modelRBFN {CEGO}  R Documentation 
RBFN Model
Description
Implementation of a distancebased Radial Basis Function Network (RBFN) model, e.g., for mixed or combinatorial input spaces. It is based on employing suitable distance measures for the samples in input space. For reference, see the paper by Moraglio and Kattan (2011).
Usage
modelRBFN(x, y, distanceFunction, control = list())
Arguments
x 
list of samples in input space 
y 
column vector of observations for each sample 
distanceFunction 
a suitable distance function of type f(x1,x2), returning a scalar distance value, preferably between 0 and 1. Maximum distances larger 1 are no problem, but may yield scaling bias when different measures are compared. Should be nonnegative and symmetric. 
control 
(list), with the options for the model building procedure:

Value
a fit (list, modelRBFN), with the options and found parameters for the model which has to be passed to the predictor function:
SSQ
Variance of the observations (y)
centers
Centers of the RBFN model, samples in input space (see parameters)
w
Model parameters (weights) w
Phi
Gram matrix
Phinv
(Pseudo)Inverse of Gram matrix
w0
Mean of observations (y)
dMax
Maximum observed distance
D
Matrix of distances between all samples
beta
See parameters
fbeta
See parameters
distanceFunction
See parameters
References
Moraglio, Alberto, and Ahmed Kattan. "Geometric generalisation of surrogate model based optimisation to combinatorial spaces." Evolutionary Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2011. 142154.
See Also
Examples
#set random number generator seed
set.seed(1)
#simple test landscape
fn < landscapeGeneratorUNI(1:5,distancePermutationHamming)
#generate data for training and test
x < unique(replicate(40,sample(5),FALSE))
xtest < x[(1:15)]
x < x[1:15]
#determin true objective function values
y < fn(x)
ytest < fn(xtest)
#build model
fit < modelRBFN(x,y,distancePermutationHamming)
#predicted obj. function values
ypred < predict(fit,xtest)$y
#plot
plot(ytest,ypred,xlab="true value",ylab="predicted value",
pch=20,xlim=c(0.3,1),ylim=c(min(ypred)0.1,max(ypred)+0.1))
abline(0,1,lty=2)