if isConv:
ppconverrorbars.main(dictAlg,outputdir,verbose)
# Performance profiles
if isPer:
config.config()
# ECDFs per noise groups
dictNoi = pproc.dictAlgByNoi(dictAlg)
for ng, tmpdictAlg in dictNoi.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
# pprldmany.main(entries, inset.summarized_target_function_values,
# from . import config
# config.config()
pprldmany.main(entries, # pass expensive flag here?
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, ng)),
verbose=verbose)
# ECDFs per function groups
dictFG = pproc.dictAlgByFuncGroup(dictAlg)
for fg, tmpdictAlg in dictFG.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
pprldmany.main(entries,
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, fg)),
verbose=verbose)
if isRldOnSingleFcts: # copy-paste from above, here for each function instead of function groups
# ECDFs for each function
dictFG = pproc.dictAlgByFun(dictAlg)
#convergence plots
if isConv:
ppconverrorbars.main(dictAlg, outputdir, verbose)
# Performance profiles
if isPer:
# ECDFs per noise groups
dictNoi = pproc.dictAlgByNoi(dictAlg)
for ng, tmpdictAlg in dictNoi.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
# pprldmany.main(entries, inset.summarized_target_function_values,
# from . import config
# config.config()
pprldmany.main(
entries, # pass expensive flag here?
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, ng)),
verbose=verbose)
# ECDFs per function groups
dictFG = pproc.dictAlgByFuncGroup(dictAlg)
for fg, tmpdictAlg in dictFG.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
pprldmany.main(entries,
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, fg)),
verbose=verbose)
print "ECDFs of run lengths figures done."
if isTab:
if isConv:
ppconverrorbars.main(dictAlg,outputdir,verbose)
# Performance profiles
if isPer:
config.config()
# ECDFs per noise groups
dictNoi = pproc.dictAlgByNoi(dictAlg)
for ng, tmpdictAlg in dictNoi.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
# pprldmany.main(entries, inset.summarized_target_function_values,
# from . import config
# config.config()
pprldmany.main(entries, # pass expensive flag here?
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, ng)),
verbose=verbose)
# ECDFs per function groups
dictFG = pproc.dictAlgByFuncGroup(dictAlg)
for fg, tmpdictAlg in dictFG.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
pprldmany.main(entries,
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, fg)),
verbose=verbose)
if isRldOnSingleFcts: # copy-paste from above, here for each function instead of function groups
# ECDFs for each function
dictFG = pproc.dictAlgByFun(dictAlg)
ppconverrorbars.main(dictAlg, outputdir, genericsettings.verbose)
# empirical cumulative distribution functions (ECDFs) aka Data profiles
if genericsettings.isRLDistr:
config.config()
# ECDFs per noise groups
dictNoi = pproc.dictAlgByNoi(dictAlg)
for ng, tmpdictAlg in dictNoi.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
# pprldmany.main(entries, inset.summarized_target_function_values,
# from . import config
# config.config()
pprldmany.main(
entries, # pass expensive flag here?
order=sortedAlgs,
outputdir=outputdir,
info=("%02dD_%s" % (d, ng)),
verbose=genericsettings.verbose,
)
# ECDFs per function groups
dictFG = pproc.dictAlgByFuncGroup(dictAlg)
for fg, tmpdictAlg in dictFG.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
pprldmany.main(
entries,
order=sortedAlgs,
outputdir=outputdir,
info=("%02dD_%s" % (d, fg)),
verbose=genericsettings.verbose,
)
#convergence plots
if isConv:
ppconverrorbars.main(dictAlg,outputdir,verbose)
# Performance profiles
if isPer:
# ECDFs per noise groups
dictNoi = pproc.dictAlgByNoi(dictAlg)
for ng, tmpdictAlg in dictNoi.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
# pprldmany.main(entries, inset.summarized_target_function_values,
# from . import config
# config.config()
pprldmany.main(entries, # pass expensive flag here?
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, ng)),
verbose=verbose)
# ECDFs per function groups
dictFG = pproc.dictAlgByFuncGroup(dictAlg)
for fg, tmpdictAlg in dictFG.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
pprldmany.main(entries,
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, fg)),
verbose=verbose)
print "ECDFs of run lengths figures done."
if isTab:
if genericsettings.isConv:
ppconverrorbars.main(dictAlg, outputdir, genericsettings.verbose)
# empirical cumulative distribution functions (ECDFs) aka Data profiles
if genericsettings.isRLDistr:
config.config(dsList[0].isBiobjective())
# ECDFs per noise groups
dictNoi = pproc.dictAlgByNoi(dictAlg)
for ng, tmpdictAlg in dictNoi.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
# pprldmany.main(entries, inset.summarized_target_function_values,
# from . import config
# config.config()
pprldmany.main(entries, # pass expensive flag here?
dsList[0].isBiobjective(),
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, ng)),
verbose=genericsettings.verbose)
# ECDFs per function groups
dictFG = pproc.dictAlgByFuncGroup(dictAlg)
for fg, tmpdictAlg in dictFG.iteritems():
dictDim = pproc.dictAlgByDim(tmpdictAlg)
for d, entries in dictDim.iteritems():
pprldmany.main(entries,
dsList[0].isBiobjective(),
order=sortedAlgs,
outputdir=outputdir,
info=('%02dD_%s' % (d, fg)),
verbose=genericsettings.verbose)
if genericsettings.isRldOnSingleFcts: # copy-paste from above, here for each function instead of function groups
# ECDFs for each function