weka.classifiers
Class Logistic

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.DistributionClassifier
              |
              +--weka.classifiers.Logistic

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable

public class Logistic

extends DistributionClassifier

implements OptionHandler

Class for building and using a two-class logistic regression model with a ridge estimator.

This class utilizes globally convergent Newtons Method adapted from Numerical Recipies in C. Reference: le Cessie, S. and van Houwelingen, J.C. (1997). Ridge Estimators in Logistic Regression. Applied Statistics, Vol. 41, No. 1, pp. 191-201.

Missing values are replaced using a ReplaceMissingValuesFilter, and nominal attributes are transformed into numeric attributes using a NominalToBinaryFilter.

Valid options are:

-D
Turn on debugging output.

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz), Tony Voyle (tv6@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

protected int	m_ClassIndex The index of the class attribute
protected boolean	m_Debug Debugging output
protected double	m_LL The log-likelihood of the built model
protected double	m_LLn The log-likelihood of the null model
protected int	m_NumPredictors The number of attributes in the model
protected double[]	m_Par The coefficients of the model
protected double	m_Ridge The ridge parameter.

Constructor Summary

Logistic()

Method Summary

void	buildClassifier(Instances train) Builds the classifier
protected double	calculateLogLikelihood(double[][] X, double[] Y, Matrix jacobian, double[] deltas) Calculates the log likelihood of the current set of coefficients (stored in m_Par), given the data.
double[]	distributionForInstance(Instance instance) Computes the distribution for a given instance
protected double	evaluateProbability(double[] instDat) Evaluate the probability for this point using the current coefficients
boolean	getDebug() Gets whether debugging output will be printed.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
void	lnsrch(int n, double[] xold, double fold, double[] g, double[] p, double[] x, double stpmax, double[][] X, double[] Y) Finds a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently.
static void	main(java.lang.String[] argv) Main method for testing this class.
protected static double	Norm(double z) Returns probability.
void	setDebug(boolean debug) Sets whether debugging output will be printed.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Gets a string describing the classifier.

Methods inherited from class weka.classifiers.DistributionClassifier

classifyInstance

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_LL

protected double m_LL

The log-likelihood of the built model

m_LLn

protected double m_LLn

The log-likelihood of the null model

m_Par

protected double[] m_Par

The coefficients of the model

m_NumPredictors

protected int m_NumPredictors

The number of attributes in the model

m_ClassIndex

protected int m_ClassIndex

The index of the class attribute

m_Ridge

protected double m_Ridge

The ridge parameter.

m_Debug

protected boolean m_Debug

Debugging output

Constructor Detail

Logistic

public Logistic()

Method Detail

lnsrch

public void lnsrch(int n,
                   double[] xold,
                   double fold,
                   double[] g,
                   double[] p,
                   double[] x,
                   double stpmax,
                   double[][] X,
                   double[] Y)
            throws java.lang.Exception

Finds a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently.

Parameters:

n - number of variables

xold - old point

fold - value at that point

g - gtradient at that point

p - direction

x - new value along direction p from xold

stpmax - maximum step length

X - instance data

Y - class values

Throws:

java.lang.Exception - if an error occurs

Norm

protected static double Norm(double z)

Returns probability.

evaluateProbability

protected double evaluateProbability(double[] instDat)

Evaluate the probability for this point using the current coefficients

Parameters:

instDat - the instance data

Returns:

the probability for this instance

calculateLogLikelihood

protected double calculateLogLikelihood(double[][] X,
                                        double[] Y,
                                        Matrix jacobian,
                                        double[] deltas)

Calculates the log likelihood of the current set of coefficients (stored in m_Par), given the data.

Parameters:

X - the instance data

Y - the class values for each instance

jacobian - the matrix which will contain the jacobian matrix after the method returns

deltas - an array which will contain the parameter adjustments after the method returns

Returns:

the log likelihood of the data.

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options. Valid options are:

-D
Turn on debugging output.

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

setDebug

public void setDebug(boolean debug)

Sets whether debugging output will be printed.

Parameters:

debug - true if debugging output should be printed

getDebug

public boolean getDebug()

Gets whether debugging output will be printed.

Returns:

true if debugging output will be printed

buildClassifier

public void buildClassifier(Instances train)
                     throws java.lang.Exception

Builds the classifier

Overrides:

buildClassifier in class Classifier

Parameters:

data - the training data to be used for generating the boosted classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Computes the distribution for a given instance

Overrides:

distributionForInstance in class DistributionClassifier

Parameters:

instance - the instance for which distribution is computed

Returns:

the distribution

Throws:

java.lang.Exception - if the distribution can't be computed successfully

toString

public java.lang.String toString()

Gets a string describing the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a string describing the classifer built.

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain the command line arguments to the scheme (see Evaluation)