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selectFeaturesRandom = function(learner, task, resampling, measures, bit.names, bits.to.features,
control, opt.path, show.info) {
states = lapply(seq_len(control$maxit), function(i) createStates(n = length(bit.names),
max.features = control$max.features, prob = control$extra.args$prob))
evalOptimizationStatesFeatSel(learner, task, resampling, measures, bits.to.features,
control, opt.path, show.info, states, 1L, NA_integer_)
makeFeatSelResultFromOptPath(learner, measures, resampling, control, opt.path)
}
# help function in order to respect max.features
createStates = function(n, max.features, prob){
if (is.na(max.features))
return(rbinom(n, 1, prob))
run.loop = TRUE
while (run.loop) {
x = rbinom(n, 1, prob)
if (sum(x) <= max.features)
run.loop = FALSE
}
return(x)
}
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