Serialized Form
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Par
double[] m_Par
m_NumClasses
int m_NumClasses
- The number of the class labels
m_IdIndex
int m_IdIndex
m_Debug
boolean m_Debug
- Debugging output
m_Classes
int[] m_Classes
- Class labels for each bag
m_Data
double[][][] m_Data
- MI data
m_Attributes
weka.core.Instances m_Attributes
- All attribute names
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Par
double[] m_Par
m_NumClasses
int m_NumClasses
- The number of the class labels
m_IdIndex
int m_IdIndex
m_Debug
boolean m_Debug
- Debugging output
m_Classes
int[] m_Classes
- Class labels for each bag
m_Data
double[][][] m_Data
- MI data
m_Attributes
weka.core.Instances m_Attributes
- All attribute names
m_Seed
long m_Seed
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Models
weka.classifiers.Classifier[] m_Models
m_NumClasses
int m_NumClasses
- The number of the class labels
m_IdIndex
int m_IdIndex
m_Debug
boolean m_Debug
- Debugging output
m_Classes
int[] m_Classes
- Class labels for each bag
m_Attributes
weka.core.Instances m_Attributes
- All attribute names
m_NumIterations
int m_NumIterations
- Number of iterations
m_Classifier
weka.classifiers.Classifier m_Classifier
- The model base classifier to use
m_Beta
double[] m_Beta
- Voting weights of models
m_MaxIterations
int m_MaxIterations
m_DiscretizeBin
int m_DiscretizeBin
m_Filter
weka.filters.unsupervised.attribute.Discretize m_Filter
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Par
double[] m_Par
m_NumClasses
int m_NumClasses
- The number of the class labels
m_IdIndex
int m_IdIndex
m_Ridge
double m_Ridge
- The ridge parameter.
m_Debug
boolean m_Debug
- Debugging output
m_Classes
int[] m_Classes
- Class labels for each bag
m_Data
double[][][] m_Data
- MI data
m_Attributes
weka.core.Instances m_Attributes
- All attribute names
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Par
double[] m_Par
m_NumClasses
int m_NumClasses
- The number of the class labels
m_IdIndex
int m_IdIndex
m_Ridge
double m_Ridge
- The ridge parameter.
m_Debug
boolean m_Debug
- Debugging output
m_Classes
int[] m_Classes
- Class labels for each bag
m_Data
double[][][] m_Data
- MI data
m_Attributes
weka.core.Instances m_Attributes
- All attribute names
xMean
double[] xMean
xSD
double[] xSD
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Par
double[] m_Par
m_NumClasses
int m_NumClasses
- The number of the class labels
m_IdIndex
int m_IdIndex
m_Ridge
double m_Ridge
- The ridge parameter.
m_Debug
boolean m_Debug
- Debugging output
m_Classes
int[] m_Classes
- Class labels for each bag
m_Data
double[][][] m_Data
- MI data
m_Attributes
weka.core.Instances m_Attributes
- All attribute names
xMean
double[] xMean
xSD
double[] xSD
m_Neighbour
int m_Neighbour
- The number of nearest neighbour for prediction
m_Mean
double[][] m_Mean
- The mean for each attribute of each exemplar
m_Variance
double[][] m_Variance
- The variance for each attribute of each exemplar
m_Dimension
int m_Dimension
- The dimension of each exemplar, i.e. (numAttributes-2)
m_Class
double[] m_Class
- The class label of each exemplar
m_NumClasses
int m_NumClasses
- The number of class labels in the data
m_Weights
double[] m_Weights
- The weight of each exemplar
m_Rate
double m_Rate
- The learning rate in the gradient descent
m_MinArray
double[] m_MinArray
- The minimum values for numeric attributes.
m_MaxArray
double[] m_MaxArray
- The maximum values for numeric attributes.
m_STOP
double m_STOP
- The stopping criteria of gradient descent
m_Change
double[][] m_Change
- The weights that alter the dimnesion of each exemplar
m_NoiseM
double[][] m_NoiseM
- The noise data of each exemplar
m_NoiseV
double[][] m_NoiseV
- The noise data of each exemplar
m_ValidM
double[][] m_ValidM
- The noise data of each exemplar
m_ValidV
double[][] m_ValidV
- The noise data of each exemplar
m_Select
int m_Select
- The number of nearest neighbour instances in the selection of noises
in the training data
m_Choose
int m_Choose
- The number of nearest neighbour exemplars in the selection of noises
in the test data
m_ClassIndex
int m_ClassIndex
- The class and ID attribute index of the data
m_IdIndex
int m_IdIndex
- The class and ID attribute index of the data
m_Decay
double m_Decay
- The decay rate of learning rate
m_logistic
MIClassifier m_logistic
- The logistic regression model
m_clm
weka.filters.unsupervised.attribute.ClusterMembership m_clm
- The RBF filter
m_num_clusters
int m_num_clusters
- The number of clusters to use
m_ridge
double m_ridge
- The ridge regression coefficient for logistic regression
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_NumClasses
int m_NumClasses
- The number of the class labels
m_IdIndex
int m_IdIndex
m_Debug
boolean m_Debug
- Debugging output
m_Attributes
weka.core.Instances m_Attributes
- All attribute names
m_Classifier
weka.classifiers.Classifier m_Classifier
m_Method
int m_Method
m_TransformMethod
int m_TransformMethod
m_Exemplars
Exemplars m_Exemplars
m_MeanP
double[][] m_MeanP
- The mean for each attribute of each positive exemplar
m_VarianceP
double[][] m_VarianceP
- The variance for each attribute of each positive exemplar
m_MeanN
double[][] m_MeanN
- The mean for each attribute of each negative exemplar
m_VarianceN
double[][] m_VarianceN
- The variance for each attribute of each negative exemplar
m_SumP
double[][] m_SumP
- The effective sum of weights of each positive exemplar in each dimension
m_SumN
double[][] m_SumN
- The effective sum of weights of each negative exemplar in each dimension
m_ParamsP
double[] m_ParamsP
- The parameters to be estimated for each positive exemplar
m_ParamsN
double[] m_ParamsN
- The parameters to be estimated for each negative exemplar
m_Dimension
int m_Dimension
- The dimension of each exemplar, i.e. (numAttributes-2)
m_Class
double[] m_Class
- The class label of each exemplar
m_NumClasses
int m_NumClasses
- The number of class labels in the data
m_ClassIndex
int m_ClassIndex
- The class and ID attribute index of the data
m_IdIndex
int m_IdIndex
- The class and ID attribute index of the data
m_Run
int m_Run
m_Seed
long m_Seed
m_Cutoff
double m_Cutoff
m_UseEmpiricalCutOff
boolean m_UseEmpiricalCutOff
m_MeanP
double[][] m_MeanP
- The mean for each attribute of each positive exemplar
m_MeanN
double[][] m_MeanN
- The mean for each attribute of each negative exemplar
m_SumP
double[][] m_SumP
- The effective sum of weights of each positive exemplar in each dimension
m_SumN
double[][] m_SumN
- The effective sum of weights of each negative exemplar in each dimension
m_SgmSqP
double[] m_SgmSqP
- Estimated sigma^2 in positive bags
m_SgmSqN
double[] m_SgmSqN
- Estimated sigma^2 in negative bags
m_ParamsP
double[] m_ParamsP
- The parameters to be estimated for each positive exemplar
m_ParamsN
double[] m_ParamsN
- The parameters to be estimated for each negative exemplar
m_Dimension
int m_Dimension
- The dimension of each exemplar, i.e. (numAttributes-2)
m_Class
double[] m_Class
- The class label of each exemplar
m_NumClasses
int m_NumClasses
- The number of class labels in the data
m_ClassIndex
int m_ClassIndex
- The class and ID attribute index of the data
m_IdIndex
int m_IdIndex
- The class and ID attribute index of the data
m_Run
int m_Run
m_Seed
long m_Seed
m_Cutoff
double m_Cutoff
m_UseEmpiricalCutOff
boolean m_UseEmpiricalCutOff
m_LkRatio
double[] m_LkRatio
m_Attribute
weka.core.Instances m_Attribute
m_IdIndex
int m_IdIndex
- The exemplar's ID attribute
m_IdValue
double m_IdValue
- The value of the ID of this exemplar
m_ClassIndex
int m_ClassIndex
- The class index of this exemplar
m_ClassValue
double m_ClassValue
- The class value of this exemplar
m_Instances
weka.core.Instances m_Instances
- The instances in the exemplar
m_Weight
double m_Weight
- The weight of this exemplar
m_RelationName
java.lang.String m_RelationName
- The dataset's name.
m_Attributes
weka.core.Attribute[] m_Attributes
- The attribute information.
m_Exemplars
java.util.Vector m_Exemplars
- The exemplars.
m_IdIndex
int m_IdIndex
- The exemplars' ID attribute
m_ClassIndex
int m_ClassIndex
- The class index of this exemplar
m_Classifier
MIClassifier 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_Instances
Exemplars m_Instances
- The dataset of interest
m_ResultListener
MIResultListener 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
weka.experiment.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
MISplitEvaluator 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
MIResultProducer m_RP
- The MIResultProducer sending us results
m_OutputFile
java.io.File m_OutputFile
- The destination output file, null sends to System.out
m_ResultProducer
MIResultProducer 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
weka.core.FastVector m_Cache
- Stores the cached values
m_DatabaseURL
java.lang.String m_DatabaseURL
- Database URL
m_Connection
java.sql.Connection m_Connection
- The database connection
m_Statement
java.sql.Statement m_Statement
- The statement used for database queries
m_Debug
boolean m_Debug
- True if debugging output should be printed
m_ResultListener
MIResultListener m_ResultListener
- Where results will be sent
m_ResultProducer
MIResultProducer 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
weka.experiment.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
Exemplars m_Instances
- The dataset of interest
m_ResultListener
MIResultListener 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
MISplitEvaluator 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
weka.experiment.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_listeners
weka.core.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
weka.core.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
MIExperiment m_baseExperiment
- The base experiment to split up into sub experiments for remote
execution
m_subExperiments
MIExperiment[] m_subExperiments
- The sub experiments
m_subExpQueue
weka.core.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_result
weka.experiment.TaskStatusInfo m_result
m_experiment
MIExperiment m_experiment
|
Package milk.gui.experiment |
m_Exp
MIExperiment 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
MIRemoteExperiment 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
MIHostListPanel 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
MISetupPanel m_SetupPanel
- The panel for configuring the experiment
m_RunPanel
MIRunPanel m_RunPanel
- The panel for running the experiment
m_ResultsPanel
MIResultsPanel 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
weka.gui.GenericArrayEditor m_ArrayEditor
- Allows editing of the custom property values
m_Exp
MIExperiment m_Exp
- The experiment this all applies to
m_Listeners
weka.core.FastVector m_Listeners
- Listeners who want to be notified about editing status of this
panel
m_Exp
MIRemoteExperiment 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
weka.gui.SaveBuffer m_SaveOut
- The buffer saving object for saving output
m_OutText
javax.swing.JTextArea m_OutText
- Displays the output of tests
m_History
weka.gui.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
weka.experiment.PairedTTester m_TTester
- The PairedTTester object
m_Instances
weka.core.Instances m_Instances
- The instances we're extracting results from
m_InstanceQuery
MIInstanceQuery 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
MIExperiment 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
MIExperiment 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
weka.gui.LogPanel m_Log
m_Exp
MIExperiment m_Exp
- The experiment to run
m_RunThread
java.lang.Thread m_RunThread
- The thread running the experiment
m_Exp
MIExperiment 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
weka.gui.GenericObjectEditor m_RPEditor
- The ResultProducer editor
m_RPEditorPanel
weka.gui.PropertyPanel m_RPEditorPanel
- The panel to contain the ResultProducer editor
m_RLEditor
weka.gui.GenericObjectEditor m_RLEditor
- The ResultListener editor
m_RLEditorPanel
weka.gui.PropertyPanel m_RLEditorPanel
- The panel to contain the ResultListener editor
m_GeneratorPropertyPanel
MIGeneratorPropertyIteratorPanel m_GeneratorPropertyPanel
- The panel that configures iteration on custom resultproducer property
m_RunNumberPanel
MIRunNumberPanel m_RunNumberPanel
- The panel for configuring run numbers
m_DistributeExperimentPanel
MIDistributeExperimentPanel m_DistributeExperimentPanel
- The panel for enabling a distributed experiment
m_DatasetListPanel
MIDatasetListPanel 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
stdDev
double stdDev
xIndex
Axis xIndex
xAttr
javax.swing.JComboBox xAttr
sd
javax.swing.JTextField sd
sdlbl
javax.swing.JLabel sdlbl
sdpnl
javax.swing.JPanel sdpnl
select
SelectPanel select
plot
PlotPanel plot
xIndex
Axis xIndex
yIndex
Axis yIndex
xAttr
javax.swing.JComboBox xAttr
yAttr
javax.swing.JComboBox yAttr
select
SelectPanel select
plot
MIPlot2D plot
m_PreprocessPanel
weka.gui.explorer.PreprocessPanel m_PreprocessPanel
- The panel for preprocessing instances
m_DistributionPanel
DistributionPanel m_DistributionPanel
- The panel to show distributions
m_GeomPanel
GeomPanel m_GeomPanel
- The panel to show data geometrically
m_TabbedPane
javax.swing.JTabbedPane m_TabbedPane
- The tabbed pane that controls which sub-pane we are working with
m_LogPanel
weka.gui.LogPanel m_LogPanel
- The panel for log and status messages
exemplars
Exemplars exemplars
cp
weka.gui.visualize.ClassPanel cp
insts
weka.core.Instances insts
ip
javax.swing.JPanel ip
clas
javax.swing.JComboBox clas
id
javax.swing.JComboBox id
colorList
weka.core.FastVector colorList
minC
double minC
maxC
double maxC
classIndex
int classIndex
idIndex
int idIndex
change
java.beans.PropertyChangeSupport change
m_DefaultColors
java.awt.Color[] m_DefaultColors
plotExemplars
Exemplars plotExemplars
- The instances to be plotted
x
Axis x
- The x and y axis, max and min values are of all the exemplars
y
Axis y
- The x and y axis, max and min values are of all the exemplars
maxC
double maxC
- Tempory variable to store maxC and minC in case the
super.determineBounds() finds wrong ones.
minC
double minC
- Tempory variable to store maxC and minC in case the
super.determineBounds() finds wrong ones.
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
plotExemplars
Exemplars plotExemplars
- The exemplars to be plotted
colorList
weka.core.FastVector colorList
- The list of the colors used
m_minC
double m_minC
- The max and min color
m_maxC
double m_maxC
- The max and min color
x
Axis x
- Indexes of the attributes to go on the x axis
stdDev
double stdDev
- The std. deviations used in deriving the distributions
maxY
double maxY
- The maximal value in Y axis
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_XaxisEnd
int m_XaxisEnd
m_YaxisStart
int m_YaxisStart
m_YaxisEnd
int m_YaxisEnd
isUp
boolean isUp
- Whether the distribution curve is up or down
m_labelFont
java.awt.Font m_labelFont
- Font for labels
m_labelMetrics
java.awt.FontMetrics m_labelMetrics
choose
javax.swing.JList choose
added
javax.swing.JList added
chooseModel
javax.swing.DefaultListModel chooseModel
addModel
javax.swing.DefaultListModel addModel
add
javax.swing.JButton add
rm
javax.swing.JButton rm
addAll
javax.swing.JButton addAll
rmAll
javax.swing.JButton rmAll
jp
javax.swing.JPanel jp
exemplars
Exemplars exemplars
addedExs
Exemplars addedExs
pcs
java.beans.PropertyChangeSupport pcs
colorList
weka.core.FastVector colorList
m_minC
double m_minC
m_maxC
double m_maxC
chooseSP
javax.swing.JScrollPane chooseSP
addSP
javax.swing.JScrollPane addSP