keel.Algorithms.Instance_Generation.ICPL

Class ICPLGenerator

java.lang.Object

keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator

public class ICPLGenerator
extends PrototypeGenerator

Version:

1.0

Author:

Isaac Triguero

Field Summary

Fields

Modifier and Type	Field and Description
protected int	numberOfClass
protected int	numberOfPrototypes

Fields inherited from class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

algorithmName, generatedDataSet, SEED, seedDefaultValueList, trainingDataSet

Constructor Summary

Constructors

Constructor and Description
ICPLGenerator(PrototypeSet _trainingDataSet, int nalg, java.lang.String Method, int k, int Q) Build a new ICPLGenerator Algorithm
ICPLGenerator(PrototypeSet t, Parameters parameters) Build a new RSPGenerator Algorithm

Method Summary

Modifier and Type	Method and Description
protected PrototypeSet	ACC(PrototypeSet T) Retaining Center Instances
protected PrototypeSet	associatesPrototype(PrototypeSet one, Prototype other) Return all the prototype in (this) that has other like the nearest neighbor
protected PrototypeSet	ENN(PrototypeSet T) Edited nearest neighbor of T.
protected PrototypeSet	filtering() Method to call the appropriate method
protected PrototypeSet	icpl1_or_3(int num) ICPL1_Pseudo-code: ICPL1( T training) 1) C1= abstraccion T 2) C2 = Filtrar T. 3) S = C1 4) Para cada prototipo P en C2 Tmp = S U P.
protected PrototypeSet	icpl2_or_4(int num) ICPL2 - pseudo-code ICPL2( T training) 1) C1= abstraccion T 2) C2 = Filtrar T. 3) S = C1 4) Para cada prototipo P en C2 Tmp = S U P.
protected boolean[]	identifyBorder(PrototypeSet[] pClass) Identify a border by typicaly.
static void	main(java.lang.String[] args) General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
protected Prototype	Merge(PrototypeSet S, boolean[] borders, Prototype I, PrototypeSet merge) Merge
PrototypeSet	reduceSet() Makes the trivial reduction.
protected PrototypeSet	RT2(PrototypeSet T) Retaining Border instances
protected PrototypeSet	TPA() TPA.
protected double	typicality(int index)

Methods inherited from class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

absoluteAccuracy, absoluteAccuracyAndError, absoluteAccuracyKNN, accuracy, accuracy2, desordenar_vector_sin, desordenar_vector, execute, generateReducedDataSet, getResultingAccuracy, getResultingAccuracy, getResults, getResultsOfAccuracy, getResultsOfAccuracy, getSeed, getSetSizeFromPercentage, getSetSizeFromPercentage, getTime, inic_vector_sin, inic_vector, saveResultsOfAccuracyIn, saveResultsOfAccuracyIn, selecRandomSet, setSeed, showResultsOfAccuracy, showResultsOfAccuracy

Methods inherited from class java.lang.Object

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

Field Detail

numberOfPrototypes

protected int numberOfPrototypes

numberOfClass

protected int numberOfClass

Constructor Detail

ICPLGenerator

public ICPLGenerator(PrototypeSet _trainingDataSet,
                     int nalg,
                     java.lang.String Method,
                     int k,
                     int Q)

Build a new ICPLGenerator Algorithm

ICPLGenerator

public ICPLGenerator(PrototypeSet t,
                     Parameters parameters)

Build a new RSPGenerator Algorithm

Parameters:

t - Original prototype set to be reduced.

parameters - Parameters of the algorithm (only % of reduced set).

Method Detail

icpl1_or_3

protected PrototypeSet icpl1_or_3(int num)

ICPL1_Pseudo-code: ICPL1( T training) 1) C1= abstraccion T 2) C2 = Filtrar T. 3) S = C1 4) Para cada prototipo P en C2 Tmp = S U P. Good = numero de instancias de T correctamente clasificadas por P en Tmp. Bad = numero de instancias de T mal clasificadas por P en Tmp. Si(Good>Bad) S= S u P. 5) Devolver S.

icpl2_or_4

protected PrototypeSet icpl2_or_4(int num)

ICPL2 - pseudo-code ICPL2( T training) 1) C1= abstraccion T 2) C2 = Filtrar T. 3) S = C1 4) Para cada prototipo P en C2 Tmp = S U P. Good = numero de instancias de T correctamente clasificadas por P en Tmp. Bad = numero de instancias de T mal clasificadas por P en Tmp. Si(Good>Bad) S= S U P. 5) Para cada prototipo P en C1 Tmp = S\ P. With= numero de instancias de T correctamente clasificadas por S Without = numero de instancias de T correctament clasificadas por Tmp. Si(Without>With) S= S \ P. 6) Devolver S.

typicality

protected double typicality(int index)

Parameters:

index -

Returns:

identifyBorder

protected boolean[] identifyBorder(PrototypeSet[] pClass)

Identify a border by typicaly.

Returns:

a boolean vector indicate which prototypes are border prototypes.

TPA

protected PrototypeSet TPA()

TPA.

Merge

protected Prototype Merge(PrototypeSet S,
                          boolean[] borders,
                          Prototype I,
                          PrototypeSet merge)

Merge

Parameters:

S -

borders -

I -

Returns:

filtering

protected PrototypeSet filtering()

Method to call the appropriate method

Returns:

ENN

protected PrototypeSet ENN(PrototypeSet T)

Edited nearest neighbor of T.

Returns:

ACC

protected PrototypeSet ACC(PrototypeSet T)

Retaining Center Instances

Parameters:

T -

Returns:

associatesPrototype

protected PrototypeSet associatesPrototype(PrototypeSet one,
                                           Prototype other)

Return all the prototype in (this) that has other like the nearest neighbor

Returns:

RT2

protected PrototypeSet RT2(PrototypeSet T)

Retaining Border instances

Parameters:

T -

Returns:

reduceSet

public PrototypeSet reduceSet()

Description copied from class: PrototypeGenerator

Makes the trivial reduction. That is none reduction of the set

Overrides:

reduceSet in class PrototypeGenerator

Returns:

A copy of the training data set

main

public static void main(java.lang.String[] args)

General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator. Always. ************************** 4: .Number of blocks

Parameters:

args - Arguments of the main function.