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Class weka.classifiers.m5.Node
java.lang.Object
|
+----weka.classifiers.m5.Node
- public final class Node
- extends java.lang.Object
- implements java.io.Serializable
Class for handing a node in the tree or the subtree under this node
- Version:
- $Revision: 1.5 $
- Author:
- Yong Wang (yongwang@cs.waikato.ac.nz)
Node(Instances, Node)
- Constructs a new node
Node(Instances, Node, Options)
- Constructs the root of a tree
copy(Node)
- Makes a copy of the tree under this node
errors(Instances, boolean)
- Evaluates a tree
factor(int, int, double)
- Calculates a multiplication factor used at this node
formulaeToString(boolean)
- Converts all the linear models at the leaves under the node to a string
function()
- Finds the appropriate order of the unsmoothed linear model at this node
leafNode()
- Sets the node to a leaf
leafNum(Instance)
- Detects which leaf a instance falls into
measures(Instances, boolean)
- Computes performance measures of a tree
measuresToString(Measures[], Instances, int, int, String)
- Converts the performance measures into a string
numberOfLinearModels()
- Counts the number of linear models in the tree.
numLeaves(int)
- Sets the leaves' numbers
predict(Instance, boolean)
- Predicts the class value of an instance by the tree
predictionsToString(Instances, int, boolean)
- Converts the predictions by the tree under this node to a string
prune()
- Prunes the model tree
regression(Function)
- Computes the coefficients of a linear model using the instances at this
node
singleNodeToString()
- Converts the information stored at this node to a string
smoothen()
- Smoothens all unsmoothed formulae at the tree leaves under this node.
smoothenFormula(Node)
- Recursively smoothens the unsmoothed linear model at this node with the
unsmoothed linear models at the nodes above this
split(Instances)
- Splits the node recursively, unless there are few instances or
instances have similar values of the class attribute
treeToString(int, double)
- Converts the tree under this node to a string
validation(Instances)
- Computes performance measures for both unsmoothed and smoothed models
valueNode()
- Takes a constant value as the function at the node
Node
public Node(Instances inst,
Node up)
Constructs a new node
- Parameters:
inst
- instances
up
- the parent node
Node
public Node(Instances inst,
Node up,
Options options)
Constructs the root of a tree
- Parameters:
inst
- instances
up
- the parent node
options
- the options
singleNodeToString
public final java.lang.String singleNodeToString() throws java.lang.Exception
Converts the information stored at this node to a string
- Returns:
- the converted string
- Throws:
- java.lang.Exception - if something goes wrong
treeToString
public final java.lang.String treeToString(int treeLevel,
double deviation)
Converts the tree under this node to a string
- Parameters:
treeLevel
- the depth of this node;
the root of a tree should have treeLevel = 0
deviation
- the global deviation of the class column,
used for evaluating relative errors
- Returns:
- the converted string
numberOfLinearModels
public final int numberOfLinearModels()
Counts the number of linear models in the tree.
formulaeToString
public final java.lang.String formulaeToString(boolean smooth) throws java.lang.Exception
Converts all the linear models at the leaves under the node to a string
- Parameters:
smooth
- either the smoothed models if true, otherwise
the unsmoothed are converted
- Returns:
- the converted string
- Throws:
- java.lang.Exception - if something goes wrong
numLeaves
public final int numLeaves(int leafCounter)
Sets the leaves' numbers
- Parameters:
leafCounter
- the number of leaves counted
- Returns:
- the number of the total leaves under the node
split
public final void split(Instances inst) throws java.lang.Exception
Splits the node recursively, unless there are few instances or
instances have similar values of the class attribute
- Parameters:
inst
- instances
- Throws:
- java.lang.Exception - if something goes wrong
leafNode
public final void leafNode() throws java.lang.Exception
Sets the node to a leaf
- Throws:
- java.lang.Exception - if something goes wrong
valueNode
public final void valueNode() throws java.lang.Exception
Takes a constant value as the function at the node
- Throws:
- java.lang.Exception - if something goes wrong
prune
public final void prune() throws java.lang.Exception
Prunes the model tree
- Parameters:
modelType
- determines what kind a model is constructed, a model tree,
a regression tree or a simple linear regression
pruningFactor
- the pruning factor influences the size of the pruned tree
- Throws:
- java.lang.Exception - if something goes wrong
regression
public final void regression(Function function)
Computes the coefficients of a linear model using the instances at this
node
- Parameters:
function
- the linear model containing the index of the attributes;
coefficients are to be computed
function
public final void function() throws java.lang.Exception
Finds the appropriate order of the unsmoothed linear model at this node
- Throws:
- java.lang.Exception - if something goes wrong
factor
public final double factor(int n,
int v,
double pruningFactor)
Calculates a multiplication factor used at this node
- Parameters:
n
- the number of instances
v
- the number of the coefficients
- Returns:
- multiplication factor
smoothen
public final void smoothen()
Smoothens all unsmoothed formulae at the tree leaves under this node.
smoothenFormula
public final void smoothenFormula(Node current)
Recursively smoothens the unsmoothed linear model at this node with the
unsmoothed linear models at the nodes above this
- Parameters:
current
- the unsmoothed linear model at the up node of the
'current' will be used for smoothening
predictionsToString
public final java.lang.String predictionsToString(Instances inst,
int lmNo,
boolean smooth) throws java.lang.Exception
Converts the predictions by the tree under this node to a string
- Parameters:
insta
- instances
smooth
- =ture using the smoothed models; otherwise, the unsmoothed
- Returns:
- the converted string
- Throws:
- java.lang.Exception - if something goes wrong
leafNum
public final int leafNum(Instance instance)
Detects which leaf a instance falls into
- Parameters:
i
- instance i
inst
- instances
- Returns:
- the leaf no.
predict
public final double predict(Instance instance,
boolean smooth)
Predicts the class value of an instance by the tree
- Parameters:
i
- instance i
- Returns:
- the predicted value
errors
public final Errors errors(Instances inst,
boolean smooth) throws java.lang.Exception
Evaluates a tree
- Parameters:
inst
- instances
- Returns:
- the evaluation results
- Throws:
- java.lang.Exception - if something goes wrong
measures
public final Measures measures(Instances inst,
boolean smooth) throws java.lang.Exception
Computes performance measures of a tree
- Parameters:
inst
- instances
smooth
- =true uses the smoothed models;
otherwise uses the unsmoothed models
- Returns:
- the performance measures
- Throws:
- java.lang.Exception - if something goes wrong
validation
public final Measures[] validation(Instances inst) throws java.lang.Exception
Computes performance measures for both unsmoothed and smoothed models
- Parameters:
inst
- instances
- Throws:
- java.lang.Exception - if something goes wrong
copy
public final Node copy(Node up) throws java.lang.Exception
Makes a copy of the tree under this node
- Parameters:
up
- the parant node of the new node
- Returns:
- a copy of the tree under this node
- Throws:
- java.lang.Exception - if something goes wrong
measuresToString
public final java.lang.String measuresToString(Measures measures[],
Instances inst,
int lmNo,
int verbosity,
java.lang.String str) throws java.lang.Exception
Converts the performance measures into a string
- Parameters:
measures[]
- contains both the unsmoothed and smoothed measures
inst
- the instances
lmNo
- also converts the predictions by all linear models if lmNo=0,
or one linear model spedified by lmNo.
verbosity
- the verbosity level
str
- the type of evaluation, one of
"t" for training, "T" for testing,
"f" for fold training, "F" for fold testing,
"x" for cross-validation
- Returns:
- the converted string
- Throws:
- java.lang.Exception - if something goes wrong
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