SciLib.Neuro.SVM
Class SVM

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

All Implemented Interfaces:

SMO

Direct Known Subclasses:

BSVM, DAGSVM, OAOSVM, OAASVM

public class SVM
extends java.lang.Object
implements SMO

Field Summary

protected vector	alpha
protected double	B
protected double	b_low
protected double	b_up
protected boolean	binary
protected double	C
protected double	eps
protected vector	error_cache
protected FileHandler	file
int	GACV1
protected int	i_low
protected int	i_up
int	JHB
protected Kernel	K
protected int	M
protected int	N variables
protected matrix	patterns
protected boolean	sparse
protected vector	sv
protected integerVector	svIndex
protected integerVector	targets
protected double	tolerance

Constructor Summary

SVM(FileHandler file, boolean binary, boolean sparse)
Constructor - you must define Kernel

SVM(FileHandler file, Kernel K, boolean binary, boolean sparse)
Constructor

SVM(matrix patterns, integerVector targets, Kernel K)
Constructor

Method Summary

protected void	computeB() compute the the threshold value b
void	crossValidation(int k, double startLog2C, int cs, double startLambda, int ls, double a, double b)
double	f(int k) compute the decision function without the threshold value b
double	f(vector x) compute the decision function without the threshold value b
vector	getAlpha() get alpha vector
double	getB() get threshold value b
double	getC() get the value of C
double	getEpsi() get the epsilon parameter of SVM
FileHandler	getFile() get the object that handles data files
Kernel	getKernel() get kernel K
int	getNoOfSV() get the number of SVs
matrix	getPatterns() get patterns matrix when data is not sparse
vector	getSV() get support vectors
integerVector	getSVIndex() get indices of SVs
integerVector	getTargets() get targets vector of training data
double	getTolerance() get the tolerance of SVM
protected void	initialize(FileHandler file)
void	initiateParameter(double startlog2C, double startlog2Lambda, int cs, int ls, vector cp, vector lp)
boolean	isBinary() check if data is binary
protected boolean	isInI0I1I2(int i) check if index i is in Iup or in Ilow
protected boolean	isInI0I3I4(int i)
protected boolean	isInI1I2(int i)
protected boolean	isInI3I4(int i)
boolean	isSparse() check if data is sparse
void	LOOValidation(int type, double[] cPar, double[] lambdaPar, double a, double b)
void	LOOValidation(int type, double startLog2C, int cs, double startLambda, int ls, double a, double b)
static integerVector	makeTargets(int k, integerVector targets)
int	misclassified(matrix p, integerVector t)
void	readSVs(java.lang.String alphaFile, vector sv, integerVector svIndex)
void	setB(double b) set the threshold b of SVM
void	setC(double C) set the parameter C of SVM
void	setEpsi(double eps) set the epsilon of SVM
void	setFile(FileHandler file) set the object that handles data files
void	setKernel(Kernel K) set the kernel of SVM
void	setN(int n)
void	setTolerance(double tolerance) set the tolerance of SVM
double	testing(FileHandler testFile)
double	testing(FileHandler testFile, double a, double b)
double	testing(FileHandler testFile, java.lang.String alphaFile)
double	testing(FileHandler testFile, java.lang.String alphaFile, double a, double b)
void	training(java.lang.String alphaFile)
void	training(java.lang.String alphaFile, double a, double b)
void	writeReport(java.lang.String fileName, java.lang.String header) write report about svm
void	writeResultToFile(java.lang.String fileName)

Methods inherited from class java.lang.Object

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

Field Detail

N

protected int N

variables

M

protected int M

binary

protected boolean binary

sparse

protected boolean sparse

C

protected double C

B

protected double B

tolerance

protected double tolerance

eps

protected double eps

alpha

protected vector alpha

error_cache

protected vector error_cache

file

protected FileHandler file

K

protected Kernel K

patterns

protected matrix patterns

targets

protected integerVector targets

sv

protected vector sv

svIndex

protected integerVector svIndex

b_up

protected double b_up

b_low

protected double b_low

i_up

protected int i_up

i_low

protected int i_low

JHB

public final int JHB

See Also:

Constant Field Values

GACV1

public final int GACV1

See Also:

Constant Field Values

Constructor Detail

SVM

public SVM(FileHandler file,
           Kernel K,
           boolean binary,
           boolean sparse)

Constructor

Parameters:

file - the file that handles training data, FileHandler

K - the kernel of SVM, Kernel

binary - the data is binary or not, boolean

sparse - the data is sparse or not, boolean

SVM

public SVM(FileHandler file,
           boolean binary,
           boolean sparse)

Constructor - you must define Kernel

Parameters:

file - the file that handles training data, FileHandler

binary - the data is binary or not, boolean

sparse - the data is sparse or not, boolean

SVM

public SVM(matrix patterns,
           integerVector targets,
           Kernel K)

Constructor

Parameters:

patterns - the training data, a matrix

targets - the labels of taining patterns, an integerVector

K - the kernel of SVM, Kernel

Method Detail

initialize

protected void initialize(FileHandler file)

isBinary

public boolean isBinary()

check if data is binary

Returns:

true or false

isSparse

public boolean isSparse()

check if data is sparse

Returns:

true or false

getAlpha

public vector getAlpha()

get alpha vector

Returns:

alpha the dual variables - alpha vector

getB

public double getB()

get threshold value b

Returns:

the threshold value, a double

getKernel

public Kernel getKernel()

get kernel K

Returns:

K the Kernel using in SVM, a Kernel

getFile

public FileHandler getFile()

get the object that handles data files

Returns:

file a FileHandler

getPatterns

public matrix getPatterns()

get patterns matrix when data is not sparse

Returns:

patterns a matrix

getTargets

public integerVector getTargets()

get targets vector of training data

Returns:

targets a vector

setFile

public void setFile(FileHandler file)

set the object that handles data files

Parameters:

file - a FileHandler

setKernel

public void setKernel(Kernel K)

set the kernel of SVM

Parameters:

K - a Kernel

setB

public void setB(double b)

set the threshold b of SVM

Parameters:

b - a double

setTolerance

public void setTolerance(double tolerance)

set the tolerance of SVM

Parameters:

tolerance - a double

setEpsi

public void setEpsi(double eps)

set the epsilon of SVM

Parameters:

eps - a double

setC

public void setC(double C)

set the parameter C of SVM

Parameters:

C - a double

setN

public void setN(int n)

getTolerance

public double getTolerance()

get the tolerance of SVM

Returns:

a double

getEpsi

public double getEpsi()

get the epsilon parameter of SVM

Returns:

a double

getC

public double getC()

get the value of C

Returns:

a double

getNoOfSV

public int getNoOfSV()

get the number of SVs

Returns:

an int

getSV

public vector getSV()

get support vectors

Returns:

a vector

getSVIndex

public integerVector getSVIndex()

get indices of SVs

Returns:

an integerVector

writeResultToFile

public void writeResultToFile(java.lang.String fileName)

readSVs

public void readSVs(java.lang.String alphaFile,
                    vector sv,
                    integerVector svIndex)

isInI0I1I2

protected boolean isInI0I1I2(int i)

check if index i is in Iup or in Ilow

Parameters:

i - the index i

Returns:

a boolean

isInI0I3I4

protected boolean isInI0I3I4(int i)

isInI1I2

protected boolean isInI1I2(int i)

isInI3I4

protected boolean isInI3I4(int i)

computeB

protected void computeB()

compute the the threshold value b

writeReport

public void writeReport(java.lang.String fileName,
                        java.lang.String header)

write report about svm

Parameters:

fileName - fileName for store the report

header - the header string

makeTargets

public static integerVector makeTargets(int k,
                                        integerVector targets)

f

public double f(int k)

compute the decision function without the threshold value b

Parameters:

k - the index of the input data from the training set

f

public double f(vector x)

compute the decision function without the threshold value b

Parameters:

x - the input vector

initiateParameter

public void initiateParameter(double startlog2C,
                              double startlog2Lambda,
                              int cs,
                              int ls,
                              vector cp,
                              vector lp)

training

public void training(java.lang.String alphaFile)

Specified by:

training in interface SMO

training

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

Specified by:

training in interface SMO

testing

public double testing(FileHandler testFile)

Specified by:

testing in interface SMO

testing

public double testing(FileHandler testFile,
                      java.lang.String alphaFile)

Specified by:

testing in interface SMO

testing

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

Specified by:

testing in interface SMO

testing

public double testing(FileHandler testFile,
                      java.lang.String alphaFile,
                      double a,
                      double b)

Specified by:

testing in interface SMO

misclassified

public int misclassified(matrix p,
                         integerVector t)

Specified by:

misclassified in interface SMO

crossValidation

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

Specified by:

crossValidation in interface SMO

LOOValidation

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

Specified by:

LOOValidation in interface SMO

LOOValidation

public void LOOValidation(int type,
                          double[] cPar,
                          double[] lambdaPar,
                          double a,
                          double b)

Specified by:

LOOValidation in interface SMO