de.bwaldvogel.liblinear

Class Problem

java.lang.Object

de.bwaldvogel.liblinear.Problem

public class Problem
extends java.lang.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 and 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 and Description
Problem()

Method Summary

Modifier and Type	Method and Description
static Problem	readFromFile(java.io.File file, java.nio.charset.Charset charset, double bias) see Train.readProblem(File, Charset, double)
static Problem	readFromFile(java.io.File file, double bias) see Train.readProblem(File, double)
static Problem	readFromStream(java.io.InputStream inputStream, java.nio.charset.Charset charset, double bias) see Train.readProblem(InputStream, Charset, double)
static Problem	readFromStream(java.io.InputStream inputStream, double bias) see Train.readProblem(InputStream, double)

Methods inherited from class java.lang.Object

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

Field Detail

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 Detail

Problem

public Problem()

Method Detail

readFromFile

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

see Train.readProblem(File, double)

Throws:

java.io.IOException

InvalidInputDataException

readFromFile

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

see Train.readProblem(File, Charset, double)

Throws:

java.io.IOException

InvalidInputDataException

readFromStream

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

see Train.readProblem(InputStream, double)

Throws:

java.io.IOException

InvalidInputDataException

readFromStream

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

see Train.readProblem(InputStream, Charset, double)

Throws:

java.io.IOException

InvalidInputDataException