de.bwaldvogel.liblinear

Class Model

java.lang.Object

de.bwaldvogel.liblinear.Model

All Implemented Interfaces:

java.io.Serializable

public final class Model
extends java.lang.Object
implements java.io.Serializable

Model stores the model obtained from the training procedure

use Linear.loadModel(File) and Linear.saveModel(File, Model) to load/save it

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Model()

Method Summary

Methods

Modifier and Type	Method and Description
protected static boolean	equals(double[] a, double[] a2) don't use Arrays.equals(double[], double[]) here, cause 0.0 and -0.0 should be handled the same
boolean	equals(java.lang.Object obj)
double	getBias()
double[]	getFeatureWeights() The nr_feature*nr_class array w gives feature weights.
int[]	getLabels()
int	getNrClass()
int	getNrFeature()
int	hashCode()
boolean	isProbabilityModel()
static Model	load(java.io.File file) see Linear.loadModel(File)
static Model	load(java.io.Reader inputReader) see Linear.loadModel(Reader)
void	save(java.io.File file) see Linear.saveModel(java.io.File, Model)
void	save(java.io.Writer writer) see Linear.saveModel(Writer, Model)
java.lang.String	toString()

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

Model

public Model()

Method Detail

getNrClass

public int getNrClass()

Returns:

number of classes

getNrFeature

public int getNrFeature()

Returns:

number of features

getLabels

public int[] getLabels()

getFeatureWeights

public double[] getFeatureWeights()

The nr_feature*nr_class array w gives feature weights. We use one against the rest for multi-class classification, so each feature index corresponds to nr_class weight values. Weights are organized in the following way

 +------------------+------------------+------------+
 | nr_class weights | nr_class weights |  ...
 | for 1st feature  | for 2nd feature  |
 +------------------+------------------+------------+
 

If bias >= 0, x becomes [x; bias]. The number of features is increased by one, so w is a (nr_feature+1)*nr_class array. The value of bias is stored in the variable bias.

Returns:

a copy of the feature weight array as described

See Also:

getBias()

isProbabilityModel

public boolean isProbabilityModel()

Returns:

true for logistic regression solvers

getBias

public double getBias()

See Also:

getFeatureWeights()

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

hashCode

public int hashCode()

Overrides:

hashCode in class java.lang.Object

equals

public boolean equals(java.lang.Object obj)

Overrides:

equals in class java.lang.Object

equals

protected static boolean equals(double[] a,
             double[] a2)

don't use Arrays.equals(double[], double[]) here, cause 0.0 and -0.0 should be handled the same

See Also:

Linear.saveModel(java.io.Writer, Model)

save

public void save(java.io.File file)
          throws java.io.IOException

see Linear.saveModel(java.io.File, Model)

Throws:

java.io.IOException

save

public void save(java.io.Writer writer)
          throws java.io.IOException

see Linear.saveModel(Writer, Model)

Throws:

java.io.IOException

load

public static Model load(java.io.File file)
                  throws java.io.IOException

see Linear.loadModel(File)

Throws:

java.io.IOException

load

public static Model load(java.io.Reader inputReader)
                  throws java.io.IOException

see Linear.loadModel(Reader)

Throws:

java.io.IOException