keel.Algorithms.Instance_Generation.GMCA

Class GMCAGenerator

java.lang.Object

keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

keel.Algorithms.Instance_Generation.PNN.PNNGenerator

keel.Algorithms.Instance_Generation.MCA.MCAGenerator

keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator

public class GMCAGenerator
extends MCAGenerator

Implements GMCAGenerator algorithm.

Author:

diegoj

Field Summary

Fields inherited from class keel.Algorithms.Instance_Generation.MCA.MCAGenerator

currentAccuracy

Fields inherited from class keel.Algorithms.Instance_Generation.PNN.PNNGenerator

numberOfPrototypes, useNumberOfPrototypes

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

algorithmName, generatedDataSet, SEED, seedDefaultValueList, trainingDataSet

Constructor Summary

Constructors

Constructor and Description
GMCAGenerator(PrototypeSet _trainingDataSet) Basic constructor
GMCAGenerator(PrototypeSet _trainingDataSet, Parameters parameters) Constructor

Method Summary

Modifier and Type	Method and Description
protected static double	d(Prototype a, Prototype b) Computes the distances between the two given prototypes.
protected void	initClusters(PrototypeSet T) Initializes the clusters for the given prototype set.
protected boolean	isConsistent(Cluster mix, PrototypeSet modified) Checks if the given cluster is consistent with the modified prototypeset.
protected boolean	isPrototypeConsistent(PrototypeSet modified) Hard-checking consistency method.
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 wich will contain the test data set 3: k Number of neighbors used in the KNN function
PrototypeSet	reduceSet() Reduce the set by the MCAGenerator method.

Methods inherited from class keel.Algorithms.Instance_Generation.MCA.MCAGenerator

isConsistent, makeAveragePrototype, removeFromCandidates

Methods inherited from class keel.Algorithms.Instance_Generation.PNN.PNNGenerator

controlledReduction, maximumReduction, nearestPrototypesIn

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

Constructor Detail

GMCAGenerator

public GMCAGenerator(PrototypeSet _trainingDataSet)

Basic constructor

Parameters:

_trainingDataSet - Prototype training data set.

GMCAGenerator

public GMCAGenerator(PrototypeSet _trainingDataSet,
                     Parameters parameters)

Constructor

Parameters:

_trainingDataSet - Prototype training data set.

parameters - Parameters of the method.

Method Detail

initClusters

protected void initClusters(PrototypeSet T)

Initializes the clusters for the given prototype set.

Parameters:

T - given prototype set.

isPrototypeConsistent

protected boolean isPrototypeConsistent(PrototypeSet modified)

Hard-checking consistency method.

Parameters:

modified - Set to be tested its consistecy.

Returns:

TRUE if the prototype si consisten, FALSE in other chase.

d

protected static double d(Prototype a,
                          Prototype b)

Computes the distances between the two given prototypes.

Parameters:

a - first prototype.

b - second prototype.

Returns:

the distances between the two given prototypes.

isConsistent

protected boolean isConsistent(Cluster mix,
                               PrototypeSet modified)

Checks if the given cluster is consistent with the modified prototypeset.

Parameters:

mix - given cluster to check.

modified - modified prototype set.

Returns:

True if the given cluster is consistent with the modified prototypeset.

reduceSet

public PrototypeSet reduceSet()

Description copied from class: MCAGenerator

Reduce the set by the MCAGenerator method.

Overrides:

reduceSet in class MCAGenerator

Returns:

Reduced prototype set by MCAGenerator method.

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 wich will contain the test data set 3: k Number of neighbors used in the KNN function

Parameters:

args - Arguments of the main function.