weka.classifiers
Class SMO

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

All Implemented Interfaces:

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

public class SMO

extends DistributionClassifier

implements OptionHandler

Implements John C. Platt's sequential minimal optimization algorithm for training a support vector classifier using polynomial kernels. Transforms output of SVM into probabilities by applying a standard sigmoid function that is not fitted to the data. This implementation globally replaces all missing values and transforms nominal attributes into binary ones. For more information on the SMO algorithm, see

J. Platt (1998). Fast Training of Support Vector Machines using Sequential Minimal Optimization. Advances in Kernel Methods - Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola, eds., MIT Press.

S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy (1999). Improvements to Platt's SMO Algorithm for SVM Classifier Design. Technical Report CD-99-14. Control Division, Dept of Mechanical and Production Engineering, National University of Singapore.

Note: for improved speed normalization should be turned off when operating on SparseInstances.

Valid options are:

-C num
The complexity constant C. (default 1)

-E num
The exponent for the polynomial kernel. (default 1)

-N
Don't normalize the training instances.

-L
Rescale kernel.

-O
Use lower-order terms.

-A num
Sets the size of the kernel cache. Should be a prime number. (default 1000003)

-T num
Sets the tolerance parameter. (default 1.0e-3)

-P num
Sets the epsilon for round-off error. (default 1.0e-12)

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Shane Legg (shane@intelligenesis.net) (sparse vector code), Stuart Inglis (stuart@intelligenesis.net) (sparse vector code)

See Also:

Serialized Form

Constructor Summary

SMO()

Method Summary

void	buildClassifier(Instances insts) Method for building the classifier.
double[]	distributionForInstance(Instance inst) Outputs the distribution for the given output.
double	getC() Get the value of C.
int	getCacheSize() Get the size of the kernel cache
double	getEpsilon() Get the value of epsilon.
double	getExponent() Get the value of exponent.
boolean	getLowerOrderTerms() Check whether lower-order terms are being used.
boolean	getNormalizeData() Check whether data is to be normalized.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
boolean	getRescaleKernel() Check whether kernel is being rescaled.
double	getToleranceParameter() Get the value of tolerance parameter.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setC(double v) Set the value of C.
void	setCacheSize(int v) Set the value of the kernel cache.
void	setEpsilon(double v) Set the value of epsilon.
void	setExponent(double v) Set the value of exponent.
void	setLowerOrderTerms(boolean v) Set whether lower-order terms are to be used.
void	setNormalizeData(boolean v) Set whether data is to be normalized.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setRescaleKernel(boolean v) Set whether kernel is to be rescaled.
void	setToleranceParameter(double v) Set the value of tolerance parameter.
java.lang.String	toString() Prints out 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

Constructor Detail

SMO

public SMO()

Method Detail

buildClassifier

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

Method for building the classifier.

Overrides:

buildClassifier in class Classifier

Parameters:

insts - the set of training instances

Throws:

java.lang.Exception - if the classifier can't be built successfully

distributionForInstance

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

Outputs the distribution for the given output. Pipes output of SVM through sigmoid function.

Overrides:

distributionForInstance in class DistributionClassifier

Parameters:

inst - the instance for which distribution is to be computed

Returns:

the distribution

Throws:

java.lang.Exception - if something goes wrong

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:

-C num
The complexity constant C. (default 1)

-E num
The exponent for the polynomial kernel. (default 1)

-N
Don't normalize the training instances.

-L
Rescale kernel.

-O
Use lower-order terms.

-A num
Sets the size of the kernel cache. Should be a prime number. (default 1000003)

-T num
Sets the tolerance parameter. (default 1.0e-3)

-P num
Sets the epsilon for round-off error. (default 1.0e-12)

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

toString

public java.lang.String toString()

Prints out the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a description of the classifier as a string

getExponent

public double getExponent()

Get the value of exponent.

Returns:

Value of exponent.

setExponent

public void setExponent(double v)

Set the value of exponent. If linear kernel is used, rescaling and lower-order terms are turned off.

Parameters:

v - Value to assign to exponent.

getC

public double getC()

Get the value of C.

Returns:

Value of C.

setC

public void setC(double v)

Set the value of C.

Parameters:

v - Value to assign to C.

getToleranceParameter

public double getToleranceParameter()

Get the value of tolerance parameter.

Returns:

Value of tolerance parameter.

setToleranceParameter

public void setToleranceParameter(double v)

Set the value of tolerance parameter.

Parameters:

v - Value to assign to tolerance parameter.

getEpsilon

public double getEpsilon()

Get the value of epsilon.

Returns:

Value of epsilon.

setEpsilon

public void setEpsilon(double v)

Set the value of epsilon.

Parameters:

v - Value to assign to epsilon.

getCacheSize

public int getCacheSize()

Get the size of the kernel cache

Returns:

Size of kernel cache.

setCacheSize

public void setCacheSize(int v)

Set the value of the kernel cache.

Parameters:

v - Size of kernel cache.

getNormalizeData

public boolean getNormalizeData()

Check whether data is to be normalized.

Returns:

true if data is to be normalized

setNormalizeData

public void setNormalizeData(boolean v)

Set whether data is to be normalized.

Parameters:

v - true if data is to be normalized

getRescaleKernel

public boolean getRescaleKernel()
                         throws java.lang.Exception

Check whether kernel is being rescaled.

Returns:

Value of rescale.

setRescaleKernel

public void setRescaleKernel(boolean v)
                      throws java.lang.Exception

Set whether kernel is to be rescaled. Defaults to false if a linear machine is built.

Parameters:

v - Value to assign to rescale.

getLowerOrderTerms

public boolean getLowerOrderTerms()

Check whether lower-order terms are being used.

Returns:

Value of lowerOrder.

setLowerOrderTerms

public void setLowerOrderTerms(boolean v)

Set whether lower-order terms are to be used. Defaults to false if a linear machine is built.

Parameters:

v - Value to assign to lowerOrder.

main

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

Main method for testing this class.