expGetKb                package:FKBL                R Documentation

_M_a_k_e_s _k_B_s _f_r_o_m _a _t_r_a_i_n _s_e_t

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

      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)

_A_r_g_u_m_e_n_t_s:

     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.

_V_a_l_u_e:

     Returns a dataset with the knowledge bases.

_E_x_a_m_p_l_e_s:

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

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

