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

Class Model

java.lang.Object

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

All Implemented Interfaces:

Serializable

public final class Model
extends Object
implements Serializable

Copyright (c) 2007-2014 The LIBLINEAR Project. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither name of copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Model stores the model obtained from the training procedure

use

invalid reference

Linear#loadModel(String)

and

invalid reference

Linear#saveModel(String, Model)

to load/save it

See Also:

• Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
(package private) double	bias
(package private) int[]	label	label of each class
(package private) int	nr_class
(package private) int	nr_feature
private static final long	serialVersionUID
(package private) SolverType	solverType
(package private) double[]	w	feature weight array

Constructor Summary

Constructors

Constructor	Description
Model()

Method Summary

Modifier and Type	Method	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(Object obj)
double	getBias()
double[]	getFeatureWeights()	The nr_feature*nr_class array w gives feature weights.
int[]	getLabels()
int	getNrClass()
int	getNrFeature()
int	hashCode()
static Model	load(File file)	see Linear.loadModel(File)
static Model	load(Reader inputReader)	see Linear.loadModel(Reader)
void	save(File file)	see Linear.saveModel(java.io.File, Model)
void	save(Writer writer)	see Linear.saveModel(Writer, Model)
String	toString()

Methods inherited from class java.lang.Object

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

Field Details

serialVersionUID

private static final long serialVersionUID

See Also:

• Constant Field Values

bias

double bias

label

int[] label

label of each class

nr_class

int nr_class

nr_feature

int nr_feature

solverType

SolverType solverType

w

double[] w

feature weight array

Constructor Details

Model

public Model()

Method Details

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()

getBias

public double getBias()

See Also:

• getFeatureWeights()

toString

public String toString()

Overrides:

toString in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class 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(File file)
          throws IOException

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

Throws:

IOException

save

public void save(Writer writer)
          throws IOException

see Linear.saveModel(Writer, Model)

Throws:

IOException

load

public static Model load(File file)
                  throws IOException

see Linear.loadModel(File)

Throws:

IOException

load

public static Model load(Reader inputReader)
                  throws IOException

see Linear.loadModel(Reader)

Throws:

IOException