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:
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