java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.trees.lmt.LogisticBase
  - - weka.classifiers.trees.ft.FTtree
    - - weka.classifiers.trees.ft.FTNode

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, WeightedInstancesHandler
```
public class FTNode
extends FTtree
```
Class for Functional tree structure.

Version:

$Revision: 1.4 $

Author:

Jo\~{a}o Gama, Carlos Ferreira

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor	Description
`FTNode(boolean errorOnProbabilities, int numBoostingIterations, int minNumInstances, double weightTrimBeta, boolean useAIC)`	Constructor for Functional tree node.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`buildClassifier(Instances data)`	Method for building a Functional tree (only called for the root node).
`void`	`buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters)`	Method for building the tree structure.
`double[]`	`distributionForInstance(Instance instance)`	Returns the class probabilities for an instance given by the Functional Tree.
`java.lang.String`	`getRevision()`	Returns the revision string.
`double`	`prune()`	Method for prunning a tree using C4.5 pruning procedure.

Methods inherited from class weka.classifiers.trees.ft.FTtree
assignIDs, assignLeafModelNumbers, cleanup, getConstError, getModelParameters, getNodes, getNodes, getNumInnerNodes, getNumLeaves, graph, hasModels, modelDistributionForInstance, modelsToString, numLeaves, numNodes, toString

Methods inherited from class weka.classifiers.trees.lmt.LogisticBase
getMaxIterations, getNumRegressions, getUseAIC, getUsedAttributes, getWeightTrimBeta, percentAttributesUsed, setHeuristicStop, setMaxIterations, setUseAIC, setWeightTrimBeta

Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - FTNode
```
public FTNode(boolean errorOnProbabilities,
              int numBoostingIterations,
              int minNumInstances,
              double weightTrimBeta,
              boolean useAIC)
```
    Constructor for Functional tree node.
    
    Parameters:
    
    errorOnProbabilities - Use error on probabilities for stopping criterion of LogitBoost?
    
    numBoostingIterations - sets the numBoostingIterations parameter
    
    minNumInstances - minimum number of instances at which a node is considered for splitting
- Method Detail
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Method for building a Functional tree (only called for the root node). Grows an initial Functional Tree.
    
    Specified by:
    
    buildClassifier in class FTtree
    
    Parameters:
    
    data - the data to train with
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - buildTree
```
public void buildTree(Instances data,
                      SimpleLinearRegression[][] higherRegressions,
                      double totalInstanceWeight,
                      double higherNumParameters)
               throws java.lang.Exception
```
    Method for building the tree structure. Builds a logistic model, splits the node and recursively builds tree for child nodes.
    
    Specified by:
    
    buildTree in class FTtree
    
    Parameters:
    
    data - the training data passed on to this node
    
    higherRegressions - An array of regression functions produced by LogitBoost at higher levels in the tree. They represent a logistic regression model that is refined locally at this node.
    
    totalInstanceWeight - the total number of training examples
    
    higherNumParameters - effective number of parameters in the logistic regression model built in parent nodes
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - prune
```
public double prune()
             throws java.lang.Exception
```
    Method for prunning a tree using C4.5 pruning procedure.
    
    Specified by:
    
    prune in class FTtree
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Returns the class probabilities for an instance given by the Functional Tree.
    
    Specified by:
    
    distributionForInstance in class FTtree
    
    Parameters:
    
    instance - the instance
    
    Returns:
    
    the array of probabilities
    
    Throws:
    
    java.lang.Exception - if distribution can't be computed successfully
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class FTtree
    
    Returns:
    
    the revision

Class FTNode

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.trees.ft.FTtree

Methods inherited from class weka.classifiers.trees.lmt.LogisticBase

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

FTNode

Method Detail

buildClassifier

buildTree

prune

distributionForInstance

getRevision