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

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, WeightedInstancesHandler

Direct Known Subclasses:

FTInnerNode, FTLeavesNode, FTNode
```
public abstract class FTtree
extends LogisticBase
```
Abstract 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

FTtree()

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method	Description
`int`	`assignIDs(int lastID)`	Assigns unique IDs to all nodes in the tree
`int`	`assignLeafModelNumbers(int leafCounter)`	Assigns numbers to the logistic regression models at the leaves of the tree
`abstract void`	`buildClassifier(Instances data)`	Method for building a Functional Tree (only called for the root node).
`abstract void`	`buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters)`	Abstract method for building the tree structure.
`void`	`cleanup()`	Cleanup in order to save memory.
`abstract double[]`	`distributionForInstance(Instance instance)`	Returns the class probabilities for an instance given by the Functional tree.
`int`	`getConstError(double[] probsConst)`
`java.lang.String`	`getModelParameters()`	Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
`java.util.Vector`	`getNodes()`	Return a list of all inner nodes in the tree
`void`	`getNodes(java.util.Vector nodeList)`	Fills a list with all inner nodes in the tree
`int`	`getNumInnerNodes()`	Method to count the number of inner nodes in the tree
`int`	`getNumLeaves()`	Returns the number of leaves in the tree.
`java.lang.String`	`getRevision()`	Returns the revision string.
`java.lang.String`	`graph()`	Returns graph describing the tree.
`boolean`	`hasModels()`	Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
`double[]`	`modelDistributionForInstance(Instance instance)`	Returns the class probabilities for an instance according to the logistic model at the node.
`java.lang.String`	`modelsToString()`	Returns a string describing the logistic regression function at the node.
`int`	`numLeaves()`	Returns the number of leaves (normal count).
`int`	`numNodes()`	Returns the number of nodes.
`abstract double`	`prune()`	Abstract Method that prunes a tree using C4.5 pruning procedure.
`java.lang.String`	`toString()`	Returns a description of the Functional tree (tree structure and logistic models)

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
  - FTtree
```
public FTtree()
```
- Method Detail
  - buildClassifier
```
public abstract 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.
    
    Overrides:
    
    buildClassifier in class LogisticBase
    
    Parameters:
    
    data - the data to train with
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - buildTree
```
public abstract void buildTree(Instances data,
                               SimpleLinearRegression[][] higherRegressions,
                               double totalInstanceWeight,
                               double higherNumParameters)
                        throws java.lang.Exception
```
    Abstract method for building the tree structure. Builds a logistic model, splits the node and recursively builds tree for child nodes.
    
    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 abstract double prune()
                      throws java.lang.Exception
```
    Abstract Method that prunes a tree using C4.5 pruning procedure.
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - getNumInnerNodes
```
public int getNumInnerNodes()
```
    Method to count the number of inner nodes in the tree
    
    Returns:
    
    the number of inner nodes
  - getNumLeaves
```
public int getNumLeaves()
```
    Returns the number of leaves in the tree. Leaves are only counted if their logistic model has changed compared to the one of the parent node.
    
    Returns:
    
    the number of leaves
  - getNodes
```
public java.util.Vector getNodes()
```
    Return a list of all inner nodes in the tree
    
    Returns:
    
    the list of nodes
  - getNodes
```
public void getNodes(java.util.Vector nodeList)
```
    Fills a list with all inner nodes in the tree
    
    Parameters:
    
    nodeList - the list to be filled
  - getConstError
```
public int getConstError(double[] probsConst)
```
    Type Parameters:
    
    any - probsConst
  - hasModels
```
public boolean hasModels()
```
    Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
    
    Returns:
    
    whether it has changed
  - modelDistributionForInstance
```
public double[] modelDistributionForInstance(Instance instance)
                                      throws java.lang.Exception
```
    Returns the class probabilities for an instance according to the logistic model at the node.
    
    Parameters:
    
    instance - the instance
    
    Returns:
    
    the array of probabilities
    
    Throws:
    
    java.lang.Exception
  - distributionForInstance
```
public abstract double[] distributionForInstance(Instance instance)
                                          throws java.lang.Exception
```
    Returns the class probabilities for an instance given by the Functional tree.
    
    Overrides:
    
    distributionForInstance in class LogisticBase
    
    Parameters:
    
    instance - the instance
    
    Returns:
    
    the array of probabilities
    
    Throws:
    
    java.lang.Exception - if distribution can't be computed successfully
  - toString
```
public java.lang.String toString()
```
    Returns a description of the Functional tree (tree structure and logistic models)
    
    Overrides:
    
    toString in class LogisticBase
    
    Returns:
    
    describing string
  - numLeaves
```
public int numLeaves()
```
    Returns the number of leaves (normal count).
    
    Returns:
    
    the number of leaves
  - numNodes
```
public int numNodes()
```
    Returns the number of nodes.
    
    Returns:
    
    the number of nodes
  - getModelParameters
```
public java.lang.String getModelParameters()
```
    Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
    
    Returns:
    
    the describing string
  - assignIDs
```
public int assignIDs(int lastID)
```
    Assigns unique IDs to all nodes in the tree
  - assignLeafModelNumbers
```
public int assignLeafModelNumbers(int leafCounter)
```
    Assigns numbers to the logistic regression models at the leaves of the tree
  - modelsToString
```
public java.lang.String modelsToString()
```
    Returns a string describing the logistic regression function at the node.
  - graph
```
public java.lang.String graph()
                       throws java.lang.Exception
```
    Returns graph describing the tree.
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - cleanup
```
public void cleanup()
```
    Cleanup in order to save memory.
    
    Overrides:
    
    cleanup in class LogisticBase
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class LogisticBase
    
    Returns:
    
    the revision

Class FTtree

Constructor Summary

Method Summary

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

FTtree

Method Detail

buildClassifier

buildTree

prune

getNumInnerNodes

getNumLeaves

getNodes

getNodes

getConstError

hasModels

modelDistributionForInstance

distributionForInstance

toString

numLeaves

numNodes

getModelParameters

assignIDs

assignLeafModelNumbers

modelsToString

graph

cleanup

getRevision