Package weka.classifiers.trees.lmt

Class SimpleLinearRegression

java.lang.Object

weka.classifiers.trees.lmt.SimpleLinearRegression

All Implemented Interfaces:

Serializable

public class SimpleLinearRegression
extends Object
implements Serializable

Stripped down version of SimpleLinearRegression. Assumes that there are no missing class values.

Version:

$Revision: 10169 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
SimpleLinearRegression()	Default constructor.
SimpleLinearRegression(int attIndex, double slope, double intercept)	Construct a simple linear regression model based on the given info.

Method Summary

Modifier and Type	Method	Description
void	addModel(SimpleLinearRegression slr)	Takes the given simple linear regression model and adds it to this one.
void	buildClassifier(Instances insts)	Builds a simple linear regression model given the supplied training data.
double	classifyInstance(Instance inst)	Generate a prediction for the supplied instance.
boolean	foundUsefulAttribute()	Returns true if a usable attribute was found.
int	getAttributeIndex()	Returns the index of the attribute used in the regression.
double	getIntercept()	Returns the intercept of the function.
double	getSlope()	Returns the slope of the function.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

SimpleLinearRegression

public SimpleLinearRegression()

Default constructor.

SimpleLinearRegression

public SimpleLinearRegression(int attIndex,
 double slope,
 double intercept)

Construct a simple linear regression model based on the given info.

Method Details

addModel

public void addModel(SimpleLinearRegression slr)
              throws Exception

Takes the given simple linear regression model and adds it to this one. Does nothing if the given model is based on a different attribute. Assumes the given model has been initialized.

Throws:

Exception

classifyInstance

public double classifyInstance(Instance inst)

Generate a prediction for the supplied instance.

Parameters:

inst - the instance to predict.

Returns:

the prediction

buildClassifier

public void buildClassifier(Instances insts)

Builds a simple linear regression model given the supplied training data.

Parameters:

insts - the training data.

foundUsefulAttribute

public boolean foundUsefulAttribute()

Returns true if a usable attribute was found.

Returns:

true if a usable attribute was found.

getAttributeIndex

public int getAttributeIndex()

Returns the index of the attribute used in the regression.

Returns:

the index of the attribute.

getSlope

public double getSlope()

Returns the slope of the function.

Returns:

the slope.

getIntercept

public double getIntercept()

Returns the intercept of the function.

Returns:

the intercept.