// StatMulti prints statistical analysis for multi-objective problems
// emin, eave, emax, edev -- errors on f1(f0)
// key -- "IGD" if IGD values are available. In this case e{...} are IGD values
func StatMulti(o *Optimiser, verbose bool) (key string, emin, eave, emax, edev float64, E []float64) {
if len(o.Multi_err) < 2 && len(o.Multi_IGD) < 2 {
io.Pfred("there are no samples for statistical analysis\n")
return
}
o.fix_formatting_data()
n := len(o.Multi_err)
key = "E"
if n < 2 {
n = len(o.Multi_IGD)
key = "IGD"
}
E = make([]float64, n)
if key == "E" {
copy(E, o.Multi_err)
} else {
copy(E, o.Multi_IGD)
}
emin, eave, emax, edev = rnd.StatBasic(E, true)
if verbose {
io.Pf("\nerror on Pareto front (multi)\n")
io.Pf("%smin = %g\n", key, emin)
io.Pf("%save = %g\n", key, eave)
io.Pf("%smax = %g\n", key, emax)
io.Pf("%sdev = %g\n", key, edev)
io.Pf(rnd.BuildTextHist(nice(emin, o.HistNdig)-o.HistDelEmin, nice(emax, o.HistNdig)+o.HistDelEmax,
o.HistNsta, E, o.HistFmt, o.HistLen))
}
return
}
// StatF computes statistical information corresponding to objective function idxF
func StatF(o *Optimiser, idxF int, verbose bool) (fmin, fave, fmax, fdev float64, F []float64) {
nsamples := len(o.BestOvas[idxF])
if nsamples == 0 {
if verbose {
io.Pfred("there are no samples for statistical analysis\n")
}
return
}
F = make([]float64, nsamples)
if nsamples == 1 {
F[0] = o.BestOvas[idxF][0]
fmin, fave, fmax = F[0], F[0], F[0]
return
}
for i, f := range o.BestOvas[idxF] {
F[i] = f
}
fmin, fave, fmax, fdev = rnd.StatBasic(F, true)
if verbose {
str := "\n"
if len(o.RptFref) == o.Nova {
str = io.Sf(" (%g)\n", o.RptFref[idxF])
}
io.Pf("fmin = %g\n", fmin)
io.Pf("fave = %g"+str, fave)
io.Pf("fmax = %g\n", fmax)
io.Pf("fdev = %g\n", fdev)
o.fix_formatting_data()
io.Pf(rnd.BuildTextHist(nice(fmin, o.HistNdig)-o.HistDelFmin, nice(fmax, o.HistNdig)+o.HistDelFmax,
o.HistNsta, F, o.HistFmt, o.HistLen))
}
return
}
// StatF1F0 prints statistical analysis for two-objective problems
// emin, eave, emax, edev -- errors on f1(f0)
// lmin, lave, lmax, ldev -- arc-lengths along f1(f0) curve
func StatF1F0(o *Optimiser, verbose bool) (emin, eave, emax, edev float64, E []float64, lmin, lave, lmax, ldev float64, L []float64) {
if len(o.F1F0_err) == 0 && len(o.F1F0_arcLen) == 0 {
io.Pfred("there are no samples for statistical analysis\n")
return
}
o.fix_formatting_data()
if len(o.F1F0_err) > 2 {
E = make([]float64, len(o.F1F0_err))
copy(E, o.F1F0_err)
emin, eave, emax, edev = rnd.StatBasic(E, true)
if verbose {
io.Pf("\nerror on Pareto front\n")
io.Pf("emin = %g\n", emin)
io.Pf("eave = %g\n", eave)
io.Pf("emax = %g\n", emax)
io.Pf("edev = %g\n", edev)
io.Pf(rnd.BuildTextHist(nice(emin, o.HistNdig)-o.HistDelEmin, nice(emax, o.HistNdig)+o.HistDelEmax,
o.HistNsta, E, o.HistFmt, o.HistLen))
}
}
if len(o.F1F0_arcLen) > 2 {
den := 1.0
if o.F1F0_arcLenRef > 0 {
den = o.F1F0_arcLenRef
}
L := make([]float64, len(o.F1F0_arcLen))
for i, l := range o.F1F0_arcLen {
L[i] = l / den
}
lmin, lave, lmax, ldev = rnd.StatBasic(L, true)
if verbose {
io.Pf("\nnormalised arc length along Pareto front (ref = %g)\n", o.F1F0_arcLenRef)
io.Pf("lmin = %g\n", lmin)
io.Pf("lave = %g\n", lave)
io.Pf("lmax = %g\n", lmax)
io.Pf("ldev = %g\n", ldev)
io.Pf(rnd.BuildTextHist(nice(lmin, o.HistNdig)-o.HistDelEmin, nice(lmax, o.HistNdig)+o.HistDelEmax,
o.HistNsta, L, o.HistFmt, o.HistLen))
}
}
return
}
// Stat prints statistical analysis
func (o *SimpleFltProb) Stat(idxF, hlen int, Fref float64) {
if o.C.Ntrials < 2 || o.Nfeasible < 2 {
return
}
F := make([]float64, o.Nfeasible)
for i := 0; i < o.Nfeasible; i++ {
o.Fcn(o.ff[0], o.gg[0], o.hh[0], o.Xbest[i])
F[i] = o.ff[0][idxF]
}
fmin, fave, fmax, fdev := rnd.StatBasic(F, true)
io.Pf("fmin = %v\n", fmin)
io.PfYel("fave = %v (%v)\n", fave, Fref)
io.Pf("fmax = %v\n", fmax)
io.Pf("fdev = %v\n\n", fdev)
io.Pf(rnd.BuildTextHist(nice_num(fmin-0.05), nice_num(fmax+0.05), 11, F, "%.2f", hlen))
}