it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver

Class Problem

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.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,?)
 

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Problem.LibLinearSolverType

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
gnu.trove.map.hash.TObjectIntHashMap<Object>	featureDict
gnu.trove.map.hash.TIntObjectHashMap<Object>	featureInverseDict
int	l the number of training data
int	n the number of features (including the bias feature if bias >= 0)
LibLinearFeature[][]	x array of sparse feature nodes
double[]	y an array containing the target values

Constructor Summary

Constructors

Constructor and Description
Problem(Dataset dataset, String reprentationName, Label label, Problem.LibLinearSolverType solverType)

Method Summary

Methods

Modifier and Type	Method and Description
Vector	getW(double[] w)
void	initializeExamples(ArrayList<Vector> vectorlist)

Methods inherited from class java.lang.Object

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

Field Detail

featureDict

public gnu.trove.map.hash.TObjectIntHashMap<Object> featureDict

featureInverseDict

public gnu.trove.map.hash.TIntObjectHashMap<Object> featureInverseDict

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 LibLinearFeature[][] 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(Dataset dataset,
       String reprentationName,
       Label label,
       Problem.LibLinearSolverType solverType)

Method Detail

getW

public Vector getW(double[] w)

initializeExamples

public void initializeExamples(ArrayList<Vector> vectorlist)