Package gov.nih.mipav.view.renderer.WildMagic.ProstateFramework.liblinearsvm

Class Problem

java.lang.Object

gov.nih.mipav.view.renderer.WildMagic.ProstateFramework.liblinearsvm.Problem

public class Problem
extends Object

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Problem describes the problem

  For example, if we have the following training data:

  LABEL       ATTR1   ATTR2   ATTR3   ATTR4   ATTR5
  -----       -----   -----   -----   -----   -----
  1           0       0.1     0.2     0       0
  2           0       0.1     0.3    -1.2     0
  1           0.4     0       0       0       0
  2           0       0.1     0       1.4     0.5
  3          -0.1    -0.2     0.1     1.1     0.1

  and bias = 1, then the components of problem are:

  l = 5
  n = 6

  y -> 1 2 1 2 3

  x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?)
       [ ] -> (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?)
       [ ] -> (1,0.4) (6,1) (-1,?)
       [ ] -> (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?)
       [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?)
 

Field Summary

Fields

Modifier and Type	Field	Description
double	bias	If bias >= 0, we assume that one additional feature is added to the end of each data instance
int	l	the number of training data
int	n	the number of features (including the bias feature if bias >= 0)
FeatureNode[][]	x	array of sparse feature nodes
int[]	y	an array containing the target values

Constructor Summary

Constructors

Constructor	Description
Problem()

Method Summary

Modifier and Type	Method	Description
static Problem	readFromFile(File file, double bias)	see Train.readProblem(File, double)

Methods inherited from class java.lang.Object

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

Field Details

l

public int l

the number of training data

n

public int n

the number of features (including the bias feature if bias >= 0)

y

public int[] y

an array containing the target values

x

public FeatureNode[][] x

array of sparse feature nodes

bias

public double bias

If bias >= 0, we assume that one additional feature is added to the end of each data instance

Constructor Details

Problem

public Problem()

Method Details

readFromFile

public static Problem readFromFile(File file,
 double bias)
                            throws IOException,
InvalidInputDataException

see Train.readProblem(File, double)

Throws:

IOException

InvalidInputDataException