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Class weka.classifiers.m5.Node
java.lang.Object
|
+----weka.classifiers.m5.Node
- public final class Node
- extends Object
- implements Serializable
Class for handing a node in the tree or the subtree under this node
- 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 String singleNodeToString() throws Exception
- Converts the information stored at this node to a string
- Returns:
- the converted string
- Throws: Exception
- if something goes wrong
treeToString
public final 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 String formulaeToString(boolean smooth) throws 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: 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 Exception
- Splits the node recursively, unless there are few instances or
instances have similar values of the class attribute
- Parameters:
- inst - instances
- Throws: Exception
- if something goes wrong
leafNode
public final void leafNode() throws Exception
- Sets the node to a leaf
- Throws: Exception
- if something goes wrong
valueNode
public final void valueNode() throws Exception
- Takes a constant value as the function at the node
- Throws: Exception
- if something goes wrong
prune
public final void prune() throws 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: 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 Exception
- Finds the appropriate order of the unsmoothed linear model at this node
- Throws: 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 String predictionsToString(Instances inst,
int lmNo,
boolean smooth) throws 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: 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 Exception
- Evaluates a tree
- Parameters:
- inst - instances
- Returns:
- the evaluation results
- Throws: Exception
- if something goes wrong
measures
public final Measures measures(Instances inst,
boolean smooth) throws 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: Exception
- if something goes wrong
validation
public final Measures[] validation(Instances inst) throws Exception
- Computes performance measures for both unsmoothed and smoothed models
- Parameters:
- inst - instances
- Throws: Exception
- if something goes wrong
copy
public final Node copy(Node up) throws 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: Exception
- if something goes wrong
measuresToString
public final String measuresToString(Measures measures[],
Instances inst,
int lmNo,
int verbosity,
String str) throws 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: Exception
- if something goes wrong
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