Package weka.classifiers.trees.ft
Class FTtree
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.trees.lmt.LogisticBase
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- weka.classifiers.trees.ft.FTtree
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- 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
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Constructor Summary
Constructors Constructor Description FTtree()
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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 treeint
assignLeafModelNumbers(int leafCounter)
Assigns numbers to the logistic regression models at the leaves of the treeabstract 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 treevoid
getNodes(java.util.Vector nodeList)
Fills a list with all inner nodes in the treeint
getNumInnerNodes()
Method to count the number of inner nodes in the treeint
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
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Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions
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Method Detail
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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 classLogisticBase
- Parameters:
data
- the data to train with- Throws:
java.lang.Exception
- if something goes wrong
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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 nodehigherRegressions
- 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 exampleshigherNumParameters
- effective number of parameters in the logistic regression model built in parent nodes- Throws:
java.lang.Exception
- if something goes wrong
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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
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getNumInnerNodes
public int getNumInnerNodes()
Method to count the number of inner nodes in the tree- Returns:
- the number of inner nodes
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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
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getNodes
public java.util.Vector getNodes()
Return a list of all inner nodes in the tree- Returns:
- the list of nodes
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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
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getConstError
public int getConstError(double[] probsConst)
- Type Parameters:
any
- probsConst
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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
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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
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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 classLogisticBase
- Parameters:
instance
- the instance- Returns:
- the array of probabilities
- Throws:
java.lang.Exception
- if distribution can't be computed successfully
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toString
public java.lang.String toString()
Returns a description of the Functional tree (tree structure and logistic models)- Overrides:
toString
in classLogisticBase
- Returns:
- describing string
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numLeaves
public int numLeaves()
Returns the number of leaves (normal count).- Returns:
- the number of leaves
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numNodes
public int numNodes()
Returns the number of nodes.- Returns:
- the number of nodes
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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
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assignIDs
public int assignIDs(int lastID)
Assigns unique IDs to all nodes in the tree
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assignLeafModelNumbers
public int assignLeafModelNumbers(int leafCounter)
Assigns numbers to the logistic regression models at the leaves of the tree
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modelsToString
public java.lang.String modelsToString()
Returns a string describing the logistic regression function at the node.
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graph
public java.lang.String graph() throws java.lang.Exception
Returns graph describing the tree.- Throws:
java.lang.Exception
- if something goes wrong
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cleanup
public void cleanup()
Cleanup in order to save memory.- Overrides:
cleanup
in classLogisticBase
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classLogisticBase
- Returns:
- the revision
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