class         cid A_CLI_monto_sucursal_1 A_CLI_monto_sucursal_2 A_CLI_monto_sucursal_3
1     N 55600018227                   0.00                   0.00                 192.00
2     N 55600021052                 144.69                   0.00                  16.19
3     N 55600028433                   0.00                 623.24                 230.23
4     N 55600029142                   0.00                  29.00                  87.00
5     N 55600035510                   0.00                 269.98                  51.50
6     N 55600037073                   0.00                   0.00                  83.00
  A_CLI_monto_sucursal_4 A_CLI_promociones A_CLI_dias_cliente A_CLI_sucursales
1                   0.00                19                511                1
2                   0.00                 4                 91                2
3                   0.00                18                694                2
4                   0.00                 8                323                2
5                  52.48                20                341                3
6                   0.00                 5                516                1
  A_CLI_promos_enviadas
1                     8
2                     0
3                    11
4                     3
5                     5
6                     2

for (i in colnames(todo6)[2:ncol(todo6)]) {
  todo6[,i]<-as.numeric(todo6[,i])}

> sapply(todo6, class)
                 class                    cid A_CLI_monto_sucursal_1 A_CLI_monto_sucursal_2
              "factor"              "numeric"              "numeric"              "numeric"
A_CLI_monto_sucursal_3 A_CLI_monto_sucursal_4      A_CLI_promociones     A_CLI_dias_cliente
             "numeric"              "numeric"              "numeric"              "numeric"
      A_CLI_sucursales  A_CLI_promos_enviadas
             "numeric"              "numeric"
colnames(todo6)[1] # class
class(todo6$class) # factor

pnn <- learn(todo6)

> pnn<-smooth(pnn)

Domains:
 0.000000e+00   <=  X1   <=    1.000000e+01

Data Type: Floating Point
Operators (code number, name, population)
  (1) Cloning...........................  0
  (2) Uniform Mutation..................  1
  (3) Boundary Mutation.................  1
  (4) Non-Uniform Mutation..............  1
  (5) Polytope Crossover................  1
  (6) Simple Crossover..................  2
  (7) Whole Non-Uniform Mutation........  1
  (8) Heuristic Crossover...............  2
  (9) Local-Minimum Crossover...........  0

HARD Maximum Number of Generations: 100
Maximum Nonchanging Generations: 2
Population size       : 10
Convergence Tolerance: 1.000000e+00

Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
Checking Gradients before Stopping.
Using Out of Bounds Individuals.

Minimization Problem.
Error in guess(model0, as.matrix(X))$category :
  $ operator is invalid for atomic vectors

pnn$sigma <- 0.5