atm.predict {atmopt}

Predict the minimum setting for ATM

Description

atm.init predicts the minimum setting for an ATM object.

Usage

  atm.predict(atm.obj,alphas=NULL,ntimes=1,nsub=100,prob.am=0.5,prob.pw=1.0,reps=NULL)

Arguments

atm.obj

Current ATM object.

alphas

A p-vector for ATM percentiles. NULL if tuned from data.

ntimes

Number of resamples for tuning ATM percentages.

nsub

Number of candidate percentiles to consider.

prob.am

In case of ties in percentage estimation, the probability of choosing marginal means (if optimal) for minimization.

prob.pw

In case of ties in percentage estimation, probability of picking-the-winner (if optimal) for minimization.

reps

Number of replications for internal OA in tuning ATM percentages.

Examples

  ## Not run: 
####################################################
# Example 1: detpep10exp (9-D)
####################################################
nfact <- 9 #number of factors
ntimes <- floor(nfact/3) #number of "repeats" for detpep10exp
lev <- 4 #number of levels
nlev <- rep(lev,nfact) #number of levels for each factor
nelim <- 3 #number of level eliminations
fn <- function(xx){detpep10exp(xx,ntimes,nlev)} #objective to minimize (assumed expensive)

#initialize objects
# (predicts & removes levels based on tuned ATM percentages)
fit.atm <- atm.init(nfact,nlev)
#initialize sel.min object
# (predicts minimum using smallest observed value & removes levels with largest minima)
fit.min <- atm.init(nfact,nlev)

#Run for nelim eliminations:
res.atm <- rep(NA,nelim) #for ATM results
res.min <- rep(NA,nelim) #for sel.min results
for (i in 1:nelim){

  # ATM updates:
  new.des <- atm.nextpts(fit.atm) #get design points
  new.obs <- apply(new.des,1,fn) #sample function
  fit.atm <- atm.addpts(fit.atm,new.des,new.obs) #add data to object
  fit.atm <- atm.predict(fit.atm) #predict minimum setting
  idx.atm <- fit.atm$idx.opt
  res.atm[i] <- fn(idx.atm)
  fit.atm <- atm.remlev(fit.atm) #removes worst performing level

  # sel.min updates:
  new.des <- atm.nextpts(fit.min) #get design points
  new.obs <- apply(new.des,1,fn) #sample function
  fit.min <- atm.addpts(fit.min,new.des,new.obs) #add data to object
  fit.min <- atm.predict(fit.min, alphas=rep(0,nfact)) #find setting with smallest observation
  idx.min <- fit.min$idx.opt
  res.min[i] <- fn(idx.min)
  #check: min(fit.min$obs.all)
  fit.min <- atm.remlev(fit.min) #removes worst performing level

}

res.atm
res.min

#conclusion: ATM finds better solutions by learning & exploiting additive structure

####################################################
# Example 2: camel6 (24-D)
####################################################
nfact <- 24 #number of factors
ntimes <- floor(nfact/2.0) #number of "repeats" for camel6
lev <- 4
nlev <- rep(lev,nfact) #number of levels for each factor
nelim <- 3 #number of level eliminations
fn <- function(xx){camel6(xx,ntimes,nlev)} #objective to minimize (assumed expensive)

#initialize objects
# (predicts & removes levels based on tuned ATM percentages)
fit.atm <- atm.init(nfact,nlev)
#initialize sel.min object
# (predicts minimum using smallest observed value & removes levels with largest minima)
fit.min <- atm.init(nfact,nlev)

#Run for nelim eliminations:
res.atm <- rep(NA,nelim) #for ATM results
res.min <- rep(NA,nelim) #for sel.min results
for (i in 1:nelim){

  # ATM updates:
  new.des <- atm.nextpts(fit.atm) #get design points
  new.obs <- apply(new.des,1,fn) #sample function
  fit.atm <- atm.addpts(fit.atm,new.des,new.obs) #add data to object
  fit.atm <- atm.predict(fit.atm) #predict minimum setting
  idx.atm <- fit.atm$idx.opt
  res.atm[i] <- fn(idx.atm)
  fit.atm <- atm.remlev(fit.atm) #removes worst performing level

  # sel.min updates:
  new.des <- atm.nextpts(fit.min) #get design points
  new.obs <- apply(new.des,1,fn) #sample function
  fit.min <- atm.addpts(fit.min,new.des,new.obs) #add data to object
  fit.min <- atm.predict(fit.min, alphas=rep(0,nfact)) #find setting with smallest observation
  idx.min <- fit.min$idx.opt
  res.min[i] <- fn(idx.min)
  #check: min(fit.min$obs.all)
  fit.min <- atm.remlev(fit.min) #removes worst performing level

}

res.atm
res.min

#conclusion: ATM finds better solutions by learning & exploiting additive structure

## End(Not run)