SciLib.Neuro.SVM
Class OAOSVM

java.lang.Object
  SciLib.Neuro.SVM.SVM
      SciLib.Neuro.SVM.OAOSVM

All Implemented Interfaces:

SMO

public class OAOSVM
extends SVM
implements SMO

Field Summary

Fields inherited from class SciLib.Neuro.SVM.SVM

alpha, B, b_low, b_up, binary, C, eps, error_cache, file, GACV1, i_low, i_up, JHB, K, N, patterns, sparse, sv, svIndex, targets, tolerance

Constructor Summary

OAOSVM(int m, FileHandler file, Kernel K)
Constructor

Method Summary

void	crossValidation(int kf, double startLog2C, int cs, double startLambda, int ls, double a, double b) cross-validation
BSVM	getKClassifier(int k) Get a specific classifier
void	LOOValidation(int type, double startLog2C, int cs, double startLambda, int ls, double a, double b) LOO-validation
void	setC(double c) Set the value of C
void	setKernel(Kernel k) Set the spesific kernel
double	testing(FileHandler testFile, double a, double b) testing svm with the data scaled between a, b ( a < b )
void	training(java.lang.String alphaFile, double a, double b) training svm with the data scaled between a, b ( a < b )

Methods inherited from class SciLib.Neuro.SVM.SVM

computeB, f, f, getAlpha, getB, getC, getEpsi, getFile, getKernel, getNoOfSV, getPatterns, getSV, getSVIndex, getTargets, getTolerance, initialize, initiateParameter, isBinary, isInI0I1I2, isInI0I3I4, isInI1I2, isInI3I4, isSparse, LOOValidation, makeTargets, misclassified, readSVs, setB, setEpsi, setFile, setN, setTolerance, testing, testing, testing, training, writeReport, writeResultToFile

Methods inherited from class java.lang.Object

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

Methods inherited from interface SciLib.Neuro.SVM.SMO

LOOValidation, misclassified, testing, testing, testing, training

Constructor Detail

OAOSVM

public OAOSVM(int m,
              FileHandler file,
              Kernel K)

Constructor

Parameters:

m - the number of classes, int

file - the file that handles training data, FileHandler

K - the kernel of SVM, Kernel

Method Detail

getKClassifier

public BSVM getKClassifier(int k)

Get a specific classifier

Parameters:

k - the index of the classifier, int

setC

public void setC(double c)

Set the value of C

Overrides:

setC in class SVM

Parameters:

c - the value of C, double

setKernel

public void setKernel(Kernel k)

Set the spesific kernel

Overrides:

setKernel in class SVM

Parameters:

k - the kernel, Kernel

training

public void training(java.lang.String alphaFile,
                     double a,
                     double b)

training svm with the data scaled between a, b ( a < b )

Specified by:

training in interface SMO

Overrides:

training in class SVM

Parameters:

alphaFile - fileName for store the support vectors

a - the lower bound of scaled data

b - the upper bound of scaled data

testing

public double testing(FileHandler testFile,
                      double a,
                      double b)

testing svm with the data scaled between a, b ( a < b )

Specified by:

testing in interface SMO

Overrides:

testing in class SVM

Parameters:

alphaFile - fileName for store the support vectors

a - the lower bound of scaled data

b - the upper bound of scaled data

LOOValidation

public void LOOValidation(int type,
                          double startLog2C,
                          int cs,
                          double startLambda,
                          int ls,
                          double a,
                          double b)

LOO-validation

Specified by:

LOOValidation in interface SMO

Overrides:

LOOValidation in class SVM

Parameters:

type - method of LOO validation, constant JHB, GACV1. etc.

startLog2C - log2 of the start value of C, double

cs - the size of the set that contains all C, int

startLambda - log2 of the start value of lambda, double

ls - the size of the set that contains all lambdas, int

a - the lower bound of scaled data

b - the upper bound of scaled data

crossValidation

public void crossValidation(int kf,
                            double startLog2C,
                            int cs,
                            double startLambda,
                            int ls,
                            double a,
                            double b)

cross-validation

Specified by:

crossValidation in interface SMO

Overrides:

crossValidation in class SVM

Parameters:

k - k-fold cross validation k > 1

startLog2C - log2 of the start value of C, double

cs - the size of the set that contains all C, int

startLambda - log2 of the start value of lambda, double

ls - the size of the set that contains all lambdas, int

a - the lower bound of scaled data

b - the upper bound of scaled data