keel.Algorithms.Instance_Generation.POC

Class POCGenerator

java.lang.Object

keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

keel.Algorithms.Instance_Generation.POC.POCGenerator

public class POCGenerator
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
POCGenerator(PrototypeSet _trainingDataSet, double ratio, java.lang.String Method) Build a new POCGenerator Algorithm
POCGenerator(PrototypeSet t, Parameters parameters) Build a new RSPGenerator Algorithm

Method Summary

Modifier and Type	Method and Description
protected Pair<Prototype,Prototype>	finding_poc_nn(PrototypeSet S, double class1, double class2) S is a training set of n pattern composed of TWO subsets, S1 y S2, with n1,n2 sizes,
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.
PrototypeSet	reduceSet() Makes the trivial reduction.
protected PrototypeSet	replacing_poc_nn(PrototypeSet S, double class1, double class2)
protected PrototypeSet	selecting_poc_nn(PrototypeSet S, double class1, double class2) Prototype Selection by Poc-NN algorithm for TWO class classification problem.

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

POCGenerator

public POCGenerator(PrototypeSet _trainingDataSet,
                    double ratio,
                    java.lang.String Method)

Build a new POCGenerator Algorithm

POCGenerator

public POCGenerator(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

finding_poc_nn

protected Pair<Prototype,Prototype> finding_poc_nn(PrototypeSet S,
                                                   double class1,
                                                   double class2)

S is a training set of n pattern composed of TWO subsets, S1 y S2, with n1,n2 sizes,

Parameters:

S -

Returns:

selecting_poc_nn

protected PrototypeSet selecting_poc_nn(PrototypeSet S,
                                        double class1,
                                        double class2)

Prototype Selection by Poc-NN algorithm for TWO class classification problem.

Parameters:

S -

Returns:

replacing_poc_nn

protected PrototypeSet replacing_poc_nn(PrototypeSet S,
                                        double class1,
                                        double class2)

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.