m_rule
- A 1-R rule
m_minBucketSize
int m_minBucketSize
- The minimum bucket size
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.
m_rules
weka.classifiers.rules.Prism.PrismRule m_rules
- The first rule in the list of rules
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
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
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 |
CF
double CF
- CF
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?
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.
|
Package weka.classifiers.trees |
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
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.
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.
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.
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
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.
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.
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 implements Serializable |
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.
m_tView
TreeVisualizer m_tView
- The tree display panel.
m_iView
VisualizePanel m_iView
- The instances display.
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_treeFrame
javax.swing.JFrame m_treeFrame
- These two frames aren't used anymore.
m_visFrame
javax.swing.JFrame m_visFrame
m_reps
javax.swing.JTabbedPane m_reps
- The tabbed window for the tree and instances view.
m_mainWin
javax.swing.JFrame m_mainWin
- The window.
m_built
boolean m_built
- The status of whether there is a decision tree ready or not.
m_classifiers
GenericObjectEditor m_classifiers
- A list of other m_classifiers.
m_propertyDialog
PropertyDialog m_propertyDialog
- A window for selecting other classifiers.
|
Package weka.classifiers.trees.m5 |
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
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
m_coefficients
double[] m_coefficients
m_intercept
double m_intercept
m_instancesHeader
Instances m_instancesHeader
m_numParameters
int m_numParameters
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
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
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 |
m_minNoObj
int m_minNoObj
- Minimum number of instances in interval.
m_allData
Instances m_allData
- The FULL training dataset.
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_minNoObj
int m_minNoObj
- Minimum number of objects in interval.
m_allData
Instances m_allData
- All the training data
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.
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.
m_distribution
Distribution m_distribution
- Distribution of class values.
m_numSubsets
int m_numSubsets
- Number of created subsets.
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.
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.
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.
|
Package weka.classifiers.trees.lmt |
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
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
m_minNumInstances
int m_minNumInstances
- Minimum number of instances for leaves
m_minInfoGain
double m_minInfoGain
- Minimum information gain for split
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 |
value
double value
- The prediction value stored in this node
children
FastVector children
- The children of this node - any number of splitter nodes
orderAdded
int orderAdded
- The number this node was in the order of nodes added to the tree
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
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 |
m_ClassNames
java.lang.String[] m_ClassNames
- Stores the names of the classes
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
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 |
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
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
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.
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 implements Serializable |
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.
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
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
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
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
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
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
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.
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 |
nf
java.text.DecimalFormat nf
sign
boolean sign
digits
int digits
exp
int exp
trailing
boolean trailing
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
nf
java.text.DecimalFormat nf
width
int width
decimal
int decimal
trailing
boolean trailing
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.
|
Package weka.classifiers.functions.neural |
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.
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.
|
Package weka.classifiers.functions.supportVector |
m_data
Instances m_data
- The dataset
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
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
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
|
Package confidenceMachine |
|
Package confidenceMachine.tcm |
m_RMIThreshold
double m_RMIThreshold
- RMI Threshold used by the classifier
m_Train
Instances m_Train
- Training data
m_OptimiseForOffline
boolean m_OptimiseForOffline
- This flag signifies whether you want the classifier to be optimised for offline calculations default: true
m_kNN
int m_kNN
- The number of neighbours to use for classification (currently)
m_kNNUpper
int m_kNNUpper
- The maximum number of nearest neighbours to use for classification (set at the start)
this is only useful for the online process (when K can increase) to ensure
that there is enough distances stored in the buffer
m_Train
Instances m_Train
- The training instances used for classification.
m_DSame
java.util.Vector m_DSame
- Stores the distances to the K nearest distances to examples with the same class
m_DExclude
java.util.Vector m_DExclude
- Stores the distances to the K nearest distances to examples with the different class
m_Alphas
DoubleVector m_Alphas
- Stores the alpha 'strangeness' values of the training examples recomputed for a test example
m_StoredAlphas
DoubleVector m_StoredAlphas
- Stores the alpha 'strangeness' values of the training examples that is pre-computed from the
training data and can be restored for efficiency
m_PValues
java.util.Vector m_PValues
- Stores the p-values of the test examples so far, this might not be efficient (change like distribution).
m_TestDistanceCache
java.util.Vector m_TestDistanceCache
- Stores the distance calculations to test instances
m_DebugEvery
int m_DebugEvery
- Specifies the frequency to output debugging information (used for debugging)
m_NumInstancesProcessedSoFar
int m_NumInstancesProcessedSoFar
- Stores the number of training instances that have been processed (used for debugging)
m_NumInstancesClassifiedSoFar
int m_NumInstancesClassifiedSoFar
- Stores the number of test instances that have been classified (used for debugging)
m_UseVDM
boolean m_UseVDM
- Whether to use the Value Difference Matric on symbolic attributes
m_DistanceMetric
DistanceMetric m_DistanceMetric
- The chosen distance metric
m_DistanceMetricString
java.lang.String m_DistanceMetricString
- The chosen distance metric string
m_DistanceMetricChoices
java.lang.String[] m_DistanceMetricChoices
- The chosen distance metrics choices
m_Model
Instances m_Model
m_Normalize
boolean m_Normalize
- True if normalization should be done
m_NumAttributesUsed
double m_NumAttributesUsed
- The number of attributes the contribute to a prediction
|
Package probabilityMachine |
m_WidthVennTypes
double m_WidthVennTypes
- Width of Venn Probability Gaussian types used by the learning machine (in standard deviations)
m_Train
Instances m_Train
- Training data
m_DistLearner
Classifier m_DistLearner
- Underlying classifier
m_Gaussians
double[][] m_Gaussians
- This array holds the mean and variance of each Gaussian
m_CumMeanAndSqrMean
double[][] m_CumMeanAndSqrMean
- The cumulative total and sqr to efficiently calculate stdev and mean
m_ClassifierOptions
java.lang.String[] m_ClassifierOptions
- The underlying classifier and options
m_KononekoMDL
boolean m_KononekoMDL
- Use Kononeko MDL
m_BetterMDL
boolean m_BetterMDL
- Use better MDL
m_Train
Instances m_Train
- Training data
m_DistLearner
Classifier m_DistLearner
- Underlying Naive Bayes classifier
m_ClassifierOptions
java.lang.String[] m_ClassifierOptions
- The underlying classifier and options
m_MaxVennTypes
int m_MaxVennTypes
- Width of Venn Probability Gaussian types used by the learning machine (in standard deviations)
m_OptimizeBins
boolean m_OptimizeBins
- Whether to optimise the number of bins used
m_Train
Instances m_Train
- Training data
m_DistLearner
Classifier m_DistLearner
- Underlying Naive Bayes classifier
m_ClassifierOptions
java.lang.String[] m_ClassifierOptions
- The underlying classifier and options
|
Package probabilityMachine.vpm |
m_RMIThreshold
double m_RMIThreshold
- RMI Threshold used by the classifier
m_NumVennTypes
int m_NumVennTypes
- Number of Venn Probability types used by the learning machine
m_Train
Instances m_Train
- Training data
m_RMIThreshold
double m_RMIThreshold
- RMI Threshold used by the classifier
m_NumVennTypes
int m_NumVennTypes
- Number of Venn Probability types used by the learning machine
m_Train
Instances m_Train
- Training data
m_kNN
int m_kNN
- The number of neighbours to use for classification (currently)
m_kNNUpper
int m_kNNUpper
- The maximum number of nearest neighbours to use for classification (set at the start)
this is only useful for the online process (when K can increase) to ensure
that there is enough distances stored in the buffer
m_Train
Instances m_Train
- The training instances used for classification.
m_TrainNeighbourList
java.util.Vector m_TrainNeighbourList
- Stores the K nearest neighbours to examples in the training set
m_TestNeighbourListCache
java.util.Vector m_TestNeighbourListCache
- Stores the neighbour list calculations to test instances (so that can be restored in online models
that update immediately)
m_DebugEvery
int m_DebugEvery
- Specifies the frequency to output debugging information (used for debugging, this is very ugly and perhaps
should be done externally!!!)
m_NumInstancesProcessedSoFar
int m_NumInstancesProcessedSoFar
- Stores the number of training instances that have been processed (used for debugging)
m_NumInstancesClassifiedSoFar
int m_NumInstancesClassifiedSoFar
- Stores the number of test instances that have been classified (used for debugging)
m_TaxonomyMode
int m_TaxonomyMode
- The chosen taxonomy mode
m_HASH_PRIME
int m_HASH_PRIME
- This is a prime number used to hash types efficiently
- See Also:
- Constant Field Values
m_NumVennTypes
int m_NumVennTypes
- Number of Venn Probability types used by the learning machine
m_Train
Instances m_Train
- Training data
m_NaiveBayes
NaiveBayes m_NaiveBayes
- Underlying Naive Bayes classifier
m_Gaussians
double[][] m_Gaussians
- This array holds the mean and variance of each Gaussian
m_CumMeanAndSqrMean
double[][] m_CumMeanAndSqrMean
- The cumulative total and sqr to efficiently calculate stdev and mean
m_classifiers
AlphaProb_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
m_alpha
double[] m_alpha
- The Lagrange multipliers.
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_data
Instances m_data
- The training data.
m_weights
double[] m_weights
- Weight vector for linear machine.
m_sparseWeights
double[] m_sparseWeights
- Variables to hold weight vector in sparse form.
(To reduce storage requirements.)
m_sparseIndices
int[] m_sparseIndices
m_kernel
Kernel m_kernel
- Kernel to use
m_class
double[] m_class
- The transformed class values.
m_errors
double[] m_errors
- 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_I4
SMOset m_I4
m_supportVectors
SMOset m_supportVectors
- The set of support vectors
m_logistic
Logistic m_logistic
- Stores logistic regression model for probability estimate
m_sumOfWeights
double m_sumOfWeights
- Stores the weight of the training instances
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_kNN
int m_kNN
- The number of neighbours to use for classification (currently)
m_DistanceMetric
DistanceMetric m_DistanceMetric
- Distance functions
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_Debug
boolean m_Debug
- True if debugging output should be printed
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
m_Ranges
double[][] m_Ranges
- Ranges of the universe of data, lowest value, highest value and width
m_DistanceMetricString
java.lang.String m_DistanceMetricString
- The chosen distance metric string
m_DistanceMetricChoices
java.lang.String[] m_DistanceMetricChoices
- The chosen distance metrics choices
m_classifiers
classifiers.PC_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
m_VDMatrix
classifiers.vdm.ValueDifferenceMetric.VDMatrix[] m_VDMatrix
- The VD Matrices for each attribute
m_DistanceTrain
Instances m_DistanceTrain
- The training set used to generate the VD Matrices
m_K
double m_K
- The type of Minkowski type distance to use (default K=1 Manhatten)
m_TentativeDistanceMetric
ValueDifferenceMetric m_TentativeDistanceMetric
|
Package classifiers.usm.distance |
m_Instance
Instance m_Instance
- The instance that the complexities have been cached for!
m_Complexity
double m_Complexity
- The complexity of the object on its own!
m_ComplexityStar
double m_ComplexityStar
- The complexity of the compressed version of the object
m_CompressedInstance
java.lang.Object m_CompressedInstance
- The compressed version of the instance
m_Attribute
Attribute m_Attribute
- The attribute that the distance has been calculated for
m_AttributePrefix
java.lang.String m_AttributePrefix
- The prefix used in the attribute names to indicate the data type analysed by this USM
m_Attributes
FastVector m_Attributes
- This vector will store all the attributes whose names have appropriate prefix indicating
they are to be analysed by USM
m_Model
Instances m_Model
- the data model
m_ComplexityCache
java.util.Vector m_ComplexityCache
- the range of the attributes
m_SingleCompressFileName
java.lang.String m_SingleCompressFileName
- File name and directory of the single wav information data set
m_SingleCompressData
Instances m_SingleCompressData
- Single compressed wav information data set
m_CombinedCompressFileName
java.lang.String m_CombinedCompressFileName
- File name and directory of the combined wav information data set
m_CombinedCompressData
Instances m_CombinedCompressData
- Combined compressed wav information data set
|
Package classifiers.stbarts |
m_Train
Instances m_Train
- Holds the training data
m_RMIThreshold
double m_RMIThreshold
- Threshold for RMI classifier
Copyright (c)
2003 David Lindsay, Computer Learning Research Centre, Dept. Computer Science, Royal Holloway, University of London