Package unifeat.gui.featureSelection.embedded.decisionTreeBased

Class DecisionTreeBasedPanel

java.lang.Object
java.awt.Component
java.awt.Container
java.awt.Window
java.awt.Dialog
javax.swing.JDialog
unifeat.gui.ParameterPanel
unifeat.gui.featureSelection.embedded.decisionTreeBased.DecisionTreeBasedPanel
All Implemented Interfaces:
ActionListener, KeyListener, ImageObserver, MenuContainer, Serializable, EventListener, Accessible, RootPaneContainer, WindowConstants
public class DecisionTreeBasedPanel extends ParameterPanel
This java class is used to create and show a panel for the parameter settings of the decision tree based method.
Author:
Sina Tabakhi
See Also:

  • Constructor Details

    • DecisionTreeBasedPanel

      public DecisionTreeBasedPanel()
      Creates new form DecisionTreeBasedPanel. This method is called from within the constructor to initialize the form.

  • Method Details

    • btn_okActionPerformed

      protected void btn_okActionPerformed(ActionEvent e)
      This method sets an action for the btn_ok button.
      Overrides:
      btn_okActionPerformed in class ParameterPanel
      Parameters:
      e - an action event

    • keyReleased

      public void keyReleased(KeyEvent e)
      The listener method for receiving keyboard events (keystrokes). Invoked when a key has been released.
      Specified by:
      keyReleased in interface KeyListener
      Specified by:
      keyReleased in class ParameterPanel
      Parameters:
      e - an action event

    • getTreeType

      public TreeType getTreeType()
      This method returns the type of the tree.
      Returns:
      the treeType parameter

    • getConfidence

      public double getConfidence()
      This method returns the confidence factor value.
      Returns:
      the Confidence factor parameter

    • setConfidence

      public void setConfidence(double confidence)
      This method sets the confidence factor value.
      Parameters:
      confidence - the confidence factor value

    • getMinNum

      public int getMinNum()
      This method returns the minimum number of samples per leaf.
      Returns:
      the MinNumSample parameter

    • setMinNum

      public void setMinNum(int minNum)
      This method sets the minimum number of samples per leaf value.
      Parameters:
      minNum - the minimum number of samples per leaf value.

    • getRandomTreeKValue

      public int getRandomTreeKValue()
      This method returns the number of randomly chosen attributes.
      Returns:
      the KValue parameter

    • setRandomTreeKValue

      public void setRandomTreeKValue(int randomTreeKValue)
      This method sets the number of randomly chosen attributes.
      Parameters:
      randomTreeKValue - the number of randomly chosen attributes

    • getRandomTreeMaxDepth

      public int getRandomTreeMaxDepth()
      This method returns the maximum depth of the tree.
      Returns:
      the MaxDepth parameter

    • setRandomTreeMaxDepth

      public void setRandomTreeMaxDepth(int randomTreeMaxDepth)
      This method sets the maximum depth of the tree.
      Parameters:
      randomTreeMaxDepth - the maximum depth of the tree

    • getRandomTreeMinNum

      public double getRandomTreeMinNum()
      This method returns the minimum total weight of the instances in a leaf.
      Returns:
      the MinNum parameter

    • setRandomTreeMinNum

      public void setRandomTreeMinNum(double randomTreeMinNum)
      This method sets the minimum total weight of the instances in a leaf.
      Parameters:
      randomTreeMinNum - the minimum total weight of the instances in a leaf

    • getRandomTreeMinVarianceProp

      public double getRandomTreeMinVarianceProp()
      This method returns the minimum proportion of the total variance (over all the data) required for split.
      Returns:
      the MinVarianceProp parameter

    • setRandomTreeMinVarianceProp

      public void setRandomTreeMinVarianceProp(double randomTreeMinVarianceProp)
      This method sets the minimum proportion of the total variance (over all the data) required for split.
      Parameters:
      randomTreeMinVarianceProp - the minimum proportion required for split

    • getRandomForestNumFeatures

      public int getRandomForestNumFeatures()
      This method returns the number of randomly selected features.
      Returns:
      the NumFeatures parameter

    • setRandomForestNumFeatures

      public void setRandomForestNumFeatures(int randomForestNumFeatures)
      This method sets the number of randomly selected features.
      Parameters:
      randomForestNumFeatures - The number of randomly selected features

    • getMaxDepth

      public int getMaxDepth()
      This method returns the maximum depth of the tree.
      Returns:
      the maxDepth parameter

    • setMaxDepth

      public void setMaxDepth(int maxDepth)
      This method sets the maximum depth of the tree.
      Parameters:
      maxDepth - The maximum depth of the tree

    • getRandomForestNumIterations

      public int getRandomForestNumIterations()
      This method returns the number of iterations to be performed.
      Returns:
      the NumIterations parameter

    • setRandomForestNumIterations

      public void setRandomForestNumIterations(int randomForestNumIterations)
      This method sets the number of iterations to be performed.
      Parameters:
      randomForestNumIterations - the number of iterations to be performed

    • setDefaultValue

      public void setDefaultValue()
      Sets the default values of the tree parameters

    • setDefaultValue

      public void setDefaultValue(TreeType tree)
      Sets the default values of the tree parameters
      Parameters:
      tree - the type of the tree

    • setUserValue

      public void setUserValue(double conf, int minSample)
      Sets the last values of the C4.5 parameters entered by user
      Parameters:
      conf - the confidence factor
      minSample - the minimum number of samples per leaf

    • setUserValue

      public void setUserValue(int kValue, int maxDepth, double minNum, double minVarianceProp)
      Sets the last values of the random tree parameters entered by user.
      Parameters:
      kValue - The number of randomly chosen attributes
      maxDepth - The maximum depth of the tree
      minNum - The minimum total weight of the instances in a leaf
      minVarianceProp - The minimum proportion of the total variance (over all the data) required for split

    • setUserValue

      public void setUserValue(int numFeatures, int maxDepth, int numIterations)
      Sets the last values of the random forest parameters entered by user.
      Parameters:
      numFeatures - The number of randomly selected features
      maxDepth - The maximum depth of the tree
      numIterations - The number of iterations to be performed

    • removeTreeType

      public void removeTreeType(Object... types)
      Removes a list of tree types from a combobox
      Parameters:
      types - the list of tree types that must be removed