expGetKb {FKBL}R Documentation

Makes kBs from a train set

Description

This eases the realization of an experiment, recieves a train set and returns a kB for every algorithm.

Usage

 expGetKb(train,P, execute=NULL, 
        iteraA=1000,e=0.01, 
        genS=100, crossS=0.5, mutaS=0.01, kS=0.01, pobS=20, 
        iteraR=1000, etaMore=0.001, etaLess=0.1, 
        crossH=0.9, mutaH=0.8, genH=50, replaceH=2,
        mutaP=0.8, crossP=0.9, genP=50,  crossM=0.9, 
        mutaM=0.01,  genM=1000)

Arguments

Takes the train data, the vector of activations of algorithms, the numbers of iterations, e, etaMore, etaLess, the list of partitions, cross and mutation probability for PittsBurgh,the number of generations for PittsBurgh, the number of generations of the ErrorSize algorithm, the cross and mutation probability for ErrorSize, the weight for the Size in ErrorSize, the size of the initial population in ErrorSize, cross and mutation probability for Hybrid, cross and mutation probability for Michigan, the generations for Hybrid and the generations for Michigan.

train The train dataset.
execute The vector of activations for the algorithms, here it is posible to determinate which algorithms are executed. If none is provided, a default one with all algorithms activated is created.
iteraR The number of iterations for rewardPunishment.
iteraA The number of iterations for analytic.
e The e parameter for analytic.
etaMore The etaMore parameter for rewardPunishment.
etaLess The etaLess parameter for rewardPunishment.
P The vector of partitions.
mutaP The mutation probability in the PittsBurgh algorithm.
crossP The cross probability in the PittsBurgh algorithm.
genP The number of generations in the PittsBurgh algorithm.
genS The number of generations in the ErrorSize algorithm.
crossS The crossing probability in the ErrorSize algorithm.
mutaS The mutation probability in the ErrorSize algorithm.
kS The weight of the size in the ErrorSize algorithm.
pobS Size of the initial population for ErrorSize.
crossH The cross probability up to 1, at Hybrid.
mutaH The mutation probability up to 1, at Hybrid.
crossM The cross probability up to 1, at Michigan.
mutaM The mutation probability up to 1, at Michigan.
genH The number of Hybrid generations.
replaceH The number of individuals to replace.
genM The number of Michigan generations.

Value

Returns a dataset with the knowledge bases.

Examples

 data(trainM)
 print(trainM)
 out<-expGetKb(trainM,P=getPart(trainM))

 #Shows the first knowledge Base
 print(out[[1]]);

[Package FKBL version 0.50-4 Index]