Package de.bwaldvogel.liblinear

Class Problem

java.lang.Object

de.bwaldvogel.liblinear.Problem

public class Problem
extends Object

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)
Feature[][]	x	array of sparse feature nodes
double[]	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)	Deprecated. use readFromFile(Path, double) instead
static Problem	readFromFile(File file, Charset charset, double bias)	Deprecated. use readFromFile(Path, Charset, double) instead
static Problem	readFromFile(Path path, double bias)	see Train.readProblem(Path, double)
static Problem	readFromFile(Path path, Charset charset, double bias)	see Train.readProblem(Path, Charset, double)
static Problem	readFromStream(InputStream inputStream, double bias)	see Train.readProblem(InputStream, double)
static Problem	readFromStream(InputStream inputStream, Charset charset, double bias)	see Train.readProblem(InputStream, Charset, 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 double[] y

an array containing the target values

x

public Feature[][] 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

Deprecated.

use readFromFile(Path, double) instead

Throws:

IOException

InvalidInputDataException

readFromFile

public static Problem readFromFile(Path path,
 double bias)
                            throws IOException,
InvalidInputDataException

see Train.readProblem(Path, double)

Throws:

IOException

InvalidInputDataException

readFromFile

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

Deprecated.

use readFromFile(Path, Charset, double) instead

Throws:

IOException

InvalidInputDataException

readFromFile

public static Problem readFromFile(Path path,
 Charset charset,
 double bias)
                            throws IOException,
InvalidInputDataException

see Train.readProblem(Path, Charset, double)

Throws:

IOException

InvalidInputDataException

readFromStream

public static Problem readFromStream(InputStream inputStream,
 double bias)
                              throws IOException,
InvalidInputDataException

see Train.readProblem(InputStream, double)

Throws:

IOException

InvalidInputDataException

readFromStream

public static Problem readFromStream(InputStream inputStream,
 Charset charset,
 double bias)
                              throws IOException,
InvalidInputDataException

see Train.readProblem(InputStream, Charset, double)

Throws:

IOException

InvalidInputDataException