eaf/0000755000176200001440000000000013255007653011013 5ustar liggesuserseaf/inst/0000755000176200001440000000000013254545527011776 5ustar liggesuserseaf/inst/CITATION0000644000176200001440000000262213254545527013135 0ustar liggesuserscitHeader("To cite package 'eaf' in publications use:") ## R >= 2.8.0 passes package metadata to citation(). if(!exists("meta") || is.null(meta)) meta <- packageDescription("eaf") year <- sub(".*(2[[:digit:]]{3})-.*", "\\1", meta$Date) vers <- paste("R package version", meta$Version) citEntry(entry="incollection", title = "Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization", author = personList(as.person("Manuel López-Ibáñez"), as.person("Luis Paquete"), as.person("Thomas Stützle")), year = 2010, doi = "10.1007/978-3-642-02538-9_9", booktitle = "Experimental Methods for the Analysis of Optimization Algorithms", publisher = "Springer, Berlin, Germany", editor = "Thomas Bartz-Beielstein and Marco Chiarandini and Luís Paquete and Mike Preuss", pages = "209--222", textVersion= paste("Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. ", "Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization. ", "In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, ", "Experimental Methods for the Analysis of Optimization Algorithms, pages 209–222. Springer, Berlin, Germany, 2010.", "\ndoi: 10.1007/978-3-642-02538-9_9", sep="")) eaf/inst/scripts/0000755000176200001440000000000013254545527013465 5ustar liggesuserseaf/inst/scripts/eafplot/0000755000176200001440000000000013254747463015122 5ustar liggesuserseaf/inst/scripts/eafplot/README0000644000176200001440000000514113254747463016003 0ustar liggesusers------------------- eafplot.pl ------------------- Perl script to plot attainment surfaces for a set of input points. Requirements of `eafplot.pl' ---------------------------- The program has been tested on GNU/Linux using bash shell. If you have success or problems using other systems, please let me know. The following programs are required: * Perl 5 * eaf R package: http://iridia.ulb.ac.be/~manuel/eaftools The following programs are only required if the corresponding features are enabled by editing the script: * ps2eps: Filter PS files and remove excessive margin whitespace. * gzip * ImageMagick (in particular, the program `convert') Using `eafplot.pl' -------------------- See the output of eafplot.pl --help License ------- Copyright (c) 2010 Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. IMPORTANT NOTE: Please be aware that the fact that this program is released as Free Software does not excuse you from scientific propriety, which obligates you to give appropriate credit! If you write a scientific paper describing research that made substantive use of this program, it is your obligation as a scientist to (a) mention the fashion in which this software was used in the Methods section; (b) mention the algorithm in the References section. The appropriate citation is: Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 209–222. Springer, Berlin, Germany, 2010. doi: 10.1007/978-3-642-02538-9_9 Moreover, as a personal note, I would appreciate it if you would email with citations of papers referencing this work so I can mention them to my funding agent and tenure committee. You can obtain a copy of the GNU General Public License at http://www.gnu.org/licenses/gpl.html or writing to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA. eaf/inst/scripts/eafplot/eafplot.pl0000755000176200001440000005076313254747463017127 0ustar liggesusers#!/usr/bin/perl -w #--------------------------------------------------------------------- # # eafplot.pl ($Revision: 251 $) # #--------------------------------------------------------------------- # # Copyright (c) 2005-2017 # Manuel Lopez-Ibanez # TeX: \copyright 2005-2017 Manuel L{\'o}pez-Ib{\'a}{\~n}ez # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You can obtain a copy of the GNU General Public License at # http://www.gnu.org/licenses/gpl.html or writing to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 # USA # #--------------------------------------------------------------------- # # IMPORTANT NOTE: Please be aware that the fact that this program is # released as Free Software does not excuse you from scientific # propriety, which obligates you to give appropriate credit! If you # write a scientific paper describing research that made substantive # use of this program, it is your obligation as a scientist to (a) # mention the fashion in which this software was used in the Methods # section; (b) mention the algorithm in the References section. The # appropriate citation is: # # Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. # Exploratory Analysis of Stochastic Local Search Algorithms in # Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, # L. Paquete, and M. Preuss, editors, Experimental Methods for the # Analysis of Optimization Algorithms, pages 209–222. Springer, # Berlin, Germany, 2010. # doi: 10.1007/978-3-642-02538-9_9 # # Moreover, as a personal note, I would appreciate it if you would # email with citations of papers # referencing this work so I can mention them to my funding agent and # tenure committee. # #--------------------------------------------------------------------- # # Literature: # # [1] Manuel Lopez-Ibanez, Luis Paquete, and Thomas Stutzle. Hybrid # population-based algorithms for the bi-objective quadratic # assignment problem. Journal of Mathematical Modelling and # Algorithms, 5(1):111-137, April 2006. # # [2] Manuel Lopez-Ibanez, Luis Paquete, and Thomas Stutzle. Hybrid # population-based algorithms for the bi-objective quadratic # assignment problem. Journal of Mathematical Modelling and # Algorithms, 5(1):111-137, April 2006. # #--------------------------------------------------------------------- # # TODO: # # * Fail if any subcommand fails. For example, do not even run R if # the call to the eaf program failed. (See execute in eafdiff.pl). # #--------------------------------------------------------------------- use strict; use warnings FATAL => 'all'; use diagnostics; use Carp; use File::Basename; use Getopt::Long qw(:config pass_through);; use File::Temp qw/ tempfile /; my $copyright =' Copyright (C) 2005-2017 Manuel Lopez-Ibanez This is free software. You may redistribute copies of it under the terms of the GNU General Public License . There is NO WARRANTY, to the extent permitted by law. '; my $progname = basename($0); my $version = ' $Revision: 251 $ '; my $Rexe = "R"; requiredprog($Rexe); &usage(1) if (@ARGV == 0); my $colors = "NULL"; my $percentiles; # FIXME: single/multiple plots are a mess. This is how it should work: # --single file1 file2 ... --output plotfile # produces a single plot 'plotfile' from all input files. Labels are input filenames # # --no-single file --output plotfile # produces a single plot 'plotfile' from input file. Labels are percentiles. # # --no-single file1 file2 --output plotfile # error: use --suffix and/or --output-dir. my $flag_single_plot = 0; my $legend; my $output_file; my @output_files; my $flag_verbose = 0; my $flag_eps = 0; my $flag_crop = (which_program("pdfcrop") ne "pdfcrop"); my $label_obj1 = "objective 1"; my $label_obj2 = "objective 2"; my $flag_area = 0; my $flag_ymaximise = 0; my $flag_xmaximise = 0; my $flag_maximise = 0; my $flag_best = 0; my $flag_median = 0; my $flag_worst = 0; GetOptions ('area' => \$flag_area, 'eps' => \$flag_eps, 'crop' => \$flag_crop, 'single' => \$flag_single_plot, 'best|b' => \$flag_best, 'median|m' => \$flag_median, 'worst|w' => \$flag_worst, 'maximise' => \$flag_maximise, 'xmaximise' => \$flag_xmaximise, 'ymaximise' => \$flag_ymaximise, 'output|o=s' => \$output_file, 'legend=s' => \$legend, 'colors=s' => \$colors, 'obj1:s' => \$label_obj1, 'obj2:s' => \$label_obj2, 'verbose|v' => \$flag_verbose); requiredprog("pdfcrop") if ($flag_crop and not $flag_eps); if ($flag_best) { $percentiles .= ", " if (defined($percentiles)); $percentiles .= "0"; } if ($flag_median) { $percentiles .= ", " if (defined($percentiles)); $percentiles .= "50"; } if ($flag_worst) { $percentiles .= ", " if (defined($percentiles)); $percentiles .= "100"; } sub version { print "$progname: version $version\n"; print $copyright; exit (0); } sub usage { my $exitcode = shift; print <<"EOF"; $progname version $version $copyright Usage: $progname [OPTIONS] FILES Plot the best/median/worst attainment surfaces for a set of input points. -h, --help print this summary and exit --version print version number and exit -v, --verbose increase verbosity and keep intermediate files -I, --iqr, --IQR plot IQR (25%-75% percentile) instead of best and worst -b, --best plot best attainment surface -m, --median plot median attainment surface -w, --worst plot worst attainment surface -p, --percentiles=INT[,INT..] plot the given percentile(s) of the EAF --extra=FILE add extra points to the plot --extra-label=STRING label in the legend for the extra points --obj1=STRING label for objective 1 (x-axis) --obj2=STRING label for objective 2 (y-axis) (labels can be R expressions, e.g., --obj1="expression(pi)") --legend=STRING A legend like: '"Algorithm A", "Algorithm B"' --legendpos={top,bottom}{left,right} position of the legend --xlim=REAL,REAL limits of x-axis --ylim=REAL,REAL limits of y-axis --area plot the area dominated by the attainment surfaces instead of lines. --colors=STRING,STRING bounds for color range in area mode (default '"gray","black"') --maximise handle a maximisation problem --xmaximise maximise first objective --ymaximise maximise second objective -o, --output=FILE output file. --[no]single generate a single plot. --eps generate EPS file (default is PDF). --no-eaf just plot the points directly. EOF exit $exitcode; } ## Format commandline. my $commandline = &format_commandline (); my @files = (); my $flag_axislog = ""; my $extra_points = "NULL"; my $extra_labels = "NULL"; my $IQR_flag = 0; my $legendpos = "topright"; my $xlim = "NULL"; my $ylim = "NULL"; my $do_eaf = 1; #FIXME: Move everything to GetOpt above. ## Handle parameters while (@ARGV) { my $argv = shift @ARGV; if ($argv =~ /^--help/ or $argv =~ /^-h/) { &usage(0); } elsif ($argv =~ /^--version/) { &version(); } elsif (# For backwards compatibility $argv =~ /--stoptime/ or $argv =~ /-stoptime/) { print "$progname: maximising y-axis\n"; $flag_ymaximise = 1; } elsif ($argv =~ /^-I/ or $argv =~ /^--iqr/i) { $IQR_flag = 1; $percentiles .= ", " if (defined($percentiles)); $percentiles .= "25, 50, 75"; } elsif ($argv =~ /^-p$/ or $argv =~ /^--percentiles=/ or $argv =~ /^--percentiles$/) { $percentiles .= ", " if (defined($percentiles)); $percentiles .= &get_arg($argv); } elsif ($argv =~ /--no-eaf/i) { $do_eaf = 0; } elsif ($argv =~ /^--extra=/ or $argv =~ /^--extra$/) { my $arg = &get_arg ($argv); if ($arg and -r $arg) { $extra_points = ($extra_points eq "NULL") ? "\"$arg\"" : $extra_points . ", \"$arg\""; } else { die "cannot read file $arg.\n"; } } elsif ($argv =~ /^--extra-label=/ or $argv =~ /^--extra-label$/) { my $arg = &get_arg ($argv); if ($arg) { $extra_labels = ($extra_labels eq "NULL") ? "\"$arg\"" : $extra_labels . ", \"$arg\""; } } elsif ($argv =~ /^--legendpos=/ or $argv =~ /^--legendpos$/) { my $arg = &get_arg ($argv); $legendpos = "$arg" if ($arg); } elsif ($argv =~ /^--ylog/) { $flag_axislog = "y"; } elsif ($argv =~ /^--xlim=/ or $argv =~ /^--xlim$/) { my $arg = &get_arg ($argv); $xlim = "c($arg)" if ($arg); } elsif ($argv =~ /^--ylim=/ or $argv =~ /^--ylim$/) { my $arg = &get_arg ($argv); $ylim = "c($arg)" if ($arg); } elsif ($argv =~ /^--/ or $argv =~ /^-/) { print "$progname: unknown option $argv\n"; &usage(1); } else { push (@files, $argv); } } @files = &unique(@files); die "$progname: error: no input files given\n" unless (@files); if (defined($percentiles) and not $do_eaf) { die "$progname: cannot specify --no-eaf and any EAF option" . " (--best, --median, --worst, --iqr, --percentiles)"; } my $ps2epsfilter = undef; $ps2epsfilter = "ps2eps"; requiredprog ($ps2epsfilter) if ($flag_eps); my $filter = ""; $filter = "|$ps2epsfilter -s b0 -q -l -B -O -P > " if ($flag_eps and defined($ps2epsfilter) and -x $ps2epsfilter); if ($extra_labels eq "NULL") { $extra_labels = $extra_points; } $percentiles = "NULL" unless ($do_eaf); # Default is best, median, worst. unless(defined($percentiles)) { $percentiles = ($flag_single_plot) ? "50" : "0, 50, 100"; } $label_obj1 = parse_expression ($label_obj1); $label_obj2 = parse_expression ($label_obj2); my $num = 0; my $eaffiles = ""; my @titles; my @eaffiles; foreach my $inpfile (@files) { unless (-r $inpfile) { die "$progname: warning: $inpfile: cannot read file.\n"; #next; } elsif (!(-s $inpfile) or `grep -v -e "#\\|^\$" $inpfile | wc --bytes` =~ /\s+0$/o) { die "$progname: warning: $inpfile: empty file.\n"; #next; } $num++; my $basefile; my $inpdir; my $file_eps; chomp($basefile = `basename $inpfile .dat`); chomp($inpdir = `dirname $inpfile`); my $filedata = $inpfile; if ($num < 10) { print "# f$num: $inpfile\n"; } else { print "#f$num: $inpfile\n"; } if ($do_eaf) { if ($IQR_flag) { $file_eps = "$inpdir/att_iqr_${basefile}"; } elsif ($flag_area) { $file_eps = "$inpdir/att_area_${basefile}"; } else { $file_eps = "$inpdir/${basefile}_att"; } } else { $file_eps = "$inpdir/${basefile}"; } $eaffiles = "\"${filedata}\""; push @output_files, $file_eps; push @titles, $inpfile; push @eaffiles, $eaffiles; } # FIXME: We can override only one output file. if (defined($output_file) and $output_file) { # Remove suffix, the R code will add the correct one. $output_file =~ s/(\.pdf|\.eps|\.ps|\.png)$//; $output_files[0] = $output_file; } my $str_outfiles = asRlist(@output_files); my $str_titles = asRlist(@titles); my $str_eaffiles = join(', ', @eaffiles); $legend = $str_titles unless(defined($legend)); if ($flag_maximise) { $flag_xmaximise = 1; $flag_ymaximise = 1; } print "$progname: maximising x-axis\n" if ($flag_xmaximise); print "$progname: maximising y-axis\n" if ($flag_ymaximise); my $Rfile = "$$.R"; open(R, ">$Rfile") or die "$progname: error: can't open $Rfile: $!\n"; print R <<"EOFR"; #!/usr/bin/r --vanilla # # R script to plot attainment surfaces # # This script is executable if you have littler installed. [*] # [*] http://code.google.com/p/littler/ # # Created by $commandline # # $version # library(eaf) filter <- "$filter" file.extra <- list(${extra_points}) extra.legend <- c(${extra_labels}) extra.pch <- c(20, 1:25) extra.col <- c("black") maximise <- as.logical(c($flag_xmaximise, $flag_ymaximise)) legend.txt <- c($legend) legend.pos <- "$legendpos" log <- "$flag_axislog" Xlim <- $xlim Ylim <- $ylim eaf.type <- ifelse(${flag_area}, "area", "point") xlab <- $label_obj1 ylab <- $label_obj2 colors <- c($colors) percentiles <- c($percentiles) percentiles <- if (is.null(percentiles)) percentiles else sort(percentiles) do.eaf <- as.logical($do_eaf) single.plot <- as.logical($flag_single_plot) flag.eps <- as.logical(${flag_eps}) output.files <- c(${str_outfiles}) titles <- c(${str_titles}) eaffiles <- list(${str_eaffiles}) EOFR print R <<'EOFR'; if (eaf.type == "area") { col <- if (!is.null(colors)) colors else c('grey', 'black') # These two are unused for "area" lty <- NULL pch <- NA } else { col <- c("black", "darkgrey", "black", "grey40", "darkgrey") lty <- c("dashed", "solid", "solid", "solid", "dashed") pch <- NA #lty <- c("blank") #pch <- c(20,21,22,23,4,5) } data <- NULL attsurfs <- NULL xlim <- NULL ylim <- NULL if (do.eaf) { data <- lapply (eaffiles, read.data.sets) xlim <- range(xlim, sapply(data, function(x) x[, 1])) ylim <- range(ylim, sapply(data, function(x) x[, 2])) } else { attsurfs <- lapply(eaffiles, function(x) { z <- read.data.sets(x) z <- split.data.frame(z, z$set) z <- lapply(z, function(y) {y$set <- 1; return(y)}) return(z)}) xlim <- range(xlim, sapply(attsurfs, function(x) do.call("rbind", x)[,1])) ylim <- range(ylim, sapply(attsurfs, function(x) do.call("rbind", x)[,2])) lty <- c("blank") pch <- c(20,21,22,23,4,5) col <- c("black", "darkgrey", "black", "grey40", "darkgrey") } if (is.null (file.extra[[1]])) { extra.points <- NULL } else { extra.points <- list() for (i in 1:length(file.extra)) { tmp <- read.table(file.extra[[i]], na.strings="NA")[,c(1,2)] extra.points[[i]] <- tmp xlim <- range(xlim, tmp[,1], na.rm=T) ylim <- range(ylim, tmp[,2], na.rm=T) } } cat(sprintf("xlim = c(%s, %s)\n", xlim[1], xlim[2])) cat(sprintf("ylim = c(%s, %s)\n", ylim[1], ylim[2])) if (!is.null(Xlim)) xlim <- Xlim if (!is.null(Ylim)) ylim <- Ylim for (k in seq_along(output.files)) { if (single.plot) { output.title <- output.files } else { output.title <- titles[k] } output.file <- output.files[k] if (flag.eps) { output.file <- paste(filter, output.file, ".eps", sep='') postscript(output.file, title = output.title, paper = "special", horizontal=F, onefile=F, width=4.5, height=4.5) } else { output.file <- paste(filter, output.file, ".pdf", sep='') pdf(output.file, title = output.title, width=4.5, height=4.5) } if (single.plot) { eafplot (data, attsurfs = attsurfs, percentiles = percentiles, xlab = xlab, ylab = ylab, las = 0, log = log, type = eaf.type, lty = lty, col = col, pch=pch, cex.pch=0.75, extra.points = extra.points, extra.pch = extra.pch, extra.col = extra.col, xlim = xlim, ylim = ylim, legend.pos = legend.pos, extra.legend = extra.legend, legend.txt = legend.txt, maximise = maximise) } else { eafplot.default (data[[k]][,1:2], sets = data[[k]][,3], attsurfs = attsurfs[[k]], percentiles = percentiles, xlab = xlab, ylab = ylab, las = 0, log = log, type = eaf.type, lty = lty, col = col, pch=pch, cex.pch=0.75, extra.points = extra.points, extra.pch = extra.pch, extra.col = extra.col, xlim=xlim, ylim=ylim, legend.pos = legend.pos, extra.legend = extra.legend, maximise = maximise) } dev.null <- dev.off() cat ("Plot: ", output.file, "\n", sep='') } EOFR close R; &execute_verbose ("$Rexe --quiet --vanilla --slave <$Rfile"); ($flag_verbose) ? print "$progname: generated R script: $Rfile\n" : unlink($Rfile); if ($flag_crop and not $flag_eps) { my ($tmpfh, $tmpfilename) = tempfile(); foreach my $outfile (@output_files) { execute ("pdfcrop ${outfile}.pdf $tmpfilename"); execute ("mv $tmpfilename ${outfile}.pdf"); } } exit 0; ################################### # helper sub-routines ################################### sub parse_expression { my $label = shift; $label = "\"$label\"" if ($label !~ /^expression\(/); return $label; } sub unique { my %seen =() ; @_ = grep { ! $seen{$_}++ } @_ ; } sub round { my($number) = shift; return int($number + .5 * ($number <=> 0)); } sub get_arg { my ($option, $arg) = split (/=/, $_[0], 2); $arg = shift @ARGV if (not $arg); return $arg; } sub format_commandline { my $cmd = $0 . " "; for (my $i=0, my $j=25; $i < @ARGV; $i++) { if ($i == $j) { $j += 25; $cmd .= "\\\n# "; } if ($ARGV[$i] =~ /\s/) { $cmd .= " \"" . $ARGV[$i] . "\""; } else { $cmd .= " " . $ARGV[$i]; } } return $cmd; } use Env '@PATH'; use Cwd; # FIXME: This should return the empty string if not found. sub which_program { my $program = shift; my $cwd = cwd(); unless ($program =~ m|^.?.?/|) { # If no path was given foreach my $path (@PATH) { if (-e "$path/$program") { $program = "$path/$program"; last; } } # Try also the current directory: $program = "$cwd/$program" if (-e "$cwd/$program"); } return $program; } sub requiredprog { my $cwd = cwd(); foreach my $program (@_) { next if not defined $program; my $pathtoprogram = which_program($program); die "$progname: cannot find required program `$program'," ." either in your PATH=\"". join(':',@PATH) ."\" or in the current directory`$cwd'\n" unless ($pathtoprogram =~ m|^.?.?/|); die "$progname: required program `$program' is not executable\n" unless (-x $pathtoprogram); } } ## See ## http://svn.collab.net/repos/svn/trunk/contrib/hook-scripts/svn-keyword-check.pl ## for a better way to read from a command output sub runcmd { my $command = shift; system ($command); if ($? == -1) { die "error: failed to execute $command: $!\n"; } elsif ($? & 127) { die "child died with signal ". ($? & 127) . ($? & 128) ? "with core dump.\n" : ".\n"; } else { return $? >> 8; } } sub execute { my $command = shift; if ($flag_verbose) { &execute_verbose ("$command"); } else { `$command`; } } sub execute_verbose { unless (@_) { croak "$progname: execute_verbose passed no arguments.\n"; } print "\n@_\n" if ($flag_verbose); my $fh = _pipe(@_); my @output; while (<$fh>) { print; chomp; push(@output, $_); } close($fh); my $result = $?; my $exit = $result >> 8; my $signal = $result & 127; my $cd = $result & 128 ? "with core dump" : ""; if ($signal or $cd) { warn "$progname: pipe from `@_' failed $cd: exit=$exit signal=$signal\n"; } if (wantarray) { return ($result, @output); } else { return $result; } } # Return the filehandle as a glob so we can loop over it elsewhere. sub _pipe { local *SAFE_READ; my $pid = open(SAFE_READ, '-|'); unless (defined $pid) { die "$progname: cannot fork: $!\n"; } unless ($pid) { open(STDERR, ">&STDOUT") or die "$progname: cannot dup STDOUT: $!\n"; exec(@_) or die "$progname: cannot exec `@_': $!\n"; } return *SAFE_READ; } sub asRlist { return "\"". join("\", \"", @_) . "\""; } eaf/inst/scripts/eafdiff/0000755000176200001440000000000013254747463015054 5ustar liggesuserseaf/inst/scripts/eafdiff/eafdiff.pl0000755000176200001440000004262613254747463017012 0ustar liggesusers#!/usr/bin/perl -w #--------------------------------------------------------------------- # # eafdiff.pl $Revision: 252 $ # #--------------------------------------------------------------------- # # Copyright (c) 2007-2014 # Manuel Lopez-Ibanez # LaTeX: Manuel L{\'o}pez-Ib{\'a}{\~n}ez # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You can obtain a copy of the GNU General Public License at # http://www.gnu.org/licenses/gpl.html or writing to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 # USA # #--------------------------------------------------------------------- # # IMPORTANT NOTE: Please be aware that the fact that this program is # released as Free Software does not excuse you from scientific # propriety, which obligates you to give appropriate credit! If you # write a scientific paper describing research that made substantive # use of this program, it is your obligation as a scientist to (a) # mention the fashion in which this software was used in the Methods # section; (b) mention the algorithm in the References section. The # appropriate citation is: # # Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. # Exploratory Analysis of Stochastic Local Search Algorithms in # Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, # L. Paquete, and M. Preuss, editors, Experimental Methods for the # Analysis of Optimization Algorithms, pages 209–222. Springer, # Berlin, Germany, 2010. # doi: 10.1007/978-3-642-02538-9_9 # # Moreover, as a personal note, I would appreciate it if you would # email with citations of papers # referencing this work so I can mention them to my funding agent and # tenure committee. # #--------------------------------------------------------------------- # # Literature: # # [1] Manuel Lopez-Ibanez, Luis Paquete, and Thomas Stutzle. Hybrid # population-based algorithms for the bi-objective quadratic # assignment problem. Journal of Mathematical Modelling and # Algorithms, 5(1):111-137, April 2006. # # [2] Manuel Lopez-Ibanez, Luis Paquete, and Thomas Stutzle. Hybrid # population-based algorithms for the bi-objective quadratic # assignment problem. Journal of Mathematical Modelling and # Algorithms, 5(1):111-137, April 2006. # #--------------------------------------------------------------------- # # TODO: # # * Fail if any subcommand fails. # #--------------------------------------------------------------------- use strict; use warnings FATAL => 'all'; use diagnostics; use File::Basename; use Getopt::Long qw(:config pass_through); use File::Temp qw/ tempfile /; my $copyright = ' Copyright (C) 2007-2017 Manuel Lopez-Ibanez This is free software. You may redistribute copies of it under the terms of the GNU General Public License . There is NO WARRANTY, to the extent permitted by law. '; my $progname = basename($0); my $version = ' $Revision: 252 $ '; my $Rexe = "R"; my $ps2epsfilter = undef; $ps2epsfilter = "ps2eps"; ## Comment this out if you do not want ps2eps. my $eps2png = "convert"; # this is only required if --png is given my $pdf2png = "convert"; requiredprog($Rexe); # Old default #my $colors = '"#FFFFFF", "#BFBFBF","#808080","#404040","#000000"'; my $colors = '"#808080","#000000"'; my $intervals = 5; my $compress_flag = 0; my $flag_xmaximise = 0; my $flag_ymaximise = 0; my $flag_fulleaf = 0; my $flag_area; my $flag_eps = 0; my $flag_crop = (which_program("pdfcrop") ne "pdfcrop"); my $jpeg_flag = 0; my $flag_png = 0; my $flag_verbose = 0; my $flag_attsurfs = 1; my $filter = ""; my $legendpos; my $xlim = "NULL"; my $ylim = "NULL"; my $label_left; my $label_right; my $label_obj1 = "objective 1"; my $label_obj2 = "objective 2"; my $output_file; my $output_dir = ""; my $overwrite = 1; my $scale = 1; &usage(1) if (@ARGV == 0); ## Format commandline. my $commandline = &format_commandline (); GetOptions ('eps' => \$flag_eps, 'png' => \$flag_png, 'crop' => \$flag_crop, 'attsurfs!' => \$flag_attsurfs, 'output|o=s' => \$output_file, 'output-dir=s' => \$output_dir, 'obj1:s' => \$label_obj1, 'obj2:s' => \$label_obj2, 'scale=f' => \$scale, 'points' => sub { $flag_area = 0; }, 'colors=s' => \$colors, 'intervals=i' => \$intervals, 'full' => sub { $flag_fulleaf = 1; $flag_area = 1 unless (defined($flag_area)); }, 'verbose|v!' => \$flag_verbose, ## Silently accept these options for backwards compatibility. 'area' => \$flag_area,, 'save-temps' => \$flag_verbose); requiredprog($ps2epsfilter) if ($flag_eps); requiredprog($eps2png) if ($flag_png and $flag_eps); requiredprog($pdf2png) if ($flag_png and not $flag_eps); requiredprog("pdfcrop") if ($flag_crop and not $flag_eps); sub usage { my $exitcode = shift; print<<"EOF"; $progname: version $version $copyright Usage: $progname FILE1 FILE2 Create a plot of the differences between the EAFs of FILE1 and FILE2. -h, --help print this summary and exit --version print version number and exit -v, --verbose increase verbosity and keep intermediate files --full plot the full EAF instead of the differences --points plot points instead of areas --left=STRING label for left plot --right=STRING label for right plot --obj1=STRING label for objective 1 (x-axis) --obj2=STRING label for objective 2 (y-axis) (labels can be R expressions, e.g., --obj1="expression(pi)") --legendpos=none|{top,bottom}{left,right} no legend or position of the legend --xlim=REAL,REAL limits of x-axis --ylim=REAL,REAL limits of y-axis --maximise handle a maximisation problem --xmaximise maximise first objective --ymaximise maximise second objective --[no]attsurfs do not add attainment surfaces to the plot --scale REAL scale the plot so fonts become larger --colors=STRING,STRING colors for 50\% and 100\% difference (default: '"#808080","#000000"') --intervals=INT number of intervals (default: 5) -o --output=FILE output file -z --gzip compress the output file (adds gz extension) --png generate also png file from the eps output (requires ImageMagick) --eps generate EPS file (default is PDF) EOF exit $exitcode; } my @files = (); ## Handle parameters while (@ARGV) { my $argv = shift @ARGV; if ($argv =~ /^--left=/ or $argv =~ /^--left$/) { $label_left = &get_arg ($argv); } elsif ($argv =~ /^--right=/ or $argv =~ /^--right$/) { $label_right = &get_arg ($argv); } elsif ($argv =~ /^--legendpos=/ or $argv =~ /^--legendpos$/) { my $arg = &get_arg ($argv); $legendpos = "$arg" if ($arg); } elsif ($argv =~ /^--xlim=/ or $argv =~ /^--xlim$/) { my $arg = &get_arg ($argv); $xlim = "c($arg)" if ($arg); } elsif ($argv =~ /^--ylim=/ or $argv =~ /^--ylim$/) { my $arg = &get_arg ($argv); $ylim = "c($arg)" if ($arg); } elsif ($argv =~ /--maxim/) { $flag_xmaximise = 1; $flag_ymaximise = 1; } elsif ($argv =~ /--xmax/) { $flag_xmaximise = 1; } elsif ($argv =~ /--ymax/) { $flag_ymaximise = 1; } elsif ($argv =~ /^-z$/ or $argv =~ /^--gzip$/) { requiredprog ("gzip"); $compress_flag = 1; } ## The remainder options are standard. elsif ($argv =~ /^--help/ or $argv =~ /^-h/) { &usage(0); } elsif ($argv =~ /^--version/) { print "$progname: version $version\n"; print $copyright; exit (0); } elsif ($argv =~ /^--/ or $argv =~ /^-/) { print "$progname: unknown option $argv\n"; &usage(1); } else { push (@files, $argv); } } unless (@files == 2) { print "$progname: you must specify two input files.\n"; print "$progname: try '$progname --help' for more information.\n"; exit 1; } my $file1 = $files[0]; die "$progname: error: cannot read $file1\n" unless (-r $file1); my $file2 = $files[1]; die "$progname: error: cannot read $file2\n" unless (-r $file2); $output_dir .= "/" if ($output_dir and not($output_dir =~ m|/$|)); my $outsuffix = ($flag_eps) ? ".eps" : ".pdf"; unless (defined($output_file) and $output_file) { $output_file = $output_dir . basename($file1)."-".basename($file2); $output_file = (($flag_fulleaf) ? "${output_file}_full$outsuffix" : "${output_file}$outsuffix"); } if ($output_file !~ m/$outsuffix$/) { $output_file =~ s/(\.pdf|\.eps|\.ps|\.png)?$/$outsuffix/; } die "$progname: error: $output_file already exists.\n" if (-e $output_file and not $overwrite); $filter = "|$ps2epsfilter -s b0 -q -l -B -O -P > " if ($flag_eps and defined($ps2epsfilter) and -x $ps2epsfilter); unless (defined($legendpos)) { $legendpos = ($flag_fulleaf) ? "bottomleft" : "topright"; } $label_left = basename($file1) unless (defined($label_left) and $label_left); $label_right= basename($file2) unless (defined($label_right) and $label_right); $flag_area = 1 unless (defined($flag_area)); $label_left = parse_expression ($label_left); $label_right = parse_expression ($label_right); $label_obj1 = parse_expression ($label_obj1); $label_obj2 = parse_expression ($label_obj2); #print "$progname: generating plot $output_file ...\n"; my $Rfile = "$$.R"; open(R, ">$Rfile") or die "$progname: error: can't open $Rfile: $!\n"; print R <<"EOFR"; #!/usr/bin/r --vanilla # # R script to plot the differences # # This script is executable if you have littler installed. [*] # [*] http://code.google.com/p/littler/ # # Input: # label1 = label for first plot # label2 = label for second plot # output_file = filename for output plot # legendpos = location of the legend or "" for no legend. # # Created by $commandline # # $version # library(eaf) col <- c("#FFFFFF", $colors) intervals <- $intervals filter <- "$filter" file.left <- "$file1" file.right <- "$file2" title.left <- $label_left title.right <- $label_right output.file <- "$output_file" legend.pos <- "$legendpos" maximise <- c(${flag_xmaximise}, ${flag_ymaximise}) xlab <- $label_obj1 ylab <- $label_obj2 Xlim <- $xlim Ylim <- $ylim full.eaf <- as.logical(${flag_fulleaf}) eaf.type <- ifelse(${flag_area}, "area", "point") flag.eps <- as.logical(${flag_eps}) flag.attsurfs <- as.logical(${flag_attsurfs}) scale <- $scale EOFR print R <<'EOFR'; data.left <- read.data.sets (file.left) data.right <- read.data.sets (file.right) xlim <- range(data.left[,1], data.right[,1]) ylim <- range(data.left[,2], data.right[,2]) cat(sprintf("xlim = c(%s, %s)\n", xlim[1], xlim[2])) cat(sprintf("ylim = c(%s, %s)\n", ylim[1], ylim[2])) if (!is.null(Xlim)) xlim <- Xlim if (!is.null(Ylim)) ylim <- Ylim output.title <- output.file output.file <- paste(filter, output.file, sep="") if (flag.eps) { postscript(output.file, title = output.title, paper = "special", horizontal=F, onefile=F, width = 9 / scale, height = 6 / scale, family = "Helvetica") } else { pdf(output.file, title = output.title, width = 9 / scale, height = 6 / scale, family = "Helvetica") } eafdiffplot (data.left, data.right, col = col, intervals = intervals, full.eaf = full.eaf, type = eaf.type, legend.pos = legend.pos, title.left = title.left, title.right = title.right, cex = 1.0, cex.lab = 1.1, cex.axis = 1.0, xlim = xlim, ylim = ylim, xlab = xlab, ylab = ylab, maximise = maximise, percentiles = if(flag.attsurfs) 50 else NA, grand.lines = flag.attsurfs) dev.off() cat (paste("eafdiffplot:", output.file, "\n")) EOFR close R; my @args = ("$Rexe --quiet --vanilla --slave < $$.R"); system(@args) == 0 or die "$progname: error: R failed to create the plots (@args)\n"; if ($flag_crop and not $flag_eps) { my ($tmpfh, $tmpfilename) = tempfile(); execute ("pdfcrop $output_file $tmpfilename"); execute ("mv $tmpfilename $output_file"); } # FIXME: EPS files tend to be huge, so sometimes pays off to convert # them to PNG or JPEG and then back to EPS. How to fix this: # # Option 1: Find the proper settings of 'convert' or an alternative # tool to do the best possible conversion. Multi-platform tools are # better. Choose either PNG or JPEG. PNG seems the best option. # # Option 2: Create the output of R directly in PNG format. Then, # conversion to EPS seems easier and pdfLaTeX handles PNG. Still, the # proper settings for PNG would need to be investigated. # # Option 3: create smaller EPS files, so no conversion is # needed. Currently, points overlapped by other points are still # present in the EPS file and take space (and time to render). Avoid # anything that is not shown in final plot. If the points are at # exactly the same position, we should be able to detect that and only # plot the point that is visible. However, a point can also be # completely overlapped by other points in different positions (since # points in a plot have a nonzero area). No idea how to avoid plotting # those points. Perhaps there is some way to detec this in R itself. # # if ($flag_png) { my $output_png = $output_file; $output_png =~ s/${outsuffix}$/.png/; #$output_png =~ s[/eps/][/png/]; if ($flag_eps) { `$eps2png -render +antialias -density 300 -background white -flatten $output_file $output_png`; } else { my $tmpfile = "/tmp/". basename($output_file); `pdfcrop $output_file $tmpfile`; `$pdf2png -render +antialias -density 300 -background white -flatten $tmpfile $output_png`; } #my $output_png_eps = $output_png; #my $output_png_eps =~ s/\.png$/_png.eps/; #`convert $output_png $output_png_eps`; #`rm -f $output_png`; #`gzip --force -v $output_png_eps`; } # if($jpeg_flag) { # $output_jpg = $output_file; # $output_jpg =~ s/\.eps$/.jpg/; # $output_jpg =~ s[/eps/][/jpg/]; # $output_jpg_eps = $output_jpg; # $output_jpg_eps =~ s/\.jpg$/_jpg.eps/; # `convert -render +antialias -density 300 $output_eps $output_jpg`; # `convert $output_jpg -compress jpeg eps2:${output_jpg_eps}`; # `rm -f $output_jpg`; # `gzip --force -v $output_jpg_eps`; # } ## FIXME: Do this on the fly within the R script by using pipes. if ($compress_flag) { print "$progname: compressing: "; `gzip --force -v $output_file`; } unless ($flag_verbose) { unlink("$$.R", "$$.Rout"); } else { print "$progname: generated R script: $$.R\n"; } ################################### # helper sub-routines ################################### sub parse_expression { my $label = shift; $label = "\"$label\"" if ($label !~ /^expression\(/); return $label; } sub get_arg { my ($option, $arg) = split (/=/, $_[0], 2); $arg = shift @ARGV if (not $arg); return $arg; } sub format_commandline { my $cmd = $0 . " "; for (my $i=0, my $j=25; $i < @ARGV; $i++) { if ($i == $j) { $j += 25; $cmd .= "\\\n# "; } if ($ARGV[$i] =~ /\s/) { $cmd .= " \"" . $ARGV[$i] . "\""; } else { $cmd .= " " . $ARGV[$i]; } } return $cmd; } use Env '@PATH'; use Cwd; # FIXME: This should return the empty string if not found. sub which_program { my $program = shift; my $cwd = cwd(); unless ($program =~ m|^.?.?/|) { # If no path was given foreach my $path (@PATH) { if (-e "$path/$program") { $program = "$path/$program"; last; } } # Try also the current directory: $program = "$cwd/$program" if (-e "$cwd/$program"); } return $program; } sub requiredprog { my $cwd = cwd(); foreach my $program (@_) { next if not defined $program; my $pathtoprogram = which_program($program); die "$progname: cannot find required program `$program'," ." either in your PATH=\"". join(':',@PATH) ."\" or in the current directory`$cwd'\n" unless ($pathtoprogram =~ m|^.?.?/|); die "$progname: required program `$program' is not executable\n" unless (-x $pathtoprogram); } } sub execute { my $command = shift; if ($flag_verbose) { print $command ."\n"; print `$command`; } else { `$command`; } my $exit = $? >> 8; die "$progname: command `$command' failed with value $exit\n" if ($exit); } eaf/inst/scripts/eafdiff/README0000644000176200001440000000513113254747463015734 0ustar liggesusers----------- eafdiff.pl ----------- Create a plot of the differences between the EAFs of two files. Requirements of `eafdiff.pl' ----------------------- The program has been tested on GNU/Linux using bash shell. If you have success or problems using other systems, please let me know. The following programs are required: * Perl 5 * eaf R package: http://iridia.ulb.ac.be/~manuel/eaftools The following programs are only required if the corresponding features are enabled by editing the script: * ps2eps: Filter PS files and remove excessive margin whitespace. * gzip * ImageMagick (in particular, the program `convert') Using `eafdiff.pl' -------------------- See the output of eafdiff.pl --help License ------- Copyright (c) 2010 Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. IMPORTANT NOTE: Please be aware that the fact that this program is released as Free Software does not excuse you from scientific propriety, which obligates you to give appropriate credit! If you write a scientific paper describing research that made substantive use of this program, it is your obligation as a scientist to (a) mention the fashion in which this software was used in the Methods section; (b) mention the algorithm in the References section. The appropriate citation is: Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 209–222. Springer, Berlin, Germany, 2010. doi: 10.1007/978-3-642-02538-9_9 Moreover, as a personal note, I would appreciate it if you would email with citations of papers referencing this work so I can mention them to my funding agent and tenure committee. You can obtain a copy of the GNU General Public License at http://www.gnu.org/licenses/gpl.html or writing to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA. eaf/inst/scripts/eaf/0000755000176200001440000000000013254747514014220 5ustar liggesuserseaf/inst/scripts/eaf/eaf_io_priv.h0000644000176200001440000001333113254747464016660 0ustar liggesusers#include #include #include /* for strerror() */ #include /* for errno */ #define PAGE_SIZE 4096 #define DATA_INC (PAGE_SIZE/sizeof(objective_t)) #ifndef DEBUG #define DEBUG 0 #endif /* * Read an array of objective values from a stream. This function may * be called repeatedly to add data to an existing data set. * * nobjs : number of objectives, also the number of columns. */ int read_objective_t_data (const char *filename, objective_t **data_p, int *nobjs_p, int **cumsizes_p, int *nsets_p) { FILE *instream; int nobjs = *nobjs_p; /* number of objectives (and columns). */ int *cumsizes = *cumsizes_p; /* cumulative sizes of data sets. */ int nsets = *nsets_p; /* number of data sets. */ objective_t *data = *data_p; int retval; /* return value for fscanf */ char newline[2]; int ntotal; /* the current element of (*datap) */ int column = 0, line = 0; int datasize; int sizessize; int errorcode = 0; if (filename == NULL) { instream = stdin; filename = ""; /* used to diagnose errors. */ } else if (NULL == (instream = fopen (filename,"rb"))) { errprintf ("%s: %s", filename, strerror (errno)); return (ERROR_FOPEN); } if (nsets == 0) { ntotal = 0; sizessize = 0; datasize = 0; } else { ntotal = nobjs * cumsizes[nsets - 1]; sizessize = ((nsets - 1) / DATA_INC + 1) * DATA_INC; datasize = ((ntotal - 1) / DATA_INC + 1) * DATA_INC; } /* if size is equal to zero, this is equivalent to free(). That is, reinitialize the data structures. */ cumsizes = realloc (cumsizes, sizessize * sizeof(int)); data = realloc (data, datasize * sizeof(objective_t)); /* skip over leading whitespace, comments and empty lines. */ do { line++; /* skip full lines starting with # */ retval = skip_comment_line (instream); } while (retval == 1); if (retval == EOF) { /* faster than !feof() */ warnprintf ("%s: file is empty.", filename); errorcode = READ_INPUT_FILE_EMPTY; goto read_data_finish; } do { /* beginning of data set */ if (nsets == sizessize) { sizessize += DATA_INC; cumsizes = realloc(cumsizes, sizessize * sizeof(int)); } cumsizes[nsets] = (nsets == 0) ? 0 : cumsizes[nsets - 1]; do { /* beginning of row */ column = 0; do { /* new column */ column++; objective_t number; if (fscanf (instream, objective_t_scanf_format, &number) != 1) { char buffer[64]; if (fscanf (instream, "%60[^ \t\r\n]", buffer) == EOF) { errprintf ("%s: line %d column %d: " "read error or EOF", filename, line, column); } else { errprintf ("%s: line %d column %d: " "could not convert string `%s' to double", filename, line, column, buffer); } errorcode = ERROR_CONVERSION; goto read_data_finish; } if (ntotal == datasize) { datasize += DATA_INC; data = realloc (data, datasize * sizeof(objective_t)); } data[ntotal] = number; ntotal++; #if DEBUG > 1 fprintf(stderr, "%s:%d:%d(%d) %d (set %d) = %.15g\n", filename, line, column, nobjs, cumsizes[nsets], nsets, (double)number); #endif /* skip possible trailing whitespace */ ignore_unused_result (fscanf (instream, "%*[ \t\r]")); retval = fscanf (instream, "%1[\n]", newline); } while (retval == 0); if (!nobjs) nobjs = column; else if (column == nobjs) ; /* OK */ else if (cumsizes[0] == 0) { /* just finished first row. */ errprintf ("%s: line %d: input has dimension %d" " while previous data has dimension %d", filename,line, column, nobjs); errorcode = READ_INPUT_WRONG_INITIAL_DIM; goto read_data_finish; } else { errprintf ("%s: line %d has different number of columns (%d)" " from first row (%d)\n", filename, line, column, nobjs); errorcode = ERROR_COLUMNS; goto read_data_finish; } cumsizes[nsets]++; /* look for an empty line */ line++; retval = skip_comment_line (instream); } while (retval == 0); nsets++; /* new data set */ #if DEBUG > 1 fprintf (stderr, "%s: set %d, read %d rows\n", filename, nsets, cumsizes[nsets - 1]); #endif /* skip over successive empty lines */ do { line++; retval = skip_comment_line (instream); } while (retval == 1); } while (retval != EOF); /* faster than !feof() */ /* adjust to real size (saves memory but probably slower). */ cumsizes = realloc (cumsizes, nsets * sizeof(int)); data = realloc (data, ntotal * sizeof(objective_t)); read_data_finish: *nobjs_p = nobjs; *nsets_p = nsets; *cumsizes_p = cumsizes; *data_p = data; if (instream != stdin) fclose(instream); return errorcode; } #undef PAGE_SIZE #undef DATA_INC eaf/inst/scripts/eaf/eaf_main.c0000644000176200001440000003335313254747464016136 0ustar liggesusers/************************************************************************* eaf: Computes the empirical attainment function (EAF) from a number of approximation sets. --------------------------------------------------------------------- Copyright (c) 2006, 2007, 2008 Carlos Fonseca Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- *************************************************************************/ #include "eaf.h" #include #include // strtol() #include // isprint() #include #include #include // for bool, true and false #include // for getopt() #define _GNU_SOURCE #include // for getopt_long() static void usage(void) { printf("\n" "Usage:\n" " %s [OPTIONS] [FILE...]\n\n", PROGRAM_NAME); printf( "Computes the empirical attainment function (EAF) of all input FILEs. \n" "With no FILE, or when FILE is -, read standard input.\n\n" "Options:\n" " -h, --help print this summary and exit \n" " --version print version number (and compilation flags) and exit \n" " -v, --verbose print some information (time, input points, output \n" " points, etc) in stderr. Default is --quiet \n" " -o, --output FILE write output to FILE instead of standard output. \n" " -q, --quiet print just the EAF (as opposed to --verbose) \n" " -b, --best compute best attainment surface \n" " -m, --median compute median attainment surface \n" " -w, --worst compute worst attainment surface \n" " -p, --percentile INT compute the given percentile of the EAF. \n" " -l, --level LEVEL compute the given level of the attainment function \n" " -i[FILE], --indices[=FILE] write attainment indices to FILE. \n" " If FILE is '-', print to stdout. \n" " If FILE is missing use the same file as for output. \n" " -d[FILE], --diff[=FILE] write difference between half of runs to FILE. \n" " If FILE is '-', print to stdout. \n" " If FILE is missing use the same file as for output. \n" "\n\n" ); } static void version(void) { printf("%s version %s" #ifdef MARCH " (optimised for "MARCH")" #endif #ifndef VERSION #define VERSION "unknown" #endif "\n\n", PROGRAM_NAME, VERSION); printf( "Copyright (C) 2009\n" "Carlos Fonseca \n" "Manuel Lopez-Ibanez \n" "\n" "This is free software, and you are welcome to redistribute it under certain\n" "conditions. See the GNU General Public License for details. There is NO\n" "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n" "\n" ); } // FIXME: How to implement this with const char *str? static int read_ints (int *levels, char *str) { char * cursor; char * endp = str; int k = 0; do { cursor = endp; levels[k] = strtol(cursor, &endp, 10); if (cursor == endp && (*endp == ',' || *endp == ';')) { endp++; continue; } k++; } while (cursor != endp); // not end of string: error while (*cursor != '\0') { if (!isspace(*cursor)) { errprintf ("invalid argument to --levels '%s'", str); exit (EXIT_FAILURE); } cursor++; } // no number: error if (k == 1) { errprintf ("invalid argument to --levels '%s'", str); exit (EXIT_FAILURE); } return k - 1; } void eaf_print (eaf_t **eaf, int nlevels, FILE *coord_file, FILE *indic_file, FILE *diff_file) { int k; for (k = 0; k < nlevels; k++) { eaf_print_attsurf (eaf[k], coord_file, indic_file, diff_file); if (coord_file) fprintf (coord_file, "\n"); else if (indic_file) fprintf(indic_file, "\n"); else if (diff_file) fprintf(diff_file, "\n"); } } void read_input_data (const char *filename, objective_t **data_p, int *nobjs_p, int **cumsizes_p, int *nsets_p) { int error = read_objective_t_data (filename, data_p, nobjs_p, cumsizes_p, nsets_p); switch (error) { case READ_INPUT_FILE_EMPTY: case READ_INPUT_WRONG_INITIAL_DIM: break; case ERROR_FOPEN: case ERROR_CONVERSION: case ERROR_COLUMNS: exit (EXIT_FAILURE); default: break; } } int main(int argc, char *argv[]) { bool verbose_flag = true; bool best_flag = false; bool median_flag = false; bool worst_flag = false; int *level; int nlevels = 0; int *percentile; int npercentiles = 0; objective_t *data = NULL; int* cumsizes = NULL; int nobj = 0, nruns = 0; eaf_t **eaf; const char *coord_filename = NULL; const char *indic_filename = NULL; const char *diff_filename = NULL; FILE *coord_file = stdout; FILE *indic_file = NULL; FILE *diff_file = NULL; int k; int option; int longopt_index; /* see the man page for getopt_long for an explanation of these fields */ static const char short_options[] = "hVvqbmwl:p:o:i::d::"; static struct option long_options[] = { {"help", no_argument, NULL, 'h'}, {"version", no_argument, NULL, 'V'}, {"verbose", no_argument, NULL, 'v'}, {"quiet", no_argument, NULL, 'q'}, {"best", no_argument, NULL, 'b'}, {"median", no_argument, NULL, 'm'}, {"worst", no_argument, NULL, 'w'}, {"output", required_argument, NULL, 'o'}, /* The optional_argument must be together with the parameter such as -iFILE or -i=FILE, otherwise it will be considered an input file. */ {"indices", optional_argument, NULL, 'i'}, {"diff", optional_argument, NULL, 'd'}, {"percentile", required_argument, NULL, 'p'}, {"level", required_argument, NULL, 'l'}, {NULL, 0, NULL, 0} /* marks end of list */ }; #define MAX_LEVELS 50 level = malloc(MAX_LEVELS * sizeof(int)); percentile = malloc(MAX_LEVELS * sizeof(int)); while (0 < (option = getopt_long(argc, argv, short_options, long_options, &longopt_index))) { switch (option) { case 'l': assert(nlevels < MAX_LEVELS); nlevels += read_ints(level + nlevels, optarg); break; case 'p': assert(npercentiles < MAX_LEVELS); npercentiles += read_ints(percentile + npercentiles, optarg); break; case 'o': if (!strcmp(optarg,"-")) { coord_file = stdout; coord_filename = NULL; } else coord_filename = optarg; break; case 'i': if (!optarg) { indic_file = stdin; /* Overwrite with coord_file later. */ indic_filename = NULL; } else if (!strcmp(optarg,"-")) { indic_file = stdout; indic_filename = NULL; } else { indic_filename = optarg; } break; case 'd': if (!optarg) { diff_file = stdin; /* Overwrite with coord_file later. */ diff_filename = NULL; } else if (!strcmp(optarg,"-")) { diff_file = stdout; diff_filename = NULL; } else { diff_filename = optarg; } break; case 'b': best_flag = true; break; case 'm': median_flag = true; break; case 'w': worst_flag = true; break; case 'q': // --quiet verbose_flag = false; break; case 'v': // --verbose verbose_flag = true; break; case 'V': // --version version(); exit(EXIT_SUCCESS); case 'h': // --help usage(); exit(EXIT_SUCCESS); case '?': // getopt prints an error message right here fprintf(stderr, "Try `%s --help' for more information.\n", PROGRAM_NAME); exit(EXIT_FAILURE); default: abort (); } } if (optind < argc) { for (k = optind; k < argc; k++) { if (strcmp (argv[k],"-")) read_input_data (argv[k], &data, &nobj, &cumsizes, &nruns); else read_input_data (NULL, &data, &nobj, &cumsizes, &nruns); } } else read_input_data (NULL, &data, &nobj, &cumsizes, &nruns); if (coord_filename) { coord_file = fopen (coord_filename, "w"); if (NULL == coord_file) { errprintf ("%s: %s", coord_filename, strerror (errno)); exit (EXIT_FAILURE); } } /* indic_file is neither NULL nor stdout and no filename was given, use same settings as for --output. */ if (indic_file && indic_file != stdout && !indic_filename) { indic_file = coord_file; indic_filename = coord_filename; } /* If a different filename as for --output was given, open it. */ if (indic_filename && (!coord_filename || strcmp (indic_filename, coord_filename))) { indic_file = fopen (indic_filename, "w"); if (NULL == indic_file) { errprintf ("%s: %s", indic_filename, strerror (errno)); exit (EXIT_FAILURE); } } /* diff_file is neither NULL nor stdout and no filename was given, use same settings as for --output. */ if (diff_file && diff_file != stdout && !diff_filename) { diff_file = coord_file; diff_filename = coord_filename; } /* If a different filename as for --output and --indices was given, open it. */ if (diff_filename && (!coord_filename || strcmp (diff_filename, coord_filename)) && (!indic_filename || strcmp (diff_filename, indic_filename))) { diff_file = fopen (diff_filename, "w"); if (NULL == diff_file) { errprintf ("%s: %s", diff_filename, strerror (errno)); exit(EXIT_FAILURE); } } if (best_flag) { level[0] = 1; nlevels = 1; } else if (median_flag) { level[0] = percentile2level (50, nruns); nlevels = 1; } else if (worst_flag) { level[0] = nruns; nlevels = 1; } else if (npercentiles > 0) { nlevels = npercentiles; for (k = 0; k < nlevels; k++) { if (percentile[k] <= 0) { errprintf ("arg to --percentile must be higher than 0.\n"); exit (EXIT_FAILURE); } else if (percentile[k] > 100) { errprintf ("arg to --percentile must be <= 100.\n"); exit (EXIT_FAILURE); } level[k] = percentile2level(percentile[k], nruns); } } else if (nlevels > 0) { level = realloc (level, nlevels * sizeof(int)); for (k = 0; k < nlevels; k++) { if (level[k] <= 0) { errprintf ("arg to --level must be higher than 0.\n"); exit (EXIT_FAILURE); } else if (level[k] > nruns) { errprintf ("arg to --level must not be higher than number of" " approximation sets (%d).\n", nruns); exit (EXIT_FAILURE); } } } else if (nlevels == 0) { nlevels = nruns; level = realloc (level, nlevels * sizeof(int)); for (k = 0; k < nruns; k++) { level[k] = k + 1; } } if (verbose_flag) { fprintf (stderr, "# objectives (%d): --\n", nobj); fprintf (stderr, "# sets: %d\n", nruns); fprintf (stderr, "# points: %d\n", cumsizes[nruns - 1]); fprintf (stderr, "%s: calculating levels:", PROGRAM_NAME); for (k = 0; k < nlevels; k++) fprintf (stderr, " %d", level[k]); fprintf (stderr, "\n"); } eaf = attsurf (data, nobj, cumsizes, nruns, level, nlevels); eaf_print (eaf, nlevels, coord_file, indic_file, diff_file); fclose (coord_file); if (indic_file && indic_file != coord_file) fclose (indic_file); if (diff_file && diff_file != coord_file && diff_file != indic_file) fclose (diff_file); /* FIXME: This needs a command-line interface. */ /* eaf_print_polygon (stdout, eaf, nlevels); */ free(level); free(data); free(cumsizes); return 0; } eaf/inst/scripts/eaf/avl.h0000644000176200001440000001677513254747464015177 0ustar liggesusers/***************************************************************************** avl.h - Source code for the AVL-tree library. Copyright (C) 1998 Michael H. Buselli Copyright (C) 2000-2002 Wessel Dankers This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Augmented AVL-tree. Original by Michael H. Buselli . Modified by Wessel Dankers to add a bunch of bloat to the sourcecode, change the interface and squash a few bugs. Mail him if you find new bugs. *****************************************************************************/ #ifndef _AVL_H #define _AVL_H #define AVL_DEPTH /* We need either depths, counts or both (the latter being the default) */ #if !defined(AVL_DEPTH) && !defined(AVL_COUNT) #define AVL_DEPTH #define AVL_COUNT #endif /* User supplied function to compare two items like strcmp() does. * For example: cmp(a,b) will return: * -1 if a < b * 0 if a = b * 1 if a > b */ typedef int (*avl_compare_t)(const void *, const void *); /* User supplied function to delete an item when a node is free()d. * If NULL, the item is not free()d. */ typedef void (*avl_freeitem_t)(void *); typedef struct avl_node_t { struct avl_node_t *next; struct avl_node_t *prev; struct avl_node_t *parent; struct avl_node_t *left; struct avl_node_t *right; void *item; #ifdef AVL_COUNT unsigned int count; #endif #ifdef AVL_DEPTH unsigned char depth; #endif /* The following members are necessary only to determine the attainment indicators. * set - set of the input point responsible for the promotion of this point * promoter - point from a lower (previous) level that is also responsible for the promotion of this point. * remover - point from the same level as this point that dominates this point. (Point * that removed this point from its level). * equal - point from a higher (next) level that is equal to this point (this point is equal's promoter) * * This point is attained by 'set' and by the same sets that attains its promoter. It is also * attained by any set that attains its remover. It is also attained by every set that attains * any point equal to itself. */ int set; struct avl_node_t *promoter; struct avl_node_t *remover; struct avl_node_t *equal; } avl_node_t; typedef struct avl_tree_t { avl_node_t *head; avl_node_t *tail; avl_node_t *top; avl_compare_t cmp; avl_freeitem_t freeitem; } avl_tree_t; /* Initializes a new tree for elements that will be ordered using * the supplied strcmp()-like function. * Returns the value of avltree (even if it's NULL). * O(1) */ extern avl_tree_t *avl_init_tree(avl_tree_t *avltree, avl_compare_t, avl_freeitem_t); /* Allocates and initializes a new tree for elements that will be * ordered using the supplied strcmp()-like function. * Returns NULL if memory could not be allocated. * O(1) */ extern avl_tree_t *avl_alloc_tree(avl_compare_t, avl_freeitem_t); /* Frees the entire tree efficiently. Nodes will be free()d. * If the tree's freeitem is not NULL it will be invoked on every item. * O(n) */ extern void avl_free_tree(avl_tree_t *); /* Reinitializes the tree structure for reuse. Nothing is free()d. * Compare and freeitem functions are left alone. * O(1) */ extern void avl_clear_tree(avl_tree_t *); /* Free()s all nodes in the tree but leaves the tree itself. * If the tree's freeitem is not NULL it will be invoked on every item. * O(n) */ extern void avl_free_nodes(avl_tree_t *); /* Initializes memory for use as a node. Returns NULL if avlnode is NULL. * O(1) */ extern avl_node_t *avl_init_node(avl_node_t *avlnode, void *item); /* Insert an item into the tree and return the new node. * Returns NULL and sets errno if memory for the new node could not be * allocated or if the node is already in the tree (EEXIST). * O(lg n) */ extern avl_node_t *avl_insert(avl_tree_t *, void *item); /* Insert a node into the tree and return it. * Returns NULL if the node is already in the tree. * O(lg n) */ extern avl_node_t *avl_insert_node(avl_tree_t *, avl_node_t *); /* Insert a node in an empty tree. If avlnode is NULL, the tree will be * cleared and ready for re-use. * If the tree is not empty, the old nodes are left dangling. * O(1) */ extern avl_node_t *avl_insert_top(avl_tree_t *, avl_node_t *avlnode); /* Insert a node before another node. Returns the new node. * If old is NULL, the item is appended to the tree. * O(lg n) */ extern avl_node_t *avl_insert_before(avl_tree_t *, avl_node_t *old, avl_node_t *new); /* Insert a node after another node. Returns the new node. * If old is NULL, the item is prepended to the tree. * O(lg n) */ extern avl_node_t *avl_insert_after(avl_tree_t *, avl_node_t *old, avl_node_t *new); /* Deletes a node from the tree. Returns immediately if the node is NULL. * The item will not be free()d regardless of the tree's freeitem handler. * This function comes in handy if you need to update the search key. * O(lg n) */ extern void avl_unlink_node(avl_tree_t *, avl_node_t *); /* Deletes a node from the tree. Returns immediately if the node is NULL. * If the tree's freeitem is not NULL, it is invoked on the item. * If it is, returns the item. * O(lg n) */ extern void *avl_delete_node(avl_tree_t *, avl_node_t *); /* Searches for an item in the tree and deletes it if found. * If the tree's freeitem is not NULL, it is invoked on the item. * If it is, returns the item. * O(lg n) */ extern void *avl_delete(avl_tree_t *, const void *item); /* If exactly one node is moved in memory, this will fix the pointers * in the tree that refer to it. It must be an exact shallow copy. * Returns the pointer to the old position. * O(1) */ extern avl_node_t *avl_fixup_node(avl_tree_t *, avl_node_t *new); /* Searches for a node with the key closest (or equal) to the given item. * If avlnode is not NULL, *avlnode will be set to the node found or NULL * if the tree is empty. Return values: * -1 if the returned node is greater * 0 if the returned node is equal or if the tree is empty * 1 if the returned node is smaller * O(lg n) */ extern int avl_search_closest(const avl_tree_t *, const void *item, avl_node_t **avlnode); /* Searches for the item in the tree and returns a matching node if found * or NULL if not. * O(lg n) */ extern avl_node_t *avl_search(const avl_tree_t *, const void *item); #ifdef AVL_COUNT /* Returns the number of nodes in the tree. * O(1) */ extern unsigned int avl_count(const avl_tree_t *); /* Searches a node by its rank in the list. Counting starts at 0. * Returns NULL if the index exceeds the number of nodes in the tree. * O(lg n) */ extern avl_node_t *avl_at(const avl_tree_t *, unsigned int); /* Returns the rank of a node in the list. Counting starts at 0. * O(lg n) */ extern unsigned int avl_index(const avl_node_t *); #endif #endif eaf/inst/scripts/eaf/eaf_input.c0000644000176200001440000000633413254747464016350 0ustar liggesusers/***************************************************************************** eaf_input.c: read EAF data --------------------------------------------------------------------- Copyright (c) 2005, 2006, 2007, 2008 Carlos Fonseca Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- TODO: things that may or may not improve reading performance. * different values for PAGE_SIZE. * reading whole lines and then sscanf the line. * mmaping the input file (or a big chunk of few megabytes), then read, then unmmap. *****************************************************************************/ #include #include #include "eaf_io.h" #ifndef ignore_unused_result #define ignore_unused_result(X) do { if(X) {}} while(0); #endif /* skip full lines starting with # */ static inline int skip_comment_line (FILE * instream) { char newline[2]; if (!fscanf (instream, "%1[#]%*[^\n]", newline)) /* and whitespace */ ignore_unused_result (fscanf (instream, "%*[ \t\r]")); return fscanf (instream, "%1[\n]", newline); } #define objective_t int #define objective_t_scanf_format "%d" #define read_objective_t_data read_int_data #include "eaf_io_priv.h" #undef objective_t #undef objective_t_scanf_format #undef read_objective_t_data #define objective_t double #define objective_t_scanf_format "%lf" #define read_objective_t_data read_double_data #include "eaf_io_priv.h" #undef objective_t #undef objective_t_scanf_format #undef read_objective_t_data #ifndef R_PACKAGE /* From: Edition 0.10, last updated 2001-07-06, of `The GNU C Library Reference Manual', for Version 2.3.x. Copyright (C) 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2001, 2002, 2003 Free Software Foundation, Inc. */ void errprintf(const char *template,...) { va_list ap; fprintf(stderr, "%s: error: ", PROGRAM_NAME); va_start(ap,template); vfprintf(stderr, template, ap); va_end(ap); fprintf(stderr, "\n"); } /* End of copyright The GNU C Library Reference Manual */ void warnprintf(const char *template,...) { va_list ap; fprintf(stderr, "%s: warning: ", PROGRAM_NAME); va_start(ap,template); vfprintf(stderr, template, ap); va_end(ap); fprintf(stderr, "\n"); } #endif eaf/inst/scripts/eaf/GNUmakefile0000644000176200001440000000742613254747464016307 0ustar liggesusers#------------------------------------------------------*- Makefile-gmake -*- # Makefile for eaf VERSION = 0.1svn$(SVN_REV) # --------------------------------------------------------------------- # Copyright (c) 2007, 2008 # Carlos Fonseca # Manuel Lopez-Ibanez # This program is free software (software libre); you can redistribute # it and/or modify it under the terms of the GNU General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program; if not, you can obtain a copy of the GNU # General Public License at: # http://www.gnu.org/copyleft/gpl.html # or by writing to: # Free Software Foundation, Inc., 59 Temple Place, # Suite 330, Boston, MA 02111-1307 USA # #----------------------------------------------------------------------- # IMPORTANT NOTE: Please be aware that the fact that this program is # released as Free Software does not excuse you from scientific # propriety, which obligates you to give appropriate credit! If you # write a scientific paper describing research that made substantive # use of this program, it is your obligation as a scientist to # acknowledge its use. Moreover, as a personal note, I would # appreciate it if you would email # with citations of papers referencing this work so I can mention them # to my funding agent and tenure committee. #----------------------------------------------------------------------- DEBUG=0 BINDIR := ./ OBJS = eaf.o eaf_input.o eaf_main.o eaf3d.o avl.o HEADERS = eaf.h eaf_io_priv.h eaf_io.h cvector.h avl.h EAFEXE=eaf ifdef march MARCH=$(march) endif # Order is important ! LINKERFLAGS = DELETE = @rm -f ECHO = @echo "$(1)" ## Do we have svnversion? ifeq ($(shell sh -c 'which svnversion 1> /dev/null 2>&1 && echo y'),y) ## Is this a working copy? ifeq ($(shell sh -c 'LC_ALL=C svnversion -n . | grep -q ^[0-9] && echo y'),y) $(shell sh -c 'svnversion -n . > svn_version') endif endif ## Set version information: SVN_REV = $(shell sh -c 'cat svn_version 2> /dev/null') -include gcc.mk ifdef ARCH override CFLAGS += -DMARCH='"$(ARCH)"' endif override CFLAGS += $(OPT_CFLAGS) -DDEBUG=$(DEBUG) \ -D VERSION='"$(VERSION)"' .PHONY : default all clean distclean ps test dist default: $(BINDIR)/$(EAFEXE) $(BINDIR)/$(EAFEXE) : $(OBJS) $(BINDIR)/$(EAFEXE) : @mkdir -p $(BINDIR) $(call ECHO,--> Building $@ version $(VERSION) <---) $(CC) -o $@ $(OBJS) $(CFLAGS) $(LINKERFLAGS) $(OBJS) : eaf.h eaf_io.h eaf_input.o : eaf_io_priv.h %.o : %.c $(CC) $(CFLAGS) -c -o $@ $< test: DEBUG=1 test: clean $(BINDIR)/$(EAFEXE) $(call ECHO,---> Testing... <--- ) @perl -w dotest.pl $(BINDIR)/$(EAFEXE) ./testsuite/ clean : $(call ECHO,---> Removing $(BINDIR)/$(EAFEXE) <---) $(DELETE) $(BINDIR)/$(EAFEXE) $(call ECHO,---> Removing object files $(OBJS) <---) $(DELETE) $(OBJS) all : clean default DIST_SRC_FILES = gcc.mk TODO GNUmakefile README LICENSE svn_version \ $(OBJS:.o=.c) $(HEADERS) DIST_SRC := eaf-$(VERSION)-src dist : DEBUG=0 dist : CDEBUG= dist : @(mkdir -p ../$(DIST_SRC) \ && rsync -rlpC --exclude=.svn $(DIST_SRC_FILES) ../$(DIST_SRC)/ \ && cd .. \ && tar cf - $(DIST_SRC) | gzip -f9 > $(DIST_SRC).tar.gz \ && rm -rf ./$(DIST_SRC)/* && rmdir ./$(DIST_SRC)/ \ && echo "$(DIST_SRC).tar.gz created." && cd $(PWD) ) eaf/inst/scripts/eaf/README0000644000176200001440000000672313254747464015114 0ustar liggesusers-------------------------------------------------------------------- Computation of the Empirical Attainment Function in 2 Dimensions Carlos Fonseca and Manuel López-Ibáñez -------------------------------------------------------------------- Contents * Introduction * Usage * License * Changelog ------------ Introduction ------------ This program computes the empirical attainment function from a number of sets of nondominated points. This version is based on the original code written by Carlos M. Fonseca available at http://www.tik.ee.ethz.ch/pisa/. Relevant literature: [1] V. Grunert da Fonseca, C. M. Fonseca, and A. Hall. Inferential performance assessment of stochastic optimizers and the attainment function. In E. Zitzler, K. Deb, L. Thiele, C. C. Coello, and D. Corne, editors, Evolutionary Multi-criterion Optimization (EMO 2001), volume 1993 of Lecture Notes in Computer Science, pages 213-225. Springer Verlag, Berlin, Germany, 2001. ------------ Building ------------ The program has been tested on GNU/Linux using bash shell and a recent version of GCC (>= 4.2). If you have success or problems using other systems, please let me know. The default compilation is done with: make eaf You can optimize for a particular computer architecture with the option "march=", for example: make eaf march=pentium4 Modern versions of GCC support a value of 'march=native' that autodetects your architecture. See the GCC manual for the names of the architectures supported by your version of GCC. ------- Usage ------- For the remainder options available, see the output of eaf --help ------------ License ------------ This software is Copyright (C) 2006, 2007, 2008, 2009. Carlos Fonseca and Manuel López-Ibáñez. This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. IMPORTANT NOTE: Please be aware that the fact that this program is released as Free Software does not excuse you from scientific propriety, which obligates you to give appropriate credit! If you write a scientific paper describing research that made substantive use of this program, it is your obligation as a scientist to (a) mention the fashion in which this software was used in the Methods section; (b) mention the algorithm in the References section. The appropriate citation is: V. Grunert da Fonseca, C. M. Fonseca, and A. Hall. Inferential performance assessment of stochastic optimizers and the attainment function. In E. Zitzler, K. Deb, L. Thiele, C. C. Coello, and D. Corne, editors, Evolutionary Multi-criterion Optimization (EMO 2001), volume 1993 of Lecture Notes in Computer Science, pages 213-225. Springer Verlag, Berlin, Germany, 2001. Moreover, as a personal note, I would appreciate it if you would email with citations of papers referencing this work so I can mention them to my funding agent and tenure committee. ------------ Changelog ------------ eaf/inst/scripts/eaf/cvector.h0000644000176200001440000003272413254747464016052 0ustar liggesusers#ifndef __CVECTOR_H__ #define __CVECTOR_H__ #ifndef cvector_assert #include #define cvector_assert(X) assert(X) #endif #include #define vector_define(VECTOR_TYPE, BASE_TYPE) \ struct VECTOR_TYPE; \ typedef struct VECTOR_TYPE VECTOR_TYPE; \ \ struct VECTOR_TYPE { \ BASE_TYPE *_begin; \ BASE_TYPE *_end; \ BASE_TYPE *_capacity; \ }; \ \ static inline \ void VECTOR_TYPE##_reserve(VECTOR_TYPE * v, size_t n); \ \ /* Default constructor: constructs an empty vector, with no content and a size of zero. */ \ static inline \ void VECTOR_TYPE##_ctor_zero(VECTOR_TYPE * v) \ { \ v->_begin = NULL; \ v->_end = NULL; \ v->_capacity = NULL; \ } \ \ /* Default constructor: constructs an empty vector, with no content and a size of zero. */ \ static inline \ void VECTOR_TYPE##_ctor(VECTOR_TYPE * v, size_t n) \ { \ VECTOR_TYPE##_ctor_zero (v); \ VECTOR_TYPE##_reserve (v, n); \ } \ \ /* Default constructor: dynamically allocates an empty vector, with no content and a size of zero. */ \ static inline \ VECTOR_TYPE * VECTOR_TYPE##_new(size_t n) \ { \ VECTOR_TYPE * v = malloc (sizeof(VECTOR_TYPE)); \ VECTOR_TYPE##_ctor (v, n); \ return v; \ } \ \ /* Destructor. It frees the memory allocated to the vector, but it does not try to free each of its elements or the vector itself. */ \ static inline \ void VECTOR_TYPE##_dtor(VECTOR_TYPE * v) \ { free(v->_begin); } \ \ /* Destructor. It frees the memory allocated to a dynamically allocated vector, but it does not try to free each of its elements. */ \ static inline \ void VECTOR_TYPE##_delete(VECTOR_TYPE * v) \ { VECTOR_TYPE##_dtor(v); free(v); } \ \ \ /* Returns the number of elements in the vector container. */ \ static inline \ size_t VECTOR_TYPE##_size(const VECTOR_TYPE * v) \ { return v->_end - v->_begin; } \ \ /* Return size of allocated storage capacity. */ \ static inline \ size_t VECTOR_TYPE##_capacity(const VECTOR_TYPE * v) \ { return v->_capacity - v->_begin; } \ \ /* Returns whether the vector container is empty, i.e. whether its size is 0. */ \ static inline \ bool VECTOR_TYPE##_empty(const VECTOR_TYPE * v) \ { return v->_begin == v->_end; } \ \ /* Requests that the capacity of the allocated storage space for the elements of the vector container be at least enough to hold n elements. This informs the vector of a planned increase in size, although notice that the parameter n informs of a minimum, so the resulting capacity may be any capacity equal or larger than this. When n is greater than the current capacity, a reallocation is attempted during the call to this function. If successful, it grants that no further automatic reallocations will happen because of a call to vector::insert or vector::push_back until the vector size surpasses at least n (this preserves the validity of iterators on all these future calls). A reallocation invalidates all previously obtained iterators, references and pointers to elements of the vector. In any case, a call to this function never affects the elements contained in the vector, nor the vector size. */ \ static inline \ void VECTOR_TYPE##_reserve(VECTOR_TYPE * v, size_t n) \ { \ size_t old_capacity = VECTOR_TYPE##_capacity(v); \ size_t old_size = VECTOR_TYPE##_size(v); \ if (n > old_capacity) { \ v->_begin = realloc(v->_begin, sizeof(BASE_TYPE) * n); \ v->_end = v->_begin + old_size; \ v->_capacity = v->_begin + n; \ } \ } \ \ /* Adds a new element at the end of the vector, after its current last element. The content of this new element is initialized to a copy of x. This effectively increases the vector size by one, which causes a reallocation of the internal allocated storage if the vector size was equal to the vector capacity before the call. Reallocations invalidate all previously obtained iterators, references and pointers. */ \ static inline \ void VECTOR_TYPE##_push_back(VECTOR_TYPE * v, BASE_TYPE x) \ { \ if (v->_end == v->_capacity) \ VECTOR_TYPE##_reserve (v, \ (VECTOR_TYPE##_capacity (v) == 0) \ ? 8 \ : 2 * VECTOR_TYPE##_capacity (v)); \ *(v->_end) = x; \ v->_end++; \ } \ \ /* Removes the last element in the vector, effectively reducing the vector size by one and invalidating all iterators and references to it. Note that the element is not freed. */ \ static inline \ BASE_TYPE VECTOR_TYPE##_pop_back(VECTOR_TYPE * v) \ { \ cvector_assert(!VECTOR_TYPE##_empty(v)); \ v->_end--; \ return *(v->_end); \ } \ \ /* Returns the first element in the vector container. This is not a reference like in C++! */ \ static inline \ BASE_TYPE VECTOR_TYPE##_front(const VECTOR_TYPE * v) \ { \ cvector_assert (!VECTOR_TYPE##_empty(v)); \ return v->_begin[0]; \ } \ \ /* Returns the last element in the vector container. This is not a reference like in C++! */ \ static inline \ BASE_TYPE VECTOR_TYPE##_back(const VECTOR_TYPE * v) \ { \ cvector_assert (!VECTOR_TYPE##_empty(v)); \ return *(v->_end - 1); \ } \ \ /* Returns an iterator (pointer) referring to the first element in the vector container. */ \ static inline \ BASE_TYPE * VECTOR_TYPE##_begin(const VECTOR_TYPE * v) \ { return v->_begin; } \ \ /* Returns an iterator (pointer) referring to the past-the-end element in the vector container. */ \ static inline \ BASE_TYPE * VECTOR_TYPE##_end(const VECTOR_TYPE * v) \ { return v->_end; } \ \ /* Returns the element at position pos. */ \ static inline \ BASE_TYPE VECTOR_TYPE##_at(const VECTOR_TYPE * v, size_t pos) \ { \ cvector_assert(pos <= VECTOR_TYPE##_size(v)); \ return v->_begin[pos]; \ } \ \ /* Set the element at position pos to value e and return it. */ \ static inline \ BASE_TYPE VECTOR_TYPE##_set(const VECTOR_TYPE * v, size_t pos, BASE_TYPE e) \ { \ cvector_assert(pos <= VECTOR_TYPE##_size(v)); \ return (v->_begin[pos] = e); \ } \ #endif eaf/inst/scripts/eaf/svn_version0000644000176200001440000000000313254747464016513 0ustar liggesusers251eaf/inst/scripts/eaf/eaf_io.h0000644000176200001440000000243213254747464015620 0ustar liggesusers#ifndef EAF_INPUT_OUTPUT_H #define EAF_INPUT_OUTPUT_H #define _GNU_SOURCE #include // for errno /*const char *program_invocation_short_name = "eaf";*/ #define PROGRAM_NAME "eaf" #define point_printf_format "% 17.16g" /* Error codes for read_data. */ enum ERROR_READ_DATA { READ_INPUT_FILE_EMPTY = -1, READ_INPUT_WRONG_INITIAL_DIM = -2, ERROR_FOPEN = -3, ERROR_CONVERSION = -4, ERROR_COLUMNS = -5, }; int read_int_data (const char *filename, int **data_p, int *nobjs_p, int **cumsizes_p, int *nsets_p); int read_double_data (const char *filename, double **data_p, int *nobjs_p, int **cumsizes_p, int *nsets_p); /* If we're not using GNU C, elide __attribute__ */ #ifndef __GNUC__ # define __attribute__(x) /* NOTHING */ #endif #ifdef R_PACKAGE #include #define errprintf error #define warnprintf warning #else void errprintf(const char * template,...) /* enables the compiler to check the format string against the parameters */ __attribute__ ((format(printf, 1, 2))); void warnprintf(const char *template,...) /* enables the compiler to check the format string against the parameters */ __attribute__ ((format(printf, 1, 2))); #endif #endif eaf/inst/scripts/eaf/LICENSE0000644000176200001440000004642013254747464015237 0ustar liggesusers --------------------------------------------------------------------- Copyright (c) 2006, 2007, 2008 Carlos Fonseca Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. IMPORTANT NOTE: Please be aware that the fact that this program is released as Free Software does not excuse you from scientific propriety, which obligates you to give appropriate credit! If you write a scientific paper describing research that made substantive use of this program, it is your obligation as a scientist to acknowledge its use. Moreover, as a personal note, I would appreciate it if you would email with citations of papers referencing this work so I can mention them to my funding agent and tenure committee. There should be a verbatim copy of the GNU General Public License below; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA --------------------------------------------------------------------- GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Lesser General Public License instead.) You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. To protect your rights, we need to make restrictions that forbid anyone to deny you these rights or to ask you to surrender the rights. These restrictions translate to certain responsibilities for you if you distribute copies of the software, or if you modify it. For example, if you distribute copies of such a program, whether gratis or for a fee, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. We protect your rights with two steps: (1) copyright the software, and (2) offer you this license which gives you legal permission to copy, distribute and/or modify the software. Also, for each author's protection and ours, we want to make certain that everyone understands that there is no warranty for this free software. If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. (Exception: if the Program itself is interactive but does not normally print such an announcement, your work based on the Program is not required to print an announcement.) These requirements apply to the modified work as a whole. If identifiable sections of that work are not derived from the Program, and can be reasonably considered independent and separate works in themselves, then this License, and its terms, do not apply to those sections when you distribute them as separate works. But when you distribute the same sections as part of a whole which is a work based on the Program, the distribution of the whole must be on the terms of this License, whose permissions for other licensees extend to the entire whole, and thus to each and every part regardless of who wrote it. Thus, it is not the intent of this section to claim rights or contest your rights to work written entirely by you; rather, the intent is to exercise the right to control the distribution of derivative or collective works based on the Program. In addition, mere aggregation of another work not based on the Program with the Program (or with a work based on the Program) on a volume of a storage or distribution medium does not bring the other work under the scope of this License. 3. You may copy and distribute the Program (or a work based on it, under Section 2) in object code or executable form under the terms of Sections 1 and 2 above provided that you also do one of the following: a) Accompany it with the complete corresponding machine-readable source code, which must be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, b) Accompany it with a written offer, valid for at least three years, to give any third party, for a charge no more than your cost of physically performing source distribution, a complete machine-readable copy of the corresponding source code, to be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, c) Accompany it with the information you received as to the offer to distribute corresponding source code. (This alternative is allowed only for noncommercial distribution and only if you received the program in object code or executable form with such an offer, in accord with Subsection b above.) The source code for a work means the preferred form of the work for making modifications to it. For an executable work, complete source code means all the source code for all modules it contains, plus any associated interface definition files, plus the scripts used to control compilation and installation of the executable. However, as a special exception, the source code distributed need not include anything that is normally distributed (in either source or binary form) with the major components (compiler, kernel, and so on) of the operating system on which the executable runs, unless that component itself accompanies the executable. If distribution of executable or object code is made by offering access to copy from a designated place, then offering equivalent access to copy the source code from the same place counts as distribution of the source code, even though third parties are not compelled to copy the source along with the object code. 4. You may not copy, modify, sublicense, or distribute the Program except as expressly provided under this License. Any attempt otherwise to copy, modify, sublicense or distribute the Program is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance. 5. You are not required to accept this License, since you have not signed it. However, nothing else grants you permission to modify or distribute the Program or its derivative works. These actions are prohibited by law if you do not accept this License. Therefore, by modifying or distributing the Program (or any work based on the Program), you indicate your acceptance of this License to do so, and all its terms and conditions for copying, distributing or modifying the Program or works based on it. 6. Each time you redistribute the Program (or any work based on the Program), the recipient automatically receives a license from the original licensor to copy, distribute or modify the Program subject to these terms and conditions. You may not impose any further restrictions on the recipients' exercise of the rights granted herein. You are not responsible for enforcing compliance by third parties to this License. 7. If, as a consequence of a court judgment or allegation of patent infringement or for any other reason (not limited to patent issues), conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot distribute so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not distribute the Program at all. For example, if a patent license would not permit royalty-free redistribution of the Program by all those who receive copies directly or indirectly through you, then the only way you could satisfy both it and this License would be to refrain entirely from distribution of the Program. If any portion of this section is held invalid or unenforceable under any particular circumstance, the balance of the section is intended to apply and the section as a whole is intended to apply in other circumstances. It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice. This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License. 8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License. 9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. NO WARRANTY 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. eaf/inst/scripts/eaf/eaf.h0000644000176200001440000001472713254747464015143 0ustar liggesusers/************************************************************************* eaf.h: prototypes and shared types of eaf and eaf-test --------------------------------------------------------------------- Copyright (c) 2006, 2007, 2008 Carlos Fonseca Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- *************************************************************************/ #ifdef R_PACKAGE #include #define eaf_assert(EXP) \ do { if (!(EXP)) { error("eaf package: error: assertion failed: '%s'", \ #EXP); }} while(0) #define EAF_MALLOC(WHAT, NMEMB, SIZE) \ do { WHAT = malloc (NMEMB * SIZE); \ if (!WHAT) { \ error(__FILE__ ": %s = malloc (%u * %u) failed", \ #WHAT, (unsigned int) NMEMB, (unsigned int) SIZE); } \ } while (0) #define fatalprintf(X) error(X) #else #include #define eaf_assert(X) assert(X) #define EAF_MALLOC(WHAT, NMEMB, SIZE) \ do { WHAT = malloc (NMEMB * SIZE); \ if (!WHAT) { perror (__FILE__ ": " #WHAT ); exit (EXIT_FAILURE); } \ } while(0) #define fatalprintf(X) \ do { errprintf (X); exit (EXIT_FAILURE); } while(0) #endif #include "eaf_io.h" #include #include #include #include #include #include /* If the input are always integers, adjusting this type will certainly improve performance. */ #define objective_t double #if objective_t == double # define objective_MAX INFINITY # define objective_MIN -INFINITY # define objective_t_scanf_format "%lf" # define read_objective_t_data read_double_data #else # define objective_MAX INT_MAX # define objective_MIN INT_MIN # define objective_t_scanf_format "%d" # define read_objective_t_data read_int_data #endif typedef struct { int nobj; /* FIXME: there is no point to store this here. */ int nruns; int size; int maxsize; bool *attained; objective_t *data; } eaf_t; void eaf_print_attsurf (eaf_t *, FILE *coord_file, /* output file (coordinates) */ FILE *indic_file, /* output file (attainment indicators) */ FILE *diff_file); /* output file (difference nruns/2) */ eaf_t * eaf_create (int nobj, int nruns, int npoints); void eaf_delete (eaf_t * eaf); objective_t * eaf_store_point_help (eaf_t * eaf, int nobj, const int *save_attained); static inline int eaf_totalpoints (eaf_t **eaf, int n) { int totalpoints = 0; int k; for (k = 0; k < n; k++) { totalpoints += eaf[k]->size; } return totalpoints; } eaf_t ** eaf2d (const objective_t *data, /* the objective vectors */ const int *cumsize, /* the cumulative sizes of the runs */ int nruns, /* the number of runs */ const int *attlevel, /* the desired attainment levels */ int nlevels /* the number of att levels */ ); eaf_t ** eaf3d (objective_t *data, const int *cumsize, int nruns, const int *attlevel, const int nlevels); static inline eaf_t ** attsurf (objective_t *data, /* the objective vectors */ int nobj, /* the number of objectives */ const int *cumsize, /* the cumulative sizes of the runs */ int nruns, /* the number of runs */ const int *attlevel, /* the desired attainment levels */ int nlevels /* the number of att levels */ ) { switch (nobj) { case 2: return eaf2d (data, cumsize, nruns, attlevel, nlevels); break; case 3: return eaf3d (data, cumsize, nruns, attlevel, nlevels); break; default: fatalprintf ("this implementation only supports two or three dimensions.\n"); } } static inline void fprint_set (FILE *stream, const objective_t **data, int ntotal) { int k; for (k = 0; k < ntotal; k++) fprintf (stream, "%6d: % .16g % .16g\n", k, (double)data[k][0], (double)data[k][1]); } static inline void attained_left_right (const bool *attained, int division, int total, int *count_left, int *count_right) { int count_l = 0; int count_r = 0; int k; eaf_assert (division < total); for (k = 0; k < division; k++) if (attained[k]) count_l++; for (k = division; k < total; k++) if (attained[k]) count_r++; *count_left = count_l; *count_right = count_r; } static inline int fast_round (double x) { return (int) (x > 0) ? x + .5 : (x < 0) ? x - .5 : x; } static inline int percentile2level (int p, int n) { int level = fast_round (n * p / 100.0); if (level > n) level = n; else if (level < 1) level = 1; return level; } #define cvector_assert(X) eaf_assert(X) #include "cvector.h" vector_define(vector_objective, objective_t) vector_define(vector_int, int) typedef struct { vector_objective xy; vector_int col; } eaf_polygon_t; #define eaf_compute_area eaf_compute_area_new eaf_polygon_t *eaf_compute_area_new (eaf_t **eaf, int nlevels); eaf_polygon_t *eaf_compute_area_old (eaf_t **eaf, int nlevels); void eaf_print_polygon (FILE* stream, eaf_t **eaf, int nlevels); eaf/inst/scripts/eaf/eaf3d.c0000644000176200001440000005711313254747464015361 0ustar liggesusers/************************************************************************* eaf3d: Compute the empirical attainment function from a sequence of non-dominated point sets (three-objective case) --------------------------------------------------------------------- Copyright (c) 2009-2011 Andreia Guerreiro Carlos M. Fonseca Luis Paquete Manuel Lopez-Ibanez This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- *************************************************************************/ #include "eaf.h" #include "avl.h" #include #include #include #include #include #include #define max( a, b ) ( ((a) > (b)) ? (a) : (b) ) typedef struct dlnode { objective_t *x; /* The data vector */ struct dlnode *next; struct dlnode *prev; int set; } dlnode_t; typedef struct removed_list { avl_node_t *head; } removed_list_t; static removed_list_t * removed_list; static inline bool avl_tree_is_empty (const avl_tree_t *avltree) { return avltree->top == NULL; } static inline objective_t *node_point(const avl_node_t *node) { return (objective_t*) node->item; } static int compare_node(const void *p1, const void *p2) { const objective_t x1 = *((*(const dlnode_t **)p1)->x); const objective_t x2 = *((*(const dlnode_t **)p2)->x); return (x1 < x2) ? -1 : ( x1 > x2 ) ? 1 : 0; } /* Create a list of points, ordered by the third coordinate. */ static dlnode_t * setup_cdllist(objective_t *data, int d, const int *cumsize, int nsets) { dlnode_t **scratch; int n = cumsize[nsets - 1]; dlnode_t *head = malloc ((n + 1) * sizeof(dlnode_t)); head->x = data; head[0].set = 0; int i, k; for (i = 0, k = 0; i < n; i++) { if (i == cumsize[k]) k++; head[i+1].set = k; // ->x points to the first coordinate of each point. head[i+1].x = head[i].x + d ;// this will be fixed a few lines below... head[i+1].next = head[i].next; head[i+1].prev = head[i].prev; } head->x = NULL; // head contains no data scratch = malloc(n * sizeof(dlnode_t*)); for (i = 0; i < n; i++) scratch[i] = head + i + 1; // ->x points to the last coordinate of each point. for (i = 0; i < n; i++) scratch[i]->x--; // Sort according to the last coordinate. qsort(scratch, n, sizeof(dlnode_t*), compare_node); head->next = scratch[0]; scratch[0]->prev = head; for (i = 1; i < n; i++) { scratch[i-1]->next = scratch[i]; scratch[i]->prev = scratch[i-1]; } scratch[n-1]->next = head; head->prev = scratch[n-1]; // ->x points to the first coordinate. for (i = 0; i < n; i++) scratch[i]->x -= d - 1; free(scratch); return head; } /* FIXME: Why this variant in one single indirection and the eaf2d equivalent is 2. */ static int compare_tree_asc_x(const void *p1, const void *p2) { const objective_t x1 = *((const objective_t *)p1); const objective_t x2 = *((const objective_t *)p2); return (x1 < x2) ? -1 : (x1 > x2) ? 1 : 0; } static int compare_tree_desc_y( const void *p1, const void *p2) { const objective_t y1 = *((const objective_t *)p1+1); const objective_t y2 = *((const objective_t *)p2+1); return (y1 > y2) ? -1 : (y1 < y2) ? 1 : 0; } static int avl_search_closest_y(const avl_tree_t *avltree, const void *item, avl_node_t **avlnode) { avl_node_t *node; int c; if(!avlnode) avlnode = &node; node = avltree->top; if(!node) return *avlnode = NULL, 0; for(;;) { c = compare_tree_desc_y(item, node->item); if(c < 0) { if(node->left) node = node->left; else return *avlnode = node, -1; } else if(c > 0) { if(node->right) node = node->right; else return *avlnode = node, 1; } else { return *avlnode = node, 0; } } } static void find_all_promoters(avl_node_t * avlnode, int * dom_sets, int nruns) { avl_node_t *node = avlnode; while (node != NULL) { dom_sets[node->set] = 1; if (node->remover != NULL) find_all_promoters(node->remover, dom_sets, nruns); node = node->promoter; } node = avlnode->equal; while (node != NULL) { dom_sets[node->set] = 1; node = node->equal; } } /* Prints the list in the tree, ordered by the second dimension, from the highest value to the lowest. The values printed are the values from dimension 0 to dimension dim of each point */ static void printlist(avl_tree_t *avltree, int dim, FILE *outfile) { avl_node_t *aux; aux = avltree->head; int i; objective_t * val; while(aux){ val = (objective_t *)aux->item; //printf("-> "); for(i = 0; i < dim; i++){ fprintf(outfile, point_printf_format "\t", val[i]); } fprintf(outfile, "\n"); aux = aux->next; } } void printset(FILE* stream, avl_tree_t **set, int nset) { int i; fprintf(stream, "# sets\n----------------------\n"); for(i = 0; i < nset; i++){ if(set[i]->top != NULL) { fprintf (stream, "set: %d", i); printlist(set[i], 3, stream); } } } void printlevel(FILE * stream, avl_tree_t **level, int nset) { int i; fprintf (stream, "#levels\n-------------------\n"); for(i = 0; i < nset; i++){ if(level[i]->top != NULL){ fprintf (stream, "level: %d\n", i); printlist(level[i], 3, stream); } } } static int printlist_points_indic(avl_tree_t *avltree, int dim, int nruns, FILE *outfile, FILE *outfileindic) { avl_node_t *aux; aux = avltree->head; int * dom_sets = (int *) malloc(nruns * sizeof(int)); int i, k, totalp = 0; objective_t * val; while(aux){ val = (objective_t *)aux->item; if(outfile){ fprintf(outfile, point_printf_format, val[0]); for(i = 1; i < dim; i++){ fprintf(outfile, "\t" point_printf_format, val[i]); } fprintf(outfile, (outfile == outfileindic) ? "\t" : "\n"); } if(outfileindic){ for(k = 0; k < nruns; k++) dom_sets[k] = 0; find_all_promoters(aux, dom_sets, nruns); fprintf(outfileindic, "%d", dom_sets[0]); for(k = 1; k < nruns; k++){ fprintf(outfileindic, "\t%d", dom_sets[k]); } fprintf(outfileindic, "\n"); } aux = aux->next; totalp++; } free(dom_sets); return totalp; } /* Returns the total number of points printed */ int printoutput(avl_tree_t **level, int nset, int d, FILE **outfile, int noutfiles, FILE **outfileindic, int noutfilesi, int * attlevel, int nlevels){ int i, k, f, fi, totalp = 0; for(i = 0; i < nlevels; i++){ k = attlevel[i] - 1; f = (noutfiles > 1) ? i : 0; fi = (noutfilesi > 1) ? i : 0; if(level[k]->head != NULL){ totalp += printlist_points_indic(level[k], d, nset, (outfile ? outfile[f] : NULL), (outfileindic ? outfileindic[fi] : NULL)); } if(i < nlevels -1){ if(outfile) fprintf(outfile[f], "\n"); if(outfileindic && (outfile == NULL || outfile[f] != outfileindic[fi])){ fprintf(outfileindic[fi], "\n"); } } } return totalp; } static void print_list_indic(avl_tree_t * level, int nruns, FILE * indicfile){ int * dom_sets = (int *) malloc(nruns * sizeof(int)); avl_node_t * avlnode; avlnode = level->head; int i; while(avlnode){ for(i = 0; i < nruns; i++){ dom_sets[i] = 0; } find_all_promoters(avlnode, dom_sets, nruns); for(i = 0; i < nruns; i++){ fprintf(indicfile, "%d\t", dom_sets[i]); } fprintf(indicfile, "\n"); avlnode = avlnode->next; } free(dom_sets); } void printindic(avl_tree_t ** levels, int nruns, FILE ** indicfile, int nfiles, int * attlevel, int nlevels){ int i, k, f; for(i=0; i < nlevels; i++){ k = attlevel[i] - 1; f = (nfiles == 1) ? 0 : i; if(levels[k]->head != NULL){ print_list_indic(levels[k], nruns, indicfile[f]); } fprintf(indicfile[f], "\n"); } } void printitem(FILE * stream, objective_t *value, int dim) { int i; for (i = 0; i < dim; i++) fprintf (stream, "%f ", value[i]); fprintf (stream, "\n"); } void add2output(avl_tree_t *output, avl_node_t *tnode) { if(output->top != NULL){ output->tail->next = tnode; output->tail = tnode; }else{ avl_insert_top(output, tnode); } } void add2output_all(avl_tree_t *output, avl_tree_t *tree_add){ avl_node_t *node = tree_add->head; avl_unlink_node(tree_add, node); free(node->item); free(node); node = tree_add->tail; avl_unlink_node(tree_add, node); free(node->item); free(node); if(tree_add->head != NULL){ if(output->tail != NULL){ output->tail->next=tree_add->head; }else{ output->head=tree_add->head; } } } //this fuction is called only when item isn't dominated by any point in tree. //note: prevnode is above and at item's left, so any dominated point that it may dominate is at its right static void add2set(avl_tree_t *tree, avl_node_t *prevnode, avl_node_t *tnode, objective_t *item) { avl_init_node (tnode, item); if(node_point(prevnode)[0] == item[0]) prevnode = prevnode->prev; avl_insert_after (tree, prevnode, tnode); tnode = tnode->next; // tnode->next is dominated by item while (node_point(tnode)[1] >= item[1]) { avl_unlink_node(tree, tnode); avl_node_t *node = tnode; tnode = tnode->next; free(node); } } static void avl_add_promoter(avl_node_t *avlnode, int set, avl_node_t *promoter) { avlnode->set = set; avlnode->promoter = promoter; avlnode->remover = NULL; avlnode->equal = NULL; } static void add_removed(avl_node_t *node) { node->next = removed_list->head; removed_list->head = node; } static avl_node_t * add2level(avl_tree_t *tree, objective_t *item, avl_tree_t *output, int set, avl_node_t * promoter) { avl_node_t *prevnode; avl_node_t *aux; switch (avl_search_closest_y(tree, item, &prevnode)) { case -1: prevnode = prevnode->prev; break; case 0: prevnode = (node_point(prevnode)[0] > item[0]) ? prevnode : prevnode->prev; break; case 1: break; } //aux represents the point immediately below item. aux = prevnode->next; // A new point (item) is added, but only if it isn't dominated. if (node_point(aux)[0] > item[0]) { avl_node_t *tnode = malloc(sizeof(avl_node_t)); avl_init_node(tnode, item); avl_add_promoter(tnode, set, promoter); avl_node_t *newnode = tnode; avl_insert_after(tree, prevnode, tnode); tnode = tnode->prev; // tnode->prev is dominated by item while (node_point(tnode)[0] >= item[0]) { avl_unlink_node(tree,tnode); aux = tnode; tnode = tnode->prev; if (node_point(aux)[2] < item[2]) add2output(output, aux); else{ free(aux->item); add_removed(aux); aux->remover = newnode; } /* Each point that is removed from this level, becomes a final point but only if the point that dominates it (item) dominates it only from dimension 0 to 1. */ } return newnode; } else{ free(item); return NULL; } } static objective_t * copy_point(const objective_t * v) { objective_t *value = malloc(3 * sizeof(objective_t)); value[0] = v[0]; value[1] = v[1]; value[2] = v[2]; return value; } //at left or equal static avl_node_t * find_point_at_left(avl_tree_t *tree, objective_t *item){ avl_node_t *leftNode; if (avl_search_closest(tree, item, &leftNode) < 0) leftNode = leftNode->prev; return leftNode; } //below and not equal static avl_node_t * find_point_below(avl_tree_t *tree, objective_t *item){ avl_node_t *belowNode; if (avl_search_closest_y(tree, item, &belowNode) >= 0) belowNode = belowNode->next; return belowNode; } static objective_t * new_point(objective_t x1, objective_t x2, objective_t x3) { objective_t *value = malloc(3 * sizeof(objective_t)); value[0] = x1; value[1] = x2; value[2] = x3; return value; } void eaf3df(dlnode_t *list, avl_tree_t **set, avl_tree_t **level, avl_tree_t **output, int nset) { // point from some level immediately at new's left, corresponds to r in pseudocode avl_node_t *leftNodeL; /*levelNode[t] is the point being verified (possibly to be promoted) from level t, corresponds to s_t in pseudocode */ avl_node_t *levelNode[nset]; avl_node_t auxNodes[nset]; /* these points are needed to represent the intersections of new with the point, of each level, immediately at new's left */ objective_t auxValues[nset][2]; avl_node_t *promoters[nset]; dlnode_t *new = list->next; //new - represents the new point //add new to its set avl_node_t * tnode = avl_init_node(malloc(sizeof(avl_node_t)), new->x); avl_insert_after(set[new->set], set[new->set]->head, tnode); //new is the only point so far, so it is added to the first level tnode = avl_init_node(malloc(sizeof(avl_node_t)), copy_point(new->x)); avl_add_promoter(tnode, new->set, NULL); avl_insert_after(level[0], level[0]->head, tnode); bool mask[nset]; // needed to know how many different sets were considered so far memset(mask, false, nset * sizeof(mask[0])); mask[new->set] = true; int start_at = 0, stop_at; // indicates which levels must be verified (from and to which) // Points are sorted in ascending order with respect to the third coordinate. while (new->next->x != NULL) { new = new->next; /* newPrev: point from new's set immediately at its left, it corresponds to q in pseudocode. */ avl_node_t *newPrev = find_point_at_left(set[new->set], new->x); // if new is dominated by a point from its set, skip it. if (node_point(newPrev)[1] <= new->x[1]) { continue; } avl_node_t *dom_new = NULL; stop_at = 0; // First part int k; for(k = start_at; k >= stop_at; k--) { leftNodeL = find_point_at_left(level[k], new->x); // new is dominated by a point from level k if (node_point(leftNodeL)[1] <= new->x[1]) { dom_new = leftNodeL; stop_at = k + 1; } else if (node_point(leftNodeL)[1] < node_point(newPrev)[1]) { /* the intersection point of new with the point in level k immediately at new's left should be added to level k+1, so this intersection point is saved in levelNode in order to be added later */ auxValues[k][0] = new->x[0]; auxValues[k][1] = node_point(leftNodeL)[1]; auxNodes[k].item = auxValues[k]; auxNodes[k].next = leftNodeL->next; levelNode[k] = &auxNodes[k]; promoters[k] = leftNodeL; } else { levelNode[k] = find_point_below(level[k], newPrev->item); promoters[k] = levelNode[k]; } } /* setNode: a point from new's set at its right, it also corresponds to q in pseudocode. */ avl_node_t *setNode = newPrev; //Second part do { setNode = setNode->next; objective_t lbound = max(node_point(setNode)[1], new->x[1]); for (k = start_at; k >= stop_at; k--) { //while levelNode is dominated by new but not by any point from new's set, levelNode is promoted while (node_point(levelNode[k])[1] >= lbound && (node_point(levelNode[k])[1] > lbound || lbound > new->x[1])) { if (node_point(setNode)[0] <= node_point(levelNode[k])[0]) { levelNode[k] = find_point_below(level[k], setNode->item); promoters[k] = levelNode[k]; } else { objective_t *value = new_point(node_point(levelNode[k])[0], node_point(levelNode[k])[1], new->x[2]); tnode = add2level(level[k+1], value, output[k+1], new->set, promoters[k]); if(tnode != NULL && new->x[2] == node_point(promoters[k])[2] && node_point(levelNode[k])[0] == node_point(promoters[k])[0]){ promoters[k]->equal = tnode; } levelNode[k] = levelNode[k]->next; promoters[k] = levelNode[k]; } } } }while(node_point(setNode)[1] > new->x[1]); //Third part for(k = start_at; k >= stop_at; k--){ //if the intersection point of new with the point from level k immediately below new //isn't dominated, it is added to level k+1 if(node_point(levelNode[k])[0] < node_point(setNode)[0]){ objective_t *value; value = new_point(node_point(levelNode[k])[0], new->x[1], new->x[2]); tnode = add2level(level[k+1], value, output[k+1], new->set, promoters[k]); if(tnode != NULL && new->x[2] == node_point(levelNode[k])[2] && new->x[1] == node_point(levelNode[k])[1]){ promoters[k]->equal = tnode; } } } //add new to its set tnode = avl_init_node(malloc(sizeof(avl_node_t)), new->x); add2set(set[new->set], newPrev, tnode, new->x); //add new to the lowest level where it isn't dominated by any point from that level tnode = add2level(level[stop_at], copy_point(new->x), output[stop_at], new->set, dom_new); if(stop_at > 0 && tnode != NULL && new->x[2] == node_point(dom_new)[2] && new->x[1] == node_point(dom_new)[1] && new->x[0] == node_point(dom_new)[0]){ dom_new->equal = tnode; } if (!mask[new->set]) { if (start_at < (nset - 2)) start_at++; mask[new->set] = true; } } } static void freetree(avl_tree_t *avltree) { avl_node_t *aux, *aux2; aux=avltree->head; if(aux){ while(aux){ aux2 = aux; aux = aux2->next; free(aux2->item); free(aux2); } } free(avltree); } static void freetree2(avl_tree_t *avltree) { avl_node_t *aux, *aux2; aux=avltree->head; free(aux->item); while(aux->next){ aux2 = aux; aux = aux2->next; free(aux2); } free(aux->item); free(aux); free(avltree); } void free_removed(){ avl_node_t * aux, * node = removed_list->head; while(node != NULL){ aux = node; node = node->next; free(aux); } free(removed_list); } void freeoutput(avl_tree_t **output, int nset){ int i; for(i = 0; i < nset; i++){ freetree(output[i]); } free(output); free_removed(); } void add_sentinels(avl_tree_t * tree, int n) { objective_t *value = malloc(n * sizeof(objective_t)); value[0] = objective_MIN; value[1] = objective_MAX; avl_node_t *tnode = avl_init_node(malloc(sizeof(avl_node_t)), value); avl_insert_top(tree, tnode); value = malloc(n * sizeof(objective_t)); value[0] = objective_MAX; value[1] = objective_MIN; avl_node_t *tnode2 = avl_init_node(malloc(sizeof(avl_node_t)), value); avl_insert_after(tree, tnode, tnode2); } static void eaf_store_point_3d (eaf_t * eaf, objective_t x, objective_t y, objective_t z, const int *save_attained) { const int nobj = 3; objective_t * pos = eaf_store_point_help (eaf, nobj, save_attained); pos[0] = x; pos[1] = y; pos[2] = z; eaf->size++; } eaf_t ** eaf3d (objective_t *data, const int *cumsize, int nruns, const int *attlevel, const int nlevels) { const int nobj = 3; const int ntotal = cumsize[nruns - 1]; /* total number of points in data */ avl_tree_t **set = malloc (nruns * sizeof(avl_tree_t)); avl_tree_t **level = malloc (nruns * sizeof(avl_tree_t)); avl_tree_t **output = malloc (nruns * sizeof(avl_tree_t)); int i; for (i = 0; i < nruns; i++) { set[i] = avl_alloc_tree ((avl_compare_t) compare_tree_asc_x, (avl_freeitem_t) free); level[i] = avl_alloc_tree ((avl_compare_t) compare_tree_asc_x, (avl_freeitem_t) free); output[i] = avl_alloc_tree ((avl_compare_t) compare_tree_asc_x, (avl_freeitem_t) free); add_sentinels(set[i], nobj); add_sentinels(level[i], nobj); } removed_list = (removed_list_t *) malloc(sizeof(removed_list)); removed_list->head = NULL; dlnode_t *list = setup_cdllist(data, nobj, cumsize, nruns); eaf3df(list, set, level, output, nruns); for (i = 0; i < nruns; i++) { add2output_all(output[i], level[i]); } for(i=0; ihead; while (aux) { objective_t * val = aux->item; for(int j = 0; j < nruns; j++) attained[j] = 0; find_all_promoters(aux, attained, nruns); eaf_store_point_3d (eaf[l], val[0], val[1], val[2], attained); aux = aux->next; } } return eaf; } eaf/inst/scripts/eaf/gcc.mk0000644000176200001440000000307313254747464015314 0ustar liggesusers# -*- Makefile-gmake -*- GCC_MAJOR := $(shell $(CC) -E -dM - < /dev/null | grep "__GNUC__" | cut -d\ -f3) GCC_MINOR := $(shell $(CC) -E -dM - < /dev/null | grep "__GNUC_MINOR__" | cut -d\ -f3) gcc-guess-march = $(strip $(shell ${CC} -march=$(MARCH) -x c -S -\#\#\# - < /dev/null 2>&1 | \ grep -m 1 -o -e "march=[^'\"]*" | \ sed 's,march=,,')) WARN_CFLAGS = -pedantic -Wall -Wextra CFLAGS += $(WARN_CFLAGS) ifeq ($(DEBUG), 0) OPT_CFLAGS := -O3 -funroll-loops -ffast-math -DNDEBUG # Options -msse -mfpmath=sse improve performance but are not portable. # Options -fstandard-precision=fast -ftree-vectorize are not well # supported in some versions/architectures. else CFLAGS += -g3 -DMALLOC_CHECK_=3 -O0 endif MISSING_MARCH_BEGIN=Cannot guess cpu type for gcc MISSING_MARCH_END=. Please specify your cpu type, \ 'make march=i686' typically works fine in most computers. \ For more fine tuning, consult compiler manual. ifndef MARCH ifeq ($(GCC_MAJOR),2) $(error Please upgrade to a recent version of GCC (>= 4.0)) else ifeq ($(GCC_MAJOR),3) $(warning $(MISSING_MARCH_BEGIN) 3$(MISSING_MARCH_END)) else ifeq ($(GCC_MINOR),0) $(warning $(MISSING_MARCH_BEGIN) 4.0$(MISSING_MARCH_END)) else ifeq ($(GCC_MINOR),1) $(warning $(MISSING_MARCH_BEGIN) 4.1$(MISSING_MARCH_END)) else ifeq ($(GCC_MINOR),2) $(warning $(MISSING_MARCH_BEGIN) 4.2$(MISSING_MARCH_END)) else MARCH := native ARCH := $(gcc-guess-march) OPT_CFLAGS += -march=$(ARCH) endif else ARCH := $(gcc-guess-march) OPT_CFLAGS += -march=$(ARCH) endif eaf/inst/scripts/eaf/eaf.c0000644000176200001440000006123713254747464015134 0ustar liggesusers/************************************************************************* eaf: Computes the empirical attainment function (EAF) from a number of approximation sets. --------------------------------------------------------------------- Copyright (c) 2005 Carlos M. Fonseca Copyright (c) 2006, 2007, 2008, 2015 Carlos M. Fonseca Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- *************************************************************************/ #include #include #include #include #include #include "eaf.h" #ifndef DEBUG #define DEBUG 0 #endif static int compare_x_asc (const void *p1, const void *p2) { objective_t x1 = **(objective_t **)p1; objective_t x2 = **(objective_t **)p2; return (x1 < x2) ? -1 : ((x1 > x2) ? 1 : 0); } static int compare_y_desc (const void *p1, const void *p2) { objective_t y1 = *(*(objective_t **)p1+1); objective_t y2 = *(*(objective_t **)p2+1); return (y1 > y2) ? -1 : ((y1 < y2) ? 1 : 0); } eaf_t * eaf_create (int nobj, int nruns, int npoints) { eaf_t *eaf; EAF_MALLOC (eaf, 1, sizeof(eaf_t)); eaf->nobj = nobj; eaf->nruns = nruns; eaf->size = 0; /* Maximum is npoints, but normally it will be smaller, so at most log2(2 * nruns) realloc will occur. */ eaf->maxsize = 256 + npoints / (2 * nruns); EAF_MALLOC (eaf->data, nobj * eaf->maxsize, sizeof(objective_t)); EAF_MALLOC (eaf->attained, nruns * eaf->maxsize, sizeof(bool)); return eaf; } void eaf_delete (eaf_t * eaf) { free (eaf->data); free (eaf->attained); free (eaf); } objective_t * eaf_store_point_help (eaf_t * eaf, int nobj, const int *save_attained) { const int nruns = eaf->nruns; if (eaf->size == eaf->maxsize) { eaf_assert (eaf->size < INT_MAX / 2); eaf->maxsize = eaf->maxsize * 2; eaf->attained = realloc (eaf->attained, sizeof(bool) * nruns * eaf->maxsize); eaf_assert(eaf->attained); eaf->data = realloc (eaf->data, sizeof(objective_t) * nobj * eaf->maxsize); eaf_assert(eaf->data); } /* We convert from int to bool to hopefully save space. */ for (int k = 0; k < nruns; k++) { eaf->attained[(nruns * eaf->size) + k] = (bool) save_attained[k]; } return eaf->data + nobj * eaf->size; } static void eaf_store_point_2d (eaf_t * eaf, objective_t x, objective_t y, const int *save_attained) { const int nobj = 2; objective_t * pos = eaf_store_point_help (eaf, nobj, save_attained); pos[0] = x; pos[1] = y; eaf->size++; } static void eaf_print_line (FILE *coord_file, FILE *indic_file, FILE *diff_file, const objective_t *x, int nobj, const bool *attained, int nruns) { int count1 = 0; int count2 = 0; int k; if (coord_file) { fprintf (coord_file, point_printf_format "\t" point_printf_format, (double)x[0], (double)x[1]); for (k = 2; k < nobj; k++) fprintf (coord_file, "\t" point_printf_format, (double)x[k]); fprintf (coord_file, (coord_file == indic_file) || (coord_file == diff_file) ? "\t" : "\n"); } if (indic_file) { fprintf (indic_file, "%d", attained[0] ? (count1++,1) : 0); for (k = 1; k < nruns/2; k++) fprintf (indic_file, "\t%d", attained[k] ? (count1++,1) : 0); for (k = nruns/2; k < nruns; k++) fprintf (indic_file, "\t%d", attained[k] ? (count2++,1) : 0); fprintf (indic_file, (indic_file == diff_file) ? "\t" : "\n"); } else if (diff_file) { attained_left_right (attained, nruns/2, nruns, &count1, &count2); } if (diff_file) fprintf (diff_file,"%d\t%d\n", count1, count2); } /* Print one attainment surface of the EAF. */ void eaf_print_attsurf (eaf_t * eaf, FILE *coord_file, FILE *indic_file, FILE *diff_file) { int i; for (i = 0; i < eaf->size; i++) { const objective_t *p = eaf->data + i * eaf->nobj; eaf_print_line (coord_file, indic_file, diff_file, p, eaf->nobj, eaf->attained + i * eaf->nruns, eaf->nruns); } } /* eaf2d: compute attainment surfaces from points in objective space, using dimension sweeping. Input arguments: data : a pointer to the data matrix, stored as a linear array of objective_t in row major order. cumsize : an array containing the cumulative number of rows in each non-dominated front (must be non-decreasing). nruns : the number of independent non-dominated fronts. attlevel : an array containing the attainment levels to compute. nlevel : number of attainment levels to compute. coord_file : stream to write the resulting attainment surfaces. indic_file : stream to write the resulting attainment indices. diff_file : stream to write the difference between the the first half and the second half of nruns. */ eaf_t ** eaf2d (const objective_t *data, const int *cumsize, int nruns, const int *attlevel, const int nlevels) { const int nobj = 2; eaf_t **eaf; const objective_t **datax, **datay; /* used to access the data sorted according to x or y */ const int ntotal = cumsize[nruns - 1]; /* total number of points in data */ int *runtab; int *attained, nattained, *save_attained; int k, j, l; /* Access to the data is made via two arrays of pointers: ix, iy These are sorted, to allow for dimension sweeping */ datax = malloc (ntotal * sizeof(objective_t *)); datay = malloc (ntotal * sizeof(objective_t *)); for (k = 0; k < ntotal ; k++) datax[k] = datay[k] = data + nobj * k; #if DEBUG > 1 fprintf (stderr, "Original data:\n"); fprint_set (stderr, datax, ntotal); #endif qsort (datax, ntotal, sizeof(*datax), &compare_x_asc); qsort (datay, ntotal, sizeof(*datay), &compare_y_desc); #if DEBUG > 1 fprintf (stderr, "Sorted data (x):\n"); fprint_set (stderr, datax, ntotal); fprintf (stderr, "Sorted data (y):\n"); fprint_set (stderr, datay, ntotal); #endif /* Setup a lookup table to go from a point to the approximation set (run) to which it belongs. */ runtab = malloc (ntotal * sizeof(int)); for (k = 0, j = 0; k < ntotal; k++) { if (k == cumsize[j]) j++; runtab[k] = j; } #if DEBUG > 1 fprintf (stderr, "Runtab:\n"); for (k = 0; k < ntotal; k++) fprintf (stderr, "%6d: %6d\n", k, runtab[k]); #endif /* Setup tables to keep attainment statistics. In particular, save_attained is needed to cope with repeated values on the same axis. */ attained = malloc (nruns * sizeof(int)); save_attained = malloc (nruns * sizeof(int)); eaf = malloc(nlevels * sizeof(eaf_t*)); for (l = 0; l < nlevels; l++) { eaf[l] = eaf_create (nobj, nruns, ntotal); int level = attlevel[l]; int x = 0; int y = 0; int run; nattained = 0; for (k = 0; k < nruns; k++) attained[k] = 0; /* Start at upper-left corner */ run = runtab[(datax[x] - data) / nobj]; attained[run]++; nattained++; do { /* Move right until desired attainment level is reached */ while (x < ntotal - 1 && (nattained < level || datax[x][0] == datax[x+1][0])) { x++; if (datax[x][1] <= datay[y][1]) { run = runtab[(datax[x] - data)/nobj]; if (!attained[run]) nattained++; attained[run]++; } } #if DEBUG > 1 for (k = 0; k < nruns; k++) fprintf (stderr, "%d ", attained[k]); fprintf (stderr, "\n"); #endif if (nattained < level) continue; /* Now move down until desired attainment level is no longer reached. */ do { /* If there are repeated values along the y axis, we need to remember where we are. */ /*save_nattained = nattained;*/ memcpy (save_attained, attained, nruns * sizeof(*attained)); do { if (datay[y][0] <= datax[x][0]) { run = runtab[(datay[y] - data)/nobj]; attained[run]--; if (!attained[run]) nattained--; } #if DEBUG > 1 for (k = 0; k < nruns; k++) fprintf (stderr, "%d ", attained[k]); fprintf (stderr, "\n"); #endif y++; } while (y < ntotal && datay[y][1] == datay[y - 1][1]); } while (nattained >= level && y < ntotal); eaf_assert (nattained < level); eaf_store_point_2d (eaf[l], datax[x][0], datay[y - 1][1], save_attained); } while (x < ntotal - 1 && y < ntotal); } free(save_attained); free(attained); free(runtab); free(datay); free(datax); return eaf; } #define PRINT_POINT(X,Y) fprintf(stderr, "%g\t%g\n", X, Y) #undef PRINT_POINT #define PRINT_POINT(X,Y) (void)0 /* Produce a polygon suitable to be plotted by the polygon function in R. */ eaf_polygon_t * eaf_compute_area_new (eaf_t **eaf, int nlevels) { /* FIXME: Don't add anything if color_0 == 0 */ #define POLY_SIZE_CHECK() \ do { _poly_size_check--; eaf_assert(_poly_size_check >= 4); \ eaf_assert(_poly_size_check % 2 == 0); _poly_size_check = 0; } while(0) #define eaf_point(A,K) (eaf[(A)]->data + (K) * nobj) #define push_point(X, Y) \ do { vector_objective_push_back (&polygon->xy, (X)); \ vector_objective_push_back (&polygon->xy, (Y)); \ _poly_size_check++; PRINT_POINT(X,Y); \ } while(0) #define polygon_close() \ do { \ vector_int_push_back (&polygon->col, color_0); \ push_point(objective_MIN, objective_MIN); POLY_SIZE_CHECK(); } while(0) int _poly_size_check = 0; eaf_polygon_t * polygon; int *color; int nruns = eaf[0]->nruns; int nobj = eaf[0]->nobj; eaf_assert(nruns % 2 == 0); int max_size = 0; for (int a = 0; a < nlevels; a++) { if (max_size < eaf[a]->size) max_size = eaf[a]->size; } EAF_MALLOC (color, sizeof(int), max_size); EAF_MALLOC(polygon, sizeof(eaf_polygon_t), 1); vector_objective_ctor (&polygon->xy, max_size); vector_int_ctor (&polygon->col, max_size); for (int b = 1; b < nlevels; b++) { const int a = b - 1; int eaf_a_size = eaf[a]->size; int eaf_b_size = eaf[b]->size; int ka; /* Init colors. */ for (ka = 0; ka < eaf_a_size; ka++) { bool *attained = eaf[a]->attained + ka * nruns; int count_left, count_right; attained_left_right (attained, nruns/2, nruns, &count_left, &count_right); color[ka] = count_left - count_right; } /* Find color transitions along the EAF level set. */ objective_t topleft_y = objective_MAX; int last_b = -1; ka = 0; while (true) { const objective_t * pka = NULL; const objective_t * pkb = NULL; int kb = last_b + 1; while (ka < eaf_a_size && kb < eaf_b_size) { pka = eaf_point (a, ka); pkb = eaf_point (b, kb); if (pkb[0] != pka[0]) break; topleft_y = pkb[1]; last_b = kb; if (pkb[1] == pka[1]) { /* Ignore points that exactly overlap. */ ka++; kb++; } else { /* b intersects a above pka. */ eaf_assert(pkb[1] > pka[1]); kb++; break; } } /* Everything in A was overlapping. */ if (ka == eaf_a_size) break; objective_t prev_pka_y = topleft_y; int color_0 = color[ka]; /* Print points and corners until we reach a different color. */ do { pka = eaf_point (a, ka); /* Find the point in B not above the current point in A. */ while (kb < eaf_b_size) { pkb = eaf_point (b, kb); eaf_assert(pkb[0] > pka[0]); if (pkb[1] <= pka[1]) break; kb++; } eaf_assert(pka[1] < prev_pka_y); push_point (pka[0], prev_pka_y); push_point (pka[0], pka[1]); prev_pka_y = pka[1]; ka++; if (kb < eaf_b_size && ka < eaf_a_size) { const objective_t * pka_next = eaf_point (a, ka); eaf_assert (pkb[0] > pka[0]); eaf_assert (pkb[1] <= pka[1]); if (pkb[0] <= pka_next[0]) { /* If B intersects with A, stop here. */ eaf_assert(pkb[1] == pka[1] || pkb[0] == pka_next[0]); eaf_assert (prev_pka_y >= pkb[1]); break; } } } while (ka < eaf_a_size && color_0 != color[ka]); /* pka is the point before changing color, but ka is the position after. */ if (ka == eaf_a_size) {/* We reached the end of eaf_a */ /* We don't have to go down the other side since eaf_a completely dominates eaf_b, so just start by the end. */ if (last_b == eaf_b_size - 1) { /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, topleft_y); eaf_assert(topleft_y > pka[1]); } else { kb = eaf_b_size - 1; pkb = eaf_point(b, kb); eaf_assert (pkb[1] >= pka[1]); eaf_assert (pkb[0] > pka[0]); if (pkb[1] > pka[1]) { /* Create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, pkb[1]); eaf_assert (pkb[1] <= topleft_y); } objective_t prev_pkb_x = pkb[0]; push_point (pkb[0], pkb[1]); kb--; while (kb > last_b) { pkb = eaf_point(b, kb); eaf_assert (pkb[0] > pka[0]); push_point (prev_pkb_x, pkb[1]); push_point (pkb[0], pkb[1]); prev_pkb_x = pkb[0]; kb--; } push_point (pkb[0], topleft_y); eaf_assert (topleft_y > pkb[1]); } /* last_b = eaf_b_size - 1; */ polygon_close(); /* DONE */ break; /* Really done! */ } else { if (kb == eaf_b_size) { eaf_assert (pka[1] < topleft_y); /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, topleft_y); last_b = eaf_b_size - 1; } else { eaf_assert (kb < eaf_b_size); pkb = eaf_point (b, kb); eaf_assert (pkb[1] <= pka[1]); eaf_assert (pkb[0] != pka[0]); /* If pkb and pka are in the same horizontal, pkb does not affect the next polygon. Otherwise, it does. */ int save_last_b = (pkb[1] == pka[1]) ? kb : kb - 1;; /* Now print in reverse. */ objective_t prev_pkb_x = pkb[0]; push_point (pkb[0], pka[1]); kb--; while (kb > last_b) { pkb = eaf_point (b, kb); eaf_assert (pkb[0] != pka[0]); push_point (prev_pkb_x, pkb[1]); push_point (pkb[0], pkb[1]); prev_pkb_x = pkb[0]; kb--; } eaf_assert (topleft_y > pkb[1]); push_point (pkb[0], topleft_y); last_b = save_last_b; } polygon_close(); /* DONE */ eaf_assert(topleft_y >= pka[1]); topleft_y = pka[1]; } } } free (color); return polygon; } /* FIXME: This version does not care about intersections, which is much simpler, but it produces artifacts when plotted with the polygon function in R. */ eaf_polygon_t * eaf_compute_area_old (eaf_t **eaf, int nlevels) { int _poly_size_check = 0; eaf_polygon_t * polygon; int *color; int max_size = 0; int nruns = eaf[0]->nruns; int nobj = eaf[0]->nobj; for (int a = 0; a < nlevels; a++) { if (max_size < eaf[a]->size) max_size = eaf[a]->size; } eaf_assert(nruns % 2 == 0); EAF_MALLOC (color, sizeof(int), max_size); EAF_MALLOC(polygon, sizeof(eaf_polygon_t), 1); vector_objective_ctor (&polygon->xy, max_size); vector_int_ctor (&polygon->col, max_size); for (int b = 1; b < nlevels; b++) { const int a = b - 1; int eaf_a_size = eaf[a]->size; int eaf_b_size = eaf[a + 1]->size; int ka; /* Init colors. */ for (ka = 0; ka < eaf_a_size; ka++) { bool *attained = eaf[a]->attained + ka * nruns; int count_left, count_right; attained_left_right (attained, nruns/2, nruns, &count_left, &count_right); color[ka] = count_left - count_right; } /* Find color transitions along the EAF level set. */ int last_b = -1; objective_t topleft_y = objective_MAX; ka = 0; while (ka < eaf_a_size) { objective_t prev_pka_y = topleft_y; const objective_t * pka; /* Print points and corners until we reach a different color. */ int color_0 = color[ka]; do { pka = eaf_point (a, ka); push_point (pka[0], prev_pka_y); push_point (pka[0], pka[1]); prev_pka_y = pka[1]; ka++; } while (ka < eaf_a_size && color_0 == color[ka]); /* pka is the point before changing color, but ka is the position after. */ if (ka == eaf_a_size) {/* We reached the end of eaf_a */ /* We don't have to go down the other side since eaf_a completely dominates eaf_b, so just start by the end. */ int kb = eaf_b_size - 1; if (last_b == kb) { /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, topleft_y); } else { const objective_t * pkb = eaf_point(a + 1, kb); eaf_assert (pkb[1] >= pka[1]); if (pkb[1] > pka[1]) { /* Create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, pkb[1]); } objective_t prev_pkb_x = pkb[0]; push_point (pkb[0], pkb[1]); kb--; while (kb > last_b) { pkb = eaf_point(a + 1, kb); push_point (prev_pkb_x, pkb[1]); push_point (pkb[0], pkb[1]); prev_pkb_x = pkb[0]; kb--; } push_point (pkb[0], topleft_y); last_b = eaf_b_size - 1; } polygon_close(); /* DONE */ } else { int kb = last_b + 1; /* Different color, go down by the other side until reaching this point. */ if (kb == eaf_b_size) { /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, topleft_y); polygon_close(); /* DONE */ } else { const objective_t * pkb; do { pkb = eaf_point (b, kb); if (pkb[1] <= pka[1]) break; kb++; } while (kb < eaf_b_size); int save_last_b = kb - 1; if (kb == eaf_b_size) { /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, pkb[1]); } else {/* pkb_y <= pka_y */ objective_t prev_pkb_x = pkb[0]; push_point (pkb[0], pka[1]); /* Now print in reverse. */ kb--; while (kb > last_b) { pkb = eaf_point (b, kb); push_point (prev_pkb_x, pkb[1]); push_point (pkb[0], pkb[1]); prev_pkb_x = pkb[0]; kb--; } push_point (pkb[0], topleft_y); } polygon_close(); /* DONE */ last_b = save_last_b; } } topleft_y = pka[1]; } } free (color); return polygon; } #undef eaf_point #undef push_point #undef polygon_close #undef PRINT_POINT #undef POLY_SIZE_CHECK void eaf_print_polygon (FILE *stream, eaf_t **eaf, int nlevels) { eaf_polygon_t *p = eaf_compute_area (eaf, nlevels); for(size_t i = 0; i < vector_objective_size(&p->xy); i += 2) { fprintf(stream, point_printf_format "\t" point_printf_format "\n", vector_objective_at(&p->xy, i), vector_objective_at(&p->xy, i + 1)); } fprintf (stream, "# col ="); for (size_t k = 0; k < vector_int_size (&p->col); k++) fprintf (stream, " %d", vector_int_at(&p->col, k)); fprintf (stream, "\n"); vector_objective_dtor (&p->xy); vector_int_dtor (&p->col); free(p); } eaf/inst/scripts/eaf/avl.c0000644000176200001440000003317713254747464015165 0ustar liggesusers/***************************************************************************** avl.c - Source code for the AVL-tree library. Copyright (C) 1998 Michael H. Buselli Copyright (C) 2000-2002 Wessel Dankers This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Augmented AVL-tree. Original by Michael H. Buselli . Modified by Wessel Dankers to add a bunch of bloat to the sourcecode, change the interface and squash a few bugs. Mail him if you find new bugs. *****************************************************************************/ #include #include #include #include "avl.h" static void avl_rebalance(avl_tree_t *, avl_node_t *); #ifdef AVL_COUNT #define NODE_COUNT(n) ((n) ? (n)->count : 0) #define L_COUNT(n) (NODE_COUNT((n)->left)) #define R_COUNT(n) (NODE_COUNT((n)->right)) #define CALC_COUNT(n) (L_COUNT(n) + R_COUNT(n) + 1) #endif #ifdef AVL_DEPTH #define NODE_DEPTH(n) ((n) ? (n)->depth : 0) #define L_DEPTH(n) (NODE_DEPTH((n)->left)) #define R_DEPTH(n) (NODE_DEPTH((n)->right)) #define CALC_DEPTH(n) ((L_DEPTH(n)>R_DEPTH(n)?L_DEPTH(n):R_DEPTH(n)) + 1) #endif #ifndef AVL_DEPTH /* Also known as ffs() (from BSD) */ static int lg(unsigned int u) { int r = 1; if(!u) return 0; if(u & 0xffff0000) { u >>= 16; r += 16; } if(u & 0x0000ff00) { u >>= 8; r += 8; } if(u & 0x000000f0) { u >>= 4; r += 4; } if(u & 0x0000000c) { u >>= 2; r += 2; } if(u & 0x00000002) r++; return r; } #endif static int avl_check_balance(avl_node_t *avlnode) { #ifdef AVL_DEPTH int d; d = R_DEPTH(avlnode) - L_DEPTH(avlnode); return d<-1?-1:d>1?1:0; #else /* int d; * d = lg(R_COUNT(avlnode)) - lg(L_COUNT(avlnode)); * d = d<-1?-1:d>1?1:0; */ #ifdef AVL_COUNT int pl, r; pl = lg(L_COUNT(avlnode)); r = R_COUNT(avlnode); if(r>>pl+1) return 1; if(pl<2 || r>>pl-2) return 0; return -1; #else #error No balancing possible. #endif #endif } #ifdef AVL_COUNT unsigned int avl_count(const avl_tree_t *avltree) { return NODE_COUNT(avltree->top); } avl_node_t *avl_at(const avl_tree_t *avltree, unsigned int index) { avl_node_t *avlnode; unsigned int c; avlnode = avltree->top; while(avlnode) { c = L_COUNT(avlnode); if(index < c) { avlnode = avlnode->left; } else if(index > c) { avlnode = avlnode->right; index -= c+1; } else { return avlnode; } } return NULL; } unsigned int avl_index(const avl_node_t *avlnode) { avl_node_t *next; unsigned int c; c = L_COUNT(avlnode); while((next = avlnode->parent)) { if(avlnode == next->right) c += L_COUNT(next) + 1; avlnode = next; } return c; } #endif int avl_search_closest(const avl_tree_t *avltree, const void *item, avl_node_t **avlnode) { avl_node_t *node; avl_compare_t cmp; int c; if(!avlnode) avlnode = &node; node = avltree->top; if(!node) return *avlnode = NULL, 0; cmp = avltree->cmp; for(;;) { c = cmp(item, node->item); if(c < 0) { if(node->left) node = node->left; else return *avlnode = node, -1; } else if(c > 0) { if(node->right) node = node->right; else return *avlnode = node, 1; } else { return *avlnode = node, 0; } } } /* * avl_search: * Return a pointer to a node with the given item in the tree. * If no such item is in the tree, then NULL is returned. */ avl_node_t *avl_search(const avl_tree_t *avltree, const void *item) { avl_node_t *node; return avl_search_closest(avltree, item, &node) ? NULL : node; } avl_tree_t *avl_init_tree(avl_tree_t *rc, avl_compare_t cmp, avl_freeitem_t freeitem) { if(rc) { rc->head = NULL; rc->tail = NULL; rc->top = NULL; rc->cmp = cmp; rc->freeitem = freeitem; } return rc; } avl_tree_t *avl_alloc_tree(avl_compare_t cmp, avl_freeitem_t freeitem) { return avl_init_tree(malloc(sizeof(avl_tree_t)), cmp, freeitem); } void avl_clear_tree(avl_tree_t *avltree) { avltree->top = avltree->head = avltree->tail = NULL; } void avl_free_nodes(avl_tree_t *avltree) { avl_node_t *node, *next; avl_freeitem_t freeitem; freeitem = avltree->freeitem; for(node = avltree->head; node; node = next) { next = node->next; if(freeitem) freeitem(node->item); free(node); } avl_clear_tree(avltree); } /* * avl_free_tree: * Free all memory used by this tree. If freeitem is not NULL, then * it is assumed to be a destructor for the items referenced in the avl_ * tree, and they are deleted as well. */ void avl_free_tree(avl_tree_t *avltree) { avl_free_nodes(avltree); free(avltree); } static void avl_clear_node(avl_node_t *newnode) { newnode->left = newnode->right = NULL; #ifdef AVL_COUNT newnode->count = 1; #endif #ifdef AVL_DEPTH newnode->depth = 1; #endif } avl_node_t *avl_init_node(avl_node_t *newnode, void *item) { if(newnode) { avl_clear_node(newnode); newnode->item = item; } return newnode; } avl_node_t *avl_insert_top(avl_tree_t *avltree, avl_node_t *newnode) { avl_clear_node(newnode); newnode->prev = newnode->next = newnode->parent = NULL; avltree->head = avltree->tail = avltree->top = newnode; return newnode; } avl_node_t *avl_insert_before(avl_tree_t *avltree, avl_node_t *node, avl_node_t *newnode) { if(!node) return avltree->tail ? avl_insert_after(avltree, avltree->tail, newnode) : avl_insert_top(avltree, newnode); if(node->left) return avl_insert_after(avltree, node->prev, newnode); avl_clear_node(newnode); newnode->next = node; newnode->parent = node; newnode->prev = node->prev; if(node->prev) node->prev->next = newnode; else avltree->head = newnode; node->prev = newnode; node->left = newnode; avl_rebalance(avltree, node); return newnode; } avl_node_t *avl_insert_after(avl_tree_t *avltree, avl_node_t *node, avl_node_t *newnode) { if(!node) return avltree->head ? avl_insert_before(avltree, avltree->head, newnode) : avl_insert_top(avltree, newnode); if(node->right) return avl_insert_before(avltree, node->next, newnode); avl_clear_node(newnode); newnode->prev = node; newnode->parent = node; newnode->next = node->next; if(node->next) node->next->prev = newnode; else avltree->tail = newnode; node->next = newnode; node->right = newnode; avl_rebalance(avltree, node); return newnode; } avl_node_t *avl_insert_node(avl_tree_t *avltree, avl_node_t *newnode) { avl_node_t *node; if(!avltree->top) return avl_insert_top(avltree, newnode); switch(avl_search_closest(avltree, newnode->item, &node)) { case -1: return avl_insert_before(avltree, node, newnode); case 1: return avl_insert_after(avltree, node, newnode); } return NULL; } /* * avl_insert: * Create a new node and insert an item there. * Returns the new node on success or NULL if no memory could be allocated. */ avl_node_t *avl_insert(avl_tree_t *avltree, void *item) { avl_node_t *newnode; newnode = avl_init_node(malloc(sizeof(avl_node_t)), item); if(newnode) { if(avl_insert_node(avltree, newnode)) return newnode; free(newnode); errno = EEXIST; } return NULL; } /* * avl_unlink_node: * Removes the given node. Does not delete the item at that node. * The item of the node may be freed before calling avl_unlink_node. * (In other words, it is not referenced by this function.) */ void avl_unlink_node(avl_tree_t *avltree, avl_node_t *avlnode) { avl_node_t *parent; avl_node_t **superparent; avl_node_t *subst, *left, *right; avl_node_t *balnode; if(avlnode->prev) avlnode->prev->next = avlnode->next; else avltree->head = avlnode->next; if(avlnode->next) avlnode->next->prev = avlnode->prev; else avltree->tail = avlnode->prev; parent = avlnode->parent; superparent = parent ? avlnode == parent->left ? &parent->left : &parent->right : &avltree->top; left = avlnode->left; right = avlnode->right; if(!left) { *superparent = right; if(right) right->parent = parent; balnode = parent; } else if(!right) { *superparent = left; left->parent = parent; balnode = parent; } else { subst = avlnode->prev; if(subst == left) { balnode = subst; } else { balnode = subst->parent; balnode->right = subst->left; if(balnode->right) balnode->right->parent = balnode; subst->left = left; left->parent = subst; } subst->right = right; subst->parent = parent; right->parent = subst; *superparent = subst; } avl_rebalance(avltree, balnode); } void *avl_delete_node(avl_tree_t *avltree, avl_node_t *avlnode) { void *item = NULL; if(avlnode) { item = avlnode->item; avl_unlink_node(avltree, avlnode); if(avltree->freeitem) avltree->freeitem(item); free(avlnode); } return item; } void *avl_delete(avl_tree_t *avltree, const void *item) { return avl_delete_node(avltree, avl_search(avltree, item)); } avl_node_t *avl_fixup_node(avl_tree_t *avltree, avl_node_t *newnode) { avl_node_t *oldnode = NULL, *node; if(!avltree || !newnode) return NULL; node = newnode->prev; if(node) { oldnode = node->next; node->next = newnode; } else { avltree->head = newnode; } node = newnode->next; if(node) { oldnode = node->prev; node->prev = newnode; } else { avltree->tail = newnode; } node = newnode->parent; if(node) { if(node->left == oldnode) node->left = newnode; else node->right = newnode; } else { oldnode = avltree->top; avltree->top = newnode; } return oldnode; } /* * avl_rebalance: * Rebalances the tree if one side becomes too heavy. This function * assumes that both subtrees are AVL-trees with consistant data. The * function has the additional side effect of recalculating the count of * the tree at this node. It should be noted that at the return of this * function, if a rebalance takes place, the top of this subtree is no * longer going to be the same node. */ void avl_rebalance(avl_tree_t *avltree, avl_node_t *avlnode) { avl_node_t *child; avl_node_t *gchild; avl_node_t *parent; avl_node_t **superparent; parent = avlnode; while(avlnode) { parent = avlnode->parent; superparent = parent ? avlnode == parent->left ? &parent->left : &parent->right : &avltree->top; switch(avl_check_balance(avlnode)) { case -1: child = avlnode->left; #ifdef AVL_DEPTH if(L_DEPTH(child) >= R_DEPTH(child)) { #else #ifdef AVL_COUNT if(L_COUNT(child) >= R_COUNT(child)) { #else #error No balancing possible. #endif #endif avlnode->left = child->right; if(avlnode->left) avlnode->left->parent = avlnode; child->right = avlnode; avlnode->parent = child; *superparent = child; child->parent = parent; #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); child->count = CALC_COUNT(child); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); child->depth = CALC_DEPTH(child); #endif } else { gchild = child->right; avlnode->left = gchild->right; if(avlnode->left) avlnode->left->parent = avlnode; child->right = gchild->left; if(child->right) child->right->parent = child; gchild->right = avlnode; if(gchild->right) gchild->right->parent = gchild; gchild->left = child; if(gchild->left) gchild->left->parent = gchild; *superparent = gchild; gchild->parent = parent; #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); child->count = CALC_COUNT(child); gchild->count = CALC_COUNT(gchild); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); child->depth = CALC_DEPTH(child); gchild->depth = CALC_DEPTH(gchild); #endif } break; case 1: child = avlnode->right; #ifdef AVL_DEPTH if(R_DEPTH(child) >= L_DEPTH(child)) { #else #ifdef AVL_COUNT if(R_COUNT(child) >= L_COUNT(child)) { #else #error No balancing possible. #endif #endif avlnode->right = child->left; if(avlnode->right) avlnode->right->parent = avlnode; child->left = avlnode; avlnode->parent = child; *superparent = child; child->parent = parent; #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); child->count = CALC_COUNT(child); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); child->depth = CALC_DEPTH(child); #endif } else { gchild = child->left; avlnode->right = gchild->left; if(avlnode->right) avlnode->right->parent = avlnode; child->left = gchild->right; if(child->left) child->left->parent = child; gchild->left = avlnode; if(gchild->left) gchild->left->parent = gchild; gchild->right = child; if(gchild->right) gchild->right->parent = gchild; *superparent = gchild; gchild->parent = parent; #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); child->count = CALC_COUNT(child); gchild->count = CALC_COUNT(gchild); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); child->depth = CALC_DEPTH(child); gchild->depth = CALC_DEPTH(gchild); #endif } break; default: #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); #endif } avlnode = parent; } } eaf/inst/extdata/0000755000176200001440000000000013254545527013430 5ustar liggesuserseaf/inst/extdata/wrots_l100w10_dat0000644000176200001440000006421213254545527016452 0ustar liggesusers# Report: # Metah: Weighted RoTS, version 2.1.88M (#DEBUG = 1) # bQAP (0.1.88Msvn) # Data File: /home/manu/personal/2005-JMMA/data/qapUni.50.0.1 Size: 50 Averages: Distance=50.47 Flow1=49.26 Flow2=50.01 # Parameter settings: # number trials : 100 # time limit : 3.40282e+38 # seed : 1242308195 # num weights: 10 # Taboo Search Length : 100 # Initialization time: 0.004 s # start_trial(0) : # end_trial(0) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.62 s # Pareto: 0 Size: 10 5483732 6495986 5489290 6333342 5550664 6113120 5564752 6013642 5701096 5859466 5714072 5844970 5946800 5662604 6032716 5640020 6101174 5581470 6319808 5571692 # start_trial(1) : # end_trial(1) 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5623536 5919430 5771846 5806208 5928420 5676678 6184582 5583356 6361946 5531276 # start_trial(32) : # end_trial(32) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.576 s # Pareto: 32 Size: 9 5464364 6424356 5489300 6245940 5551000 6094160 5627598 5967092 5795344 5771036 5919084 5678558 6133650 5587244 6169884 5581002 6346504 5570752 # start_trial(33) : # end_trial(33) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.5 s # Pareto: 33 Size: 8 5460750 6433122 5466458 6391590 5503404 6233514 5519244 6102006 5647964 5957684 5790592 5787072 5934146 5680492 6187478 5553366 # start_trial(34) : # end_trial(34) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.764 s # Pareto: 34 Size: 9 5474864 6503948 5486566 6322748 5513724 6205906 5578960 6033890 5742728 5816594 5801244 5776214 6001986 5628534 6104554 5608010 6332184 5537058 # start_trial(35) : # end_trial(35) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.392 s # Pareto: 35 Size: 10 5479296 6430192 5495022 6369136 5510396 6248004 5550820 6092512 5685986 5871606 5806876 5737868 6027886 5627798 6193668 5588732 6236102 5566948 6331186 5531908 # start_trial(36) : # end_trial(36) : Pareto Size = 7, Scalarizations = 10, Elapsed time = 7.536 s # Pareto: 36 Size: 7 5483966 6199576 5575572 6046984 5610512 5944608 5782966 5795920 5971140 5646644 6105930 5611402 6202634 5565176 # start_trial(37) : # end_trial(37) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 37 Size: 9 5464946 6499558 5485680 6305490 5494854 6261270 5552210 6094662 5647176 5882590 5915906 5671558 5982364 5616618 6085892 5596798 6318416 5572054 # start_trial(38) : # end_trial(38) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.576 s # Pareto: 38 Size: 9 5454310 6437978 5461550 6415528 5498290 6255278 5546774 6023252 5625700 5938602 5799232 5780092 6006738 5645848 6143226 5575104 6279376 5565148 # start_trial(39) : # end_trial(39) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.612 s # Pareto: 39 Size: 10 5467252 6363380 5473134 6337508 5483468 6230544 5526506 6095344 5675484 5907978 5832082 5736258 5970478 5654660 6106816 5607456 6277528 5563606 6301206 5558658 # start_trial(40) : # end_trial(40) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.652 s # Pareto: 40 Size: 9 5466174 6385356 5529690 6142526 5539262 6140734 5719820 5843134 5818802 5753756 5946768 5679776 6226612 5570494 6235454 5557220 6360876 5549518 # start_trial(41) : # end_trial(41) : Pareto Size = 7, Scalarizations = 10, Elapsed time = 7.724 s # Pareto: 41 Size: 7 5455528 6334414 5502094 6256098 5555354 6083696 5655250 5948698 5741158 5807514 6005698 5638516 6233194 5563604 # start_trial(42) : # end_trial(42) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 42 Size: 10 5463378 6437412 5481822 6359986 5483864 6350828 5569450 6076518 5676774 5850722 5763862 5810086 5978130 5632102 6050626 5608058 6263776 5551620 6324434 5550076 # start_trial(43) : # end_trial(43) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 43 Size: 10 5469406 6434860 5484810 6305698 5495272 6181080 5587226 6047608 5688266 5903462 5746270 5760304 5968946 5632342 6184926 5590270 6285702 5572314 6339500 5557634 # start_trial(44) : # end_trial(44) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 44 Size: 9 5471664 6330898 5495172 6226150 5548168 6106378 5633140 5902586 5738738 5818824 5953524 5647994 6165036 5580810 6285146 5571246 6383930 5547810 # start_trial(45) : # end_trial(45) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 45 Size: 10 5470492 6458962 5489422 6398304 5508312 6242176 5558066 6095364 5685674 5897196 5757878 5778170 6086038 5603552 6170146 5562190 6313884 5557252 6393268 5555930 # start_trial(46) : # end_trial(46) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 46 Size: 8 5477084 6512012 5484474 6297218 5496548 6262188 5555660 6061186 5693346 5872916 5751296 5818486 5930916 5658800 6187520 5567152 # start_trial(47) : # end_trial(47) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.548 s # Pareto: 47 Size: 10 5465976 6456996 5491598 6278660 5531044 6218308 5543950 6105976 5637230 5933480 5871690 5733884 5956464 5648750 6089656 5598250 6272108 5564212 6332114 5553188 # start_trial(48) : # end_trial(48) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.548 s # Pareto: 48 Size: 10 5472908 6540230 5481178 6434846 5505916 6249932 5576938 6061496 5600984 5936078 5874574 5730972 5992638 5658312 6182806 5575516 6291204 5542740 6309370 5541624 # start_trial(49) : # end_trial(49) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 49 Size: 9 5479804 6406962 5499124 6304978 5500008 6218310 5572160 6035382 5645788 5930298 5827620 5751816 6002856 5652932 6131254 5606048 6217820 5542794 # start_trial(50) : # end_trial(50) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 50 Size: 9 5449602 6446264 5470546 6390498 5515868 6190614 5585380 6039228 5673028 5904506 5773280 5786788 5917014 5689732 6188006 5574970 6212160 5553864 # start_trial(51) : # end_trial(51) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 51 Size: 9 5474162 6465420 5475758 6324220 5519234 6182248 5587576 5944476 5673530 5920274 5787396 5786702 5849848 5712760 6131332 5565358 6299776 5561922 # start_trial(52) : # end_trial(52) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 52 Size: 10 5472490 6436546 5493492 6303968 5508536 6174908 5552312 6073712 5679886 5872698 5825846 5738288 5986804 5657114 6106192 5620990 6241884 5566658 6388158 5551352 # start_trial(53) : # end_trial(53) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 53 Size: 10 5458498 6487190 5481138 6341500 5541128 6153820 5555170 6101932 5617668 5924606 5759206 5790292 6012676 5636282 6130326 5585418 6300574 5574330 6343550 5553848 # start_trial(54) : # end_trial(54) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 54 Size: 8 5466954 6459456 5520228 6211528 5583042 6054646 5589228 6014920 5762860 5796350 6016864 5608092 6230692 5583562 6441990 5568290 # start_trial(55) : # end_trial(55) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 55 Size: 9 5470314 6480582 5486822 6359190 5522318 6195530 5588268 6030406 5708962 5848816 5811700 5739808 5954132 5683530 6167880 5572726 6315912 5532832 # start_trial(56) : # end_trial(56) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 56 Size: 9 5470596 6474606 5487874 6349446 5511938 6203716 5565862 6074052 5583774 5993526 5773112 5788750 5960584 5667316 6099064 5605386 6171402 5565010 # start_trial(57) : # end_trial(57) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 57 Size: 10 5477100 6528908 5492254 6333282 5501630 6274466 5519680 6164794 5621164 5936774 5812012 5768478 5966912 5645736 6103366 5598210 6165548 5585928 6372828 5572316 # start_trial(58) : # end_trial(58) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 58 Size: 8 5457492 6486828 5486566 6288570 5504814 6271680 5573526 6015678 5655400 5931026 5843502 5733974 6080386 5607698 6174176 5572188 # start_trial(59) : # end_trial(59) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 59 Size: 9 5472920 6463318 5475100 6340824 5517596 6200722 5565050 6056146 5633486 5956706 5787884 5779690 5956102 5662724 6111112 5614596 6319974 5552806 # start_trial(60) : # end_trial(60) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 60 Size: 9 5465348 6378218 5473920 6372968 5527628 6167508 5542070 6135966 5711744 5837182 5765912 5795032 5884738 5718638 6101078 5604102 6289648 5567552 # start_trial(61) : # end_trial(61) : Pareto Size = 7, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 61 Size: 7 5465940 6316172 5565802 6058994 5643038 5934428 5741770 5834288 5985814 5636732 6053970 5611484 6228668 5562444 # start_trial(62) : # end_trial(62) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 62 Size: 9 5452174 6365710 5496572 6247790 5546452 6104310 5661866 5907994 5795958 5750526 5977772 5638566 6101066 5621908 6242124 5587084 6308032 5547688 # start_trial(63) : # end_trial(63) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.464 s # Pareto: 63 Size: 10 5450646 6414700 5470216 6357480 5486480 6234756 5582028 6030238 5650946 5898674 5728802 5837314 6024596 5646352 6059530 5618566 6209976 5570116 6444200 5566768 # start_trial(64) : # end_trial(64) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.624 s # Pareto: 64 Size: 9 5459930 6534674 5467896 6337118 5491252 6234486 5512280 6148118 5655036 5872586 5837600 5710264 6050656 5599640 6210750 5571176 6415458 5567600 # start_trial(65) : # end_trial(65) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.564 s # Pareto: 65 Size: 10 5474116 6366908 5480168 6262148 5501520 6205054 5555786 6106970 5707834 5864130 5804582 5779480 5920908 5697270 6039484 5614556 6147178 5581516 6351212 5574316 # start_trial(66) : # end_trial(66) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.492 s # Pareto: 66 Size: 9 5466874 6389726 5526846 6210960 5612222 5991440 5715654 5808282 5812574 5771790 6052270 5615154 6113928 5601678 6261288 5559220 6479972 5552642 # start_trial(67) : # end_trial(67) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.668 s # Pareto: 67 Size: 9 5457806 6349490 5508354 6188872 5578396 6063868 5643166 5949920 5791658 5786814 5960792 5633658 6158548 5577402 6226762 5567636 6303250 5556000 # start_trial(68) : # end_trial(68) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.6 s # Pareto: 68 Size: 10 5469348 6447340 5490944 6318908 5515312 6191752 5566176 6078888 5655048 5920422 5896516 5701290 5983364 5629296 6094102 5624962 6234210 5570696 6372890 5554268 # start_trial(69) : # end_trial(69) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.4 s # Pareto: 69 Size: 9 5467116 6259322 5509754 6183956 5560798 6055162 5575458 6021490 5846776 5734820 5899162 5708530 6055842 5614082 6272342 5562104 6446820 5551004 # start_trial(70) : # end_trial(70) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.768 s # Pareto: 70 Size: 9 5472500 6409244 5494584 6258546 5574936 6049120 5679958 5884950 5870454 5733232 5904838 5673706 6091942 5611026 6225848 5589946 6308358 5547258 # start_trial(71) : # end_trial(71) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.52 s # Pareto: 71 Size: 9 5482464 6275596 5501430 6213628 5583816 6057380 5632500 5946644 5847048 5737196 5889552 5687062 6008690 5615990 6202852 5588898 6415806 5563188 # start_trial(72) : # end_trial(72) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.44 s # Pareto: 72 Size: 8 5449782 6483298 5471210 6342944 5519230 6199686 5549896 6075720 5646298 5917374 5871992 5718768 5977120 5648240 6236852 5544006 # start_trial(73) : # end_trial(73) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.836 s # Pareto: 73 Size: 9 5456298 6419094 5474094 6264862 5552510 6102484 5672398 5868706 5875616 5690950 5928430 5674910 6111602 5581866 6197636 5577668 6285514 5571988 # start_trial(74) : # end_trial(74) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 74 Size: 10 5456466 6410824 5469346 6356418 5480544 6297076 5556026 6119542 5627160 5967772 5865616 5718566 5933242 5664250 6214844 5569144 6322918 5563714 6458830 5562594 # start_trial(75) : # end_trial(75) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.464 s # Pareto: 75 Size: 9 5427334 6395560 5500316 6240626 5545806 6090792 5658902 5894594 5819984 5759110 5940508 5665850 6156844 5608454 6217506 5584204 6320022 5558302 # start_trial(76) : # end_trial(76) : Pareto Size = 7, Scalarizations = 10, Elapsed time = 7.892 s # Pareto: 76 Size: 7 5453456 6303654 5518814 6150732 5760482 5797044 5836492 5728654 5942422 5631794 6109768 5623176 6335516 5546426 # start_trial(77) : # end_trial(77) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.588 s # Pareto: 77 Size: 9 5481100 6355034 5505828 6178522 5533126 6165954 5655210 5905450 5768896 5801764 5943870 5665808 6249664 5566960 6272784 5554388 6309646 5544486 # start_trial(78) : # end_trial(78) : Pareto Size = 7, Scalarizations = 10, Elapsed time = 7.612 s # Pareto: 78 Size: 7 5474842 6391098 5510476 6198248 5567404 6055036 5686520 5871306 5793776 5773138 6002474 5647216 6191466 5526768 # start_trial(79) : # end_trial(79) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.776 s # Pareto: 79 Size: 8 5462054 6302676 5466670 6262648 5494230 6201042 5683996 5904130 5788108 5771464 5929358 5650986 6057540 5621166 6175798 5577148 # start_trial(80) : # end_trial(80) : Pareto Size = 7, Scalarizations = 10, Elapsed time = 7.56 s # Pareto: 80 Size: 7 5461970 6319240 5503840 6236246 5530942 6134874 5742922 5773648 5918548 5687640 6178900 5572652 6283980 5544396 # start_trial(81) : # end_trial(81) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.604 s # Pareto: 81 Size: 9 5475818 6426268 5482546 6316172 5511874 6183640 5552698 6091540 5684826 5902532 5748256 5829988 5991182 5654958 6183922 5569438 6343706 5558770 # start_trial(82) : # end_trial(82) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.792 s # Pareto: 82 Size: 10 5454380 6490644 5494020 6401882 5496952 6259430 5555082 6095164 5714598 5859320 5826914 5752076 5930814 5667166 6137982 5595728 6181432 5584188 6294372 5570056 # start_trial(83) : # end_trial(83) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.472 s # Pareto: 83 Size: 10 5476294 6453592 5481144 6314904 5503244 6229860 5573066 6061852 5683296 5901926 5887694 5706562 6018290 5640230 6110216 5602750 6226046 5578226 6309536 5569346 # start_trial(84) : # end_trial(84) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.42 s # Pareto: 84 Size: 8 5461520 6409454 5502736 6286354 5616868 5904096 5893356 5687982 6013554 5644338 6068540 5601686 6321966 5561408 6328150 5547548 # start_trial(85) : # end_trial(85) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.788 s # Pareto: 85 Size: 9 5440400 6378466 5479794 6297932 5525910 6125262 5581258 6035782 5662780 5888358 5786134 5770462 5930072 5673142 6074790 5594830 6298186 5559392 # start_trial(86) : # end_trial(86) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.484 s # Pareto: 86 Size: 8 5474360 6520712 5503740 6239402 5505762 6154052 5645772 5898168 5791092 5784764 5930828 5686390 6133224 5603120 6248228 5562056 # start_trial(87) : # end_trial(87) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.556 s # Pareto: 87 Size: 8 5475600 6374582 5503280 6178796 5522796 6145946 5658344 5886500 5850094 5730296 6016756 5619020 6090798 5597146 6244574 5574980 # start_trial(88) : # end_trial(88) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 88 Size: 8 5478278 6286976 5512044 6215628 5588082 6017112 5705176 5870614 5752378 5808252 5951500 5646556 6137224 5576298 6348750 5551522 # start_trial(89) : # end_trial(89) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 89 Size: 10 5451960 6407926 5494754 6303792 5500712 6248508 5580588 6023686 5676502 5882462 5767318 5773006 5959630 5653962 6157060 5579384 6202784 5570690 6337328 5551148 # start_trial(90) : # end_trial(90) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 90 Size: 8 5454256 6406538 5505964 6172394 5623062 5945814 5666012 5899346 5823416 5776752 5933776 5640712 6303492 5571864 6394170 5559036 # start_trial(91) : # end_trial(91) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 91 Size: 10 5473092 6434892 5483758 6346654 5503582 6273586 5584672 6042688 5606238 5970870 5835668 5737220 6044596 5628414 6046402 5574712 6311694 5554934 6315350 5548278 # start_trial(92) : # end_trial(92) : Pareto Size = 10, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 92 Size: 10 5470936 6517966 5475690 6320618 5500834 6239524 5544000 6070862 5677350 5855750 5832728 5743664 5926284 5679876 6108596 5609460 6311280 5578192 6452774 5560716 # start_trial(93) : # end_trial(93) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 93 Size: 9 5462638 6412128 5482590 6284282 5497544 6240212 5550588 6056364 5730798 5844904 5800418 5775816 6017668 5625184 6115646 5604498 6284432 5562496 # start_trial(94) : # end_trial(94) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.552 s # Pareto: 94 Size: 8 5459500 6477452 5485472 6355412 5510534 6191900 5542482 6136756 5667176 5888524 5806300 5744756 5984090 5661664 6242524 5552126 # start_trial(95) : # end_trial(95) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 95 Size: 8 5455376 6432280 5487974 6315414 5506744 6208822 5579864 6035980 5702816 5885406 5821640 5736174 6008106 5602344 6188000 5564444 # start_trial(96) : # end_trial(96) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.544 s # Pareto: 96 Size: 9 5478474 6439740 5481166 6303486 5512912 6191432 5553648 6101466 5691254 5892726 5806686 5759848 6025744 5632446 6116446 5598472 6368894 5568552 # start_trial(97) : # end_trial(97) : Pareto Size = 9, Scalarizations = 10, Elapsed time = 7.548 s # Pareto: 97 Size: 9 5454650 6387114 5520800 6182892 5538604 6127718 5611070 5949594 5853072 5732714 6032496 5647702 6083704 5620360 6271070 5592926 6377154 5572286 # start_trial(98) : # end_trial(98) : Pareto Size = 7, Scalarizations = 10, Elapsed time = 7.548 s # Pareto: 98 Size: 7 5475392 6416886 5492298 6410812 5527576 6115370 5653766 5887336 5798162 5776322 5977888 5650516 6174526 5576070 # start_trial(99) : # end_trial(99) : Pareto Size = 8, Scalarizations = 10, Elapsed time = 7.54 s # Pareto: 99 Size: 8 5465638 6331462 5493238 6240190 5567252 6055564 5709936 5879216 5828292 5760602 5938028 5679240 6230124 5568504 6449060 5559802 # Average Number Scalarizations = 10, Average Trial Time = 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5368942 5249068 5328154 5277710 5321710 5280132 5281080 # start_trial(2) : # end_trial(2) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 2 Size: 2 5255914 5312620 5285994 5294248 # start_trial(3) : # end_trial(3) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 3 Size: 6 5254586 5341368 5265448 5336434 5266332 5329290 5278158 5328006 5293084 5304026 5316378 5285680 # start_trial(4) : # end_trial(4) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 4 Size: 5 5257008 5385896 5260020 5354180 5261932 5351978 5265120 5343482 5272604 5280450 # start_trial(5) : # end_trial(5) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 5 Size: 3 5251436 5304740 5260634 5289432 5261388 5278662 # start_trial(6) : # end_trial(6) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 6 Size: 3 5260462 5303782 5309662 5298112 5338894 5292426 # start_trial(7) : # end_trial(7) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 7 Size: 4 5238158 5302220 5266616 5301604 5277362 5291346 5328374 5283152 # start_trial(8) : # end_trial(8) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 8 Size: 5 5259568 5344514 5260078 5323590 5275688 5304758 5295860 5299490 5321894 5280216 # start_trial(9) : # end_trial(9) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 9 Size: 5 5252950 5337456 5258962 5303668 5273430 5301298 5311040 5291910 5319530 5279154 # start_trial(10) : # end_trial(10) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 10 Size: 6 5255088 5362366 5260872 5337164 5276254 5307774 5278846 5302382 5287324 5301484 5290398 5284198 # start_trial(11) : # end_trial(11) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 11 Size: 3 5257462 5328600 5262892 5302082 5306682 5293358 # start_trial(12) : # end_trial(12) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 12 Size: 5 5255536 5369466 5265974 5314044 5276688 5311924 5301892 5288482 5319394 5278344 # start_trial(13) : # end_trial(13) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 13 Size: 4 5243484 5327056 5262810 5312496 5265234 5288336 5305096 5285540 # start_trial(14) : # end_trial(14) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 14 Size: 3 5254326 5373688 5258282 5328368 5277040 5290704 # start_trial(15) : # end_trial(15) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 15 Size: 5 5251220 5334532 5258622 5318388 5274694 5318056 5275588 5299916 5307866 5281706 # start_trial(16) : # end_trial(16) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 16 Size: 4 5252238 5320246 5297660 5319870 5302690 5316084 5305034 5293474 # start_trial(17) : # end_trial(17) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 17 Size: 3 5256636 5364154 5262016 5294988 5264892 5269504 # start_trial(18) : # end_trial(18) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 18 Size: 6 5228108 5347760 5281796 5327312 5286788 5320758 5292364 5312668 5305786 5297640 5319554 5296596 # start_trial(19) : # end_trial(19) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 19 Size: 3 5258012 5349460 5262532 5294728 5311364 5285878 # start_trial(20) : # end_trial(20) : Pareto Size = 9, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 20 Size: 9 5262846 5351252 5268224 5332648 5286336 5329542 5291024 5323824 5291052 5311388 5322204 5305956 5337348 5305340 5338120 5302878 5350520 5288884 # start_trial(21) : # end_trial(21) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 21 Size: 6 5257942 5373498 5259552 5357156 5267384 5330886 5279304 5315890 5286088 5309012 5287040 5284718 # start_trial(22) : # end_trial(22) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 22 Size: 3 5253764 5351678 5265558 5309298 5290820 5295606 # start_trial(23) : # end_trial(23) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 23 Size: 3 5243858 5338614 5261776 5332340 5286980 5290820 # start_trial(24) : # end_trial(24) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 24 Size: 4 5251984 5322490 5293588 5309414 5295572 5298376 5313146 5297746 # start_trial(25) : # end_trial(25) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 25 Size: 3 5238488 5294964 5265768 5287358 5274538 5282446 # start_trial(26) : # end_trial(26) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 26 Size: 3 5240112 5318798 5285086 5296794 5297420 5288042 # start_trial(27) : # end_trial(27) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 27 Size: 3 5253562 5315656 5258408 5295302 5297444 5294720 # start_trial(28) : # end_trial(28) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 28 Size: 3 5243092 5314062 5243248 5286716 5296000 5284678 # start_trial(29) : # end_trial(29) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 29 Size: 4 5254042 5338584 5260876 5330882 5276202 5313170 5286874 5286552 # start_trial(30) : # end_trial(30) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 30 Size: 4 5249126 5334774 5263022 5312306 5281242 5309470 5284318 5282482 # start_trial(31) : # end_trial(31) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 31 Size: 6 5257878 5347536 5275036 5339680 5281738 5334044 5282030 5309294 5292442 5286754 5305288 5276742 # start_trial(32) : # end_trial(32) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 32 Size: 4 5262822 5336322 5287408 5314374 5288480 5303274 5297530 5296626 # start_trial(33) : # end_trial(33) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 33 Size: 4 5262302 5350178 5268796 5320690 5287784 5307578 5303060 5291696 # start_trial(34) : # end_trial(34) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.9001 s # Pareto: 34 Size: 4 5255158 5363060 5265740 5316854 5280842 5287822 5295982 5271860 # start_trial(35) : # end_trial(35) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 35 Size: 4 5236920 5323144 5275658 5316252 5295276 5291846 5320036 5287242 # start_trial(36) : # end_trial(36) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 36 Size: 7 5245320 5350028 5265846 5346862 5269172 5329214 5289792 5317436 5291128 5309678 5300432 5300306 5320356 5299120 # start_trial(37) : # end_trial(37) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 37 Size: 6 5256692 5369864 5258244 5351398 5260990 5318888 5276810 5304172 5307654 5301888 5349612 5294114 # start_trial(38) : # end_trial(38) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 38 Size: 4 5245000 5402680 5261294 5355072 5280504 5342928 5281916 5279952 # start_trial(39) : # end_trial(39) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 39 Size: 1 5256756 5291420 # start_trial(40) : # end_trial(40) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 40 Size: 3 5240372 5339244 5269234 5300968 5292588 5291350 # start_trial(41) : # end_trial(41) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 41 Size: 3 5238810 5321212 5254626 5315172 5260078 5296038 # start_trial(42) : # end_trial(42) : Pareto Size = 8, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 42 Size: 8 5262120 5384314 5262892 5343884 5264312 5331836 5279288 5323156 5287766 5316696 5288662 5313878 5299640 5305808 5304502 5281312 # start_trial(43) : # end_trial(43) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 43 Size: 6 5251856 5339406 5260988 5322928 5285486 5322566 5290320 5308872 5302434 5302166 5318698 5289714 # start_trial(44) : # end_trial(44) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 44 Size: 4 5235812 5381636 5236334 5354730 5270128 5301072 5305764 5283634 # start_trial(45) : # end_trial(45) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 45 Size: 2 5230660 5301340 5293106 5289332 # start_trial(46) : # end_trial(46) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 46 Size: 4 5228398 5320810 5238488 5307248 5282654 5305988 5299924 5287560 # start_trial(47) : # end_trial(47) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8641 s # Pareto: 47 Size: 2 5260116 5305420 5269468 5286560 # start_trial(48) : # end_trial(48) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8641 s # Pareto: 48 Size: 5 5249848 5325302 5296612 5310710 5309120 5306430 5317752 5305550 5342704 5304442 # start_trial(49) : # end_trial(49) : Pareto Size = 4, 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0.949901401244599 0.215603995439070 0.588952304395791 0.21330388448436 0.779510510519756 eaf/inst/extdata/wrots_l10w100_dat0000644000176200001440000017675013254545527016465 0ustar liggesusers# Report: # Metah: Weighted RoTS, version 2.1.88M (#DEBUG = 1) # bQAP (0.1.88Msvn) # Data File: /home/manu/personal/2005-JMMA/data/qapUni.50.0.1 Size: 50 Averages: Distance=50.47 Flow1=49.26 Flow2=50.01 # Parameter settings: # number trials : 100 # time limit : 3.40282e+38 # seed : 1242309217 # num weights: 100 # Taboo Search Length : 10 # Initialization time: 0.004 s # start_trial(0) : # end_trial(0) : Pareto Size = 33, Scalarizations = 100, Elapsed time = 7.716 s # Pareto: 0 Size: 33 5473826 6526814 5493250 6343474 5496176 6282882 5502448 6218950 5510780 6180946 5539966 6127292 5565586 6052860 5584022 6050386 5590094 6048274 5601432 6041098 5603938 6036508 5604250 6035400 5642958 6009858 5643314 5996484 5655268 5954504 5681332 5885864 5685066 5881662 5761538 5869308 5762428 5860318 5792684 5772712 5823414 5767558 5860734 5740240 5890544 5724424 5906560 5704548 5961104 5688026 5985044 5666152 6065866 5653804 6079994 5610878 6177896 5605006 6187024 5598184 6211210 5595258 6247004 5580700 6289572 5553288 # start_trial(1) : # end_trial(1) : Pareto Size = 39, Scalarizations = 100, Elapsed time = 7.604 s # Pareto: 1 Size: 39 5464286 6468510 5483338 6425684 5486560 6398072 5490172 6321560 5492190 6249646 5501474 6243086 5506814 6242362 5530720 6222704 5540230 6195922 5544276 6191576 5546330 6178918 5547704 6173070 5551010 6144084 5582976 6109600 5609460 6057996 5624086 6049322 5625720 5993428 5638102 5971312 5647638 5921298 5663048 5915032 5704138 5888876 5729684 5836944 5747252 5819924 5759610 5812382 5782070 5768652 5873978 5762538 5874272 5761044 5882830 5760430 5904552 5723300 5939924 5707386 5947614 5701140 5971320 5697846 5982022 5661266 6063414 5654264 6065726 5632240 6069364 5596736 6272108 5587046 6315974 5583950 6368090 5578316 # start_trial(2) : # end_trial(2) : Pareto Size = 31, Scalarizations = 100, Elapsed time = 7.776 s # Pareto: 2 Size: 31 5459400 6391458 5479426 6334344 5495376 6334052 5499092 6252340 5504398 6186928 5525714 6168288 5547188 6143768 5559092 6086104 5587542 6070154 5587768 6045176 5606566 6009568 5645972 5973140 5659796 5967768 5685066 5903518 5700516 5884792 5708298 5837288 5786040 5816636 5808188 5810686 5827514 5802070 5836668 5767048 5870506 5732568 5892102 5721780 5924134 5705214 5934668 5677728 6004674 5669380 6027072 5659732 6031592 5638978 6044782 5637662 6082808 5609204 6092324 5603094 6253214 5575184 # start_trial(3) : # end_trial(3) : Pareto Size = 29, Scalarizations = 100, Elapsed time = 7.548 s # Pareto: 3 Size: 29 5463102 6438364 5473856 6324104 5482618 6290900 5504258 6287356 5505584 6193506 5532676 6189400 5546722 6156310 5572324 6061118 5620220 6025180 5625344 6016892 5631372 5956486 5659450 5928660 5720274 5898402 5733618 5857754 5743250 5838360 5758642 5818112 5829984 5796312 5848816 5749614 5854722 5735726 5898104 5707610 5941982 5702718 5972232 5675872 6021562 5660390 6023888 5654430 6075856 5638782 6106930 5614914 6117966 5608692 6171870 5587604 6209836 5566250 # start_trial(4) : # end_trial(4) : Pareto Size = 34, Scalarizations = 100, Elapsed time = 7.624 s # Pareto: 4 Size: 34 5462904 6447758 5471358 6403078 5478530 6374152 5488616 6373720 5489318 6286472 5505816 6207974 5525600 6194892 5540534 6183010 5563076 6147576 5575522 6129916 5585150 6059392 5589382 6042868 5605688 6018882 5615056 5998676 5639552 5966008 5646670 5944664 5697582 5934608 5698828 5934298 5701456 5917160 5707924 5882614 5730436 5861732 5740210 5856682 5789570 5781642 5830076 5746930 5919034 5725796 5933716 5669830 5953002 5668762 6025550 5652298 6065570 5647412 6098626 5606028 6172266 5584250 6277404 5576724 6283842 5566726 6349150 5563204 # start_trial(5) : # end_trial(5) : Pareto Size = 36, Scalarizations = 100, Elapsed time = 7.768 s # Pareto: 5 Size: 36 5473170 6393360 5476340 6380998 5491846 6353018 5495148 6296226 5519062 6265090 5519812 6212052 5534358 6174368 5537162 6161216 5564424 6150734 5576744 6127658 5581700 6103550 5581934 6056392 5590954 6039310 5611828 6032792 5622246 6010060 5640802 5969970 5679068 5951746 5693260 5893258 5696058 5887714 5715428 5879502 5745114 5861730 5770308 5812920 5786984 5779996 5804950 5755002 5828922 5746320 5906244 5728720 5935080 5706794 5963402 5686978 5975454 5675910 5988840 5667338 6028566 5658016 6048388 5628088 6066286 5625498 6105872 5598794 6250842 5577366 6288742 5565098 # start_trial(6) : # end_trial(6) : Pareto Size = 28, Scalarizations = 100, Elapsed time = 7.696 s # Pareto: 6 Size: 28 5463368 6432452 5472040 6379916 5486028 6301162 5522330 6189608 5543346 6142814 5544048 6103468 5547890 6095124 5584602 6070942 5601820 6057146 5606908 6046368 5628534 5996806 5639754 5979188 5667862 5903616 5706776 5857700 5737668 5831240 5777824 5781582 5798668 5779106 5826374 5753854 5840298 5742252 5899532 5686184 5981008 5671694 6045318 5649770 6061648 5635462 6126548 5613592 6154312 5607456 6165072 5601942 6227740 5585918 6263758 5570116 # start_trial(7) : # end_trial(7) : Pareto Size = 30, Scalarizations = 100, Elapsed time = 7.724 s # Pareto: 7 Size: 30 5468774 6382734 5498568 6304846 5499490 6235464 5517008 6227206 5519794 6225236 5525784 6196098 5527344 6196068 5540808 6134132 5556582 6114292 5566184 6097612 5569316 6025674 5618874 5954694 5630924 5951274 5672118 5947266 5677566 5916114 5722486 5867416 5758392 5846684 5777734 5787298 5833602 5779658 5834850 5755424 5852146 5754470 5861922 5720298 5963272 5698242 5973552 5695486 5979604 5679158 6001482 5643540 6020326 5626466 6050568 5595726 6154474 5589552 6209536 5563840 # start_trial(8) : # end_trial(8) : Pareto Size = 39, Scalarizations = 100, Elapsed time = 7.752 s # Pareto: 8 Size: 39 5487164 6387864 5493858 6347562 5495452 6314548 5503952 6288260 5508524 6269818 5520270 6239256 5528570 6207896 5529942 6147766 5560010 6132202 5562612 6101794 5593108 6081356 5615656 6047176 5630456 6014302 5633416 5981558 5662954 5916380 5701298 5893354 5715524 5884950 5727872 5870568 5739352 5858264 5772402 5823914 5780418 5818252 5786840 5816708 5830932 5790760 5834644 5764390 5869730 5762196 5875676 5737886 5889824 5732110 5917000 5719372 5919168 5710156 5933164 5696970 5941176 5687456 5968848 5670862 5988362 5641660 6088704 5635518 6091792 5623608 6106984 5599008 6195550 5585052 6297880 5569958 6306082 5553538 # start_trial(9) : # end_trial(9) : Pareto Size = 36, Scalarizations = 100, Elapsed time = 7.66 s # Pareto: 9 Size: 36 5462044 6435818 5484260 6386302 5488498 6285006 5502622 6242846 5520082 6215464 5527790 6212844 5536864 6208854 5556500 6180892 5557828 6105258 5560916 6087702 5585718 6067392 5602506 6049554 5607700 6003382 5629368 6001370 5661986 5963470 5671440 5958196 5673518 5945960 5692180 5934804 5700026 5900610 5700538 5877378 5720530 5864594 5720804 5811566 5805576 5782574 5821954 5768060 5883228 5722900 5927952 5713414 5928762 5686332 5949524 5671262 6022138 5649370 6073150 5627932 6083922 5620382 6189274 5616004 6192004 5602090 6202578 5583326 6205034 5555302 6236032 5541288 # start_trial(10) : # end_trial(10) : Pareto Size = 31, Scalarizations = 100, Elapsed time = 7.736 s # Pareto: 10 Size: 31 5464096 6302030 5512218 6157876 5539916 6137688 5540734 6109298 5591870 6107332 5594846 6081628 5609524 6056874 5612712 6016244 5635752 6008636 5643372 5967030 5672520 5958904 5673058 5919582 5698226 5915150 5736220 5870434 5747934 5865668 5782542 5832806 5783226 5805662 5825482 5786756 5833726 5774638 5863254 5740006 5900318 5732572 5912698 5680052 5958580 5673376 5974446 5670744 5996902 5641786 6018904 5628254 6068804 5618760 6134070 5617998 6147618 5611076 6154364 5585526 6231908 5570114 # start_trial(11) : # end_trial(11) : Pareto Size = 35, Scalarizations = 100, Elapsed time = 7.708 s # Pareto: 11 Size: 35 5478162 6302532 5492580 6272064 5514554 6249728 5521940 6236398 5523922 6222574 5526614 6161368 5559890 6123700 5563894 6033748 5597430 6006592 5612680 5986774 5624044 5931842 5664846 5921454 5712834 5878164 5731736 5861610 5751760 5845224 5759554 5804516 5792678 5802622 5827862 5785862 5828000 5783260 5855824 5759194 5858444 5746108 5865216 5737808 5866704 5731096 5871804 5726804 5956844 5667532 6039342 5635498 6110914 5633938 6111532 5619508 6130620 5615284 6146544 5610928 6165116 5609980 6219808 5599314 6272020 5581332 6306308 5570008 6327900 5562564 # start_trial(12) : # end_trial(12) : Pareto Size = 28, Scalarizations = 100, Elapsed time = 7.548 s # Pareto: 12 Size: 28 5459306 6472574 5478000 6335576 5496942 6330424 5515726 6326626 5518730 6250746 5521632 6149208 5568464 6107872 5570200 6082650 5598276 6075422 5611736 6007338 5617260 6006774 5654824 5973176 5667198 5900706 5709896 5874518 5725116 5837692 5760102 5836974 5772502 5821754 5817124 5786934 5827338 5755606 5841200 5732114 5887968 5729354 5927252 5684720 5935826 5672138 5978816 5659646 6016392 5622784 6073776 5621626 6097370 5581836 6289038 5566690 # start_trial(13) : # end_trial(13) : Pareto Size = 35, Scalarizations = 100, Elapsed time = 7.772 s # Pareto: 13 Size: 35 5491858 6403678 5492276 6347128 5501992 6321612 5507342 6314246 5508776 6279032 5510304 6163454 5525860 6120256 5554948 6119586 5563934 6106438 5580268 6089920 5590538 6069026 5598274 6048738 5622562 6037658 5624374 6018308 5629954 6010616 5634070 5971220 5670456 5921322 5684026 5918784 5703500 5903930 5736216 5844806 5758852 5828618 5771054 5807936 5824234 5754078 5830534 5747444 5850850 5742776 5858832 5728474 5924410 5717742 5946666 5692180 5973574 5665240 5982892 5645420 6057330 5625640 6132696 5606594 6179432 5583116 6310588 5581560 6357798 5565104 # start_trial(14) : # end_trial(14) : Pareto Size = 36, Scalarizations = 100, Elapsed time = 7.612 s # Pareto: 14 Size: 36 5473616 6365124 5483300 6313064 5504884 6253696 5507284 6248576 5521370 6221830 5533570 6197372 5535110 6194642 5556436 6135286 5568184 6073172 5596556 6066640 5604918 6048232 5611088 6007282 5613452 5977660 5648454 5957486 5666660 5927316 5691638 5917364 5696656 5881888 5746996 5855012 5781462 5829580 5783374 5827822 5801384 5782180 5802008 5772632 5836906 5745708 5898018 5728342 5914834 5710606 5923260 5694894 5944566 5675980 5989300 5655444 6015852 5654926 6071484 5651702 6085962 5614748 6155170 5605904 6219216 5597388 6229872 5596136 6355788 5585346 6358938 5580490 # start_trial(15) : # end_trial(15) : Pareto Size = 27, Scalarizations = 100, Elapsed time = 7.58 s # Pareto: 15 Size: 27 5451054 6443550 5468206 6292932 5513040 6278006 5519200 6158560 5541188 6144916 5575260 6094414 5584080 6041258 5603840 6032658 5611498 6005860 5670768 5981726 5671922 5901218 5736616 5865220 5743112 5852820 5756044 5814416 5858480 5757432 5859216 5740162 5862256 5736824 5890464 5727424 5925262 5714692 5931186 5702696 5991426 5672862 5996850 5644532 6029484 5632112 6101178 5595104 6150300 5583016 6163900 5571306 6262890 5559534 # start_trial(16) : # end_trial(16) : Pareto Size = 36, Scalarizations = 100, Elapsed time = 7.868 s # Pareto: 16 Size: 36 5466818 6467160 5484180 6308694 5490738 6278358 5512120 6248044 5523598 6150674 5557638 6093354 5557834 6088440 5578156 6068020 5596418 6041396 5615368 6002640 5629738 6001232 5630074 5987482 5647204 5969202 5675654 5931724 5698938 5862152 5771070 5837874 5792712 5821942 5798774 5821542 5816834 5811922 5837762 5767234 5861694 5766624 5864226 5762180 5877058 5730980 5916290 5709438 5964328 5692998 6004902 5681346 6012366 5680162 6025224 5666758 6030442 5657732 6049238 5626340 6098358 5618936 6112590 5600128 6172164 5598820 6234638 5580636 6276460 5567134 6391042 5560836 # start_trial(17) : # end_trial(17) : Pareto Size = 35, Scalarizations = 100, Elapsed time = 7.636 s # Pareto: 17 Size: 35 5476496 6448550 5484800 6441964 5486666 6326926 5509022 6185866 5526670 6113278 5561814 6102252 5585682 6100740 5605992 6074462 5611434 6000132 5615300 5999414 5618462 5995558 5627828 5993486 5639274 5949160 5680480 5879822 5740488 5837192 5750178 5834752 5815454 5776118 5819886 5774184 5874084 5749848 5882244 5733082 5901402 5725664 5934924 5706842 5941048 5697296 5968522 5689110 5979526 5679058 5987056 5676004 5999960 5671618 6004028 5661610 6065500 5657232 6102992 5628946 6118938 5618594 6156916 5614400 6166074 5599882 6187334 5568340 6291410 5551648 # start_trial(18) : # end_trial(18) : Pareto Size = 39, Scalarizations = 100, Elapsed time = 7.816 s # Pareto: 18 Size: 39 5462092 6578838 5469474 6312478 5470536 6308272 5506202 6202130 5537080 6094448 5575758 6038920 5594824 6012806 5621804 6010540 5622548 6002610 5654616 5965410 5659166 5948048 5659856 5945366 5673952 5943166 5681424 5923854 5711768 5916320 5713990 5895934 5725642 5828812 5748354 5819730 5783182 5816492 5786506 5813720 5842456 5796984 5850396 5783282 5857620 5769208 5864936 5762616 5870328 5747932 5873652 5727540 5946020 5693944 5972542 5688728 5983342 5650576 6017740 5649586 6022766 5639208 6074832 5609498 6144096 5609100 6197054 5607916 6205762 5586168 6254604 5584254 6313180 5576628 6372986 5574362 6389744 5571078 # start_trial(19) : # end_trial(19) : Pareto Size = 30, Scalarizations = 100, Elapsed time = 7.78 s # Pareto: 19 Size: 30 5477962 6305670 5502112 6303922 5518518 6285740 5525556 6240952 5529618 6174678 5572690 6112580 5583130 6105584 5592230 6066632 5601494 6052948 5632598 5993334 5635970 5952384 5653452 5950026 5656292 5924886 5688570 5892638 5728464 5864952 5768474 5819182 5792722 5795344 5810534 5775870 5823686 5753896 5826790 5748290 5901464 5711600 5953112 5694464 5964726 5681660 5993236 5633966 6091666 5620806 6161836 5619102 6187506 5594304 6207150 5573984 6329262 5572274 6444050 5571240 # start_trial(20) : # end_trial(20) : Pareto Size = 32, Scalarizations = 100, Elapsed time = 7.636 s # Pareto: 20 Size: 32 5476994 6413058 5487532 6345980 5497006 6334266 5498226 6310770 5502026 6298096 5502806 6249656 5525448 6234870 5536278 6199468 5540218 6178268 5553554 6176648 5555558 6126162 5586254 6119268 5587004 6095478 5599208 6053264 5605482 5956758 5693504 5946334 5719632 5942226 5721290 5875510 5768950 5818234 5774276 5789072 5805764 5769564 5820410 5760244 5872976 5741360 5911662 5709442 5942788 5699226 5959804 5670764 6007238 5654574 6045790 5622638 6123814 5619560 6188898 5593504 6212412 5571680 6366708 5565044 # start_trial(21) : # end_trial(21) : Pareto Size = 36, Scalarizations = 100, Elapsed time = 7.672 s # Pareto: 21 Size: 36 5471336 6315342 5491628 6284588 5501326 6277322 5502674 6262448 5508838 6254442 5513624 6227666 5535670 6176916 5538978 6162688 5548068 6134918 5552816 6126506 5563438 6099144 5586172 6060446 5600224 6032344 5635182 5999762 5660892 5940974 5699962 5897722 5708900 5857856 5769490 5840154 5787466 5817440 5804206 5813018 5826076 5772564 5827764 5771020 5831912 5761062 5847890 5757042 5867020 5714660 5982128 5681038 6001028 5672288 6010194 5666498 6045448 5663608 6050148 5658530 6052004 5652818 6084784 5618548 6155900 5590124 6259082 5586160 6349080 5580132 6415522 5571378 # start_trial(22) : # end_trial(22) : Pareto Size = 34, Scalarizations = 100, Elapsed time = 7.636 s # Pareto: 22 Size: 34 5480036 6476354 5482270 6326440 5491422 6275512 5505634 6240896 5519850 6221902 5527274 6218344 5541714 6188026 5565924 6045534 5609560 6042750 5619128 6020164 5631696 5988372 5659906 5964766 5662036 5960206 5665044 5947768 5683374 5890356 5715530 5875230 5742592 5849416 5752076 5843532 5776004 5823888 5803218 5788098 5858410 5750908 5900096 5734998 5905468 5727668 5910454 5725540 5948994 5662558 6029008 5658500 6066820 5654874 6067114 5623270 6097022 5617182 6128324 5611634 6180012 5608960 6189356 5602644 6239140 5568314 6244782 5565176 # start_trial(23) : # end_trial(23) : Pareto Size = 35, Scalarizations = 100, Elapsed time = 7.668 s # Pareto: 23 Size: 35 5477242 6497954 5485042 6418628 5488040 6388640 5498664 6385192 5499852 6361860 5504184 6343310 5504698 6224208 5521574 6149532 5543222 6098508 5577962 6089138 5593958 5998976 5626292 5972690 5668196 5941802 5688920 5937596 5700628 5910000 5730530 5897624 5735462 5852836 5754254 5848404 5759728 5832682 5763328 5822410 5773490 5803096 5839716 5770870 5877774 5768684 5885802 5698244 5966396 5685326 5974018 5673048 6001706 5658124 6013834 5649982 6031780 5643234 6073592 5622762 6080262 5621646 6111294 5591928 6210600 5580586 6278276 5572274 6359836 5571896 # start_trial(24) : # end_trial(24) : Pareto Size = 40, Scalarizations = 100, Elapsed time = 7.68 s # Pareto: 24 Size: 40 5483734 6527794 5484548 6401346 5489654 6362178 5500958 6348516 5505038 6288340 5507650 6253554 5514452 6205286 5527924 6173394 5553540 6154908 5563394 6124660 5570452 6037498 5621038 5981702 5629386 5969184 5662568 5938110 5674806 5938020 5680216 5915034 5703292 5884440 5741838 5833770 5771504 5824890 5780040 5816532 5797864 5805076 5810398 5788146 5813636 5762580 5836414 5750126 5883484 5745130 5898980 5698260 5931390 5697906 5958732 5685018 5965588 5682380 5979396 5677592 5995504 5672166 6033678 5668602 6047856 5643420 6102928 5614184 6126138 5605970 6132790 5601730 6182082 5583418 6231718 5583088 6261116 5578752 6393790 5558642 # start_trial(25) : # end_trial(25) : Pareto Size = 28, Scalarizations = 100, Elapsed time = 7.868 s # Pareto: 25 Size: 28 5479152 6370306 5483326 6346230 5485092 6251692 5517202 6222060 5535384 6178272 5536120 6170102 5536752 6143918 5554910 6108976 5567966 6106792 5569914 6051718 5605876 6040696 5641924 5993790 5661286 5968578 5662206 5967642 5673364 5935440 5716222 5855508 5773588 5831476 5785820 5823902 5823924 5749578 5849948 5739752 5944590 5683332 5994348 5671270 6017810 5634482 6109146 5634340 6117362 5595428 6208684 5586720 6269642 5571504 6271632 5552066 # start_trial(26) : # end_trial(26) : Pareto Size = 30, Scalarizations = 100, Elapsed time = 7.652 s # Pareto: 26 Size: 30 5482234 6406148 5498040 6340538 5502866 6197636 5530488 6185752 5543008 6165486 5559882 6143190 5573468 6130378 5575658 6129774 5576584 6044426 5649386 5949764 5654956 5944032 5687204 5901440 5695532 5893306 5733938 5836310 5775554 5791398 5814190 5755000 5845614 5742466 5879506 5720208 5900890 5718980 5904184 5693338 5970758 5677364 6002442 5676588 6004862 5643736 6027296 5634274 6129564 5615078 6160288 5613492 6204154 5603138 6249170 5595702 6258006 5587184 6289434 5578930 # start_trial(27) : # end_trial(27) : Pareto Size = 35, Scalarizations = 100, Elapsed time = 7.636 s # Pareto: 27 Size: 35 5449790 6360588 5497376 6317394 5509168 6243800 5515094 6221302 5517326 6213532 5543098 6150386 5569186 6098304 5575970 6095022 5583952 6042126 5612434 6036164 5617618 6027198 5619280 5993738 5628856 5992568 5654492 5947008 5691142 5916532 5693644 5910702 5705094 5872634 5744078 5846660 5758548 5830634 5794420 5805406 5801654 5788142 5864826 5726540 5877074 5726028 5919900 5710778 5929708 5692868 5931392 5681482 5947286 5674014 6002630 5630226 6087886 5623256 6145286 5612586 6146552 5612552 6155652 5609596 6179356 5600760 6190666 5574378 6318134 5567528 # start_trial(28) : # end_trial(28) : Pareto Size = 31, Scalarizations = 100, Elapsed time = 7.636 s # Pareto: 28 Size: 31 5471220 6468532 5482532 6395690 5486656 6314146 5501106 6302906 5512580 6271016 5528464 6207280 5530358 6201316 5532944 6171108 5565482 6157722 5571040 6087264 5580004 6084082 5614482 6034960 5615832 5989788 5646232 5984510 5676516 5889250 5705932 5868272 5763350 5860400 5775828 5802802 5841996 5777068 5891626 5743364 5916262 5718944 5935150 5706706 5961250 5685776 5981954 5670504 6018184 5670042 6033064 5654170 6050652 5599726 6175722 5598556 6279254 5595090 6332050 5587276 6465206 5564530 # start_trial(29) : # end_trial(29) : Pareto Size = 37, Scalarizations = 100, Elapsed time = 7.596 s # Pareto: 29 Size: 37 5476436 6408608 5478444 6387120 5482118 6350128 5493142 6246986 5508674 6244768 5524268 6228466 5527382 6215856 5531182 6180854 5553874 6119424 5572456 6101534 5578762 6069204 5602250 6026392 5651678 6010604 5655438 5960172 5669662 5941576 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5565580 # start_trial(31) : # end_trial(31) : Pareto Size = 38, Scalarizations = 100, Elapsed time = 7.904 s # Pareto: 31 Size: 38 5466502 6446336 5467992 6390878 5479664 6362974 5492064 6326966 5492484 6322866 5499922 6291382 5504254 6167980 5556712 6164764 5558676 6126026 5563106 6117316 5572622 6083426 5591680 6053926 5606676 6005232 5649200 5973054 5649594 5903672 5682254 5896098 5733892 5875122 5738722 5867076 5767500 5843076 5800616 5783878 5823378 5762688 5833484 5754320 5850344 5737568 5873752 5726626 5970470 5675280 6006328 5671568 6006754 5651258 6013652 5649844 6057966 5647930 6087738 5613908 6159902 5607402 6173798 5605132 6175228 5603782 6187338 5600156 6200572 5583828 6204620 5582636 6280986 5573892 6412792 5572106 # start_trial(32) : # end_trial(32) : Pareto Size = 34, Scalarizations = 100, Elapsed time = 7.544 s # Pareto: 32 Size: 34 5485682 6446206 5494238 6398914 5500454 6391388 5501406 6237808 5520706 6220172 5535056 6127840 5566128 6041192 5642124 5938724 5684194 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Pareto Size = 33, Scalarizations = 100, Elapsed time = 7.944 s # Pareto: 34 Size: 33 5487404 6505136 5488066 6469774 5491100 6468776 5494454 6466944 5496488 6265610 5505678 6203570 5530012 6199960 5540236 6179770 5540758 6120200 5569652 6097142 5594360 6094274 5605600 5987872 5641364 5965428 5642756 5906184 5660574 5904474 5668140 5893668 5714694 5892624 5719186 5857312 5756394 5824572 5800002 5814280 5815284 5788864 5830600 5772410 5845572 5759124 5899410 5739376 5913894 5707548 5932538 5692260 5942804 5650630 6067430 5645978 6099890 5634122 6102370 5615454 6185322 5602756 6229324 5581660 6383824 5575026 # start_trial(35) : # end_trial(35) : Pareto Size = 33, Scalarizations = 100, Elapsed time = 7.512 s # Pareto: 35 Size: 33 5459624 6428096 5482434 6382876 5482998 6367468 5488694 6355134 5503436 6317548 5510740 6311844 5514852 6123142 5559384 6090636 5578092 6063416 5587144 6045020 5591742 6004966 5658562 5920124 5700632 5903432 5712048 5885158 5721200 5876400 5737732 5873206 5766018 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31, Scalarizations = 100, Elapsed time = 7.776 s # Pareto: 61 Size: 31 5478014 6266504 5490102 6208560 5539310 6137958 5551192 6104634 5557284 6095580 5568324 6079160 5611712 6012100 5631866 5956536 5661938 5908692 5672572 5908628 5694688 5892876 5741392 5822414 5775330 5807942 5790956 5795942 5822138 5794996 5834468 5775870 5856556 5768892 5860474 5759838 5887966 5740248 5899352 5721566 5931358 5712210 5945898 5702258 5983476 5666820 5998128 5645850 6014776 5633844 6042002 5632744 6123340 5626164 6168380 5621354 6199922 5616446 6204290 5616132 6210686 5567286 # start_trial(62) : # end_trial(62) : Pareto Size = 39, Scalarizations = 100, Elapsed time = 7.656 s # Pareto: 62 Size: 39 5467196 6380972 5480696 6285836 5508440 6280948 5512098 6265504 5529052 6242770 5531436 6212368 5534440 6187688 5540258 6123834 5566330 6092520 5593640 6065944 5605206 6059426 5606172 6024128 5618850 6014730 5623060 5998894 5638146 5990052 5663350 5938828 5667320 5893784 5721574 5889506 5742176 5880520 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end_trial(86) : Pareto Size = 34, Scalarizations = 100, Elapsed time = 7.672 s # Pareto: 86 Size: 34 5467992 6460600 5485956 6431882 5493690 6242948 5501798 6191070 5527026 6169682 5528344 6157654 5546316 6105380 5596484 6025462 5635058 6009536 5635466 5984054 5641666 5966840 5669264 5956824 5684830 5928248 5699020 5923320 5700662 5908786 5726628 5855506 5779488 5834870 5798046 5804238 5799994 5795828 5844930 5746740 5863204 5730040 5904156 5725124 5909916 5705614 5930010 5701756 5936064 5688028 5981844 5679594 6022790 5664102 6023120 5661508 6034532 5643226 6076010 5636052 6141616 5631280 6175982 5613500 6185426 5582092 6282026 5568500 # start_trial(87) : # end_trial(87) : Pareto Size = 36, Scalarizations = 100, Elapsed time = 7.744 s # Pareto: 87 Size: 36 5477868 6402504 5484038 6369776 5500272 6295844 5509032 6286762 5516594 6211524 5523046 6187678 5546248 6156344 5555622 6147466 5565362 6070788 5591052 5983940 5642976 5962358 5669520 5950046 5675326 5935904 5678956 5933120 5679234 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start_trial(92) : # end_trial(92) : Pareto Size = 37, Scalarizations = 100, Elapsed time = 7.64 s # Pareto: 92 Size: 37 5478730 6463030 5486662 6358724 5502362 6311094 5525386 6210170 5533384 6191092 5540184 6182408 5556486 6126910 5564586 6085588 5577552 6085436 5610078 6020540 5621150 6010578 5638802 5982966 5644612 5979140 5667176 5951924 5667502 5937994 5692334 5918000 5692384 5895974 5706642 5887416 5724840 5874878 5770594 5824382 5796292 5804600 5814546 5743420 5866490 5727344 5899236 5727032 5921676 5723326 5921872 5694282 5966352 5687830 6022984 5668028 6036764 5667210 6043222 5665306 6046488 5649166 6054362 5634366 6064540 5633006 6075394 5599924 6224800 5588636 6244632 5584730 6296342 5575304 # start_trial(93) : # end_trial(93) : Pareto Size = 29, Scalarizations = 100, Elapsed time = 7.64 s # Pareto: 93 Size: 29 5488044 6368944 5497632 6332378 5505636 6307722 5507432 6208806 5537302 6203454 5542134 6191844 5545672 6139488 5552012 6131764 5563816 6102846 5600206 6047434 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5458918 6915260 5461586 6912246 5463182 6909398 5470894 6903364 5472760 6903128 5476640 6902676 5478320 6887482 5479408 6880680 5488318 6872452 5494014 6863820 5499344 6856248 5499644 6840218 5507398 6816882 5522204 6808980 5525698 6786460 5525896 6786360 5536092 6778618 5541588 6776942 5541796 6763548 5551096 6762860 5551898 6760972 5551988 6747004 5563434 6736864 5573632 6720908 5584794 6711820 5589598 6707662 5592946 6706984 5594524 6703684 5596552 6689388 5597924 6688528 5600142 6686288 5603636 6683672 5607084 6675658 5612500 6659996 5615622 6657264 5625760 6647548 5636314 6644632 5642172 6628486 5646778 6619064 5652486 6610292 5656990 6604406 5660700 6600792 5664552 6599400 5680730 6576660 5682216 6574504 5700920 6554304 5707998 6543322 5709246 6535710 5716266 6530126 5720810 6525902 5727754 6510510 5735016 6501214 5745334 6497882 5749040 6489334 5760270 6470056 5767572 6465090 5773602 6464400 5778276 6449698 5784226 6441892 5804950 6427712 5805264 6422932 5822560 6404420 5845166 6381764 5856204 6374498 5858830 6365234 5869292 6356466 5869588 6351120 5874282 6339500 5911668 6309244 5913010 6301044 5915050 6297660 5924090 6297356 5933668 6269262 5943694 6268610 5949466 6265246 5954182 6264608 5954184 6262606 5965438 6242738 5967970 6237710 5983168 6234944 5983620 6230938 5992880 6212058 6010458 6194140 6025322 6182910 6042762 6173178 6048362 6169442 6071186 6147856 6074966 6142036 6075684 6138010 6094288 6128816 6096554 6123478 6110178 6107920 6124966 6089824 6126276 6083722 6127616 6079526 6135124 6076246 6174032 6041498 6174262 6041384 6178268 6035712 6200294 6017980 6209158 6011610 6214360 6006128 6227066 5990750 6230024 5990356 6235190 5989272 6260610 5967642 6261322 5959590 6281184 5939284 6285920 5939058 6295488 5930032 6296972 5926610 6297110 5924930 6305370 5924252 6314780 5910824 6329062 5910684 6334944 5903488 6338582 5900806 6354554 5867370 6376726 5861444 6379056 5857892 6384960 5853704 6402568 5845406 6402678 5828394 6417182 5826492 6432248 5826268 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5450900 7019358 5444180 7024490 5440050 7035700 5430098 7115742 5404814 # Average Number Scalarizations = 1000, Average Trial Time = 807.2 s eaf/inst/extdata/README0000644000176200001440000000104413254545527014307 0ustar liggesusersThis directory contains the files needed to reproduce the examples described in Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuß, editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 209-233, Springer, 2010. The script run.sh contains the commands necessary to reproduce all examples. Please, read the comments within run.sh. eaf/inst/extdata/uniform-250-10-3d.txt0000644000176200001440000041402313254545527016702 0ustar liggesusers 5.4952587487048605 2.7306949214499197 3.9730413624604792 3.595707923021565 4.362395109239126 2.638454540676543 1.9095004095834445 1.964463493673704 6.940545205397193 6.76573067692009 1.3300975525705367 7.855225087261113 7.180421856052806 2.3795767341110796 2.775429570354854 0.6393838813969355 5.566696143713011 7.033094161954498 7.540136826292178 9.677708472993157 2.4026917182497423 2.1034724437057672 2.2937227893955354 6.594643133819253 7.941451798051468 1.2963971611918872 3.188156724357548 2.1315842137163465 8.499492620455449 2.865240053114304 7.998637424863092 1.8744517086130683 1.148555223294197 1.5938653509700353 6.276274824590952 4.04977277149441 5.1236764048164884 2.7444965831224097 4.62701808514004 1.5777975447540626 6.727870458266202 6.579110650841406 7.764622863548487 1.3055169538956868 4.799295135552365 1.7985008265936044 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5331366 5292270 5318304 5298876 5290408 5312952 5277832 # start_trial(1) : # end_trial(1) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 1 Size: 4 5246946 5368942 5249068 5328154 5277710 5321710 5280132 5281080 # start_trial(2) : # end_trial(2) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 2 Size: 2 5255914 5312620 5285994 5294248 # start_trial(3) : # end_trial(3) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 3 Size: 6 5254586 5341368 5265448 5336434 5266332 5329290 5278158 5328006 5293084 5304026 5316378 5285680 # start_trial(4) : # end_trial(4) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 4 Size: 5 5257008 5385896 5260020 5354180 5261932 5351978 5265120 5343482 5272604 5280450 # start_trial(5) : # end_trial(5) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 5 Size: 3 5251436 5304740 5260634 5289432 5261388 5278662 # start_trial(6) : # end_trial(6) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 6 Size: 3 5260462 5303782 5309662 5298112 5338894 5292426 # start_trial(7) : # end_trial(7) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 7 Size: 4 5238158 5302220 5266616 5301604 5277362 5291346 5328374 5283152 # start_trial(8) : # end_trial(8) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 8 Size: 5 5259568 5344514 5260078 5323590 5275688 5304758 5295860 5299490 5321894 5280216 # start_trial(9) : # end_trial(9) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 9 Size: 5 5252950 5337456 5258962 5303668 5273430 5301298 5311040 5291910 5319530 5279154 # start_trial(10) : # end_trial(10) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 10 Size: 6 5255088 5362366 5260872 5337164 5276254 5307774 5278846 5302382 5287324 5301484 5290398 5284198 # start_trial(11) : # end_trial(11) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 11 Size: 3 5257462 5328600 5262892 5302082 5306682 5293358 # start_trial(12) : # end_trial(12) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 12 Size: 5 5255536 5369466 5265974 5314044 5276688 5311924 5301892 5288482 5319394 5278344 # start_trial(13) : # end_trial(13) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 13 Size: 4 5243484 5327056 5262810 5312496 5265234 5288336 5305096 5285540 # start_trial(14) : # end_trial(14) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 14 Size: 3 5254326 5373688 5258282 5328368 5277040 5290704 # start_trial(15) : # end_trial(15) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 15 Size: 5 5251220 5334532 5258622 5318388 5274694 5318056 5275588 5299916 5307866 5281706 # start_trial(16) : # end_trial(16) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 16 Size: 4 5252238 5320246 5297660 5319870 5302690 5316084 5305034 5293474 # start_trial(17) : # end_trial(17) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 17 Size: 3 5256636 5364154 5262016 5294988 5264892 5269504 # start_trial(18) : # end_trial(18) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 18 Size: 6 5228108 5347760 5281796 5327312 5286788 5320758 5292364 5312668 5305786 5297640 5319554 5296596 # start_trial(19) : # end_trial(19) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 19 Size: 3 5258012 5349460 5262532 5294728 5311364 5285878 # start_trial(20) : # end_trial(20) : Pareto Size = 9, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 20 Size: 9 5262846 5351252 5268224 5332648 5286336 5329542 5291024 5323824 5291052 5311388 5322204 5305956 5337348 5305340 5338120 5302878 5350520 5288884 # start_trial(21) : # end_trial(21) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 21 Size: 6 5257942 5373498 5259552 5357156 5267384 5330886 5279304 5315890 5286088 5309012 5287040 5284718 # start_trial(22) : # end_trial(22) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 22 Size: 3 5253764 5351678 5265558 5309298 5290820 5295606 # start_trial(23) : # end_trial(23) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 23 Size: 3 5243858 5338614 5261776 5332340 5286980 5290820 # start_trial(24) : # end_trial(24) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 24 Size: 4 5251984 5322490 5293588 5309414 5295572 5298376 5313146 5297746 # start_trial(25) : # end_trial(25) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 25 Size: 3 5238488 5294964 5265768 5287358 5274538 5282446 # start_trial(26) : # end_trial(26) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 26 Size: 3 5240112 5318798 5285086 5296794 5297420 5288042 # start_trial(27) : # end_trial(27) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 27 Size: 3 5253562 5315656 5258408 5295302 5297444 5294720 # start_trial(28) : # end_trial(28) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 28 Size: 3 5243092 5314062 5243248 5286716 5296000 5284678 # start_trial(29) : # end_trial(29) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 29 Size: 4 5254042 5338584 5260876 5330882 5276202 5313170 5286874 5286552 # start_trial(30) : # end_trial(30) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 30 Size: 4 5249126 5334774 5263022 5312306 5281242 5309470 5284318 5282482 # start_trial(31) : # end_trial(31) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 31 Size: 6 5257878 5347536 5275036 5339680 5281738 5334044 5282030 5309294 5292442 5286754 5305288 5276742 # start_trial(32) : # end_trial(32) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 32 Size: 4 5262822 5336322 5287408 5314374 5288480 5303274 5297530 5296626 # start_trial(33) : # end_trial(33) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 33 Size: 4 5262302 5350178 5268796 5320690 5287784 5307578 5303060 5291696 # start_trial(34) : # end_trial(34) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 34 Size: 4 5255158 5363060 5265740 5316854 5280842 5287822 5295982 5271860 # start_trial(35) : # end_trial(35) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 35 Size: 4 5236920 5323144 5275658 5316252 5295276 5291846 5320036 5287242 # start_trial(36) : # end_trial(36) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 36 Size: 7 5245320 5350028 5265846 5346862 5269172 5329214 5289792 5317436 5291128 5309678 5300432 5300306 5320356 5299120 # start_trial(37) : # end_trial(37) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 37 Size: 6 5256692 5369864 5258244 5351398 5260990 5318888 5276810 5304172 5307654 5301888 5349612 5294114 # start_trial(38) : # end_trial(38) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 38 Size: 4 5245000 5402680 5261294 5355072 5280504 5342928 5281916 5279952 # start_trial(39) : # end_trial(39) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 39 Size: 1 5256756 5291420 # start_trial(40) : # end_trial(40) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 40 Size: 3 5240372 5339244 5269234 5300968 5292588 5291350 # start_trial(41) : # end_trial(41) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 41 Size: 3 5238810 5321212 5254626 5315172 5260078 5296038 # start_trial(42) : # end_trial(42) : Pareto Size = 8, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 42 Size: 8 5262120 5384314 5262892 5343884 5264312 5331836 5279288 5323156 5287766 5316696 5288662 5313878 5299640 5305808 5304502 5281312 # start_trial(43) : # end_trial(43) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 43 Size: 6 5251856 5339406 5260988 5322928 5285486 5322566 5290320 5308872 5302434 5302166 5318698 5289714 # start_trial(44) : # end_trial(44) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 44 Size: 4 5235812 5381636 5236334 5354730 5270128 5301072 5305764 5283634 # start_trial(45) : # end_trial(45) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 45 Size: 2 5230660 5301340 5293106 5289332 # start_trial(46) : # end_trial(46) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 46 Size: 4 5228398 5320810 5238488 5307248 5282654 5305988 5299924 5287560 # start_trial(47) : # end_trial(47) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 47 Size: 2 5260116 5305420 5269468 5286560 # start_trial(48) : # end_trial(48) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 48 Size: 5 5249848 5325302 5296612 5310710 5309120 5306430 5317752 5305550 5342704 5304442 # start_trial(49) : # end_trial(49) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 49 Size: 4 5261944 5309926 5288296 5307688 5312776 5304054 5327484 5298724 # start_trial(50) : # end_trial(50) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 50 Size: 5 5253764 5347418 5257354 5335486 5259120 5308196 5319244 5299520 5342200 5282958 # start_trial(51) : # end_trial(51) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 51 Size: 4 5261206 5322822 5288592 5320284 5316748 5302208 5335480 5286420 # start_trial(52) : # end_trial(52) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 52 Size: 6 5247862 5344898 5267480 5338488 5279426 5311808 5286042 5310946 5297888 5298750 5304450 5295776 # start_trial(53) : # end_trial(53) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 53 Size: 4 5242284 5315430 5299172 5300900 5342822 5296088 5358172 5295926 # start_trial(54) : # end_trial(54) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 54 Size: 6 5235754 5344592 5258078 5328266 5264712 5304666 5280648 5300618 5285308 5276622 5310290 5276322 # start_trial(55) : # end_trial(55) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 55 Size: 2 5242082 5306670 5328674 5299828 # start_trial(56) : # end_trial(56) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.9081 s # Pareto: 56 Size: 1 5256734 5284984 # start_trial(57) : # end_trial(57) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 57 Size: 7 5242010 5369404 5248032 5330344 5286322 5316662 5289386 5307442 5326316 5306350 5328532 5295078 5341172 5287608 # start_trial(58) : # end_trial(58) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.9401 s # Pareto: 58 Size: 6 5260496 5391364 5262604 5351568 5263442 5324760 5277952 5316794 5292546 5307408 5304486 5289422 # start_trial(59) : # end_trial(59) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 59 Size: 6 5259588 5363620 5260464 5334398 5271884 5333010 5276300 5315924 5299660 5308920 5326834 5306774 # start_trial(60) : # end_trial(60) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.9281 s # Pareto: 60 Size: 1 5234320 5262976 # start_trial(61) : # end_trial(61) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 61 Size: 3 5254722 5330798 5258722 5312162 5306164 5277030 # start_trial(62) : # end_trial(62) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 62 Size: 6 5252284 5385672 5270528 5327488 5281022 5314692 5296408 5305058 5318856 5301588 5330258 5288516 # start_trial(63) : # end_trial(63) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 63 Size: 4 5252048 5332752 5259582 5296404 5290924 5292414 5296594 5282124 # start_trial(64) : # end_trial(64) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 64 Size: 4 5254176 5344870 5264204 5335046 5268688 5308428 5275096 5304396 # start_trial(65) : # end_trial(65) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.9081 s # Pareto: 65 Size: 6 5253734 5350752 5266398 5327920 5274224 5324300 5281920 5313874 5313554 5307624 5316664 5301172 # start_trial(66) : # end_trial(66) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 66 Size: 5 5266576 5355532 5269280 5344320 5276966 5342668 5278770 5314612 5315252 5296102 # start_trial(67) : # end_trial(67) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 67 Size: 6 5269788 5328620 5273672 5324206 5287004 5315594 5302912 5314092 5308178 5283444 5347098 5281286 # start_trial(68) : # end_trial(68) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8961 s # Pareto: 68 Size: 2 5250266 5283778 5291518 5274832 # start_trial(69) : # end_trial(69) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.9521 s # Pareto: 69 Size: 6 5263826 5354972 5273308 5346828 5274692 5341542 5280736 5323828 5282256 5319488 5286150 5286728 # start_trial(70) : # end_trial(70) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.9401 s # Pareto: 70 Size: 2 5256726 5303656 5291596 5289432 # start_trial(71) : # end_trial(71) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.9401 s # Pareto: 71 Size: 4 5258136 5343598 5269662 5334022 5289226 5315086 5292162 5293562 # start_trial(72) : # end_trial(72) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 72 Size: 1 5248776 5290344 # start_trial(73) : # end_trial(73) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 73 Size: 5 5218454 5334926 5260938 5322806 5291370 5297718 5304592 5287938 5314982 5285268 # start_trial(74) : # end_trial(74) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.9081 s # Pareto: 74 Size: 4 5258702 5360220 5263454 5334720 5275412 5330956 5291384 5277334 # start_trial(75) : # end_trial(75) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 75 Size: 1 5245122 5270788 # start_trial(76) : # end_trial(76) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 76 Size: 3 5258602 5325318 5290268 5311812 5314862 5296934 # start_trial(77) : # end_trial(77) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 77 Size: 2 5241944 5296114 5282260 5280460 # start_trial(78) : # end_trial(78) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 78 Size: 4 5250786 5332162 5280702 5314016 5290956 5292046 5293478 5285544 # start_trial(79) : # end_trial(79) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.8881 s # Pareto: 79 Size: 1 5244758 5281034 # start_trial(80) : # end_trial(80) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 80 Size: 6 5244094 5358484 5252686 5336058 5261452 5327250 5281694 5319124 5283896 5307338 5304106 5265076 # start_trial(81) : # end_trial(81) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 81 Size: 7 5263662 5380452 5269740 5337070 5285200 5324712 5289394 5324456 5292320 5297070 5320574 5292384 5350206 5284230 # start_trial(82) : # end_trial(82) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 82 Size: 7 5254466 5343314 5273292 5327148 5273646 5321210 5281792 5318952 5297958 5308136 5305154 5302466 5313004 5296520 # start_trial(83) : # end_trial(83) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 83 Size: 3 5274740 5318122 5278544 5301430 5362482 5295038 # start_trial(84) : # end_trial(84) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.9081 s # Pareto: 84 Size: 2 5249768 5339684 5258262 5293812 # start_trial(85) : # end_trial(85) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 85 Size: 2 5256640 5315028 5306908 5297420 # start_trial(86) : # end_trial(86) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 86 Size: 4 5245982 5344194 5258992 5333040 5287224 5324826 5288672 5284450 # start_trial(87) : # end_trial(87) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 87 Size: 7 5255464 5371998 5261372 5346970 5263790 5322536 5286590 5321710 5289168 5317450 5297930 5309896 5307946 5278906 # start_trial(88) : # end_trial(88) : Pareto Size = 10, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 88 Size: 10 5262622 5400986 5265912 5382320 5270982 5378326 5278382 5371032 5281176 5350530 5285532 5339452 5292918 5326392 5294868 5309878 5327076 5306252 5332918 5296722 # start_trial(89) : # end_trial(89) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 89 Size: 6 5249178 5355160 5258192 5321030 5264166 5315014 5284102 5310674 5292936 5307192 5304938 5274502 # start_trial(90) : # end_trial(90) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 90 Size: 5 5251208 5351068 5257866 5344442 5266680 5319614 5273044 5309560 5331924 5291808 # start_trial(91) : # end_trial(91) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 91 Size: 6 5235292 5347606 5262632 5312126 5275892 5306426 5298398 5301830 5325094 5297634 5334632 5281888 # start_trial(92) : # end_trial(92) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 92 Size: 4 5250116 5352672 5258824 5343600 5279978 5309544 5295378 5301240 # start_trial(93) : # end_trial(93) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 93 Size: 4 5246462 5310508 5291544 5302102 5319288 5299960 5325884 5285786 # start_trial(94) : # end_trial(94) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 94 Size: 3 5252000 5302482 5293538 5300674 5303910 5288250 # start_trial(95) : # end_trial(95) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8881 s # Pareto: 95 Size: 2 5245366 5308014 5256896 5298362 # start_trial(96) : # end_trial(96) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 96 Size: 2 5256672 5307198 5260802 5304284 # start_trial(97) : # end_trial(97) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 97 Size: 3 5259914 5376072 5261922 5352874 5266982 5285696 # start_trial(98) : # end_trial(98) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 98 Size: 3 5260882 5348806 5263884 5318024 5270396 5286306 # start_trial(99) : # end_trial(99) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 99 Size: 2 5250308 5316120 5252958 5293300 # start_trial(100) : # end_trial(100) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 100 Size: 4 5250390 5329390 5261540 5328954 5289164 5328796 5290310 5275050 # start_trial(101) : # end_trial(101) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 101 Size: 5 5253072 5361462 5254608 5337038 5270882 5335940 5275714 5298076 5300910 5293890 # start_trial(102) : # end_trial(102) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 102 Size: 5 5243478 5344086 5267936 5341394 5279958 5332910 5280782 5314720 5285232 5275010 # start_trial(103) : # end_trial(103) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 103 Size: 3 5235504 5320670 5263384 5310056 5302036 5304832 # start_trial(104) : # end_trial(104) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 104 Size: 3 5227330 5330544 5240298 5280132 5308070 5275390 # start_trial(105) : # end_trial(105) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 105 Size: 1 5248426 5277914 # start_trial(106) : # end_trial(106) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 106 Size: 2 5255756 5317174 5274796 5273690 # start_trial(107) : # end_trial(107) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 107 Size: 5 5244958 5357934 5261804 5337972 5265596 5314642 5295872 5301766 5302656 5296712 # start_trial(108) : # end_trial(108) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.9201 s # Pareto: 108 Size: 4 5265300 5396848 5278934 5355400 5286066 5343848 5288282 5277112 # start_trial(109) : # end_trial(109) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.9241 s # Pareto: 109 Size: 3 5248312 5313382 5278584 5288376 5350914 5273140 # start_trial(110) : # end_trial(110) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.9201 s # Pareto: 110 Size: 5 5260570 5366182 5261138 5327354 5265384 5319300 5276072 5315454 5320900 5292374 # start_trial(111) : # end_trial(111) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.9241 s # Pareto: 111 Size: 6 5259020 5371016 5262886 5316222 5266652 5314362 5268656 5314344 5287836 5305634 5340968 5303026 # start_trial(112) : # end_trial(112) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.9201 s # Pareto: 112 Size: 4 5246474 5346104 5263294 5310064 5270142 5303812 5272096 5300444 # start_trial(113) : # end_trial(113) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.9201 s # Pareto: 113 Size: 4 5263220 5378332 5263998 5327640 5267114 5307732 5330296 5307354 # start_trial(114) : # end_trial(114) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 114 Size: 4 5262548 5402950 5270176 5345758 5275424 5318398 5276992 5236250 # start_trial(115) : # end_trial(115) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 115 Size: 6 5233790 5319090 5264518 5319008 5264536 5318920 5276542 5316952 5288210 5298584 5304666 5292444 # start_trial(116) : # end_trial(116) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8641 s # Pareto: 116 Size: 4 5254910 5338252 5260136 5318360 5290766 5300026 5334682 5290058 # start_trial(117) : # end_trial(117) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 117 Size: 3 5249998 5357374 5267810 5305306 5297790 5288126 # start_trial(118) : # end_trial(118) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 118 Size: 4 5246196 5351106 5255360 5314966 5265678 5297074 5295686 5284642 # start_trial(119) : # end_trial(119) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 119 Size: 3 5262708 5311982 5269482 5307826 5305344 5288686 # start_trial(120) : # end_trial(120) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 120 Size: 5 5271874 5369380 5277576 5340570 5280818 5291898 5282712 5289454 5300780 5280476 # start_trial(121) : # end_trial(121) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 121 Size: 4 5248424 5335144 5249960 5331024 5256802 5285296 5298484 5283738 # start_trial(122) : # end_trial(122) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 122 Size: 6 5256520 5336596 5260378 5322566 5273802 5311874 5288902 5307538 5311962 5293074 5323786 5288510 # start_trial(123) : # end_trial(123) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 123 Size: 4 5258636 5364368 5259878 5327640 5268400 5303050 5307924 5295254 # start_trial(124) : # end_trial(124) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 124 Size: 4 5237430 5357268 5265556 5342644 5286146 5304570 5325270 5280718 # start_trial(125) : # end_trial(125) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 125 Size: 2 5262556 5301992 5304798 5287750 # start_trial(126) : # end_trial(126) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 126 Size: 5 5244792 5329616 5302508 5327468 5305302 5316252 5320352 5313348 5339836 5298872 # start_trial(127) : # end_trial(127) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 127 Size: 2 5259772 5308780 5278412 5280542 # start_trial(128) : # end_trial(128) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 128 Size: 3 5258142 5333230 5284148 5329706 5305316 5292932 # start_trial(129) : # end_trial(129) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 129 Size: 2 5251582 5295028 5325122 5292932 # start_trial(130) : # end_trial(130) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 130 Size: 7 5253764 5377948 5261100 5376560 5262886 5348078 5268516 5331016 5284602 5308580 5304954 5307342 5350956 5306594 # start_trial(131) : # end_trial(131) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 131 Size: 3 5247364 5341168 5255072 5309098 5257812 5293934 # start_trial(132) : # end_trial(132) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 132 Size: 1 5248088 5300746 # start_trial(133) : # end_trial(133) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 133 Size: 5 5243774 5339600 5260430 5331044 5261064 5317618 5284230 5294324 5309232 5282052 # start_trial(134) : # end_trial(134) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 134 Size: 7 5210172 5342470 5268604 5332856 5272310 5329524 5287224 5313622 5297324 5306942 5297552 5295804 5319088 5290708 # start_trial(135) : # end_trial(135) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 135 Size: 4 5237578 5320140 5267828 5311316 5293642 5289788 5315048 5287112 # start_trial(136) : # end_trial(136) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 136 Size: 4 5256000 5378382 5265996 5347036 5272834 5320852 5296750 5294544 # start_trial(137) : # end_trial(137) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 137 Size: 7 5248054 5337248 5271552 5336002 5276270 5319376 5282786 5318792 5289352 5307728 5299360 5294498 5335696 5287664 # start_trial(138) : # end_trial(138) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 138 Size: 4 5249970 5351738 5262156 5329990 5273030 5300160 5284834 5268814 # start_trial(139) : # end_trial(139) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 139 Size: 4 5260472 5322856 5267366 5309662 5294148 5305270 5356184 5300892 # start_trial(140) : # end_trial(140) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 140 Size: 4 5247614 5319096 5255140 5297600 5292146 5295294 5292856 5274260 # start_trial(141) : # end_trial(141) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 141 Size: 4 5241520 5324764 5269646 5323274 5271470 5303358 5280230 5283868 # start_trial(142) : # end_trial(142) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 142 Size: 3 5251808 5307900 5319016 5306842 5325528 5302748 # start_trial(143) : # end_trial(143) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 143 Size: 4 5232352 5331690 5273332 5316496 5279668 5305118 5331000 5299232 # start_trial(144) : # end_trial(144) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 144 Size: 6 5256082 5351070 5270108 5341410 5271946 5331824 5289960 5321778 5298824 5298102 5316372 5297098 # start_trial(145) : # end_trial(145) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 145 Size: 7 5240608 5349646 5256128 5346218 5261404 5345828 5270440 5328984 5275350 5303866 5279460 5299088 5312672 5290524 # start_trial(146) : # end_trial(146) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 146 Size: 5 5229068 5331358 5247820 5319718 5279928 5314066 5293434 5301422 5293870 5284408 # start_trial(147) : # end_trial(147) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 147 Size: 4 5265888 5381896 5268672 5311472 5302546 5299700 5336824 5287200 # start_trial(148) : # end_trial(148) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 148 Size: 5 5246992 5371468 5266244 5308672 5276688 5302090 5315516 5294950 5319938 5284618 # start_trial(149) : # end_trial(149) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 149 Size: 4 5241468 5358336 5244606 5340366 5267342 5297432 5332938 5276904 # start_trial(150) : # end_trial(150) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 150 Size: 7 5244244 5376114 5250468 5354044 5278652 5340352 5281488 5333600 5284324 5310560 5331200 5296610 5333906 5293128 # start_trial(151) : # end_trial(151) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 151 Size: 7 5255280 5352872 5268406 5343326 5289330 5342400 5292076 5336504 5292286 5312784 5301596 5307448 5391774 5307228 # start_trial(152) : # end_trial(152) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 152 Size: 3 5255694 5351886 5271362 5307726 5283454 5285084 # start_trial(153) : # end_trial(153) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 153 Size: 3 5253714 5313704 5263676 5299808 5275022 5284336 # start_trial(154) : # end_trial(154) : Pareto Size = 8, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 154 Size: 8 5241616 5362236 5255426 5346276 5262784 5332708 5269108 5325460 5277288 5323876 5277498 5307024 5310020 5301512 5352002 5293254 # start_trial(155) : # end_trial(155) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 155 Size: 6 5252906 5366638 5257382 5334862 5258428 5308712 5303022 5298936 5327690 5289806 5339530 5283106 # start_trial(156) : # end_trial(156) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 156 Size: 6 5214054 5387936 5244092 5333448 5290884 5324472 5299138 5315958 5301466 5313770 5315072 5295644 # start_trial(157) : # end_trial(157) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 157 Size: 5 5251526 5385648 5266708 5365138 5267846 5303184 5293826 5297860 5319616 5295318 # start_trial(158) : # end_trial(158) : Pareto Size = 8, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 158 Size: 8 5246382 5364572 5255166 5321574 5287178 5321104 5288952 5315294 5289520 5313774 5289734 5312258 5295552 5298740 5320318 5292216 # start_trial(159) : # end_trial(159) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 159 Size: 7 5255790 5350878 5270496 5328194 5273050 5323448 5283064 5322406 5299868 5311574 5311458 5296820 5369294 5296484 # start_trial(160) : # end_trial(160) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 160 Size: 4 5263060 5345028 5263346 5329268 5263630 5309538 5270548 5274536 # start_trial(161) : # end_trial(161) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 161 Size: 4 5249782 5343552 5278364 5331734 5293258 5321236 5314122 5294300 # start_trial(162) : # end_trial(162) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 162 Size: 4 5248654 5311618 5273862 5309422 5282368 5301892 5323902 5294724 # start_trial(163) : # end_trial(163) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8961 s # Pareto: 163 Size: 4 5242876 5337732 5272238 5304110 5296404 5302756 5302736 5300562 # start_trial(164) : # end_trial(164) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.9201 s # Pareto: 164 Size: 2 5257106 5322472 5257872 5291056 # start_trial(165) : # end_trial(165) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.9281 s # Pareto: 165 Size: 3 5246412 5301200 5257102 5296406 5282938 5281232 # start_trial(166) : # end_trial(166) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.9041 s # Pareto: 166 Size: 1 5261344 5278434 # start_trial(167) : # end_trial(167) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 167 Size: 2 5207768 5285096 5251774 5262174 # start_trial(168) : # end_trial(168) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 168 Size: 5 5252476 5341930 5259994 5339162 5261246 5300000 5287272 5296960 5295622 5294608 # start_trial(169) : # end_trial(169) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 169 Size: 6 5226264 5328500 5277464 5326176 5283500 5325496 5292540 5307144 5297542 5293280 5302886 5289706 # start_trial(170) : # end_trial(170) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.9001 s # Pareto: 170 Size: 6 5258608 5349086 5262004 5348740 5271162 5328506 5278050 5304684 5298656 5286344 5351610 5279252 # start_trial(171) : # end_trial(171) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.9081 s # Pareto: 171 Size: 4 5255400 5336540 5255508 5317544 5266526 5311184 5295340 5285696 # start_trial(172) : # end_trial(172) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8961 s # Pareto: 172 Size: 2 5250304 5297874 5283074 5265846 # start_trial(173) : # end_trial(173) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8881 s # Pareto: 173 Size: 4 5210334 5328068 5269816 5306036 5311530 5305230 5364628 5302930 # start_trial(174) : # end_trial(174) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 174 Size: 3 5240600 5302258 5303732 5299116 5315600 5295118 # start_trial(175) : # end_trial(175) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8641 s # Pareto: 175 Size: 2 5245444 5328246 5278910 5297264 # start_trial(176) : # end_trial(176) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 176 Size: 3 5262744 5333626 5286060 5321404 5303996 5286016 # start_trial(177) : # end_trial(177) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8881 s # Pareto: 177 Size: 4 5266860 5381622 5268472 5326842 5272178 5318748 5272624 5286186 # start_trial(178) : # end_trial(178) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8641 s # Pareto: 178 Size: 3 5252908 5329620 5266834 5322330 5271910 5286698 # start_trial(179) : # end_trial(179) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 179 Size: 3 5262210 5341652 5286860 5325636 5290370 5281614 # start_trial(180) : # end_trial(180) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 180 Size: 3 5266766 5332104 5274192 5325700 5290394 5286536 # start_trial(181) : # end_trial(181) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 181 Size: 2 5223996 5264934 5276944 5259324 # start_trial(182) : # end_trial(182) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 182 Size: 6 5254596 5376180 5269014 5329152 5274636 5306446 5297442 5301974 5299942 5300198 5311514 5299176 # start_trial(183) : # end_trial(183) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 183 Size: 6 5261394 5354636 5265378 5346184 5268934 5338140 5273124 5334504 5273154 5318460 5285648 5287704 # start_trial(184) : # end_trial(184) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 184 Size: 2 5244120 5337354 5256384 5285550 # start_trial(185) : # end_trial(185) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 185 Size: 2 5262862 5302662 5277674 5291722 # start_trial(186) : # end_trial(186) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 186 Size: 4 5255174 5330402 5276800 5312808 5293146 5279082 5346592 5266638 # start_trial(187) : # end_trial(187) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 187 Size: 7 5246804 5400388 5250178 5352570 5266038 5328910 5280858 5320198 5290784 5316988 5309158 5298146 5354416 5291594 # start_trial(188) : # end_trial(188) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 188 Size: 2 5259684 5349314 5262814 5286348 # start_trial(189) : # end_trial(189) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 189 Size: 5 5247922 5341922 5267178 5329290 5271752 5318494 5291980 5306488 5322930 5301076 # start_trial(190) : # end_trial(190) : Pareto Size = 1, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 190 Size: 1 5234970 5275712 # start_trial(191) : # end_trial(191) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 191 Size: 6 5254424 5378850 5255612 5369634 5260926 5349398 5267362 5338284 5273240 5303014 5280820 5289176 # start_trial(192) : # end_trial(192) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 192 Size: 4 5241760 5327686 5274282 5324544 5275572 5308086 5275822 5287264 # start_trial(193) : # end_trial(193) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 193 Size: 3 5255512 5333632 5267168 5322190 5282082 5279046 # start_trial(194) : # end_trial(194) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 194 Size: 6 5244070 5360390 5244866 5318190 5268138 5317572 5289852 5314910 5321016 5314668 5325172 5302966 # start_trial(195) : # end_trial(195) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 195 Size: 2 5223050 5316882 5339314 5304744 # start_trial(196) : # end_trial(196) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 196 Size: 5 5247650 5360362 5254704 5324760 5263442 5309510 5312364 5303634 5316266 5295334 # start_trial(197) : # end_trial(197) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 197 Size: 5 5264462 5341904 5267506 5312252 5268108 5297324 5294678 5290524 5304892 5288808 # start_trial(198) : # end_trial(198) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8721 s # Pareto: 198 Size: 3 5263722 5332598 5290170 5327584 5295962 5289366 # start_trial(199) : # end_trial(199) : Pareto Size = 9, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 199 Size: 9 5239548 5364766 5253792 5355244 5265338 5344000 5272150 5343382 5276076 5325734 5283462 5321062 5285448 5299064 5290246 5297754 5325178 5288796 # Average Number Scalarizations = 100, 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Exploratory # analysis of stochastic local search algorithms in biobjective # optimization. In EMAA, pages X-Y, publisher, 2009. # # The directory containing the programs eaf, eafplot.pl and eafdiff.pl # must be in your PATH. For example, to link the programs to ~/bin # ## mkdir -p ~/bin ## ln -s eaftools/eaf/eaf ~/bin/eaf ## ln -s eaftools/eafplot/eafplot.pl ~/bin/eafplot.pl ## ln -s eaftools/eafplot/eafdiff.pl ~/bin/eafdiff.pl # # and uncomment the following line: # #PATH=$PATH:~/bin/ # # You may add the above line to your startup scripts (~/.profile, # ~/.bashrc, ~/.shrc or similar) to make its effect permanent. # error () { echo "$0: example \`$@\' FAILED!" exit 1 } verbose () { echo "==== EXAMPLE: $@" eval $@ || error $@ echo "=====================================================================" } verbose 'eafplot.pl example1_dat' verbose 'eafplot.pl --best --median --worst --percentiles=25,75 r15_dat r50_dat r200_dat' verbose 'eafdiff.pl --full --left="Algorithm 1" ALG_1_dat --right="Algorithm 2" ALG_2_dat' verbose 'eafdiff.pl --left="Algorithm 1" ALG_1_dat --right="Algorithm 2" ALG_2_dat' verbose eafplot.pl n75_dat verbose eafplot.pl p75_dat verbose 'eafdiff.pl --left="WRoTS, l=100, w=10" --right="WRoTS, l=10, w=100" wrots_l100w10_dat wrots_l10w100_dat' verbose 'eafdiff.pl --left="TPLS" --right="Restart" tpls rest' # Cleanup rm -f Rplots.ps *.R *.med *.eaf *.best *.diff *.worst att_*.p?? eaf/inst/extdata/ran.10pts.3d.100000644000176200001440000001256413254545527015635 0ustar liggesusers 8.075596533325871 2.4070255356042236 5.7900584474997725 8.660944458216182 3.640501439918415 4.449547646497271 0.20816431319298268 4.622754690859642 5.207141132875611 4.881432799420187 9.094731372977776 2.653138500755452 0.22997366985771173 1.1177220504888612 8.981893105113855 1.516436358882681 3.0793373131494355 5.521641101169316 6.081528410951497 4.587438533417284 3.28238204604303 2.3530968030921873 0.790551723688969 7.297223710722109 8.747545403381238 1.7157586196357748 1.033957674001333 0.587994749876203 0.7389118125403558 8.675789804506532 8.588487717331967 3.6978131307633157 2.909288653302772 1.5964887994915449 5.98825094023213 7.869613377381958 5.281236703887404 3.4780096885580587 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8.922358124720017 6.163150300484419 7.938401212563832 5.474888116173653 4.104768524680931 9.678917821945836 3.93738426670604 8.579118682450876 0.35169752391560394 1.4493731767636193 4.9652583719732375 1.9435330516410834 4.284620594148203 8.17231096446568 9.769778530979478 1.1638275060136398 6.784984926701287 0.5638079644445808 8.061786891662466 2.718912135243628 6.943274809465109 9.397290557238108 3.41869650212319 9.384374667642259 4.761145502773846 6.454319548281916 4.060443879503394 2.256057566498885 1.1309630636314307 9.726454362756678 4.824054924439186 8.34008115019884 5.7069891876906595 1.2116034419419246 9.301370427932444 2.1432853237244496 9.958841791937324 9.695319492357159 7.2221252286508975 5.930624696677696 2.0113940991604218 9.387384421945871 9.488958888835963 4.0895251515646285 8.269917273689043 5.6896017947792075 5.193575918195671 9.430837249658266 4.328847902798756 9.306081015187335 7.694332464305761 4.014964357685522 3.3403539670375997 2.893774444550223 9.0098457086505 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7.046944669377279 1.8348435769055138 8.915052230973973 5.490272289337653 7.197279188094493 1.23306741620951 0.6510164008245374 9.423812130809253 7.921884118107121 1.3029144856969155 4.504176981643349 7.539297818529722 4.810156892044506 8.91432444750432 1.3582474223124437 0.6223027131455723 3.5694532411916766 4.206323126879596 1.3913956384270905 2.8479460274795874 1.3011355608587931 6.830412903586499 9.23146982658223 0.9287020455996275 0.8672396507619968 1.5859908886337628 9.520507331139061 6.431355372602436 1.0015356923806324 5.697775896323792 6.828978930106149 5.770930022029423 0.9644883471342007 4.925997256924397 2.704925186572346 2.465146637711475 1.2223439449845679 5.6895031075085285 3.1273859794431793 7.994669587594613 2.811225370890746 1.3907733489621363 2.127002885257127 2.43114173891515 6.997555157157489 eaf/inst/extdata/r15_dat0000644000176200001440000000636013254545527014617 0ustar liggesusers# Report: # Metah: Weighted RoTS, version 2.1.88M (#DEBUG = 1) # bQAP (0.1.88Msvn) # Data File: /home/manu/personal/2005-JMMA/data/qapUni.50.p75.1 Size: 50 Averages: Distance=47.49 Flow1=49.90 Flow2=49.96 # Parameter settings: # number trials : 15 # time limit : 3.40282e+38 # seed : 1234567 # num weights: 100 # Taboo Search Length : 1 # Initialization time: 0.004 s # start_trial(0) : # end_trial(0) : Pareto Size = 7, Scalarizations = 100, Elapsed time = 0.8921 s # Pareto: 0 Size: 7 5261548 5340738 5283204 5340630 5287522 5336320 5291202 5331366 5292270 5318304 5298876 5290408 5312952 5277832 # start_trial(1) : # end_trial(1) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8641 s # Pareto: 1 Size: 4 5246946 5368942 5249068 5328154 5277710 5321710 5280132 5281080 # start_trial(2) : # end_trial(2) : Pareto Size = 2, Scalarizations = 100, Elapsed time = 0.8641 s # Pareto: 2 Size: 2 5255914 5312620 5285994 5294248 # start_trial(3) : # end_trial(3) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 3 Size: 6 5254586 5341368 5265448 5336434 5266332 5329290 5278158 5328006 5293084 5304026 5316378 5285680 # start_trial(4) : # end_trial(4) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8641 s # Pareto: 4 Size: 5 5257008 5385896 5260020 5354180 5261932 5351978 5265120 5343482 5272604 5280450 # start_trial(5) : # end_trial(5) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.9121 s # Pareto: 5 Size: 3 5251436 5304740 5260634 5289432 5261388 5278662 # start_trial(6) : # end_trial(6) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 6 Size: 3 5260462 5303782 5309662 5298112 5338894 5292426 # start_trial(7) : # end_trial(7) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 7 Size: 4 5238158 5302220 5266616 5301604 5277362 5291346 5328374 5283152 # start_trial(8) : # end_trial(8) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 8 Size: 5 5259568 5344514 5260078 5323590 5275688 5304758 5295860 5299490 5321894 5280216 # start_trial(9) : # end_trial(9) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8801 s # Pareto: 9 Size: 5 5252950 5337456 5258962 5303668 5273430 5301298 5311040 5291910 5319530 5279154 # start_trial(10) : # end_trial(10) : Pareto Size = 6, Scalarizations = 100, Elapsed time = 0.8761 s # Pareto: 10 Size: 6 5255088 5362366 5260872 5337164 5276254 5307774 5278846 5302382 5287324 5301484 5290398 5284198 # start_trial(11) : # end_trial(11) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.8681 s # Pareto: 11 Size: 3 5257462 5328600 5262892 5302082 5306682 5293358 # start_trial(12) : # end_trial(12) : Pareto Size = 5, Scalarizations = 100, Elapsed time = 0.8841 s # Pareto: 12 Size: 5 5255536 5369466 5265974 5314044 5276688 5311924 5301892 5288482 5319394 5278344 # start_trial(13) : # end_trial(13) : Pareto Size = 4, Scalarizations = 100, Elapsed time = 0.9041 s # Pareto: 13 Size: 4 5243484 5327056 5262810 5312496 5265234 5288336 5305096 5285540 # start_trial(14) : # end_trial(14) : Pareto Size = 3, Scalarizations = 100, Elapsed time = 0.9161 s # Pareto: 14 Size: 3 5254326 5373688 5258282 5328368 5277040 5290704 # Average Number Scalarizations = 100, Average Trial Time = 0.8817 s eaf/inst/extdata/example1_dat0000644000176200001440000000203213254545527015714 0ustar liggesusers5136906 5252884 5142568 5219868 5167616 5217238 5169726 5210654 5188260 5201808 5207548 5195594 5208824 5191646 5231722 5183804 5135414 5268372 5147846 5210542 5174600 5209152 5181262 5179358 5146734 5264400 5149830 5258196 5151682 5217482 5162856 5203302 5186380 5201066 5208800 5190706 5220492 5185632 5248376 5183246 5134240 5232272 5160938 5205572 5172032 5205298 5175168 5192196 5193370 5186240 5208364 5184852 5153534 5295574 5155512 5219180 5162270 5211316 5177516 5207258 5193688 5206734 5196258 5198842 5196400 5194080 5200046 5187082 5128224 5266830 5147362 5262574 5155716 5198006 5169034 5186656 5128176 5238886 5153066 5238024 5155408 5204124 5185294 5200040 5191528 5192334 5223828 5189078 5137952 5233566 5158466 5229466 5158568 5215710 5162240 5209706 5168678 5202144 5175072 5196856 5221198 5186816 5135300 5276110 5145614 5247358 5146024 5226582 5154280 5223756 5165138 5217210 5165272 5215804 5168218 5206596 5199178 5202584 5210042 5194988 5226770 5191850 5143188 5235016 5144532 5209802 5164108 5188228 5213378 5185264 eaf/tests/0000755000176200001440000000000013254747514012163 5ustar liggesuserseaf/tests/eafplot.R0000644000176200001440000000324613254545527013745 0ustar liggesuserslibrary(eaf) pdf(file = "eafplot.pdf", title = "eafplot.pdf", width = 6, height = 6) data(gcp2x2) tabucol <- subset(gcp2x2, alg != "TSinN1") tabucol$alg <- tabucol$alg[drop = TRUE] eafplot(time + best ~ run, data = tabucol, subset = tabucol$inst == "DSJC500.5") eafplot(time + best ~ run | inst, groups = alg, data = gcp2x2) eafplot(time + best ~ run | inst, groups = alg, data = gcp2x2, percentiles = c(0, 50, 100), include.extremes = TRUE, cex = 1.4, lty = c(2, 1, 2), lwd = c(2, 2, 2), col = c("black", "blue", "grey50")) A1 <- read.data.sets(file.path(system.file(package = "eaf"), "extdata", "ALG_1_dat")) A2 <- read.data.sets(file.path(system.file(package = "eaf"), "extdata", "ALG_2_dat")) eafplot(A1, A2, percentiles = c(50)) eafplot(list(A1 = A1, A2 = A2), percentiles = c(50)) eafplot(A1, type="area", legend.pos="bottomleft") ## Using extra.points data(HybridGA) data(SPEA2relativeVanzyl) eafplot(SPEA2relativeVanzyl, percentiles = c(25, 50, 75), xlab = expression(C[E]), ylab = "Total switches", xlim = c(320, 400), extra.points = HybridGA$vanzyl, extra.legend = "Hybrid GA") data(SPEA2relativeRichmond) eafplot(SPEA2relativeRichmond, percentiles = c(25, 50, 75), xlab = expression(C[E]), ylab = "Total switches", xlim = c(90, 140), ylim = c(0, 25), extra.points = HybridGA$richmond, extra.lty = "dashed", extra.legend = "Hybrid GA") data(SPEA2minstoptimeRichmond) SPEA2minstoptimeRichmond[, 2] <- SPEA2minstoptimeRichmond[, 2] / 60 eafplot(SPEA2minstoptimeRichmond, xlab = expression(C[E]), ylab = "Minimum idle time (minutes)", las = 1, log = "y", maximise = c(FALSE, TRUE), main = "SPEA2 (Richmond)") eafplot(A1, A2, percentiles = c(50)) dev.off() eaf/tests/eafplot.Rout.saved0000644000176200001440000000472113254545527015575 0ustar liggesusers R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree" Copyright (C) 2015 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(eaf) > pdf(file = "eafplot.pdf", title = "eafplot.pdf", width = 6, height = 6) > data(gcp2x2) > tabucol <- subset(gcp2x2, alg != "TSinN1") > tabucol$alg <- tabucol$alg[drop = TRUE] > eafplot(time + best ~ run, data = tabucol, subset = tabucol$inst == "DSJC500.5") > > eafplot(time + best ~ run | inst, groups = alg, data = gcp2x2) > > eafplot(time + best ~ run | inst, groups = alg, data = gcp2x2, percentiles = c(0, + 50, 100), include.extremes = TRUE, cex = 1.4, lty = c(2, 1, 2), lwd = c(2, 2, + 2), col = c("black", "blue", "grey50")) > > A1 <- read.data.sets(file.path(system.file(package = "eaf"), "extdata", "ALG_1_dat")) > A2 <- read.data.sets(file.path(system.file(package = "eaf"), "extdata", "ALG_2_dat")) > eafplot(A1, A2, percentiles = c(50)) > > eafplot(list(A1 = A1, A2 = A2), percentiles = c(50)) > > eafplot(A1, type="area", legend.pos="bottomleft") > > ## Using extra.points > data(HybridGA) > data(SPEA2relativeVanzyl) > eafplot(SPEA2relativeVanzyl, percentiles = c(25, 50, 75), xlab = expression(C[E]), + ylab = "Total switches", xlim = c(320, 400), extra.points = HybridGA$vanzyl, + extra.legend = "Hybrid GA") > > data(SPEA2relativeRichmond) > eafplot(SPEA2relativeRichmond, percentiles = c(25, 50, 75), xlab = expression(C[E]), + ylab = "Total switches", xlim = c(90, 140), ylim = c(0, 25), extra.points = HybridGA$richmond, + extra.lty = "dashed", extra.legend = "Hybrid GA") > > data(SPEA2minstoptimeRichmond) > SPEA2minstoptimeRichmond[, 2] <- SPEA2minstoptimeRichmond[, 2] / 60 > eafplot(SPEA2minstoptimeRichmond, xlab = expression(C[E]), ylab = "Minimum idle time (minutes)", + las = 1, log = "y", maximise = c(FALSE, TRUE), main = "SPEA2 (Richmond)") > > eafplot(A1, A2, percentiles = c(50)) > > dev.off() null device 1 > > proc.time() user system elapsed 8.100 0.308 8.321 eaf/tests/ran.100pts.3d.10.R0000644000176200001440000000001513254545527014634 0ustar liggesuserslibrary(eaf) eaf/tests/lin.S-sph.S-diff.txt0000644000176200001440000177002613254545527015623 0ustar liggesusers 0.7227364801398616 0.3249308341784661 0.002982026681672245 1 0 0.6739354499164126 0.4474391071892501 0.005100986357436471 0 1 0.09842304848443974 0.7898456103655892 0.0006526537690230337 0 1 0.7855678086518828 0.1279795799819541 0.006932226281955484 0 1 0.784783215856367 0.1326718537281219 0.007556055994071511 0 1 0.4898744794343283 0.5882761606600168 0.009589793905655064 1 0 0.3815483559748831 0.6478199660967801 0.006377798928336897 1 0 0.801439439681168 0.1089192801067229 0.01575373932556754 0 1 0.7132115550532701 0.1218505256924447 0.01276941439187318 0 1 0.9605029256695775 0.03459345887305176 0.009875822457370731 1 0 0.2659219451112546 0.6729391868732062 0.01094078130733762 0 1 0.5578027954215485 0.5128249446167905 0.01711269396166104 1 0 0.7233298043816512 0.00930624903859424 0.01971594884792657 0 1 0.7226690522677043 0.02681616179247296 0.02667705340096801 0 1 0.6698448925972653 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DIFF <- eaf:::compute.eafdiff.helper(data.combined, intervals = nruns.left) x <- as.matrix(read.table("lin.S-sph.S-diff.txt", header = FALSE)) dimnames(x) <- NULL x[, nobjs + 1] <- x[, nobjs + 1] - x[, nobjs + 2] stopifnot(all.equal(DIFF[, 1 : (nobjs + 1)], x[, 1 : (nobjs + 1)])) eaf/tests/ran.100pts.3d.100000644000176200001440000015311113254545527014442 0ustar liggesusers 7.567852079267114 2.0559109694571025 0.6562393901651342 8.722946970740553 1.9480814044359325 8.179005234089914 6.403252071247728 8.776084418694044 5.251559214480584 4.62087998308029 0.8187018723387034 9.279752899922839 6.519503769853502 8.415981572951651 7.308809643322325 9.488433078429297 1.0715090264383487 0.775007383670892 3.4481421661093226 9.557551610704408 9.052493848093434 3.3341476086257367 1.9452893688099664 9.948231962974702 5.032571427845995 9.220469851668568 4.28921192640886 2.7445054620724414 5.487450472809712 9.927167188789968 7.117340620250287 7.581813654241109 7.457103506407492 9.300358743954067 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0.47036565689378484 7.48354865462369 4.85133960596317 8.000685691682316 8.68389656248826 2.112706111479249 3.3187319972479554 9.176440574491034 0.8946728726021647 9.86928999964597 4.40022698993567 0.9075112765323157 1.304552513676358 5.765772041263006 3.8980562206935265 5.680454240373907 8.263506120865584 2.6644712357582687 1.2250744341814594 4.777170052921206 4.590525270891498 0.6851665437536232 2.340228747341369 7.96093522017145 5.562978915500683 5.801423547335766 3.560380178226712 4.020170239142647 0.24207430034691324 7.604529781745404 3.1552309660486015 0.21396373355853449 8.931295820577372 9.907766231186168 2.194021285382104 0.6379487807601567 0.34799002059688533 6.589476714362268 5.002325925359578 5.499595528014376 9.273767551761999 0.4734496098997649 0.12262798456303425 9.439052839318535 4.361194902009143 1.4658617603862334 8.429239242193113 3.534384329776369 2.845262344087302 0.3847571948624111 6.622504990890739 0.6199109263880898 9.495994497846345 2.6819776692967805 8.29885270231144 6.6154765551130925 3.424833336420085 0.9775118744064848 4.475683462881875 5.97974989417236 8.9934377386103 7.298643877112457 0.6201769422204642 6.198606040922135 8.44995038199703 2.326485114632287 4.217583470318369 7.145615855603389 7.084357077310411 6.53098209704761 2.3154390277260535 2.139686310940814 4.587817875723777 2.513395760362827 0.7730139717978917 1.7474734085606114 9.111832364430146 5.605047311600075 9.326984470006309 0.979487809592273 0.7703141578520443 7.343841819580142 3.8866287884154884 0.7678091114661205 4.938191353806339 2.1218980145342408 5.360364558131053 8.95186125293883 0.2986084560976847 6.76948388885229 7.139310369661382 0.7392987337636985 4.360309909030401 9.876284203521507 9.365208186236025 0.41658429835610633 2.64281348509081 8.851513155294004 7.484317875498514 2.0908522517282635 2.876085773558841 9.018568666225432 0.1759541977955852 1.5101543311635388 9.898477793752328 2.1133816884745333 5.07170152536633 8.107296201297384 3.9478280233447034 7.699420296295877 6.181505007979345 9.428189840418796 7.357887553262499 0.6650040894083213 3.260434180523835 1.9392834390954303 8.305087557727258 2.688239949880689 2.7822340224013273 8.461940265333267 3.433693193943168 8.404028341792065 1.1704123469582783 3.8022070330922038 1.3676782894455073 7.739088826530867 1.2564903860461172 0.4752342189255243 9.602009387116881 9.412257894585434 0.5885924516568402 3.881847229490952 1.6295191136606775 9.985567650509898 7.813595424700434 6.611681910385769 1.4721782182394936 1.8913743281367639 9.861113194308968 0.5200411003478891 5.975881806790335 4.54084474025081 4.266232655131312 1.2582006050525694 8.795208785018234 2.0333539876815285 1.3828876450268752 0.8215821811397515 5.4327428567648735 8.599928661657016 1.9287557104552988 9.772564628865236 5.129009878537387 5.944389782239308 8.398558784101288 0.5251835590869147 6.114029618530541 1.2911043526933303 2.6739268934177063 2.2518907734430136 9.726130426296994 3.6327938451391626 7.7975810927216855 2.2689767730233665 1.427869788901384 2.7230006819760892 8.100541759038006 7.309611127609296 7.2679905929411746 0.3292939552756238 6.607214784309551 1.5397982571250286 1.3985529987720193 9.459001113197068 2.0631997829799538 6.589574401252804 7.491906648627927 1.64357320305276 9.94499844391714 6.468056558658848 2.9492660566697046 2.0857335992706316 8.420052693707415 6.10866512841514 1.7956699882041822 4.378036317864441 2.657909723955185 9.684755196826242 0.31435445569556997 6.141235710282632 1.269855692964013 2.7338901840345673 8.00458363142204 0.6585099741836329 3.0082566942689604 4.43633348457197 1.7583527016705718 7.675247018301219 8.26685679504109 0.9500469700200584 2.919704643629888 5.42024152101256 0.913001270560315 9.541333920343625 1.9612314290858874 9.018853500268621 6.385422611733764 6.325427827582561 1.2176276942872502 8.965780468498306 7.910450049666792 1.5708264902282565 1.604606043024595 3.047078697755252 8.380063797493946 3.1636780976116836 5.230238658767249 1.248055690084131 7.800976080971532 3.3884187984602567 5.794899010412604 7.541426671271079 9.78929695948942 0.5210786443834836 4.803353147640208 3.8455681462296205 8.325863895117273 6.398817616572794 2.8138758342861836 8.69581018412621 4.871990128262432 4.319936740065444 5.638649340716341 2.079507486808464 1.8403148287884687 8.875345492671556 8.468944318573676 4.298754534159962 2.526887221209911 7.331623416226735 0.6783387902258751 7.959615582765457 9.563555694095221 6.805021100341115 1.0063373626340963 6.032747187484695 4.328560775257312 4.870615327037083 6.803165083996292 6.311951146545343 0.46745269743666285 9.107396522131143 2.603944336961476 4.883876559844677 8.538286156903087 8.627884005866452 1.6054743361090011 1.4706253470435326 2.234630848324073 2.889712426331758 8.818778077738541 1.2421386466808935 8.986953530376068 8.598213544475897 3.637877348295384 0.953518270678706 9.18137110707615 2.7280415813853125 2.7038340046428684 7.55446525956608 0.59567473739052 8.492034786483746 9.687809498011557 4.2740201950362 7.40709980457488 6.469434978770892 7.555750300876273 0.8651832011098 3.5166075861469395 4.992402919601277 1.257772610617748 7.723301202820917 0.4117662785718599 9.322066815586776 9.710817648926941 5.930116893113618 1.4564322993174268 6.4826085146592485 4.406465171500217 4.715409620866508 1.0002179082134308 3.876456895453496 8.167275622467807 4.009997995817102 7.151655671466528 1.2893230052756426 1.7583113841266163 0.603793792671941 9.049478939757561 8.784690795101852 2.8488250850231442 2.5951350908365876 9.136353599533056 2.0375440199830437 3.2539697263399066 9.024161185388024 8.245990358897483 0.29070279757508555 7.417736572345657 8.966117066451375 0.11366594535383978 8.555173212221165 2.694534876510625 8.523028997441994 6.460754599587702 9.756529971190385 0.7336873410697295 2.3054033777805514 9.911109287259428 6.066922950748569 0.6031195456751428 2.477979101596395 2.8116401506265714 9.095720689009344 4.350733196153476 3.9385739722495368 3.880735615834299 4.1220259188842086 6.131681734156255 7.359521522095781 4.163506543243654 7.565960678508496 0.5297305772732308 2.0088816029389576 9.9225594747282 0.28912642666584 9.70362319153585 0.5219288499494841 5.1368249265912045 7.924974158679827 0.6781183493156258 4.037950827551063 5.26957630520211 2.0556731164002957 2.4200370583147826 0.718525757097761 7.025671272137126 9.778319776646867 9.112769310330686 1.5938001649090552 1.2516205355063126 6.023051251883863 2.281377768803243 2.282519811073439 3.4196557998353745 8.349740551179215 1.7688816335329038 9.244905224650843 1.53780687597762 1.0242581402590165 7.59094799164239 9.890880356040057 0.16114298198631527 9.57188229368112 1.8185064840290006 0.7363344967566272 eaf/tests/testthat.R0000644000176200001440000000006213254545527014144 0ustar liggesuserslibrary(testthat) library(eaf) test_check("eaf") eaf/tests/testthat/0000755000176200001440000000000013255007653014015 5ustar liggesuserseaf/tests/testthat/test-eafdiffplot.R0000644000176200001440000000216313254545527017410 0ustar liggesuserslibrary(eaf) context("eafdiffplot") test_that("eafdiffplot", { skip_on_cran() pdf(file = "eafdiffplot.pdf", title = "eafdiffplot.pdf", width = 9, height = 6) ## FIXME: Add main=invokation ## FIXME: We need smaller data! eaftest <- function(a, b, maximise = c(FALSE, FALSE)) { A1 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", a)) A2 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", b)) A1m <- A1; A1m[, which(maximise)] <- -A1m[, which(maximise)] A2m <- A2; A2m[, which(maximise)] <- -A2m[, which(maximise)] eafdiffplot(A1m, A2m, type = "area", maximise = maximise) eafdiffplot(A1m, A2m, type = "point", maximise = maximise) eafdiffplot(A1m, A2m, full.eaf = TRUE, maximise = maximise) return(TRUE) } expect_true(eaftest("wrots_l10w100_dat", "wrots_l100w10_dat")) expect_true(eaftest("tpls", "rest")) expect_true(eaftest("ALG_1_dat", "ALG_2_dat")) expect_true(eaftest("ALG_1_dat", "ALG_2_dat", maximise = c(TRUE, FALSE))) expect_true(eaftest("ALG_1_dat", "ALG_2_dat", maximise = c(FALSE, TRUE))) expect_true(eaftest("ALG_1_dat", "ALG_2_dat", maximise = c(TRUE, TRUE))) dev.off() }) eaf/src/0000755000176200001440000000000013254747514011610 5ustar liggesuserseaf/src/eaf_io_priv.h0000644000176200001440000001333113254747514014244 0ustar liggesusers#include #include #include /* for strerror() */ #include /* for errno */ #define PAGE_SIZE 4096 #define DATA_INC (PAGE_SIZE/sizeof(objective_t)) #ifndef DEBUG #define DEBUG 0 #endif /* * Read an array of objective values from a stream. This function may * be called repeatedly to add data to an existing data set. * * nobjs : number of objectives, also the number of columns. */ int read_objective_t_data (const char *filename, objective_t **data_p, int *nobjs_p, int **cumsizes_p, int *nsets_p) { FILE *instream; int nobjs = *nobjs_p; /* number of objectives (and columns). */ int *cumsizes = *cumsizes_p; /* cumulative sizes of data sets. */ int nsets = *nsets_p; /* number of data sets. */ objective_t *data = *data_p; int retval; /* return value for fscanf */ char newline[2]; int ntotal; /* the current element of (*datap) */ int column = 0, line = 0; int datasize; int sizessize; int errorcode = 0; if (filename == NULL) { instream = stdin; filename = ""; /* used to diagnose errors. */ } else if (NULL == (instream = fopen (filename,"rb"))) { errprintf ("%s: %s", filename, strerror (errno)); return (ERROR_FOPEN); } if (nsets == 0) { ntotal = 0; sizessize = 0; datasize = 0; } else { ntotal = nobjs * cumsizes[nsets - 1]; sizessize = ((nsets - 1) / DATA_INC + 1) * DATA_INC; datasize = ((ntotal - 1) / DATA_INC + 1) * DATA_INC; } /* if size is equal to zero, this is equivalent to free(). That is, reinitialize the data structures. */ cumsizes = realloc (cumsizes, sizessize * sizeof(int)); data = realloc (data, datasize * sizeof(objective_t)); /* skip over leading whitespace, comments and empty lines. */ do { line++; /* skip full lines starting with # */ retval = skip_comment_line (instream); } while (retval == 1); if (retval == EOF) { /* faster than !feof() */ warnprintf ("%s: file is empty.", filename); errorcode = READ_INPUT_FILE_EMPTY; goto read_data_finish; } do { /* beginning of data set */ if (nsets == sizessize) { sizessize += DATA_INC; cumsizes = realloc(cumsizes, sizessize * sizeof(int)); } cumsizes[nsets] = (nsets == 0) ? 0 : cumsizes[nsets - 1]; do { /* beginning of row */ column = 0; do { /* new column */ column++; objective_t number; if (fscanf (instream, objective_t_scanf_format, &number) != 1) { char buffer[64]; if (fscanf (instream, "%60[^ \t\r\n]", buffer) == EOF) { errprintf ("%s: line %d column %d: " "read error or EOF", filename, line, column); } else { errprintf ("%s: line %d column %d: " "could not convert string `%s' to double", filename, line, column, buffer); } errorcode = ERROR_CONVERSION; goto read_data_finish; } if (ntotal == datasize) { datasize += DATA_INC; data = realloc (data, datasize * sizeof(objective_t)); } data[ntotal] = number; ntotal++; #if DEBUG > 1 fprintf(stderr, "%s:%d:%d(%d) %d (set %d) = %.15g\n", filename, line, column, nobjs, cumsizes[nsets], nsets, (double)number); #endif /* skip possible trailing whitespace */ ignore_unused_result (fscanf (instream, "%*[ \t\r]")); retval = fscanf (instream, "%1[\n]", newline); } while (retval == 0); if (!nobjs) nobjs = column; else if (column == nobjs) ; /* OK */ else if (cumsizes[0] == 0) { /* just finished first row. */ errprintf ("%s: line %d: input has dimension %d" " while previous data has dimension %d", filename,line, column, nobjs); errorcode = READ_INPUT_WRONG_INITIAL_DIM; goto read_data_finish; } else { errprintf ("%s: line %d has different number of columns (%d)" " from first row (%d)\n", filename, line, column, nobjs); errorcode = ERROR_COLUMNS; goto read_data_finish; } cumsizes[nsets]++; /* look for an empty line */ line++; retval = skip_comment_line (instream); } while (retval == 0); nsets++; /* new data set */ #if DEBUG > 1 fprintf (stderr, "%s: set %d, read %d rows\n", filename, nsets, cumsizes[nsets - 1]); #endif /* skip over successive empty lines */ do { line++; retval = skip_comment_line (instream); } while (retval == 1); } while (retval != EOF); /* faster than !feof() */ /* adjust to real size (saves memory but probably slower). */ cumsizes = realloc (cumsizes, nsets * sizeof(int)); data = realloc (data, ntotal * sizeof(objective_t)); read_data_finish: *nobjs_p = nobjs; *nsets_p = nsets; *cumsizes_p = cumsizes; *data_p = data; if (instream != stdin) fclose(instream); return errorcode; } #undef PAGE_SIZE #undef DATA_INC eaf/src/Makevars0000644000176200001440000000020413254747514013300 0ustar liggesusers# Do not put GCC specific flags here. Put them instead in CLAGS and # CXXLAGS in ~/.R/Makevars PKG_CPPFLAGS = -DR_PACKAGE -DDEBUG=1 eaf/src/Reaf.c0000644000176200001440000003002613254747514012632 0ustar liggesusers#include #include #include #include #include "eaf.h" #ifndef DEBUG #define DEBUG 0 #endif #if DEBUG >= 1 #define DEBUG1(X) X;(void)0 #else #define DEBUG1(X) (void)0 #endif #if DEBUG >= 2 #define DEBUG2(X) X;(void)0 #else #define DEBUG2(X) (void)0 #endif #define CHECK_ARG_IS_INT_VECTOR(A) \ if (!isInteger(A) || !isVector(A)) \ error("Argument '" #A "' is not an integer vector"); /* The C API of R is awfully ugly and unpractical (and poorly documented). These wrappers make it a little more bearable. */ #define Rexp(VAR) Rexp_##VAR #define new_real_matrix(DOUBLEVAR, DIM1, DIM2) \ SEXP Rexp_##DOUBLEVAR; double *DOUBLEVAR; \ PROTECT(Rexp_##DOUBLEVAR = allocMatrix(REALSXP, (DIM1), (DIM2))); \ nprotected++; DOUBLEVAR = REAL(Rexp_##DOUBLEVAR) #define new_real_vector(DOUBLEVAR, DIM) \ SEXP Rexp_##DOUBLEVAR; double *DOUBLEVAR; \ PROTECT(Rexp_##DOUBLEVAR = allocVector(REALSXP, (DIM))); \ nprotected++; DOUBLEVAR = REAL(Rexp_##DOUBLEVAR) #define new_string_vector(VAR, DIM) \ SEXP Rexp_##VAR; int Rexp_##VAR##_len = 0; \ PROTECT(Rexp_##VAR = allocVector(STRSXP, (DIM))); \ nprotected++ #define string_vector_push_back(VAR, ELEMENT) \ SET_STRING_ELT(Rexp_##VAR, Rexp_##VAR##_len, mkChar(ELEMENT)); \ Rexp_##VAR##_len++ #define new_list(LISTVAR, LENGTH) \ SEXP Rexp_##LISTVAR; int Rexp_##LISTVAR##_len = 0; \ PROTECT(Rexp_##LISTVAR = allocVector(VECSXP, (LENGTH))); \ ++nprotected #define list_len(VAR) Rexp_##VAR##_len #define list_push_back(LISTVAR, ELEMENT) \ SET_VECTOR_ELT(Rexp_##LISTVAR, Rexp_##LISTVAR##_len, Rexp_##ELEMENT); \ Rexp_##LISTVAR##_len++ #define set_names(VAR, NAMES) \ setAttrib(Rexp_##VAR, R_NamesSymbol, Rexp_##NAMES) #define set_attribute(VAR, ATTRIBUTE, VALUE) \ setAttrib(Rexp_##VAR, install(ATTRIBUTE), Rexp_##VALUE) /* * Unpack an integer vector stored in SEXP S. */ #define SEXP_2_INT_VECTOR(S, I, N) \ CHECK_ARG_IS_INT_VECTOR(S); \ int *I = INTEGER(S); \ const R_len_t N = length(S); #define SEXP_2_INT(S,var) \ int var = asInteger(S); \ if (var == NA_INTEGER) \ error ("Argument '" #S "' is not an integer"); #define SEXP_2_STRING(S,var) \ if (!isString(S) || length(S) != 1) \ error ("Argument '" #S "' is not a string"); \ const char * var = CHAR(STRING_ELT(S,0)); static eaf_t ** compute_eaf_helper (SEXP DATA, int nobj, SEXP CUMSIZES, int nruns, const int *percentile, int nlevels) { int k; SEXP_2_INT_VECTOR(CUMSIZES, cumsizes, cumsizes_len); if (cumsizes_len < nruns) error("length of cumsizes (%d) is less than nruns (%d)", cumsizes_len, nruns); int *level; if (percentile != NULL) { level = malloc(sizeof(int) * nlevels); for (k = 0; k < nlevels; k++) level[k] = percentile2level(percentile[k], nruns); } else { eaf_assert (nlevels == nruns); level = malloc(sizeof(int) * nruns); for (k = 0; k < nruns; k++) level[k] = k + 1; } double *data = REAL(DATA); DEBUG2( Rprintf ("attsurf ({(%f, %f", data[0], data[1]); for (k = 2; k < nobj; k++) { Rprintf (", %f", data[k]); } Rprintf (")...}, %d, { %d", nobj, cumsizes[0]); for (k = 1; k < nruns; k++) { Rprintf (", %d", cumsizes[k]); } Rprintf ("}, %d, { %d", nruns, level[0]); for (k = 1; k < nlevels; k++) { Rprintf (", %d", level[k]); } Rprintf ("}, %d)\n", nlevels); ); eaf_t **eaf = attsurf (data, nobj, cumsizes, nruns, level, nlevels); free (level); DEBUG2( Rprintf ("eaf computed\n"); for (k = 0; k < nlevels; k++) { Rprintf ("eaf[%d] = %d\n", k, eaf[k]->size); }); return eaf; } extern SEXP compute_eaf_C(SEXP DATA, SEXP NOBJ, SEXP CUMSIZES, SEXP NRUNS, SEXP PERCENTILES); SEXP compute_eaf_C(SEXP DATA, SEXP NOBJ, SEXP CUMSIZES, SEXP NRUNS, SEXP PERCENTILE) { SEXP_2_INT(NOBJ, nobj); SEXP_2_INT(NRUNS, nruns); SEXP_2_INT_VECTOR(PERCENTILE, percentile, nlevels); eaf_t **eaf = compute_eaf_helper(DATA, nobj, CUMSIZES, nruns, percentile, nlevels); int totalpoints = eaf_totalpoints (eaf, nlevels); SEXP mat; PROTECT(mat = allocMatrix(REALSXP, totalpoints, nobj + 1)); double * rmat = REAL(mat); int pos = 0; int k; for (k = 0; k < nlevels; k++) { int npoints = eaf[k]->size; DEBUG2( int totalsize = npoints * nobj; Rprintf ("totalpoints eaf[%d] = %d\n", k, totalsize) ); int i; for (i = 0; i < npoints; i++) { int j; for (j = 0; j < nobj; j++) { rmat[pos + j * totalpoints] = eaf[k]->data[j + i * nobj]; } rmat[pos + nobj * totalpoints] = percentile[k]; pos++; } eaf_delete (eaf[k]); } free(eaf); UNPROTECT (1); return mat; } SEXP compute_eafdiff_C(SEXP DATA, SEXP NOBJ, SEXP CUMSIZES, SEXP NRUNS, SEXP INTERVALS); SEXP compute_eafdiff_C(SEXP DATA, SEXP NOBJ, SEXP CUMSIZES, SEXP NRUNS, SEXP INTERVALS) { int k; SEXP_2_INT(NOBJ, nobj); SEXP_2_INT(NRUNS, nruns); SEXP_2_INT(INTERVALS, intervals); eaf_t **eaf = compute_eaf_helper(DATA, nobj, CUMSIZES, nruns, NULL, nruns); int nsets1 = nruns / 2; int nsets2 = nruns - nsets1; int totalpoints = eaf_totalpoints (eaf, nruns); SEXP mat; PROTECT(mat = allocMatrix(REALSXP, totalpoints, nobj + 1)); double *rmat = REAL(mat); int pos = 0; for (k = 0; k < nruns; k++) { int npoints = eaf[k]->size; int i, j; DEBUG2( int totalsize = npoints * nobj; Rprintf ("totalpoints eaf[%d] = %d\n", k, totalsize) ); // FIXME: Find the most efficient order of the loop. for (i = 0; i < npoints; i++) { for (j = 0; j < nobj; j++) { rmat[pos + j * totalpoints] = eaf[k]->data[j + i * nobj]; } pos++; } } pos += (nobj - 1) * totalpoints; for (k = 0; k < nruns; k++) { int i; int npoints = eaf[k]->size; for (i = 0; i < npoints; i++) { int count_left; int count_right; attained_left_right (eaf[k]->attained + i * eaf[k]->nruns, nruns / 2, nruns, &count_left, &count_right); rmat[pos] = intervals * (double) ((count_left / (double) nsets1) - (count_right / (double) nsets2)); pos++; } eaf_delete (eaf[k]); } free(eaf); UNPROTECT (1); return mat; } static int polygon_len (const double *src, int nobj) { const double *src_orig = src; while (*src != objective_MIN) src += nobj; src += nobj; return (src - src_orig) / nobj; } static int polygon_copy (double *dest, int start, int nrows, const double *src) { int len = start; while (*src != objective_MIN) { dest[len] = *src; dest[len + nrows] = *(src + 1); len++; src += 2; } dest[len] = NA_REAL; dest[len + nrows] = NA_REAL; len++; return len - start; } SEXP compute_eafdiff_area_C(SEXP DATA, SEXP NOBJ, SEXP CUMSIZES, SEXP NRUNS, SEXP INTERVALS); SEXP compute_eafdiff_area_C(SEXP DATA, SEXP NOBJ, SEXP CUMSIZES, SEXP NRUNS, SEXP INTERVALS) { int nprotected = 0; int k; SEXP_2_INT(NOBJ, nobj); SEXP_2_INT(NRUNS, nruns); SEXP_2_INT(INTERVALS, intervals); eaf_t **eaf = compute_eaf_helper(DATA, nobj, CUMSIZES, nruns, NULL, nruns); eaf_polygon_t *p = eaf_compute_area (eaf, nruns); for (k = 0; k < nruns; k++) eaf_delete (eaf[k]); free(eaf); const int division = nruns / 2; int ncol = vector_int_size(&p->col); DEBUG2(Rprintf ("ncol: %d\n", ncol)); int left_ncol = 0, right_ncol = 0; int left_len = 0, right_len = 0; /* First compute the adjusted colors, and how much space we need on each side. */ double * p_xy = vector_objective_begin(&p->xy); for (k = 0; k < ncol; k++) { double color = vector_int_at(&p->col, k); color = intervals * color / (double) division; int len = polygon_len (p_xy, nobj); p_xy += len * nobj; DEBUG2(Rprintf ("color: %d, len = %d\n", color, len)); if (color >= 1) { left_len += len; left_ncol++; } else if (color <= 1) { right_len += len; right_ncol++; } vector_int_set(&p->col, k, color); } DEBUG2(Rprintf ("left_len: %d, right_len: %d, left_ncol: %d, right_ncol: %d\n", left_len, right_len, left_ncol, right_ncol)); /* Now assign points to each side. */ new_real_vector(left_col, left_ncol); new_real_vector(right_col, right_ncol); const int left_npoints = left_len; new_real_matrix (left, left_npoints, nobj); const int right_npoints = right_len; new_real_matrix (right, right_npoints, nobj); p_xy = vector_objective_begin(&p->xy); left_len = right_len = 0; left_ncol = right_ncol = 0; for (k = 0; k < ncol; k++) { int len; int color = vector_int_at(&p->col, k); if (color >= 1) { len = polygon_copy (left, left_len, left_npoints, p_xy); left_len += len; left_col[left_ncol++] = color + 1; } else if (color <= 1) { len = polygon_copy (right, right_len, right_npoints, p_xy); right_len += len; right_col[right_ncol++] = (-color) + 1; } else { len = polygon_len (p_xy, nobj); } p_xy += nobj * len; } vector_int_dtor (&p->col); vector_objective_dtor (&p->xy); free(p); set_attribute(left, "col", left_col); set_attribute(right, "col", right_col); new_list(poly, 2); list_push_back (poly, left); list_push_back (poly, right); new_string_vector (names, list_len (poly)); string_vector_push_back (names, "left"); string_vector_push_back (names, "right"); set_names (poly, names); UNPROTECT (nprotected); return Rexp(poly); } SEXP read_data_sets (SEXP FILENAME); SEXP read_data_sets (SEXP FILENAME) { SEXP_2_STRING(FILENAME, filename); objective_t *data = NULL; int* cumsizes = NULL; int nobj = 0, nruns = 0; /* Rprintf ("filename: %s\n", filename); */ read_objective_t_data (filename, &data, &nobj, &cumsizes, &nruns); const int ntotal = cumsizes[nruns - 1]; int * runtab = malloc (ntotal * sizeof(int)); int k, j, i; for (k = 0, j = 0; k < ntotal; k++) { if (k == cumsizes[j]) j++; runtab[k] = j + 1; } SEXP DATA; PROTECT(DATA = allocMatrix(REALSXP, cumsizes[nruns-1], nobj + 1)); double *rdata = REAL(DATA); int pos = 0; for (j = 0; j < nobj; j++) { for (i = 0; i < ntotal; i++) { rdata[pos] = data[j + i * nobj]; pos++; } } for (j = 0; j < ntotal; j++, pos++) { rdata[pos] = runtab[j]; } free(data); free(cumsizes); free(runtab); UNPROTECT (1); return DATA; } eaf/src/avl.h0000644000176200001440000001677513254747514012563 0ustar liggesusers/***************************************************************************** avl.h - Source code for the AVL-tree library. Copyright (C) 1998 Michael H. Buselli Copyright (C) 2000-2002 Wessel Dankers This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Augmented AVL-tree. Original by Michael H. Buselli . Modified by Wessel Dankers to add a bunch of bloat to the sourcecode, change the interface and squash a few bugs. Mail him if you find new bugs. *****************************************************************************/ #ifndef _AVL_H #define _AVL_H #define AVL_DEPTH /* We need either depths, counts or both (the latter being the default) */ #if !defined(AVL_DEPTH) && !defined(AVL_COUNT) #define AVL_DEPTH #define AVL_COUNT #endif /* User supplied function to compare two items like strcmp() does. * For example: cmp(a,b) will return: * -1 if a < b * 0 if a = b * 1 if a > b */ typedef int (*avl_compare_t)(const void *, const void *); /* User supplied function to delete an item when a node is free()d. * If NULL, the item is not free()d. */ typedef void (*avl_freeitem_t)(void *); typedef struct avl_node_t { struct avl_node_t *next; struct avl_node_t *prev; struct avl_node_t *parent; struct avl_node_t *left; struct avl_node_t *right; void *item; #ifdef AVL_COUNT unsigned int count; #endif #ifdef AVL_DEPTH unsigned char depth; #endif /* The following members are necessary only to determine the attainment indicators. * set - set of the input point responsible for the promotion of this point * promoter - point from a lower (previous) level that is also responsible for the promotion of this point. * remover - point from the same level as this point that dominates this point. (Point * that removed this point from its level). * equal - point from a higher (next) level that is equal to this point (this point is equal's promoter) * * This point is attained by 'set' and by the same sets that attains its promoter. It is also * attained by any set that attains its remover. It is also attained by every set that attains * any point equal to itself. */ int set; struct avl_node_t *promoter; struct avl_node_t *remover; struct avl_node_t *equal; } avl_node_t; typedef struct avl_tree_t { avl_node_t *head; avl_node_t *tail; avl_node_t *top; avl_compare_t cmp; avl_freeitem_t freeitem; } avl_tree_t; /* Initializes a new tree for elements that will be ordered using * the supplied strcmp()-like function. * Returns the value of avltree (even if it's NULL). * O(1) */ extern avl_tree_t *avl_init_tree(avl_tree_t *avltree, avl_compare_t, avl_freeitem_t); /* Allocates and initializes a new tree for elements that will be * ordered using the supplied strcmp()-like function. * Returns NULL if memory could not be allocated. * O(1) */ extern avl_tree_t *avl_alloc_tree(avl_compare_t, avl_freeitem_t); /* Frees the entire tree efficiently. Nodes will be free()d. * If the tree's freeitem is not NULL it will be invoked on every item. * O(n) */ extern void avl_free_tree(avl_tree_t *); /* Reinitializes the tree structure for reuse. Nothing is free()d. * Compare and freeitem functions are left alone. * O(1) */ extern void avl_clear_tree(avl_tree_t *); /* Free()s all nodes in the tree but leaves the tree itself. * If the tree's freeitem is not NULL it will be invoked on every item. * O(n) */ extern void avl_free_nodes(avl_tree_t *); /* Initializes memory for use as a node. Returns NULL if avlnode is NULL. * O(1) */ extern avl_node_t *avl_init_node(avl_node_t *avlnode, void *item); /* Insert an item into the tree and return the new node. * Returns NULL and sets errno if memory for the new node could not be * allocated or if the node is already in the tree (EEXIST). * O(lg n) */ extern avl_node_t *avl_insert(avl_tree_t *, void *item); /* Insert a node into the tree and return it. * Returns NULL if the node is already in the tree. * O(lg n) */ extern avl_node_t *avl_insert_node(avl_tree_t *, avl_node_t *); /* Insert a node in an empty tree. If avlnode is NULL, the tree will be * cleared and ready for re-use. * If the tree is not empty, the old nodes are left dangling. * O(1) */ extern avl_node_t *avl_insert_top(avl_tree_t *, avl_node_t *avlnode); /* Insert a node before another node. Returns the new node. * If old is NULL, the item is appended to the tree. * O(lg n) */ extern avl_node_t *avl_insert_before(avl_tree_t *, avl_node_t *old, avl_node_t *new); /* Insert a node after another node. Returns the new node. * If old is NULL, the item is prepended to the tree. * O(lg n) */ extern avl_node_t *avl_insert_after(avl_tree_t *, avl_node_t *old, avl_node_t *new); /* Deletes a node from the tree. Returns immediately if the node is NULL. * The item will not be free()d regardless of the tree's freeitem handler. * This function comes in handy if you need to update the search key. * O(lg n) */ extern void avl_unlink_node(avl_tree_t *, avl_node_t *); /* Deletes a node from the tree. Returns immediately if the node is NULL. * If the tree's freeitem is not NULL, it is invoked on the item. * If it is, returns the item. * O(lg n) */ extern void *avl_delete_node(avl_tree_t *, avl_node_t *); /* Searches for an item in the tree and deletes it if found. * If the tree's freeitem is not NULL, it is invoked on the item. * If it is, returns the item. * O(lg n) */ extern void *avl_delete(avl_tree_t *, const void *item); /* If exactly one node is moved in memory, this will fix the pointers * in the tree that refer to it. It must be an exact shallow copy. * Returns the pointer to the old position. * O(1) */ extern avl_node_t *avl_fixup_node(avl_tree_t *, avl_node_t *new); /* Searches for a node with the key closest (or equal) to the given item. * If avlnode is not NULL, *avlnode will be set to the node found or NULL * if the tree is empty. Return values: * -1 if the returned node is greater * 0 if the returned node is equal or if the tree is empty * 1 if the returned node is smaller * O(lg n) */ extern int avl_search_closest(const avl_tree_t *, const void *item, avl_node_t **avlnode); /* Searches for the item in the tree and returns a matching node if found * or NULL if not. * O(lg n) */ extern avl_node_t *avl_search(const avl_tree_t *, const void *item); #ifdef AVL_COUNT /* Returns the number of nodes in the tree. * O(1) */ extern unsigned int avl_count(const avl_tree_t *); /* Searches a node by its rank in the list. Counting starts at 0. * Returns NULL if the index exceeds the number of nodes in the tree. * O(lg n) */ extern avl_node_t *avl_at(const avl_tree_t *, unsigned int); /* Returns the rank of a node in the list. Counting starts at 0. * O(lg n) */ extern unsigned int avl_index(const avl_node_t *); #endif #endif eaf/src/eaf_input.c0000644000176200001440000000633413254747514013734 0ustar liggesusers/***************************************************************************** eaf_input.c: read EAF data --------------------------------------------------------------------- Copyright (c) 2005, 2006, 2007, 2008 Carlos Fonseca Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- TODO: things that may or may not improve reading performance. * different values for PAGE_SIZE. * reading whole lines and then sscanf the line. * mmaping the input file (or a big chunk of few megabytes), then read, then unmmap. *****************************************************************************/ #include #include #include "eaf_io.h" #ifndef ignore_unused_result #define ignore_unused_result(X) do { if(X) {}} while(0); #endif /* skip full lines starting with # */ static inline int skip_comment_line (FILE * instream) { char newline[2]; if (!fscanf (instream, "%1[#]%*[^\n]", newline)) /* and whitespace */ ignore_unused_result (fscanf (instream, "%*[ \t\r]")); return fscanf (instream, "%1[\n]", newline); } #define objective_t int #define objective_t_scanf_format "%d" #define read_objective_t_data read_int_data #include "eaf_io_priv.h" #undef objective_t #undef objective_t_scanf_format #undef read_objective_t_data #define objective_t double #define objective_t_scanf_format "%lf" #define read_objective_t_data read_double_data #include "eaf_io_priv.h" #undef objective_t #undef objective_t_scanf_format #undef read_objective_t_data #ifndef R_PACKAGE /* From: Edition 0.10, last updated 2001-07-06, of `The GNU C Library Reference Manual', for Version 2.3.x. Copyright (C) 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2001, 2002, 2003 Free Software Foundation, Inc. */ void errprintf(const char *template,...) { va_list ap; fprintf(stderr, "%s: error: ", PROGRAM_NAME); va_start(ap,template); vfprintf(stderr, template, ap); va_end(ap); fprintf(stderr, "\n"); } /* End of copyright The GNU C Library Reference Manual */ void warnprintf(const char *template,...) { va_list ap; fprintf(stderr, "%s: warning: ", PROGRAM_NAME); va_start(ap,template); vfprintf(stderr, template, ap); va_end(ap); fprintf(stderr, "\n"); } #endif eaf/src/cvector.h0000644000176200001440000003272413254747514013436 0ustar liggesusers#ifndef __CVECTOR_H__ #define __CVECTOR_H__ #ifndef cvector_assert #include #define cvector_assert(X) assert(X) #endif #include #define vector_define(VECTOR_TYPE, BASE_TYPE) \ struct VECTOR_TYPE; \ typedef struct VECTOR_TYPE VECTOR_TYPE; \ \ struct VECTOR_TYPE { \ BASE_TYPE *_begin; \ BASE_TYPE *_end; \ BASE_TYPE *_capacity; \ }; \ \ static inline \ void VECTOR_TYPE##_reserve(VECTOR_TYPE * v, size_t n); \ \ /* Default constructor: constructs an empty vector, with no content and a size of zero. */ \ static inline \ void VECTOR_TYPE##_ctor_zero(VECTOR_TYPE * v) \ { \ v->_begin = NULL; \ v->_end = NULL; \ v->_capacity = NULL; \ } \ \ /* Default constructor: constructs an empty vector, with no content and a size of zero. */ \ static inline \ void VECTOR_TYPE##_ctor(VECTOR_TYPE * v, size_t n) \ { \ VECTOR_TYPE##_ctor_zero (v); \ VECTOR_TYPE##_reserve (v, n); \ } \ \ /* Default constructor: dynamically allocates an empty vector, with no content and a size of zero. */ \ static inline \ VECTOR_TYPE * VECTOR_TYPE##_new(size_t n) \ { \ VECTOR_TYPE * v = malloc (sizeof(VECTOR_TYPE)); \ VECTOR_TYPE##_ctor (v, n); \ return v; \ } \ \ /* Destructor. It frees the memory allocated to the vector, but it does not try to free each of its elements or the vector itself. */ \ static inline \ void VECTOR_TYPE##_dtor(VECTOR_TYPE * v) \ { free(v->_begin); } \ \ /* Destructor. It frees the memory allocated to a dynamically allocated vector, but it does not try to free each of its elements. */ \ static inline \ void VECTOR_TYPE##_delete(VECTOR_TYPE * v) \ { VECTOR_TYPE##_dtor(v); free(v); } \ \ \ /* Returns the number of elements in the vector container. */ \ static inline \ size_t VECTOR_TYPE##_size(const VECTOR_TYPE * v) \ { return v->_end - v->_begin; } \ \ /* Return size of allocated storage capacity. */ \ static inline \ size_t VECTOR_TYPE##_capacity(const VECTOR_TYPE * v) \ { return v->_capacity - v->_begin; } \ \ /* Returns whether the vector container is empty, i.e. whether its size is 0. */ \ static inline \ bool VECTOR_TYPE##_empty(const VECTOR_TYPE * v) \ { return v->_begin == v->_end; } \ \ /* Requests that the capacity of the allocated storage space for the elements of the vector container be at least enough to hold n elements. This informs the vector of a planned increase in size, although notice that the parameter n informs of a minimum, so the resulting capacity may be any capacity equal or larger than this. When n is greater than the current capacity, a reallocation is attempted during the call to this function. If successful, it grants that no further automatic reallocations will happen because of a call to vector::insert or vector::push_back until the vector size surpasses at least n (this preserves the validity of iterators on all these future calls). A reallocation invalidates all previously obtained iterators, references and pointers to elements of the vector. In any case, a call to this function never affects the elements contained in the vector, nor the vector size. */ \ static inline \ void VECTOR_TYPE##_reserve(VECTOR_TYPE * v, size_t n) \ { \ size_t old_capacity = VECTOR_TYPE##_capacity(v); \ size_t old_size = VECTOR_TYPE##_size(v); \ if (n > old_capacity) { \ v->_begin = realloc(v->_begin, sizeof(BASE_TYPE) * n); \ v->_end = v->_begin + old_size; \ v->_capacity = v->_begin + n; \ } \ } \ \ /* Adds a new element at the end of the vector, after its current last element. The content of this new element is initialized to a copy of x. This effectively increases the vector size by one, which causes a reallocation of the internal allocated storage if the vector size was equal to the vector capacity before the call. Reallocations invalidate all previously obtained iterators, references and pointers. */ \ static inline \ void VECTOR_TYPE##_push_back(VECTOR_TYPE * v, BASE_TYPE x) \ { \ if (v->_end == v->_capacity) \ VECTOR_TYPE##_reserve (v, \ (VECTOR_TYPE##_capacity (v) == 0) \ ? 8 \ : 2 * VECTOR_TYPE##_capacity (v)); \ *(v->_end) = x; \ v->_end++; \ } \ \ /* Removes the last element in the vector, effectively reducing the vector size by one and invalidating all iterators and references to it. Note that the element is not freed. */ \ static inline \ BASE_TYPE VECTOR_TYPE##_pop_back(VECTOR_TYPE * v) \ { \ cvector_assert(!VECTOR_TYPE##_empty(v)); \ v->_end--; \ return *(v->_end); \ } \ \ /* Returns the first element in the vector container. This is not a reference like in C++! */ \ static inline \ BASE_TYPE VECTOR_TYPE##_front(const VECTOR_TYPE * v) \ { \ cvector_assert (!VECTOR_TYPE##_empty(v)); \ return v->_begin[0]; \ } \ \ /* Returns the last element in the vector container. This is not a reference like in C++! */ \ static inline \ BASE_TYPE VECTOR_TYPE##_back(const VECTOR_TYPE * v) \ { \ cvector_assert (!VECTOR_TYPE##_empty(v)); \ return *(v->_end - 1); \ } \ \ /* Returns an iterator (pointer) referring to the first element in the vector container. */ \ static inline \ BASE_TYPE * VECTOR_TYPE##_begin(const VECTOR_TYPE * v) \ { return v->_begin; } \ \ /* Returns an iterator (pointer) referring to the past-the-end element in the vector container. */ \ static inline \ BASE_TYPE * VECTOR_TYPE##_end(const VECTOR_TYPE * v) \ { return v->_end; } \ \ /* Returns the element at position pos. */ \ static inline \ BASE_TYPE VECTOR_TYPE##_at(const VECTOR_TYPE * v, size_t pos) \ { \ cvector_assert(pos <= VECTOR_TYPE##_size(v)); \ return v->_begin[pos]; \ } \ \ /* Set the element at position pos to value e and return it. */ \ static inline \ BASE_TYPE VECTOR_TYPE##_set(const VECTOR_TYPE * v, size_t pos, BASE_TYPE e) \ { \ cvector_assert(pos <= VECTOR_TYPE##_size(v)); \ return (v->_begin[pos] = e); \ } \ #endif eaf/src/eaf_io.h0000644000176200001440000000243213254747514013204 0ustar liggesusers#ifndef EAF_INPUT_OUTPUT_H #define EAF_INPUT_OUTPUT_H #define _GNU_SOURCE #include // for errno /*const char *program_invocation_short_name = "eaf";*/ #define PROGRAM_NAME "eaf" #define point_printf_format "% 17.16g" /* Error codes for read_data. */ enum ERROR_READ_DATA { READ_INPUT_FILE_EMPTY = -1, READ_INPUT_WRONG_INITIAL_DIM = -2, ERROR_FOPEN = -3, ERROR_CONVERSION = -4, ERROR_COLUMNS = -5, }; int read_int_data (const char *filename, int **data_p, int *nobjs_p, int **cumsizes_p, int *nsets_p); int read_double_data (const char *filename, double **data_p, int *nobjs_p, int **cumsizes_p, int *nsets_p); /* If we're not using GNU C, elide __attribute__ */ #ifndef __GNUC__ # define __attribute__(x) /* NOTHING */ #endif #ifdef R_PACKAGE #include #define errprintf error #define warnprintf warning #else void errprintf(const char * template,...) /* enables the compiler to check the format string against the parameters */ __attribute__ ((format(printf, 1, 2))); void warnprintf(const char *template,...) /* enables the compiler to check the format string against the parameters */ __attribute__ ((format(printf, 1, 2))); #endif #endif eaf/src/init.c0000644000176200001440000000146313254747514012723 0ustar liggesusers#include #include #include // for NULL #include /* .Call calls */ extern SEXP compute_eaf_C(SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP compute_eafdiff_area_C(SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP compute_eafdiff_C(SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP read_data_sets(SEXP); static const R_CallMethodDef CallEntries[] = { {"compute_eaf_C", (DL_FUNC) &compute_eaf_C, 5}, {"compute_eafdiff_area_C", (DL_FUNC) &compute_eafdiff_area_C, 5}, {"compute_eafdiff_C", (DL_FUNC) &compute_eafdiff_C, 5}, {"read_data_sets", (DL_FUNC) &read_data_sets, 1}, {NULL, NULL, 0} }; void R_init_eaf(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } eaf/src/eaf.h0000644000176200001440000001472713254747514012527 0ustar liggesusers/************************************************************************* eaf.h: prototypes and shared types of eaf and eaf-test --------------------------------------------------------------------- Copyright (c) 2006, 2007, 2008 Carlos Fonseca Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- *************************************************************************/ #ifdef R_PACKAGE #include #define eaf_assert(EXP) \ do { if (!(EXP)) { error("eaf package: error: assertion failed: '%s'", \ #EXP); }} while(0) #define EAF_MALLOC(WHAT, NMEMB, SIZE) \ do { WHAT = malloc (NMEMB * SIZE); \ if (!WHAT) { \ error(__FILE__ ": %s = malloc (%u * %u) failed", \ #WHAT, (unsigned int) NMEMB, (unsigned int) SIZE); } \ } while (0) #define fatalprintf(X) error(X) #else #include #define eaf_assert(X) assert(X) #define EAF_MALLOC(WHAT, NMEMB, SIZE) \ do { WHAT = malloc (NMEMB * SIZE); \ if (!WHAT) { perror (__FILE__ ": " #WHAT ); exit (EXIT_FAILURE); } \ } while(0) #define fatalprintf(X) \ do { errprintf (X); exit (EXIT_FAILURE); } while(0) #endif #include "eaf_io.h" #include #include #include #include #include #include /* If the input are always integers, adjusting this type will certainly improve performance. */ #define objective_t double #if objective_t == double # define objective_MAX INFINITY # define objective_MIN -INFINITY # define objective_t_scanf_format "%lf" # define read_objective_t_data read_double_data #else # define objective_MAX INT_MAX # define objective_MIN INT_MIN # define objective_t_scanf_format "%d" # define read_objective_t_data read_int_data #endif typedef struct { int nobj; /* FIXME: there is no point to store this here. */ int nruns; int size; int maxsize; bool *attained; objective_t *data; } eaf_t; void eaf_print_attsurf (eaf_t *, FILE *coord_file, /* output file (coordinates) */ FILE *indic_file, /* output file (attainment indicators) */ FILE *diff_file); /* output file (difference nruns/2) */ eaf_t * eaf_create (int nobj, int nruns, int npoints); void eaf_delete (eaf_t * eaf); objective_t * eaf_store_point_help (eaf_t * eaf, int nobj, const int *save_attained); static inline int eaf_totalpoints (eaf_t **eaf, int n) { int totalpoints = 0; int k; for (k = 0; k < n; k++) { totalpoints += eaf[k]->size; } return totalpoints; } eaf_t ** eaf2d (const objective_t *data, /* the objective vectors */ const int *cumsize, /* the cumulative sizes of the runs */ int nruns, /* the number of runs */ const int *attlevel, /* the desired attainment levels */ int nlevels /* the number of att levels */ ); eaf_t ** eaf3d (objective_t *data, const int *cumsize, int nruns, const int *attlevel, const int nlevels); static inline eaf_t ** attsurf (objective_t *data, /* the objective vectors */ int nobj, /* the number of objectives */ const int *cumsize, /* the cumulative sizes of the runs */ int nruns, /* the number of runs */ const int *attlevel, /* the desired attainment levels */ int nlevels /* the number of att levels */ ) { switch (nobj) { case 2: return eaf2d (data, cumsize, nruns, attlevel, nlevels); break; case 3: return eaf3d (data, cumsize, nruns, attlevel, nlevels); break; default: fatalprintf ("this implementation only supports two or three dimensions.\n"); } } static inline void fprint_set (FILE *stream, const objective_t **data, int ntotal) { int k; for (k = 0; k < ntotal; k++) fprintf (stream, "%6d: % .16g % .16g\n", k, (double)data[k][0], (double)data[k][1]); } static inline void attained_left_right (const bool *attained, int division, int total, int *count_left, int *count_right) { int count_l = 0; int count_r = 0; int k; eaf_assert (division < total); for (k = 0; k < division; k++) if (attained[k]) count_l++; for (k = division; k < total; k++) if (attained[k]) count_r++; *count_left = count_l; *count_right = count_r; } static inline int fast_round (double x) { return (int) (x > 0) ? x + .5 : (x < 0) ? x - .5 : x; } static inline int percentile2level (int p, int n) { int level = fast_round (n * p / 100.0); if (level > n) level = n; else if (level < 1) level = 1; return level; } #define cvector_assert(X) eaf_assert(X) #include "cvector.h" vector_define(vector_objective, objective_t) vector_define(vector_int, int) typedef struct { vector_objective xy; vector_int col; } eaf_polygon_t; #define eaf_compute_area eaf_compute_area_new eaf_polygon_t *eaf_compute_area_new (eaf_t **eaf, int nlevels); eaf_polygon_t *eaf_compute_area_old (eaf_t **eaf, int nlevels); void eaf_print_polygon (FILE* stream, eaf_t **eaf, int nlevels); eaf/src/eaf3d.c0000644000176200001440000005711313254747514012745 0ustar liggesusers/************************************************************************* eaf3d: Compute the empirical attainment function from a sequence of non-dominated point sets (three-objective case) --------------------------------------------------------------------- Copyright (c) 2009-2011 Andreia Guerreiro Carlos M. Fonseca Luis Paquete Manuel Lopez-Ibanez This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- *************************************************************************/ #include "eaf.h" #include "avl.h" #include #include #include #include #include #include #define max( a, b ) ( ((a) > (b)) ? (a) : (b) ) typedef struct dlnode { objective_t *x; /* The data vector */ struct dlnode *next; struct dlnode *prev; int set; } dlnode_t; typedef struct removed_list { avl_node_t *head; } removed_list_t; static removed_list_t * removed_list; static inline bool avl_tree_is_empty (const avl_tree_t *avltree) { return avltree->top == NULL; } static inline objective_t *node_point(const avl_node_t *node) { return (objective_t*) node->item; } static int compare_node(const void *p1, const void *p2) { const objective_t x1 = *((*(const dlnode_t **)p1)->x); const objective_t x2 = *((*(const dlnode_t **)p2)->x); return (x1 < x2) ? -1 : ( x1 > x2 ) ? 1 : 0; } /* Create a list of points, ordered by the third coordinate. */ static dlnode_t * setup_cdllist(objective_t *data, int d, const int *cumsize, int nsets) { dlnode_t **scratch; int n = cumsize[nsets - 1]; dlnode_t *head = malloc ((n + 1) * sizeof(dlnode_t)); head->x = data; head[0].set = 0; int i, k; for (i = 0, k = 0; i < n; i++) { if (i == cumsize[k]) k++; head[i+1].set = k; // ->x points to the first coordinate of each point. head[i+1].x = head[i].x + d ;// this will be fixed a few lines below... head[i+1].next = head[i].next; head[i+1].prev = head[i].prev; } head->x = NULL; // head contains no data scratch = malloc(n * sizeof(dlnode_t*)); for (i = 0; i < n; i++) scratch[i] = head + i + 1; // ->x points to the last coordinate of each point. for (i = 0; i < n; i++) scratch[i]->x--; // Sort according to the last coordinate. qsort(scratch, n, sizeof(dlnode_t*), compare_node); head->next = scratch[0]; scratch[0]->prev = head; for (i = 1; i < n; i++) { scratch[i-1]->next = scratch[i]; scratch[i]->prev = scratch[i-1]; } scratch[n-1]->next = head; head->prev = scratch[n-1]; // ->x points to the first coordinate. for (i = 0; i < n; i++) scratch[i]->x -= d - 1; free(scratch); return head; } /* FIXME: Why this variant in one single indirection and the eaf2d equivalent is 2. */ static int compare_tree_asc_x(const void *p1, const void *p2) { const objective_t x1 = *((const objective_t *)p1); const objective_t x2 = *((const objective_t *)p2); return (x1 < x2) ? -1 : (x1 > x2) ? 1 : 0; } static int compare_tree_desc_y( const void *p1, const void *p2) { const objective_t y1 = *((const objective_t *)p1+1); const objective_t y2 = *((const objective_t *)p2+1); return (y1 > y2) ? -1 : (y1 < y2) ? 1 : 0; } static int avl_search_closest_y(const avl_tree_t *avltree, const void *item, avl_node_t **avlnode) { avl_node_t *node; int c; if(!avlnode) avlnode = &node; node = avltree->top; if(!node) return *avlnode = NULL, 0; for(;;) { c = compare_tree_desc_y(item, node->item); if(c < 0) { if(node->left) node = node->left; else return *avlnode = node, -1; } else if(c > 0) { if(node->right) node = node->right; else return *avlnode = node, 1; } else { return *avlnode = node, 0; } } } static void find_all_promoters(avl_node_t * avlnode, int * dom_sets, int nruns) { avl_node_t *node = avlnode; while (node != NULL) { dom_sets[node->set] = 1; if (node->remover != NULL) find_all_promoters(node->remover, dom_sets, nruns); node = node->promoter; } node = avlnode->equal; while (node != NULL) { dom_sets[node->set] = 1; node = node->equal; } } /* Prints the list in the tree, ordered by the second dimension, from the highest value to the lowest. The values printed are the values from dimension 0 to dimension dim of each point */ static void printlist(avl_tree_t *avltree, int dim, FILE *outfile) { avl_node_t *aux; aux = avltree->head; int i; objective_t * val; while(aux){ val = (objective_t *)aux->item; //printf("-> "); for(i = 0; i < dim; i++){ fprintf(outfile, point_printf_format "\t", val[i]); } fprintf(outfile, "\n"); aux = aux->next; } } void printset(FILE* stream, avl_tree_t **set, int nset) { int i; fprintf(stream, "# sets\n----------------------\n"); for(i = 0; i < nset; i++){ if(set[i]->top != NULL) { fprintf (stream, "set: %d", i); printlist(set[i], 3, stream); } } } void printlevel(FILE * stream, avl_tree_t **level, int nset) { int i; fprintf (stream, "#levels\n-------------------\n"); for(i = 0; i < nset; i++){ if(level[i]->top != NULL){ fprintf (stream, "level: %d\n", i); printlist(level[i], 3, stream); } } } static int printlist_points_indic(avl_tree_t *avltree, int dim, int nruns, FILE *outfile, FILE *outfileindic) { avl_node_t *aux; aux = avltree->head; int * dom_sets = (int *) malloc(nruns * sizeof(int)); int i, k, totalp = 0; objective_t * val; while(aux){ val = (objective_t *)aux->item; if(outfile){ fprintf(outfile, point_printf_format, val[0]); for(i = 1; i < dim; i++){ fprintf(outfile, "\t" point_printf_format, val[i]); } fprintf(outfile, (outfile == outfileindic) ? "\t" : "\n"); } if(outfileindic){ for(k = 0; k < nruns; k++) dom_sets[k] = 0; find_all_promoters(aux, dom_sets, nruns); fprintf(outfileindic, "%d", dom_sets[0]); for(k = 1; k < nruns; k++){ fprintf(outfileindic, "\t%d", dom_sets[k]); } fprintf(outfileindic, "\n"); } aux = aux->next; totalp++; } free(dom_sets); return totalp; } /* Returns the total number of points printed */ int printoutput(avl_tree_t **level, int nset, int d, FILE **outfile, int noutfiles, FILE **outfileindic, int noutfilesi, int * attlevel, int nlevels){ int i, k, f, fi, totalp = 0; for(i = 0; i < nlevels; i++){ k = attlevel[i] - 1; f = (noutfiles > 1) ? i : 0; fi = (noutfilesi > 1) ? i : 0; if(level[k]->head != NULL){ totalp += printlist_points_indic(level[k], d, nset, (outfile ? outfile[f] : NULL), (outfileindic ? outfileindic[fi] : NULL)); } if(i < nlevels -1){ if(outfile) fprintf(outfile[f], "\n"); if(outfileindic && (outfile == NULL || outfile[f] != outfileindic[fi])){ fprintf(outfileindic[fi], "\n"); } } } return totalp; } static void print_list_indic(avl_tree_t * level, int nruns, FILE * indicfile){ int * dom_sets = (int *) malloc(nruns * sizeof(int)); avl_node_t * avlnode; avlnode = level->head; int i; while(avlnode){ for(i = 0; i < nruns; i++){ dom_sets[i] = 0; } find_all_promoters(avlnode, dom_sets, nruns); for(i = 0; i < nruns; i++){ fprintf(indicfile, "%d\t", dom_sets[i]); } fprintf(indicfile, "\n"); avlnode = avlnode->next; } free(dom_sets); } void printindic(avl_tree_t ** levels, int nruns, FILE ** indicfile, int nfiles, int * attlevel, int nlevels){ int i, k, f; for(i=0; i < nlevels; i++){ k = attlevel[i] - 1; f = (nfiles == 1) ? 0 : i; if(levels[k]->head != NULL){ print_list_indic(levels[k], nruns, indicfile[f]); } fprintf(indicfile[f], "\n"); } } void printitem(FILE * stream, objective_t *value, int dim) { int i; for (i = 0; i < dim; i++) fprintf (stream, "%f ", value[i]); fprintf (stream, "\n"); } void add2output(avl_tree_t *output, avl_node_t *tnode) { if(output->top != NULL){ output->tail->next = tnode; output->tail = tnode; }else{ avl_insert_top(output, tnode); } } void add2output_all(avl_tree_t *output, avl_tree_t *tree_add){ avl_node_t *node = tree_add->head; avl_unlink_node(tree_add, node); free(node->item); free(node); node = tree_add->tail; avl_unlink_node(tree_add, node); free(node->item); free(node); if(tree_add->head != NULL){ if(output->tail != NULL){ output->tail->next=tree_add->head; }else{ output->head=tree_add->head; } } } //this fuction is called only when item isn't dominated by any point in tree. //note: prevnode is above and at item's left, so any dominated point that it may dominate is at its right static void add2set(avl_tree_t *tree, avl_node_t *prevnode, avl_node_t *tnode, objective_t *item) { avl_init_node (tnode, item); if(node_point(prevnode)[0] == item[0]) prevnode = prevnode->prev; avl_insert_after (tree, prevnode, tnode); tnode = tnode->next; // tnode->next is dominated by item while (node_point(tnode)[1] >= item[1]) { avl_unlink_node(tree, tnode); avl_node_t *node = tnode; tnode = tnode->next; free(node); } } static void avl_add_promoter(avl_node_t *avlnode, int set, avl_node_t *promoter) { avlnode->set = set; avlnode->promoter = promoter; avlnode->remover = NULL; avlnode->equal = NULL; } static void add_removed(avl_node_t *node) { node->next = removed_list->head; removed_list->head = node; } static avl_node_t * add2level(avl_tree_t *tree, objective_t *item, avl_tree_t *output, int set, avl_node_t * promoter) { avl_node_t *prevnode; avl_node_t *aux; switch (avl_search_closest_y(tree, item, &prevnode)) { case -1: prevnode = prevnode->prev; break; case 0: prevnode = (node_point(prevnode)[0] > item[0]) ? prevnode : prevnode->prev; break; case 1: break; } //aux represents the point immediately below item. aux = prevnode->next; // A new point (item) is added, but only if it isn't dominated. if (node_point(aux)[0] > item[0]) { avl_node_t *tnode = malloc(sizeof(avl_node_t)); avl_init_node(tnode, item); avl_add_promoter(tnode, set, promoter); avl_node_t *newnode = tnode; avl_insert_after(tree, prevnode, tnode); tnode = tnode->prev; // tnode->prev is dominated by item while (node_point(tnode)[0] >= item[0]) { avl_unlink_node(tree,tnode); aux = tnode; tnode = tnode->prev; if (node_point(aux)[2] < item[2]) add2output(output, aux); else{ free(aux->item); add_removed(aux); aux->remover = newnode; } /* Each point that is removed from this level, becomes a final point but only if the point that dominates it (item) dominates it only from dimension 0 to 1. */ } return newnode; } else{ free(item); return NULL; } } static objective_t * copy_point(const objective_t * v) { objective_t *value = malloc(3 * sizeof(objective_t)); value[0] = v[0]; value[1] = v[1]; value[2] = v[2]; return value; } //at left or equal static avl_node_t * find_point_at_left(avl_tree_t *tree, objective_t *item){ avl_node_t *leftNode; if (avl_search_closest(tree, item, &leftNode) < 0) leftNode = leftNode->prev; return leftNode; } //below and not equal static avl_node_t * find_point_below(avl_tree_t *tree, objective_t *item){ avl_node_t *belowNode; if (avl_search_closest_y(tree, item, &belowNode) >= 0) belowNode = belowNode->next; return belowNode; } static objective_t * new_point(objective_t x1, objective_t x2, objective_t x3) { objective_t *value = malloc(3 * sizeof(objective_t)); value[0] = x1; value[1] = x2; value[2] = x3; return value; } void eaf3df(dlnode_t *list, avl_tree_t **set, avl_tree_t **level, avl_tree_t **output, int nset) { // point from some level immediately at new's left, corresponds to r in pseudocode avl_node_t *leftNodeL; /*levelNode[t] is the point being verified (possibly to be promoted) from level t, corresponds to s_t in pseudocode */ avl_node_t *levelNode[nset]; avl_node_t auxNodes[nset]; /* these points are needed to represent the intersections of new with the point, of each level, immediately at new's left */ objective_t auxValues[nset][2]; avl_node_t *promoters[nset]; dlnode_t *new = list->next; //new - represents the new point //add new to its set avl_node_t * tnode = avl_init_node(malloc(sizeof(avl_node_t)), new->x); avl_insert_after(set[new->set], set[new->set]->head, tnode); //new is the only point so far, so it is added to the first level tnode = avl_init_node(malloc(sizeof(avl_node_t)), copy_point(new->x)); avl_add_promoter(tnode, new->set, NULL); avl_insert_after(level[0], level[0]->head, tnode); bool mask[nset]; // needed to know how many different sets were considered so far memset(mask, false, nset * sizeof(mask[0])); mask[new->set] = true; int start_at = 0, stop_at; // indicates which levels must be verified (from and to which) // Points are sorted in ascending order with respect to the third coordinate. while (new->next->x != NULL) { new = new->next; /* newPrev: point from new's set immediately at its left, it corresponds to q in pseudocode. */ avl_node_t *newPrev = find_point_at_left(set[new->set], new->x); // if new is dominated by a point from its set, skip it. if (node_point(newPrev)[1] <= new->x[1]) { continue; } avl_node_t *dom_new = NULL; stop_at = 0; // First part int k; for(k = start_at; k >= stop_at; k--) { leftNodeL = find_point_at_left(level[k], new->x); // new is dominated by a point from level k if (node_point(leftNodeL)[1] <= new->x[1]) { dom_new = leftNodeL; stop_at = k + 1; } else if (node_point(leftNodeL)[1] < node_point(newPrev)[1]) { /* the intersection point of new with the point in level k immediately at new's left should be added to level k+1, so this intersection point is saved in levelNode in order to be added later */ auxValues[k][0] = new->x[0]; auxValues[k][1] = node_point(leftNodeL)[1]; auxNodes[k].item = auxValues[k]; auxNodes[k].next = leftNodeL->next; levelNode[k] = &auxNodes[k]; promoters[k] = leftNodeL; } else { levelNode[k] = find_point_below(level[k], newPrev->item); promoters[k] = levelNode[k]; } } /* setNode: a point from new's set at its right, it also corresponds to q in pseudocode. */ avl_node_t *setNode = newPrev; //Second part do { setNode = setNode->next; objective_t lbound = max(node_point(setNode)[1], new->x[1]); for (k = start_at; k >= stop_at; k--) { //while levelNode is dominated by new but not by any point from new's set, levelNode is promoted while (node_point(levelNode[k])[1] >= lbound && (node_point(levelNode[k])[1] > lbound || lbound > new->x[1])) { if (node_point(setNode)[0] <= node_point(levelNode[k])[0]) { levelNode[k] = find_point_below(level[k], setNode->item); promoters[k] = levelNode[k]; } else { objective_t *value = new_point(node_point(levelNode[k])[0], node_point(levelNode[k])[1], new->x[2]); tnode = add2level(level[k+1], value, output[k+1], new->set, promoters[k]); if(tnode != NULL && new->x[2] == node_point(promoters[k])[2] && node_point(levelNode[k])[0] == node_point(promoters[k])[0]){ promoters[k]->equal = tnode; } levelNode[k] = levelNode[k]->next; promoters[k] = levelNode[k]; } } } }while(node_point(setNode)[1] > new->x[1]); //Third part for(k = start_at; k >= stop_at; k--){ //if the intersection point of new with the point from level k immediately below new //isn't dominated, it is added to level k+1 if(node_point(levelNode[k])[0] < node_point(setNode)[0]){ objective_t *value; value = new_point(node_point(levelNode[k])[0], new->x[1], new->x[2]); tnode = add2level(level[k+1], value, output[k+1], new->set, promoters[k]); if(tnode != NULL && new->x[2] == node_point(levelNode[k])[2] && new->x[1] == node_point(levelNode[k])[1]){ promoters[k]->equal = tnode; } } } //add new to its set tnode = avl_init_node(malloc(sizeof(avl_node_t)), new->x); add2set(set[new->set], newPrev, tnode, new->x); //add new to the lowest level where it isn't dominated by any point from that level tnode = add2level(level[stop_at], copy_point(new->x), output[stop_at], new->set, dom_new); if(stop_at > 0 && tnode != NULL && new->x[2] == node_point(dom_new)[2] && new->x[1] == node_point(dom_new)[1] && new->x[0] == node_point(dom_new)[0]){ dom_new->equal = tnode; } if (!mask[new->set]) { if (start_at < (nset - 2)) start_at++; mask[new->set] = true; } } } static void freetree(avl_tree_t *avltree) { avl_node_t *aux, *aux2; aux=avltree->head; if(aux){ while(aux){ aux2 = aux; aux = aux2->next; free(aux2->item); free(aux2); } } free(avltree); } static void freetree2(avl_tree_t *avltree) { avl_node_t *aux, *aux2; aux=avltree->head; free(aux->item); while(aux->next){ aux2 = aux; aux = aux2->next; free(aux2); } free(aux->item); free(aux); free(avltree); } void free_removed(){ avl_node_t * aux, * node = removed_list->head; while(node != NULL){ aux = node; node = node->next; free(aux); } free(removed_list); } void freeoutput(avl_tree_t **output, int nset){ int i; for(i = 0; i < nset; i++){ freetree(output[i]); } free(output); free_removed(); } void add_sentinels(avl_tree_t * tree, int n) { objective_t *value = malloc(n * sizeof(objective_t)); value[0] = objective_MIN; value[1] = objective_MAX; avl_node_t *tnode = avl_init_node(malloc(sizeof(avl_node_t)), value); avl_insert_top(tree, tnode); value = malloc(n * sizeof(objective_t)); value[0] = objective_MAX; value[1] = objective_MIN; avl_node_t *tnode2 = avl_init_node(malloc(sizeof(avl_node_t)), value); avl_insert_after(tree, tnode, tnode2); } static void eaf_store_point_3d (eaf_t * eaf, objective_t x, objective_t y, objective_t z, const int *save_attained) { const int nobj = 3; objective_t * pos = eaf_store_point_help (eaf, nobj, save_attained); pos[0] = x; pos[1] = y; pos[2] = z; eaf->size++; } eaf_t ** eaf3d (objective_t *data, const int *cumsize, int nruns, const int *attlevel, const int nlevels) { const int nobj = 3; const int ntotal = cumsize[nruns - 1]; /* total number of points in data */ avl_tree_t **set = malloc (nruns * sizeof(avl_tree_t)); avl_tree_t **level = malloc (nruns * sizeof(avl_tree_t)); avl_tree_t **output = malloc (nruns * sizeof(avl_tree_t)); int i; for (i = 0; i < nruns; i++) { set[i] = avl_alloc_tree ((avl_compare_t) compare_tree_asc_x, (avl_freeitem_t) free); level[i] = avl_alloc_tree ((avl_compare_t) compare_tree_asc_x, (avl_freeitem_t) free); output[i] = avl_alloc_tree ((avl_compare_t) compare_tree_asc_x, (avl_freeitem_t) free); add_sentinels(set[i], nobj); add_sentinels(level[i], nobj); } removed_list = (removed_list_t *) malloc(sizeof(removed_list)); removed_list->head = NULL; dlnode_t *list = setup_cdllist(data, nobj, cumsize, nruns); eaf3df(list, set, level, output, nruns); for (i = 0; i < nruns; i++) { add2output_all(output[i], level[i]); } for(i=0; ihead; while (aux) { objective_t * val = aux->item; for(int j = 0; j < nruns; j++) attained[j] = 0; find_all_promoters(aux, attained, nruns); eaf_store_point_3d (eaf[l], val[0], val[1], val[2], attained); aux = aux->next; } } return eaf; } eaf/src/eaf.c0000644000176200001440000006123713254747514012520 0ustar liggesusers/************************************************************************* eaf: Computes the empirical attainment function (EAF) from a number of approximation sets. --------------------------------------------------------------------- Copyright (c) 2005 Carlos M. Fonseca Copyright (c) 2006, 2007, 2008, 2015 Carlos M. Fonseca Manuel Lopez-Ibanez This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, you can obtain a copy of the GNU General Public License at: http://www.gnu.org/copyleft/gpl.html or by writing to: Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ---------------------------------------------------------------------- *************************************************************************/ #include #include #include #include #include #include "eaf.h" #ifndef DEBUG #define DEBUG 0 #endif static int compare_x_asc (const void *p1, const void *p2) { objective_t x1 = **(objective_t **)p1; objective_t x2 = **(objective_t **)p2; return (x1 < x2) ? -1 : ((x1 > x2) ? 1 : 0); } static int compare_y_desc (const void *p1, const void *p2) { objective_t y1 = *(*(objective_t **)p1+1); objective_t y2 = *(*(objective_t **)p2+1); return (y1 > y2) ? -1 : ((y1 < y2) ? 1 : 0); } eaf_t * eaf_create (int nobj, int nruns, int npoints) { eaf_t *eaf; EAF_MALLOC (eaf, 1, sizeof(eaf_t)); eaf->nobj = nobj; eaf->nruns = nruns; eaf->size = 0; /* Maximum is npoints, but normally it will be smaller, so at most log2(2 * nruns) realloc will occur. */ eaf->maxsize = 256 + npoints / (2 * nruns); EAF_MALLOC (eaf->data, nobj * eaf->maxsize, sizeof(objective_t)); EAF_MALLOC (eaf->attained, nruns * eaf->maxsize, sizeof(bool)); return eaf; } void eaf_delete (eaf_t * eaf) { free (eaf->data); free (eaf->attained); free (eaf); } objective_t * eaf_store_point_help (eaf_t * eaf, int nobj, const int *save_attained) { const int nruns = eaf->nruns; if (eaf->size == eaf->maxsize) { eaf_assert (eaf->size < INT_MAX / 2); eaf->maxsize = eaf->maxsize * 2; eaf->attained = realloc (eaf->attained, sizeof(bool) * nruns * eaf->maxsize); eaf_assert(eaf->attained); eaf->data = realloc (eaf->data, sizeof(objective_t) * nobj * eaf->maxsize); eaf_assert(eaf->data); } /* We convert from int to bool to hopefully save space. */ for (int k = 0; k < nruns; k++) { eaf->attained[(nruns * eaf->size) + k] = (bool) save_attained[k]; } return eaf->data + nobj * eaf->size; } static void eaf_store_point_2d (eaf_t * eaf, objective_t x, objective_t y, const int *save_attained) { const int nobj = 2; objective_t * pos = eaf_store_point_help (eaf, nobj, save_attained); pos[0] = x; pos[1] = y; eaf->size++; } static void eaf_print_line (FILE *coord_file, FILE *indic_file, FILE *diff_file, const objective_t *x, int nobj, const bool *attained, int nruns) { int count1 = 0; int count2 = 0; int k; if (coord_file) { fprintf (coord_file, point_printf_format "\t" point_printf_format, (double)x[0], (double)x[1]); for (k = 2; k < nobj; k++) fprintf (coord_file, "\t" point_printf_format, (double)x[k]); fprintf (coord_file, (coord_file == indic_file) || (coord_file == diff_file) ? "\t" : "\n"); } if (indic_file) { fprintf (indic_file, "%d", attained[0] ? (count1++,1) : 0); for (k = 1; k < nruns/2; k++) fprintf (indic_file, "\t%d", attained[k] ? (count1++,1) : 0); for (k = nruns/2; k < nruns; k++) fprintf (indic_file, "\t%d", attained[k] ? (count2++,1) : 0); fprintf (indic_file, (indic_file == diff_file) ? "\t" : "\n"); } else if (diff_file) { attained_left_right (attained, nruns/2, nruns, &count1, &count2); } if (diff_file) fprintf (diff_file,"%d\t%d\n", count1, count2); } /* Print one attainment surface of the EAF. */ void eaf_print_attsurf (eaf_t * eaf, FILE *coord_file, FILE *indic_file, FILE *diff_file) { int i; for (i = 0; i < eaf->size; i++) { const objective_t *p = eaf->data + i * eaf->nobj; eaf_print_line (coord_file, indic_file, diff_file, p, eaf->nobj, eaf->attained + i * eaf->nruns, eaf->nruns); } } /* eaf2d: compute attainment surfaces from points in objective space, using dimension sweeping. Input arguments: data : a pointer to the data matrix, stored as a linear array of objective_t in row major order. cumsize : an array containing the cumulative number of rows in each non-dominated front (must be non-decreasing). nruns : the number of independent non-dominated fronts. attlevel : an array containing the attainment levels to compute. nlevel : number of attainment levels to compute. coord_file : stream to write the resulting attainment surfaces. indic_file : stream to write the resulting attainment indices. diff_file : stream to write the difference between the the first half and the second half of nruns. */ eaf_t ** eaf2d (const objective_t *data, const int *cumsize, int nruns, const int *attlevel, const int nlevels) { const int nobj = 2; eaf_t **eaf; const objective_t **datax, **datay; /* used to access the data sorted according to x or y */ const int ntotal = cumsize[nruns - 1]; /* total number of points in data */ int *runtab; int *attained, nattained, *save_attained; int k, j, l; /* Access to the data is made via two arrays of pointers: ix, iy These are sorted, to allow for dimension sweeping */ datax = malloc (ntotal * sizeof(objective_t *)); datay = malloc (ntotal * sizeof(objective_t *)); for (k = 0; k < ntotal ; k++) datax[k] = datay[k] = data + nobj * k; #if DEBUG > 1 fprintf (stderr, "Original data:\n"); fprint_set (stderr, datax, ntotal); #endif qsort (datax, ntotal, sizeof(*datax), &compare_x_asc); qsort (datay, ntotal, sizeof(*datay), &compare_y_desc); #if DEBUG > 1 fprintf (stderr, "Sorted data (x):\n"); fprint_set (stderr, datax, ntotal); fprintf (stderr, "Sorted data (y):\n"); fprint_set (stderr, datay, ntotal); #endif /* Setup a lookup table to go from a point to the approximation set (run) to which it belongs. */ runtab = malloc (ntotal * sizeof(int)); for (k = 0, j = 0; k < ntotal; k++) { if (k == cumsize[j]) j++; runtab[k] = j; } #if DEBUG > 1 fprintf (stderr, "Runtab:\n"); for (k = 0; k < ntotal; k++) fprintf (stderr, "%6d: %6d\n", k, runtab[k]); #endif /* Setup tables to keep attainment statistics. In particular, save_attained is needed to cope with repeated values on the same axis. */ attained = malloc (nruns * sizeof(int)); save_attained = malloc (nruns * sizeof(int)); eaf = malloc(nlevels * sizeof(eaf_t*)); for (l = 0; l < nlevels; l++) { eaf[l] = eaf_create (nobj, nruns, ntotal); int level = attlevel[l]; int x = 0; int y = 0; int run; nattained = 0; for (k = 0; k < nruns; k++) attained[k] = 0; /* Start at upper-left corner */ run = runtab[(datax[x] - data) / nobj]; attained[run]++; nattained++; do { /* Move right until desired attainment level is reached */ while (x < ntotal - 1 && (nattained < level || datax[x][0] == datax[x+1][0])) { x++; if (datax[x][1] <= datay[y][1]) { run = runtab[(datax[x] - data)/nobj]; if (!attained[run]) nattained++; attained[run]++; } } #if DEBUG > 1 for (k = 0; k < nruns; k++) fprintf (stderr, "%d ", attained[k]); fprintf (stderr, "\n"); #endif if (nattained < level) continue; /* Now move down until desired attainment level is no longer reached. */ do { /* If there are repeated values along the y axis, we need to remember where we are. */ /*save_nattained = nattained;*/ memcpy (save_attained, attained, nruns * sizeof(*attained)); do { if (datay[y][0] <= datax[x][0]) { run = runtab[(datay[y] - data)/nobj]; attained[run]--; if (!attained[run]) nattained--; } #if DEBUG > 1 for (k = 0; k < nruns; k++) fprintf (stderr, "%d ", attained[k]); fprintf (stderr, "\n"); #endif y++; } while (y < ntotal && datay[y][1] == datay[y - 1][1]); } while (nattained >= level && y < ntotal); eaf_assert (nattained < level); eaf_store_point_2d (eaf[l], datax[x][0], datay[y - 1][1], save_attained); } while (x < ntotal - 1 && y < ntotal); } free(save_attained); free(attained); free(runtab); free(datay); free(datax); return eaf; } #define PRINT_POINT(X,Y) fprintf(stderr, "%g\t%g\n", X, Y) #undef PRINT_POINT #define PRINT_POINT(X,Y) (void)0 /* Produce a polygon suitable to be plotted by the polygon function in R. */ eaf_polygon_t * eaf_compute_area_new (eaf_t **eaf, int nlevels) { /* FIXME: Don't add anything if color_0 == 0 */ #define POLY_SIZE_CHECK() \ do { _poly_size_check--; eaf_assert(_poly_size_check >= 4); \ eaf_assert(_poly_size_check % 2 == 0); _poly_size_check = 0; } while(0) #define eaf_point(A,K) (eaf[(A)]->data + (K) * nobj) #define push_point(X, Y) \ do { vector_objective_push_back (&polygon->xy, (X)); \ vector_objective_push_back (&polygon->xy, (Y)); \ _poly_size_check++; PRINT_POINT(X,Y); \ } while(0) #define polygon_close() \ do { \ vector_int_push_back (&polygon->col, color_0); \ push_point(objective_MIN, objective_MIN); POLY_SIZE_CHECK(); } while(0) int _poly_size_check = 0; eaf_polygon_t * polygon; int *color; int nruns = eaf[0]->nruns; int nobj = eaf[0]->nobj; eaf_assert(nruns % 2 == 0); int max_size = 0; for (int a = 0; a < nlevels; a++) { if (max_size < eaf[a]->size) max_size = eaf[a]->size; } EAF_MALLOC (color, sizeof(int), max_size); EAF_MALLOC(polygon, sizeof(eaf_polygon_t), 1); vector_objective_ctor (&polygon->xy, max_size); vector_int_ctor (&polygon->col, max_size); for (int b = 1; b < nlevels; b++) { const int a = b - 1; int eaf_a_size = eaf[a]->size; int eaf_b_size = eaf[b]->size; int ka; /* Init colors. */ for (ka = 0; ka < eaf_a_size; ka++) { bool *attained = eaf[a]->attained + ka * nruns; int count_left, count_right; attained_left_right (attained, nruns/2, nruns, &count_left, &count_right); color[ka] = count_left - count_right; } /* Find color transitions along the EAF level set. */ objective_t topleft_y = objective_MAX; int last_b = -1; ka = 0; while (true) { const objective_t * pka = NULL; const objective_t * pkb = NULL; int kb = last_b + 1; while (ka < eaf_a_size && kb < eaf_b_size) { pka = eaf_point (a, ka); pkb = eaf_point (b, kb); if (pkb[0] != pka[0]) break; topleft_y = pkb[1]; last_b = kb; if (pkb[1] == pka[1]) { /* Ignore points that exactly overlap. */ ka++; kb++; } else { /* b intersects a above pka. */ eaf_assert(pkb[1] > pka[1]); kb++; break; } } /* Everything in A was overlapping. */ if (ka == eaf_a_size) break; objective_t prev_pka_y = topleft_y; int color_0 = color[ka]; /* Print points and corners until we reach a different color. */ do { pka = eaf_point (a, ka); /* Find the point in B not above the current point in A. */ while (kb < eaf_b_size) { pkb = eaf_point (b, kb); eaf_assert(pkb[0] > pka[0]); if (pkb[1] <= pka[1]) break; kb++; } eaf_assert(pka[1] < prev_pka_y); push_point (pka[0], prev_pka_y); push_point (pka[0], pka[1]); prev_pka_y = pka[1]; ka++; if (kb < eaf_b_size && ka < eaf_a_size) { const objective_t * pka_next = eaf_point (a, ka); eaf_assert (pkb[0] > pka[0]); eaf_assert (pkb[1] <= pka[1]); if (pkb[0] <= pka_next[0]) { /* If B intersects with A, stop here. */ eaf_assert(pkb[1] == pka[1] || pkb[0] == pka_next[0]); eaf_assert (prev_pka_y >= pkb[1]); break; } } } while (ka < eaf_a_size && color_0 != color[ka]); /* pka is the point before changing color, but ka is the position after. */ if (ka == eaf_a_size) {/* We reached the end of eaf_a */ /* We don't have to go down the other side since eaf_a completely dominates eaf_b, so just start by the end. */ if (last_b == eaf_b_size - 1) { /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, topleft_y); eaf_assert(topleft_y > pka[1]); } else { kb = eaf_b_size - 1; pkb = eaf_point(b, kb); eaf_assert (pkb[1] >= pka[1]); eaf_assert (pkb[0] > pka[0]); if (pkb[1] > pka[1]) { /* Create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, pkb[1]); eaf_assert (pkb[1] <= topleft_y); } objective_t prev_pkb_x = pkb[0]; push_point (pkb[0], pkb[1]); kb--; while (kb > last_b) { pkb = eaf_point(b, kb); eaf_assert (pkb[0] > pka[0]); push_point (prev_pkb_x, pkb[1]); push_point (pkb[0], pkb[1]); prev_pkb_x = pkb[0]; kb--; } push_point (pkb[0], topleft_y); eaf_assert (topleft_y > pkb[1]); } /* last_b = eaf_b_size - 1; */ polygon_close(); /* DONE */ break; /* Really done! */ } else { if (kb == eaf_b_size) { eaf_assert (pka[1] < topleft_y); /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, topleft_y); last_b = eaf_b_size - 1; } else { eaf_assert (kb < eaf_b_size); pkb = eaf_point (b, kb); eaf_assert (pkb[1] <= pka[1]); eaf_assert (pkb[0] != pka[0]); /* If pkb and pka are in the same horizontal, pkb does not affect the next polygon. Otherwise, it does. */ int save_last_b = (pkb[1] == pka[1]) ? kb : kb - 1;; /* Now print in reverse. */ objective_t prev_pkb_x = pkb[0]; push_point (pkb[0], pka[1]); kb--; while (kb > last_b) { pkb = eaf_point (b, kb); eaf_assert (pkb[0] != pka[0]); push_point (prev_pkb_x, pkb[1]); push_point (pkb[0], pkb[1]); prev_pkb_x = pkb[0]; kb--; } eaf_assert (topleft_y > pkb[1]); push_point (pkb[0], topleft_y); last_b = save_last_b; } polygon_close(); /* DONE */ eaf_assert(topleft_y >= pka[1]); topleft_y = pka[1]; } } } free (color); return polygon; } /* FIXME: This version does not care about intersections, which is much simpler, but it produces artifacts when plotted with the polygon function in R. */ eaf_polygon_t * eaf_compute_area_old (eaf_t **eaf, int nlevels) { int _poly_size_check = 0; eaf_polygon_t * polygon; int *color; int max_size = 0; int nruns = eaf[0]->nruns; int nobj = eaf[0]->nobj; for (int a = 0; a < nlevels; a++) { if (max_size < eaf[a]->size) max_size = eaf[a]->size; } eaf_assert(nruns % 2 == 0); EAF_MALLOC (color, sizeof(int), max_size); EAF_MALLOC(polygon, sizeof(eaf_polygon_t), 1); vector_objective_ctor (&polygon->xy, max_size); vector_int_ctor (&polygon->col, max_size); for (int b = 1; b < nlevels; b++) { const int a = b - 1; int eaf_a_size = eaf[a]->size; int eaf_b_size = eaf[a + 1]->size; int ka; /* Init colors. */ for (ka = 0; ka < eaf_a_size; ka++) { bool *attained = eaf[a]->attained + ka * nruns; int count_left, count_right; attained_left_right (attained, nruns/2, nruns, &count_left, &count_right); color[ka] = count_left - count_right; } /* Find color transitions along the EAF level set. */ int last_b = -1; objective_t topleft_y = objective_MAX; ka = 0; while (ka < eaf_a_size) { objective_t prev_pka_y = topleft_y; const objective_t * pka; /* Print points and corners until we reach a different color. */ int color_0 = color[ka]; do { pka = eaf_point (a, ka); push_point (pka[0], prev_pka_y); push_point (pka[0], pka[1]); prev_pka_y = pka[1]; ka++; } while (ka < eaf_a_size && color_0 == color[ka]); /* pka is the point before changing color, but ka is the position after. */ if (ka == eaf_a_size) {/* We reached the end of eaf_a */ /* We don't have to go down the other side since eaf_a completely dominates eaf_b, so just start by the end. */ int kb = eaf_b_size - 1; if (last_b == kb) { /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, topleft_y); } else { const objective_t * pkb = eaf_point(a + 1, kb); eaf_assert (pkb[1] >= pka[1]); if (pkb[1] > pka[1]) { /* Create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, pkb[1]); } objective_t prev_pkb_x = pkb[0]; push_point (pkb[0], pkb[1]); kb--; while (kb > last_b) { pkb = eaf_point(a + 1, kb); push_point (prev_pkb_x, pkb[1]); push_point (pkb[0], pkb[1]); prev_pkb_x = pkb[0]; kb--; } push_point (pkb[0], topleft_y); last_b = eaf_b_size - 1; } polygon_close(); /* DONE */ } else { int kb = last_b + 1; /* Different color, go down by the other side until reaching this point. */ if (kb == eaf_b_size) { /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, topleft_y); polygon_close(); /* DONE */ } else { const objective_t * pkb; do { pkb = eaf_point (b, kb); if (pkb[1] <= pka[1]) break; kb++; } while (kb < eaf_b_size); int save_last_b = kb - 1; if (kb == eaf_b_size) { /* There is nothing on the other side, just create two points in the infinity. */ push_point (objective_MAX, pka[1]); push_point (objective_MAX, pkb[1]); } else {/* pkb_y <= pka_y */ objective_t prev_pkb_x = pkb[0]; push_point (pkb[0], pka[1]); /* Now print in reverse. */ kb--; while (kb > last_b) { pkb = eaf_point (b, kb); push_point (prev_pkb_x, pkb[1]); push_point (pkb[0], pkb[1]); prev_pkb_x = pkb[0]; kb--; } push_point (pkb[0], topleft_y); } polygon_close(); /* DONE */ last_b = save_last_b; } } topleft_y = pka[1]; } } free (color); return polygon; } #undef eaf_point #undef push_point #undef polygon_close #undef PRINT_POINT #undef POLY_SIZE_CHECK void eaf_print_polygon (FILE *stream, eaf_t **eaf, int nlevels) { eaf_polygon_t *p = eaf_compute_area (eaf, nlevels); for(size_t i = 0; i < vector_objective_size(&p->xy); i += 2) { fprintf(stream, point_printf_format "\t" point_printf_format "\n", vector_objective_at(&p->xy, i), vector_objective_at(&p->xy, i + 1)); } fprintf (stream, "# col ="); for (size_t k = 0; k < vector_int_size (&p->col); k++) fprintf (stream, " %d", vector_int_at(&p->col, k)); fprintf (stream, "\n"); vector_objective_dtor (&p->xy); vector_int_dtor (&p->col); free(p); } eaf/src/avl.c0000644000176200001440000003317713254747514012551 0ustar liggesusers/***************************************************************************** avl.c - Source code for the AVL-tree library. Copyright (C) 1998 Michael H. Buselli Copyright (C) 2000-2002 Wessel Dankers This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Augmented AVL-tree. Original by Michael H. Buselli . Modified by Wessel Dankers to add a bunch of bloat to the sourcecode, change the interface and squash a few bugs. Mail him if you find new bugs. *****************************************************************************/ #include #include #include #include "avl.h" static void avl_rebalance(avl_tree_t *, avl_node_t *); #ifdef AVL_COUNT #define NODE_COUNT(n) ((n) ? (n)->count : 0) #define L_COUNT(n) (NODE_COUNT((n)->left)) #define R_COUNT(n) (NODE_COUNT((n)->right)) #define CALC_COUNT(n) (L_COUNT(n) + R_COUNT(n) + 1) #endif #ifdef AVL_DEPTH #define NODE_DEPTH(n) ((n) ? (n)->depth : 0) #define L_DEPTH(n) (NODE_DEPTH((n)->left)) #define R_DEPTH(n) (NODE_DEPTH((n)->right)) #define CALC_DEPTH(n) ((L_DEPTH(n)>R_DEPTH(n)?L_DEPTH(n):R_DEPTH(n)) + 1) #endif #ifndef AVL_DEPTH /* Also known as ffs() (from BSD) */ static int lg(unsigned int u) { int r = 1; if(!u) return 0; if(u & 0xffff0000) { u >>= 16; r += 16; } if(u & 0x0000ff00) { u >>= 8; r += 8; } if(u & 0x000000f0) { u >>= 4; r += 4; } if(u & 0x0000000c) { u >>= 2; r += 2; } if(u & 0x00000002) r++; return r; } #endif static int avl_check_balance(avl_node_t *avlnode) { #ifdef AVL_DEPTH int d; d = R_DEPTH(avlnode) - L_DEPTH(avlnode); return d<-1?-1:d>1?1:0; #else /* int d; * d = lg(R_COUNT(avlnode)) - lg(L_COUNT(avlnode)); * d = d<-1?-1:d>1?1:0; */ #ifdef AVL_COUNT int pl, r; pl = lg(L_COUNT(avlnode)); r = R_COUNT(avlnode); if(r>>pl+1) return 1; if(pl<2 || r>>pl-2) return 0; return -1; #else #error No balancing possible. #endif #endif } #ifdef AVL_COUNT unsigned int avl_count(const avl_tree_t *avltree) { return NODE_COUNT(avltree->top); } avl_node_t *avl_at(const avl_tree_t *avltree, unsigned int index) { avl_node_t *avlnode; unsigned int c; avlnode = avltree->top; while(avlnode) { c = L_COUNT(avlnode); if(index < c) { avlnode = avlnode->left; } else if(index > c) { avlnode = avlnode->right; index -= c+1; } else { return avlnode; } } return NULL; } unsigned int avl_index(const avl_node_t *avlnode) { avl_node_t *next; unsigned int c; c = L_COUNT(avlnode); while((next = avlnode->parent)) { if(avlnode == next->right) c += L_COUNT(next) + 1; avlnode = next; } return c; } #endif int avl_search_closest(const avl_tree_t *avltree, const void *item, avl_node_t **avlnode) { avl_node_t *node; avl_compare_t cmp; int c; if(!avlnode) avlnode = &node; node = avltree->top; if(!node) return *avlnode = NULL, 0; cmp = avltree->cmp; for(;;) { c = cmp(item, node->item); if(c < 0) { if(node->left) node = node->left; else return *avlnode = node, -1; } else if(c > 0) { if(node->right) node = node->right; else return *avlnode = node, 1; } else { return *avlnode = node, 0; } } } /* * avl_search: * Return a pointer to a node with the given item in the tree. * If no such item is in the tree, then NULL is returned. */ avl_node_t *avl_search(const avl_tree_t *avltree, const void *item) { avl_node_t *node; return avl_search_closest(avltree, item, &node) ? NULL : node; } avl_tree_t *avl_init_tree(avl_tree_t *rc, avl_compare_t cmp, avl_freeitem_t freeitem) { if(rc) { rc->head = NULL; rc->tail = NULL; rc->top = NULL; rc->cmp = cmp; rc->freeitem = freeitem; } return rc; } avl_tree_t *avl_alloc_tree(avl_compare_t cmp, avl_freeitem_t freeitem) { return avl_init_tree(malloc(sizeof(avl_tree_t)), cmp, freeitem); } void avl_clear_tree(avl_tree_t *avltree) { avltree->top = avltree->head = avltree->tail = NULL; } void avl_free_nodes(avl_tree_t *avltree) { avl_node_t *node, *next; avl_freeitem_t freeitem; freeitem = avltree->freeitem; for(node = avltree->head; node; node = next) { next = node->next; if(freeitem) freeitem(node->item); free(node); } avl_clear_tree(avltree); } /* * avl_free_tree: * Free all memory used by this tree. If freeitem is not NULL, then * it is assumed to be a destructor for the items referenced in the avl_ * tree, and they are deleted as well. */ void avl_free_tree(avl_tree_t *avltree) { avl_free_nodes(avltree); free(avltree); } static void avl_clear_node(avl_node_t *newnode) { newnode->left = newnode->right = NULL; #ifdef AVL_COUNT newnode->count = 1; #endif #ifdef AVL_DEPTH newnode->depth = 1; #endif } avl_node_t *avl_init_node(avl_node_t *newnode, void *item) { if(newnode) { avl_clear_node(newnode); newnode->item = item; } return newnode; } avl_node_t *avl_insert_top(avl_tree_t *avltree, avl_node_t *newnode) { avl_clear_node(newnode); newnode->prev = newnode->next = newnode->parent = NULL; avltree->head = avltree->tail = avltree->top = newnode; return newnode; } avl_node_t *avl_insert_before(avl_tree_t *avltree, avl_node_t *node, avl_node_t *newnode) { if(!node) return avltree->tail ? avl_insert_after(avltree, avltree->tail, newnode) : avl_insert_top(avltree, newnode); if(node->left) return avl_insert_after(avltree, node->prev, newnode); avl_clear_node(newnode); newnode->next = node; newnode->parent = node; newnode->prev = node->prev; if(node->prev) node->prev->next = newnode; else avltree->head = newnode; node->prev = newnode; node->left = newnode; avl_rebalance(avltree, node); return newnode; } avl_node_t *avl_insert_after(avl_tree_t *avltree, avl_node_t *node, avl_node_t *newnode) { if(!node) return avltree->head ? avl_insert_before(avltree, avltree->head, newnode) : avl_insert_top(avltree, newnode); if(node->right) return avl_insert_before(avltree, node->next, newnode); avl_clear_node(newnode); newnode->prev = node; newnode->parent = node; newnode->next = node->next; if(node->next) node->next->prev = newnode; else avltree->tail = newnode; node->next = newnode; node->right = newnode; avl_rebalance(avltree, node); return newnode; } avl_node_t *avl_insert_node(avl_tree_t *avltree, avl_node_t *newnode) { avl_node_t *node; if(!avltree->top) return avl_insert_top(avltree, newnode); switch(avl_search_closest(avltree, newnode->item, &node)) { case -1: return avl_insert_before(avltree, node, newnode); case 1: return avl_insert_after(avltree, node, newnode); } return NULL; } /* * avl_insert: * Create a new node and insert an item there. * Returns the new node on success or NULL if no memory could be allocated. */ avl_node_t *avl_insert(avl_tree_t *avltree, void *item) { avl_node_t *newnode; newnode = avl_init_node(malloc(sizeof(avl_node_t)), item); if(newnode) { if(avl_insert_node(avltree, newnode)) return newnode; free(newnode); errno = EEXIST; } return NULL; } /* * avl_unlink_node: * Removes the given node. Does not delete the item at that node. * The item of the node may be freed before calling avl_unlink_node. * (In other words, it is not referenced by this function.) */ void avl_unlink_node(avl_tree_t *avltree, avl_node_t *avlnode) { avl_node_t *parent; avl_node_t **superparent; avl_node_t *subst, *left, *right; avl_node_t *balnode; if(avlnode->prev) avlnode->prev->next = avlnode->next; else avltree->head = avlnode->next; if(avlnode->next) avlnode->next->prev = avlnode->prev; else avltree->tail = avlnode->prev; parent = avlnode->parent; superparent = parent ? avlnode == parent->left ? &parent->left : &parent->right : &avltree->top; left = avlnode->left; right = avlnode->right; if(!left) { *superparent = right; if(right) right->parent = parent; balnode = parent; } else if(!right) { *superparent = left; left->parent = parent; balnode = parent; } else { subst = avlnode->prev; if(subst == left) { balnode = subst; } else { balnode = subst->parent; balnode->right = subst->left; if(balnode->right) balnode->right->parent = balnode; subst->left = left; left->parent = subst; } subst->right = right; subst->parent = parent; right->parent = subst; *superparent = subst; } avl_rebalance(avltree, balnode); } void *avl_delete_node(avl_tree_t *avltree, avl_node_t *avlnode) { void *item = NULL; if(avlnode) { item = avlnode->item; avl_unlink_node(avltree, avlnode); if(avltree->freeitem) avltree->freeitem(item); free(avlnode); } return item; } void *avl_delete(avl_tree_t *avltree, const void *item) { return avl_delete_node(avltree, avl_search(avltree, item)); } avl_node_t *avl_fixup_node(avl_tree_t *avltree, avl_node_t *newnode) { avl_node_t *oldnode = NULL, *node; if(!avltree || !newnode) return NULL; node = newnode->prev; if(node) { oldnode = node->next; node->next = newnode; } else { avltree->head = newnode; } node = newnode->next; if(node) { oldnode = node->prev; node->prev = newnode; } else { avltree->tail = newnode; } node = newnode->parent; if(node) { if(node->left == oldnode) node->left = newnode; else node->right = newnode; } else { oldnode = avltree->top; avltree->top = newnode; } return oldnode; } /* * avl_rebalance: * Rebalances the tree if one side becomes too heavy. This function * assumes that both subtrees are AVL-trees with consistant data. The * function has the additional side effect of recalculating the count of * the tree at this node. It should be noted that at the return of this * function, if a rebalance takes place, the top of this subtree is no * longer going to be the same node. */ void avl_rebalance(avl_tree_t *avltree, avl_node_t *avlnode) { avl_node_t *child; avl_node_t *gchild; avl_node_t *parent; avl_node_t **superparent; parent = avlnode; while(avlnode) { parent = avlnode->parent; superparent = parent ? avlnode == parent->left ? &parent->left : &parent->right : &avltree->top; switch(avl_check_balance(avlnode)) { case -1: child = avlnode->left; #ifdef AVL_DEPTH if(L_DEPTH(child) >= R_DEPTH(child)) { #else #ifdef AVL_COUNT if(L_COUNT(child) >= R_COUNT(child)) { #else #error No balancing possible. #endif #endif avlnode->left = child->right; if(avlnode->left) avlnode->left->parent = avlnode; child->right = avlnode; avlnode->parent = child; *superparent = child; child->parent = parent; #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); child->count = CALC_COUNT(child); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); child->depth = CALC_DEPTH(child); #endif } else { gchild = child->right; avlnode->left = gchild->right; if(avlnode->left) avlnode->left->parent = avlnode; child->right = gchild->left; if(child->right) child->right->parent = child; gchild->right = avlnode; if(gchild->right) gchild->right->parent = gchild; gchild->left = child; if(gchild->left) gchild->left->parent = gchild; *superparent = gchild; gchild->parent = parent; #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); child->count = CALC_COUNT(child); gchild->count = CALC_COUNT(gchild); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); child->depth = CALC_DEPTH(child); gchild->depth = CALC_DEPTH(gchild); #endif } break; case 1: child = avlnode->right; #ifdef AVL_DEPTH if(R_DEPTH(child) >= L_DEPTH(child)) { #else #ifdef AVL_COUNT if(R_COUNT(child) >= L_COUNT(child)) { #else #error No balancing possible. #endif #endif avlnode->right = child->left; if(avlnode->right) avlnode->right->parent = avlnode; child->left = avlnode; avlnode->parent = child; *superparent = child; child->parent = parent; #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); child->count = CALC_COUNT(child); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); child->depth = CALC_DEPTH(child); #endif } else { gchild = child->left; avlnode->right = gchild->left; if(avlnode->right) avlnode->right->parent = avlnode; child->left = gchild->right; if(child->left) child->left->parent = child; gchild->left = avlnode; if(gchild->left) gchild->left->parent = gchild; gchild->right = child; if(gchild->right) gchild->right->parent = gchild; *superparent = gchild; gchild->parent = parent; #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); child->count = CALC_COUNT(child); gchild->count = CALC_COUNT(gchild); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); child->depth = CALC_DEPTH(child); gchild->depth = CALC_DEPTH(gchild); #endif } break; default: #ifdef AVL_COUNT avlnode->count = CALC_COUNT(avlnode); #endif #ifdef AVL_DEPTH avlnode->depth = CALC_DEPTH(avlnode); #endif } avlnode = parent; } } eaf/NAMESPACE0000644000176200001440000000046413254745030012233 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(eafplot,data.frame) S3method(eafplot,default) S3method(eafplot,formula) S3method(eafplot,list) export(eafdiffplot) export(eafplot) export(eafs) export(read.data.sets) import(grDevices) import(graphics) import(stats) useDynLib(eaf, .registration = TRUE) eaf/NEWS0000644000176200001440000000623613254747421011524 0ustar liggesusers1.8 * Development version moved to GitHub: https://github.com/MLopez-Ibanez/eaf * Remove leading zeros from version number. * New parameters left.panel.last and right.panel.last of eafdiffplot. * Export and document function 'eafs' to compute EAFs. * eafdiff.pl: Handle --colors=, --intervals=. * Fix crash in eafplot if 'sets' is a vector of strings. * Reset layout in eafdiffplot. * Compute eaf for 3D. * Added testthat testing framework. * The documentation is now generated with Roxygen2. * Entry points to C code are now properly registered. 1.07 * Silence CRAN warning for GNU extensions in Makefiles in inst/scripts/eaf/Makefile. 1.06 * Fix bug when automatically generating a legend in eafplot with the formula interface (Thanks to Bernd Bischl for reporting this) * Improve handling of various newline character formats. * Reduce memory consumption (up to four times less memory). * eafdiff.pl: Mention option --legendpos=none to hide the legend. * eafplot.pl: Add options --maximise and --xmaximise and --colors=. Fix bug with --area. * eafplot.default now requires two colors when type=="area" and a palette is interpolated between these two colors for all the different levels plotted (Thanks to Alexandre Quemy for the suggestion). 1.05 * Implement eafdiffplot(, type = "area"). This is now the default. The idea for the algorithm to compute the areas was provided by Carlos M. Fonseca. The implementation uses R polygons, which some PDF viewers may have trouble rendering correctly (See https://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-are-there-unwanted-borders). Plots will look correct when printed. To get the previous behaviour use eafdiffplot(, type = "point"). * Attempt to deal with DOS/Unix newlines correctly. * eafplot.pl: Lots of cleanup. (--single, --output, --legend): New options. Default to PDF. * eafdiff.pl: Many cleanups. EAF diff with type="area" is the default now. Parameter --cex replaced by --scale. Do not require ps2eps. Handle --obj1= better. Crop pdf before converting to png. (--noattsurfs): New option. (--output-dir,--output): New options. (--eps): New option. PDF is the default now. * Force eafdiff plots to use a square plotting region. * read.data.sets() normalizes paths, thus it works with files such as ~/file. * Function eafdiffplot() handles percentiles=NA and grand.lines parameter. * Fix bug with ranges and maximise=TRUE in eafdiffplot(). * Fix points.steps to work correctly for all values of maximise. * Fix bug in eafdiffplot(..., maximise=c(TRUE,FALSE), full.eaf = TRUE) * Fix "log" parameter in eafplot and eafdiffplot. * Avoid that eafplot.formula modifies global options. * Add 'axes' parameter to eafplot. * To avoid confusion, the arguments 'xaxis.side' and 'yaxis.side' of eafplot can only take values c("below", "above") and c("left", "right"), respectively. * Add missing Makefile in inst/scripts/eaf/. 1.04 * Fix issues with only one point in the EAF. 1.03 * Handle maximise argument in eafdiffplot and --maximise command-line option in eafdiff.pl * R/calls.R (eafplot.data.frame): Fix problem with main parameter. 1.00 * Initial release available in CRAN. eaf/data/0000755000176200001440000000000013254545527011732 5ustar liggesuserseaf/data/SPEA2relativeVanzyl.rda0000644000176200001440000000165613254545527016204 0ustar liggesusersV{HTYSLCfR5a/Z"FS9쒻D=v7{.FDHX=(7"ika{}s?.L8;~wagܚ94HA?}h\pZneyֈR͡%$ӕMxwYC~;/.C< ^[ ֕jIj_NK,먭>; !Z("?8ܩsrYҊNLmpyG!\ٍYs}g UXwk&}G˧ tXe'F`h]) }uF[ΐMys[#n `k+/Ρu2c3ޔ:Sp*O8?6֮{F4j(`R~Şf62zNR ?>)WWh=w^:fI]^ Ò!BA )#2F<<]Xw'TuFꛎigC|miK3 zX')y^zg(<(d#i|QآgesW:tL9c]2pAdveEg6R9kWsi K *KD°{$&YI.g9`RӳH}|<y_+=UwW}\pxþ ]J/c8V\.zL?~یVvlf|_g31$S,0&lj#La#mbc^8k6qB h8) i4rRo)8%T3oʍzvJ?ݵ$+hK*?gSI^mD eaf/data/gcp2x2.rda0000644000176200001440000006636313254545527013545 0ustar liggesusersXTk}0tybwwQDPBJJBD|flppK5s8vKY "2ɀ?MMtVjl)OP+ U31bĈ#F1bĈ#F1bĈ#F L?Ϳm_F1bĈ#F1bĈ#F~ο=C2bĈ?/#gHVtwF1bĈ#F1bĈ#F1bĈ#Fb ی1bĈ#F1bĈ#F1b[H*aeEV k-QXٴTW=fFS#FaTF@mȿ#G1bnĈ=, g{q{nĸ7̿=K6b_ߞ'g~FsP#7 >_go>_򟠒*H2݆ ٕ!;#TE3vGwW~u-emSO_e/鯉G-ꯎO}'_ [O_i{;_a+EOc̟;Ej{m|7kW{8JJO6m{ʿ{sZ_ʭ2{Y.J}]5ٻRGT=ӍtA_7ZyѪkՎ>cuKMoNUPۊV/kᓔu}y7gvVߊfi:,}Ƿ7xwҷ5?`amC kcsz<ȻzzjX>~]ٟԓaXJ_x'7{Xϔ2wuΞekƞ\Ʒfvưxܦk~QX- 6)QopjSԦ*>8zN|ĥl|aHO:oGmrWö'ZƋ{N XG2k>JG[wڗ8vY6VԼn橺1a7ǤeZv`;|C7N\¿ ?>VZ־7+$l)Y|N۹3\TQU^7 mb >u]Փݭv+L~9Z; %خ2IxDVtWs9xkDhm髎`  6uR}Wλ$-lGƷa-ɩa&\e誨dnjk]X1{={/O{Nc+z^@EݨѮnE%o;@O=J= F_?yvN~́o=qR,R\_iPŷ/ҽliX7,ʰ9/`3RFh% >˾t[}?;G vɖL'62oz)>-KOOoX'j*۾TG|8Z.LN[L`'}ʌ{krg o+9AufX2`Nɟ?@ѷ³%I\RY41ĺH٭pEU&2vRu2"  X! 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See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, you can obtain a copy of the GNU # General Public License at http://www.gnu.org/copyleft/gpl.html # # IMPORTANT NOTE: Please be aware that the fact that this program is # released as Free Software does not excuse you from scientific # propriety, which obligates you to give appropriate credit! If you # write a scientific paper describing research that made substantive use # of this program, it is your obligation as a scientist to (a) mention # the fashion in which this software was used in the Methods section; # (b) mention the algorithm in the References section. The appropriate # citation is: # # Manuel Lopez-Ibanez, Luis Paquete, and Thomas Stuetzle. # Exploratory Analysis of Stochastic Local Search Algorithms in # Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, # L. Paquete, and M. Preuss, editors, Experimental Methods for the # Analysis of Optimization Algorithms, pages 209-222. Springer, # Berlin, Germany, 2010. doi: 10.1007/978-3-642-02538-9_9 # # Moreover, as a personal note, I would appreciate it if you would email # manuel.lopez-ibanez@ulb.ac.be with citations of papers referencing this # work so I can mention them to my funding agent and tenure committee. # ################################################################################ # # TODO: # # * Follow this style for coding: # http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html # ################################################################################ #' Plot the Empirical Attainment Function for two objectives generic #' #' Computes and plots the Empirical Attainment Function, either as #' attainment surfaces for certain percentiles or as points. #' #' This function can be used to plot random sets of points like those obtained #' by different runs of biobjective stochastic optimization algorithms. An EAF #' curve represents the boundary separating points that are known to be #' attainable (that is, dominated in Pareto sense) in at least a fraction #' (quantile) of the runs from those that are not. The median EAF represents #' the curve where the fraction of attainable points is 50\%. In single #' objective optimization the function can be used to plot the profile of #' solution quality over time of a collection of runs of a stochastic optimizer. #' #' @param x Either a matrix of data values, or a data frame, or a list of #' data frames of exactly three columns. #' #' @keywords graphs #' @export eafplot <- function(x, ...) UseMethod("eafplot") #' @describeIn eafplot Formula interface #' #'@param formula A formula of the type: \code{time + cost ~ run | instance} #' will draw \code{time} on the x-axis and \code{cost} on the y-axis. If \code{instance} is #' present the plot is conditional to the instances. #' #'@param data Dataframe containing the fields mentioned in the formula and in groups. #'@export eafplot.formula <- function(formula, data, groups = NULL, subset = NULL, ...) { ## formula of type time+cost~run|inst, groups=alg if (missing(formula)) stop("formula missing") if ((length(formula) != 3L) || (length(formula[[2L]]) != 3L)) stop("incorrect specification for 'formula'") mf <- modeltools::ModelEnvFormula(formula = formula, data = data, subset = subset, designMatrix = FALSE, responseMatrix = FALSE, ...) ### extract data from the ModelEnv object points <- mf@get("response") sets <- mf@get("input") cond <- NULL if (length(mf@formula) == 3L) cond <- as.list(mf@get("blocks")) groups <- eval(substitute(groups), data, environment(formula)) if (!is.null(groups) && !is.factor(groups)) stop("groups must be a factor") if (length(cond) == 0) { strip <- FALSE cond <- list(gl(1, length(points))) } condlevels <- lapply(cond, levels) cond.max.level <- unlist(lapply(cond, nlevels)) npackets <- prod(cond.max.level) panel.args <- vector(mode = "list", length = npackets) packet.sizes <- numeric(npackets) if (npackets > 1) { dim(packet.sizes) <- sapply(condlevels, length) dimnames(packet.sizes) <- lapply(condlevels, as.character) } cond.current.level <- rep(1, length(cond)) for (packet.number in seq_len(npackets)) { id <- .compute.packet(cond, cond.current.level) packet.sizes[packet.number] <- sum(id) panel.args[[packet.number]] <- list(points = as.matrix(points[id,]), sets = as.numeric(sets[id,]), groups=groups[id]) ## MARCO: I do not think we need to care about subscripts... or do we? #if (subscripts) # panel.args[[packet.number]]$subscripts <- # subscr[id] cond.current.level <- .cupdate(cond.current.level, cond.max.level) } # FIXME: I don't think this is doing the right thing. op <- par(mfrow = .check.layout(NULL,cond.max.level)[2:3]) on.exit(par(op)) for (i in seq_len(length(panel.args))) { eafplot.default(panel.args[[i]]$points, panel.args[[i]]$sets, panel.args[[i]]$groups, ...) } invisible() } #' @describeIn eafplot List interface for lists of data.frames #' #'@export eafplot.list <- function(x,...) { if (!is.list(x)) stop("'x' must be a list of data.frames with exactly three columns") DT <- data.frame() if (!is.null(names(x))) groups <- names(x) else groups <- 1:length(x) for (i in seq_len(length(x)) ) { if (!is.data.frame(x[[i]])) stop("Each element of the list must be a data.frame with exactly three columns.") DT <- rbind(DT, data.frame(x[[i]], groups = groups[i])) } eafplot(as.matrix(DT[,c(1,2)]), as.numeric(as.factor(DT[,3])), as.factor(DT[,4]),...) } #' @describeIn eafplot Data.frame interface #' #' @param y Either a matrix of data values, or a data frame. #' @export eafplot.data.frame <- function(x, y = NULL, ...) { namex <- deparse(substitute(x)) namey <- deparse(substitute(y)) # FIXME: Why keep x and y as data.frame to convert it here to matrix? eafplot.data.frame2 <- function(x, groups, main = DNAME, ...) eafplot(as.matrix(x[,c(1,2)]), as.numeric(x[,3]), groups = groups, main = main, ...) check.eaf.data.frame <- function(x) { xname <- deparse(substitute(x)) if (!is.data.frame(x) || ncol(x) != 3L) stop("'", xname, "' must be a data.frame with exactly three columns.\n", " If you have grouping and conditioning variables, please consider using this format: 'eafplot(formula, data, ...)'") if (nrow(x) < 1L) stop("not enough (finite) '", xname, "' observations") if (!is.numeric(x[,1]) || !is.numeric(x[,2])) stop("columns 1 and 2 of '", xname, "' must be numeric") if (!is.numeric(x[,3]) && !is.factor(x[,3])) x[,3] <- as.factor(x[,3]) return(x) } x <- check.eaf.data.frame(x) if (!is.null(y)) { y <- check.eaf.data.frame(y) DNAME <- paste0(namex, " and ", namey) DT <- rbind(data.frame(x, groups = namex), data.frame(y, groups = namey)) groups <- as.factor(DT[, 4]) } else { DNAME <- namex DT <- x groups <- NULL } eafplot.data.frame2(DT, groups = groups, ...) } ### Local Variables: ### ess-style: DEFAULT ### indent-tabs-mode: nil ### ess-fancy-comments: nil ### End: eaf/R/eaf.R0000644000176200001440000013212513254744575012110 0ustar liggesusers############################################################################### # # Copyright (c) 2011-2013 # Manuel Lopez-Ibanez # Marco Chiarandini # # This program is free software (software libre); you can redistribute # it and/or modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 of the # License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, you can obtain a copy of the GNU # General Public License at http://www.gnu.org/copyleft/gpl.html # # IMPORTANT NOTE: Please be aware that the fact that this program is # released as Free Software does not excuse you from scientific # propriety, which obligates you to give appropriate credit! If you # write a scientific paper describing research that made substantive use # of this program, it is your obligation as a scientist to (a) mention # the fashion in which this software was used in the Methods section; # (b) mention the algorithm in the References section. The appropriate # citation is: # # Manuel Lopez-Ibanez, Luis Paquete, and Thomas Stuetzle. # Exploratory Analysis of Stochastic Local Search Algorithms in # Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, # L. Paquete, and M. Preuss, editors, Experimental Methods for the # Analysis of Optimization Algorithms, pages 209-222. Springer, # Berlin, Germany, 2010. doi: 10.1007/978-3-642-02538-9_9 # # Moreover, as a personal note, I would appreciate it if you would email # manuel.lopez-ibanez@ulb.ac.be with citations of papers referencing this # work so I can mention them to my funding agent and tenure committee. # ################################################################################ # # TODO: # # * Follow this style for coding: # http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html # ################################################################################ #dyn.load("../src/eaf.so") check.eaf.data <- function(x) { name <- deparse(substitute(x)) if (length(dim(x)) != 2L) stop("'", name, "' must be a data.frame or a matrix") if (nrow(x) < 1L) stop("not enough points (rows) in '", name, "'") if (ncol(x) < 3) stop("'", name, "' must have at least 3 columns: 2D points and set index") # Re-encode the sets so that they are consecutive and numeric x[,3] <- as.numeric(as.factor(x[,3])) x <- as.matrix(x) if (!is.numeric(x)) stop("The two first columns of '", name, "' must be numeric") return(x) } compute.eaf <- function(data, percentiles = NULL) { data <- check.eaf.data(data) nobjs <- ncol(data) - 1L # The C code expects points within a set to be contiguous. data <- data[order(data[, nobjs + 1L]), ] sets <- data[, nobjs + 1L] nsets <- length(unique(sets)) npoints <- tabulate(sets) if (is.null(percentiles)) { percentiles <- 1:nsets * 100 / nsets } return(.Call("compute_eaf_C", as.double(t(as.matrix(data[, 1L:nobjs]))), nobjs, as.integer(cumsum(npoints)), as.integer(nsets), as.integer(percentiles) )) } compute.eaf.as.list <- function(data, percentiles = NULL) { eaf <- compute.eaf (data, percentiles = percentiles) nobjs <- 2 # FIXME: Is this ncol (eaf) - 1L ? return (lapply(split.data.frame(eaf, as.factor(eaf[, nobjs + 1L])), function(x) { x[, -(nobjs + 1L), drop = FALSE] })) } compute.eafdiff.helper <- function(data, intervals) { # Last column is the set number. nobjs <- ncol(data) - 1L # the C code expects points within a set to be contiguous. data <- data[order(data[, nobjs + 1L]), ] sets <- data[ , nobjs + 1L] nsets <- length(unique(sets)) npoints <- tabulate (sets) # FIXME: Ideally this would be computed by the C code, but it is hard-coded. ## division <- nsets %/% 2 ## nsets1 <- division ## nsets2 <- nsets - division return(.Call("compute_eafdiff_C", as.double(t(as.matrix(data[, 1L:nobjs]))), nobjs, as.integer(cumsum(npoints)), as.integer(nsets), as.integer(intervals))) } compute.eafdiff.area <- function(data, intervals) { # Last column is the set number. nobjs <- ncol(data) - 1L # the C code expects points within a set to be contiguous. data <- data[order(data[, nobjs + 1L]), ] sets <- data[ , nobjs + 1L] nsets <- length(unique(sets)) npoints <- tabulate (sets) # FIXME: Ideally this would be computed by the C code, but it is hard-coded. ## division <- nsets %/% 2 ## nsets1 <- division ## nsets2 <- nsets - division return(.Call("compute_eafdiff_area_C", as.double(t(as.matrix(data[, 1L:nobjs]))), nobjs, as.integer(cumsum(npoints)), as.integer(nsets), as.integer(intervals))) } # FIXME: The default intervals should be nsets / 2 compute.eafdiff <- function(data, intervals = 1) { DIFF <- compute.eafdiff.helper(data, intervals) #print(DIFF) # FIXME: Do this computation in C code. eafval <- DIFF[, ncol(data)] eafdiff <- list(left = NULL, right = NULL) eafdiff$left <- unique(DIFF[ eafval >= 1L, , drop = FALSE]) eafdiff$right <- unique(DIFF[ eafval <= -1L, , drop = FALSE]) eafdiff$right[, ncol(data)] <- -eafdiff$right[, ncol(data)] return(eafdiff) } # FIXME: The default intervals should be nsets / 2 compute.eafdiff.polygon <- function(data, intervals = 1L) { return(compute.eafdiff.area (data, intervals)) } rm.inf <- function(x, xmax) { x[is.infinite(x)] <- xmax return(x) } # FIXME: Accept ... max.finite <- function (x) { x <- as.vector(x) x <- x[is.finite(x)] if (length(x)) return(max(x)) return(NULL) } # FIXME: Accept ... min.finite <- function (x) { x <- as.vector(x) x <- x[is.finite(x)] if (length(x)) return(min(x)) return(NULL) } # FIXME: Accept ... range.finite <- function(x) { return(c(min.finite(x), max.finite(x))) } matrix.maximise <- function(z, maximise) { # R bug?: If z is data.frame with rownames != NULL, and # maximise == (FALSE, FALSE), then -z[, which(FALSE)] # gives error: Error in # data.frame(value, row.names = rn, check.names = FALSE, check.rows = FALSE) : # row names supplied are of the wrong length rownames(z) <- NULL z[, which(maximise)] <- -z[, which(maximise)] return(z) } #' Read several data.frame sets #' #' Reads a text file in table format and creates a data frame from it. The file #' may contain several sets, separated by empty lines. The function adds an #' additional column \code{set} to indicate to which set each row belongs. #' #' @param file Filename that contains the data. Each row of the table appears #' as one line of the file. If it does not contain an \emph{absolute} path, #' the file name is \emph{relative} to the current working directory, #' \code{getwd()}. Tilde-expansion is performed where supported. #' #' @param col.names Vector of optional names for the variables. The #' default is to use \samp{"V"} followed by the column number. #' #' @return A data frame (\code{data.frame}) containing a representation of the #' data in the file. An extra column \code{set} is added to indicate to #' which set each row belongs. #' #' @author Manuel \enc{López-Ibáñez}{Lopez-Ibanez} #' #'@note There are several examples of data sets in \code{file.path(system.file(package="eaf"),"extdata")}. #' #'@section Warning: #' A known limitation is that the input file must use newline characters #' native to the host system, otherwise they will be, possibly silently, #' misinterpreted. In GNU/Linux the program \code{dos2unix} may be used #' to fix newline characters. #' #'@seealso \code{\link{read.table}}, \code{\link{eafplot}}, \code{\link{eafdiffplot}} #' #'@examples #'A1<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_1_dat")) #'str(A1) #'A2<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_2_dat")) #'str(A2) #' #' @keywords file #' @export read.data.sets <- function(file, col.names) { if (!file.exists(file)) stop("error: ", file, ": No such file or directory"); file <- normalizePath(file) out <- .Call("read_data_sets", as.character(file)) if (missing(col.names)) col.names <- paste0("V", 1L:(ncol(out)-1)) colnames(out) <- c(col.names, "set") return(as.data.frame(out)) } ## Calculate the intermediate points in order to plot a staircase-like ## polygon. ## Example: given ((1,2), (2,1)), it returns ((1,2), (2,2), (2,1)). ## Input should be already in the correct order. points.steps <- function(x) { n <- nrow(x) if (n == 1) return(x) x <- rbind(x, cbind(x[-1, 1], x[-n, 2])) return(x[c(as.vector(outer(c(0, n), 1:(n - 1), "+")), n),]) } sciNotation <- function(x, digits = 1) { if (length(x) > 1) { return(append(sciNotation(x[1]), sciNotation(x[-1]))) } if (!x) return(0) exponent <- floor(log10(x)) base <- round(x / 10^exponent, digits) as.expression(substitute(base %*% 10^exponent, list(base = base, exponent = exponent))) } #' Exact computation of the EAF #' #' This function computes the EAF given a set of points and a vector \code{set} #' that indicates to which set each point belongs. #' #' @param points Either a matrix or a data frame of numerical values, where #' each row gives the coordinates of a point. #' #' @param sets A vector indicating which set each point belongs to. #' #' @param groups Indicates that the EAF must be computed separately for data #' belonging to different groups. #' #' @param percentiles The percentiles of the EAF of each side that will be #' plotted as attainment surfaces. \code{NA} does not plot any. See #' \code{\link{eafplot.default}}. #' #' @return A data frame (\code{data.frame}) containing the exact representation #' of EAF. The last column gives the percentile that corresponds to each #' point. If groups is not \code{NULL}, then an additional column #' indicates to which group the point belongs. #' #' @author Manuel \enc{López-Ibáñez}{Lopez-Ibanez} #' #'@note There are several examples of data sets in \code{file.path(system.file(package="eaf"),"extdata")}. #' #'@seealso \code{\link{read.data.sets}} #' #'@examples #' #' eaf.path <- system.file(package="eaf") #' #' x <- read.data.sets(file.path(eaf.path, "extdata","example1_dat")) #' # Compute full EAF #' eafs(x[,1:2], x[,3]) #' #' # Compute only best, median and worst #' eafs(x[,1:2], x[,3], percentiles = c(0, 50, 100)) #' #' x <- read.data.sets(file.path(eaf.path,"extdata","spherical-250-10-3d.txt")) #' y <- read.data.sets(file.path(eaf.path,"extdata","uniform-250-10-3d.txt")) #' x <- data.frame(x, group = "spherical") #' x <- rbind(x, data.frame(y, group = "uniform")) #' #' # Compute only median separately for each group #' eafs(x[,1:3], sets = x[,4], groups = x[,5], percentiles = 50) #' #'@export eafs <- function (points, sets, groups = NULL, percentiles = NULL) { points <- cbind(points, sets) if (is.null(groups)) { attsurfs <- compute.eaf (points, percentiles) } else { attsurfs <- data.frame() groups <- factor(groups) for (g in levels(groups)) { tmp <- compute.eaf(points[groups == g,], percentiles) attsurfs <- rbind(attsurfs, data.frame(tmp, groups = g)) } } return (attsurfs) } get.extremes <- function(xlim, ylim, maximise, log) { if (length(log) && log != "") log <- strsplit(log, NULL)[[1L]] if ("x" %in% log) xlim <- log(xlim) if ("y" %in% log) ylim <- log(ylim) extreme1 <- ifelse(maximise[1], xlim[1] - 0.05 * diff(xlim), xlim[2] + 0.05 * diff(xlim)) extreme2 <- ifelse(maximise[2], ylim[1] - 0.05 * diff(ylim), ylim[2] + 0.05 * diff(ylim)) if ("x" %in% log) extreme1 <- exp(extreme1) if ("y" %in% log) extreme2 <- exp(extreme2) return(c(extreme1, extreme2)) } #' @describeIn eafplot Main function #' #' @param groups This may be used to plot profiles of different algorithms on the same plot. #' #' @param subset A vector indicating which rows of the data should be used. If left to default \code{NULL} all data in the data frame are used. #' #' @param sets Vector indicating which set each point belongs to. #' #' @param percentiles Vector indicating which percentile should be plot. The #' default is to plot only the median attainment curve. #' #' @param attsurfs TODO #' #' @param type string giving the type of plot desired. The following values #' are possible, \samp{points} and \samp{area}. #' #' @param xlab,ylab,xlim,ylim,log,col,lty,lwd,pch,cex.pch,las Graphical #' parameters, see \code{\link{plot.default}}. #' #'@param legend.pos the position of the legend, see \code{\link{legend}}. #' #'@param legend.txt a character or expression vector to appear in the #' legend. If \code{NULL}, appropriate labels will be generated. #' #' @param extra.points A list of matrices or data.frames with #' two-columns. Each element of the list defines a set of points, or #' lines if one of the columns is \code{NA}. #' #' @param extra.pch,extra.lwd,extra.lty,extra.col Control the graphical aspect #' of the points. See \code{\link{points}} and \code{\link{lines}}. #' #' @param extra.legend A character vector providing labels for the #' groups of points. #' #' @param maximise Whether the first and/or second objective correspond to a #' maximisation problem. #' #' @param xaxis.side On which side that xaxis is drawn. Valid values are #' "below" and "above". See \code{\link{axis}}. #' #' @param yaxis.side On which side that yaxis is drawn. Valid values are "left" #' and "right". See \code{\link{axis}}. #' #' @param axes A logical value indicating whether both axes should be drawn #' on the plot. #' #' @param ... Other graphical parameters to \code{\link{plot.default}}. #' #' @return No value is returned. #' #' @seealso \code{\link{read.data.sets}} \code{\link{eafdiffplot}} #' #'@examples #'data(gcp2x2) #' tabucol <- subset(gcp2x2, alg != "TSinN1") #' tabucol$alg <- tabucol$alg[drop=TRUE] #' eafplot(time+best~run,data=tabucol,subset=tabucol$inst=="DSJC500.5") #' #' \dontrun{ # These take time #' eafplot(time+best~run|inst,groups=alg,data=gcp2x2) #' eafplot(time+best~run|inst,groups=alg,data=gcp2x2, #' percentiles=c(0,50,100),include.extremes=TRUE, #' cex=1.4, lty=c(2,1,2),lwd=c(2,2,2), #' col=c("black","blue","grey50")) #' #' A1 <- read.data.sets(file.path(system.file(package = "eaf"), "extdata", "ALG_1_dat")) #' A2 <- read.data.sets(file.path(system.file(package = "eaf"), "extdata", "ALG_2_dat")) #' eafplot(A1, A2, percentiles = c(50)) #' eafplot(list(A1 = A1, A2 = A2), percentiles = c(50)) #' #' ## Save as a PDF file. #' # dev.copy2pdf(file = "eaf.pdf", onefile = TRUE, width = 5, height = 4) #' } #' #' ## Using extra.points #' \dontrun{ #' data(HybridGA) #' data(SPEA2relativeVanzyl) #' eafplot(SPEA2relativeVanzyl, percentiles = c(25, 50, 75), #' xlab = expression(C[E]), ylab = "Total switches", xlim = c(320, 400), #' extra.points = HybridGA$vanzyl, extra.legend = "Hybrid GA") #' #' data(SPEA2relativeRichmond) #' eafplot (SPEA2relativeRichmond, percentiles = c(25, 50, 75), #' xlab = expression(C[E]), ylab = "Total switches", #' xlim = c(90, 140), ylim = c(0, 25), #' extra.points = HybridGA$richmond, extra.lty = "dashed", #' extra.legend = "Hybrid GA") #' #' data(SPEA2minstoptimeRichmond) #' SPEA2minstoptimeRichmond[,2] <- SPEA2minstoptimeRichmond[,2] / 60 #' eafplot (SPEA2minstoptimeRichmond, xlab = expression(C[E]), #' ylab = "Minimum idle time (minutes)", #' las = 1, log = "y", maximise = c(FALSE, TRUE), main = "SPEA2 (Richmond)") #' } #' @export eafplot.default <- function (x, sets = NULL, groups = NULL, percentiles = c(0,50,100), attsurfs = NULL, xlab = "objective 1", ylab = "objective 2", xlim = NULL, ylim = NULL, log = "", type = "point", # FIXME: This default doesn't work when type == "area" col = NULL, lty = c("dashed", "solid", "solid", "solid", "dashed"), lwd = c(1.75), pch = NA, cex.pch = par("cex"), las = par("las"), legend.pos = "topright", legend.txt = NULL, # FIXME: Can we get rid of the extra. stuff? Replace it with calling points after eafplot.default in examples and eafplot.pl. extra.points = NULL, extra.legend = NULL, extra.pch = c(4:25), extra.lwd = 0.5, extra.lty = "dashed", extra.col = "black", maximise = c(FALSE, FALSE), xaxis.side = "below", yaxis.side = "left", axes = TRUE, ... ) { type <- match.arg (type, c("point", "area")) maximise <- as.logical(maximise) xaxis.side <- switch(match.arg (xaxis.side, c("below", "above")), below = 1, above = 3) yaxis.side <- switch(match.arg (yaxis.side, c("left", "right")), left = 2, right = 4) if (is.null(col)) { if (type == "point") { col <- c("black", "darkgrey", "black", "grey40", "darkgrey") } else { col <- c("grey", "black") } } if (!is.null (attsurfs)) { # Don't we need to apply maximise? attsurfs <- lapply(attsurfs, function(x) { as.matrix(x[, 1:2, drop = FALSE]) }) } else { if (length(dim(x)) != 2L) stop("'x' must be a data.frame or a matrix") if (nrow(x) < 1L) stop("not enough points (rows) in 'x'") if (ncol(x) < 2) stop("'x' must have at least 2 columns") # Re-encode the sets so that they are consecutive and numeric sets <- as.numeric(as.factor(sets)) if (length(sets) != nrow(x)) stop("'sets' must be a vector of length equal to the number of rows in 'x'") x <- as.matrix(x) if (!is.numeric(x)) stop("The two first columns of 'x' must be numeric") x <- matrix.maximise(x, maximise) # Transform EAF matrix into attsurfs list. if (is.null(groups)) { attsurfs <- compute.eaf.as.list(cbind(x, sets), percentiles) } else { # FIXME: Is this equivalent to compute.eaf.as.list for each g? EAF <- eafs (x, sets, groups, percentiles) attsurfs <- list() groups <- EAF$groups for (g in levels(groups)) { tmp <- lapply(split.data.frame(EAF[groups == g,], as.factor(EAF[groups == g, 3])), function(x) { as.matrix(x[, 1:2, drop=FALSE]) }) attsurfs <- c(attsurfs, tmp) } } # FIXME: rm(EAF) to save memory ? attsurfs <- lapply(attsurfs, matrix.maximise, maximise = maximise) } # FIXME: This seems too complicated, what is going on? if (!is.null(xlim) && maximise[1]) xlim <- -xlim if (!is.null(ylim) && maximise[2]) ylim <- -ylim # FIXME: We should take the range from the attsurfs to not make x mandatory. if (is.null (xlim)) xlim <- range(x[,1]) if (is.null (ylim)) ylim <- range(x[,2]) if (maximise[1]) xlim <- range(-xlim) if (maximise[2]) ylim <- range(-ylim) best1 <- if (maximise[1]) max else min best2 <- if (maximise[2]) max else min extreme <- get.extremes(xlim, ylim, maximise, log) # FIXME: Find a better way to handle different x-y scale. yscale <- 1 ## if (ymaximise) { ## #yscale <- 60 ## yreverse <- -1 ## attsurfs <- lapply (attsurfs, function (x) ## { x[,2] <- yreverse * x[,2] / yscale; x }) ## ylim <- yreverse * ylim / yscale ## extreme[2] <- yreverse * extreme[2] ## if (log == "y") extreme[2] <- 1 ## } ## lab' A numerical vector of the form 'c(x, y, len)' which modifies ## the default way that axes are annotated. The values of 'x' ## and 'y' give the (approximate) number of tickmarks on the x ## and y axes and 'len' specifies the label length. The default ## is 'c(5, 5, 7)'. Note that this only affects the way the ## parameters 'xaxp' and 'yaxp' are set when the user coordinate ## system is set up, and is not consulted when axes are drawn. ## 'len' _is unimplemented_ in R. op <- par(cex = 1.0, cex.lab = 1.1, cex.axis = 1.0, lab = c(10,5,7)) on.exit(par(op)) if (!is.null(colnames(x)[1])) xlab <- colnames(x)[1] if (!is.null(colnames(x)[2])) ylab <- colnames(x)[2] plot(xlim, ylim, type = "n", xlab = "", ylab = "", xlim = xlim, ylim = ylim, log = log, axes = FALSE, panel.first = ({ if (axes) { at <- axTicks(1) labels <- formatC(at, format="g") ## tck=1 draws the vertical grid lines (grid() is seriously broken). axis(xaxis.side, at=at, labels=FALSE, tck=1, col='lightgray', ## Work-around for R bug: lwd=0.5, lty="26") ## This should be instead: lty='dotted', lwd=par("lwd")) axis(xaxis.side, at=at, labels=labels, las = las) mtext(xlab, xaxis.side, line=2.1, cex=par("cex.axis"), las=0) at <- axTicks(2) labels <- formatC(at, format="g") ## if (log == "y") { ## ## Custom log axis (like gnuplot but in R is hard) ## max.pow <- 6 ## at <- c(1, 5, 10, 50, 100, 500, 1000, 1500, 10^c(4:max.pow)) ## labels <- c(1, 5, 10, 50, 100, 500, 1000, 1500, ## parse(text = paste("10^", 4:max.pow, sep = ""))) ## #at <- c(60, 120, 180, 240, 300, 480, 600, 900, 1200, 1440) ## #labels <- formatC(at,format="g") ## ## Now do the minor ticks, at 1/10 of each power of 10 interval ## ##at.minor <- 2:9 * rep(c(10^c(1:max.pow)) / 10, each = length(2:9)) ## at.minor <- 1:10 * rep(c(10^c(1:max.pow)) / 10, each = length(1:10)) ## axis (yaxis.side, at = at.minor, tcl = -0.25, labels = FALSE, las=las) ## axis (yaxis.side, at = at.minor, labels = FALSE, tck=1, ## col='lightgray', lty='dotted', lwd=par("lwd")) ## } ## tck=1 draws the horizontal grid lines (grid() is seriously broken). axis (yaxis.side, at=at, labels=FALSE, tck=1, col='lightgray', lty='dotted', lwd=par("lwd")) axis (yaxis.side, at=at, labels=labels, las = las) mtext(ylab, yaxis.side, line=2.75, cex=par("cex.axis"), las=0) } # FIXME: Perhaps have a function plot.eaf.lines that computes # several percentiles for a single algorithm and then calls # points() or polygon() as appropriate to add attainment # surfaces to an existing plot. This way we can factor out # the code below and use it in plot.eaf and plot.eafdiff if (type == "area") { if (length(col) != 2) { stop ("length(col) != 2, but with 'type=area', eafplot.default needs just two colors") } colfunc <- colorRampPalette(col) col <- colfunc(length(attsurfs)) for (k in 1:length(attsurfs)) { poli <- points.steps(attsurfs[[k]]) poli <- rbind(c(best1(poli[,1]), extreme[2]), poli, c(extreme[1], best2(poli[,2])), extreme) polygon(poli[,1], poli[,2], border = NA, col = col[k]) } } else { ## Recycle values lwd <- rep(lwd, length=length(attsurfs)) lty <- rep(lty, length=length(attsurfs)) col <- rep(col, length=length(attsurfs)) pch <- rep(pch, length=length(attsurfs)) for (k in 1:length(attsurfs)) { tmp <- attsurfs[[k]] tmp <- rbind(c(best1(tmp[,1]), extreme[2]), tmp, c(extreme[1], best2(tmp[,2]))) points(tmp, type="p", col=col[k], pch = pch[k], cex = cex.pch) points(tmp, type="s", col=col[k], lty = lty[k], lwd = lwd[k]) } } }), las = las, ...) if (!is.null (extra.points)) { if (!is.list (extra.points[[1]])) { extra.points <- list(extra.points) } ## Recycle values extra.length <- length(extra.points) extra.lwd <- rep(extra.lwd, length=extra.length) extra.lty <- rep(extra.lty, length=extra.length) extra.col <- rep(extra.col, length=extra.length) extra.pch <- rep(extra.pch, length=extra.length) for (i in 1:length(extra.points)) { if (any(is.na(extra.points[[i]][,1]))) { ## Extra points are given in the correct order so no reverse extra.points[[i]][,2] <- extra.points[[i]][,2] / yscale abline(h=extra.points[[i]][,2], lwd = extra.lwd[i], col = extra.col[i], lty = extra.lty[i]) extra.pch[i] <- 0 } else if (any(is.na(extra.points[[i]][,2]))) { abline(v=extra.points[[i]][,1], lwd = extra.lwd[i], col = extra.col[i], lty = extra.lty[i]) extra.pch[i] <- NA } else { ## Extra points are given in the correct order so no reverse extra.points[[i]][,2] <- extra.points[[i]][,2] / yscale points (extra.points[[i]], type="p", pch=extra.pch[i], col=extra.col[i], cex = cex.pch) extra.lty[i] <- "blank" extra.lwd[i] <- NA } lwd <- c(lwd, extra.lwd[i]) lty <- c(lty, extra.lty[i]) col <- c(col, extra.col[i]) pch <- c(pch, extra.pch[i]) } } # Setup legend. if (is.null(legend.txt) && !is.null(percentiles)) { legend.txt <- paste0(percentiles, "%") legend.txt <- sub("^0%$", "best", legend.txt) legend.txt <- sub("^50%$", "median", legend.txt) legend.txt <- sub("^100%$", "worst", legend.txt) if (!is.null(groups)) { legend.txt <- as.vector(t(outer(levels(groups), legend.txt, paste))) } } legend.txt <- c(legend.txt, extra.legend) if (!is.null (legend.txt) && is.na(pmatch(legend.pos,"none"))) { if (type == "area") { legend(x = legend.pos, y = NULL, legend = rev(legend.txt), fill = c(rev(col), "#FFFFFF"), bg="white",bty="n", xjust=0, yjust=0, cex=0.9) } else { legend(legend.pos, legend = legend.txt, xjust=1, yjust=1, bty="n", lty = lty, lwd = lwd, pch = pch, col = col, merge=T) } } box() invisible() } .plot.eafdiff.side <- function (eafdiff, attsurfs = list(), col = c("#FFFFFF", "#BFBFBF","#808080","#404040","#000000"), side = stop("Argument 'side' is required"), type = "point", xlim = NULL, ylim = NULL, log = "", las = par("las"), full.eaf = FALSE, title = "", maximise = c(FALSE, FALSE), xlab = "objective 1", ylab = "objective 2", ...) { side <- match.arg (side, c("left", "right")) type <- match.arg (type, c("point", "area")) xaxis.side <- if (side == "left") 1 else 3 yaxis.side <- if (side == "left") 2 else 4 maximise <- as.logical(maximise) # We do not paint with the same color as the background since this # will override the grid lines ## FIXME: This should actually look at the bg color of the plot col[col == "#FFFFFF"] <- NA # For !full.eaf && type == "area", str(eafdiff) is a polygon: ## $ num [, 1:2] ## - attr(*, "col")= num [] # Colors are correct for !full.eaf && type == "area" if (full.eaf || type == "point") { # Why flooring and not ceiling? If a point has value 2.05, it should # be painted with color 2 rather than 3. # +1 because col[1] is white. eafdiff[,3] <- floor(eafdiff[,3]) + 1 if (length(unique(eafdiff[,3])) > length(col)) { stop ("Too few colors: length(unique(eafdiff[,3])) > length(col)") } } extreme <- get.extremes(xlim, ylim, maximise, log) yscale <- 1 # yscale <- 60 ## if (yscale != 1) { ## # This doesn't work with polygons. ## stopifnot (full.eaf || type == "point") ## eafdiff[,2] <- eafdiff[,2] / yscale ## attsurfs <- lapply (attsurfs, function (x) ## { x[,2] <- x[,2] / yscale; x }) ## ylim <- ylim / yscale ## if (log == "y") extreme[2] <- 1 ## } best1 <- if (maximise[1]) max else min best2 <- if (maximise[2]) max else min attsurfs <- lapply (attsurfs, function (x) { x <- rbind(c(best1(x[,1]), extreme[2]), x[,1:2], c(extreme[1], best2(x[,2]))) }) plot(xlim, ylim, type = "n", xlab = "", ylab = "", ylim = ylim, xlim = xlim, log = log, axes = FALSE, panel.first = ({ at <- axTicks(1) labels <- formatC(at, format="g") ## tck=1 draws the vertical grid lines (grid() is seriously broken). axis(xaxis.side, at=at, labels=FALSE, tck=1, col='lightgray', ## Work-around for R bug: ## This should be instead: lty='dotted', lwd=par("lwd")) lwd = 0.5, lty = "26") axis(xaxis.side, at=at, labels=labels, las = las) mtext(xlab, xaxis.side, line=2.1, las = 0, cex = par("cex") * par("cex.axis")) at <- axTicks(2) labels <- formatC(at, format="g") ## if (log == "y") { ## ## Custom log axis (like gnuplot but in R is hard) ## max.pow <- 6 ## ##at <- c(1, 5, 10, 50, 100, 500, 1000, 1500, 10^c(4:max.pow)) ## ##labels <- c(1, 5, 10, 50, 100, 500, 1000, 1500, ## ## parse(text = paste("10^", 4:max.pow, sep = ""))) ## at <- c(60, 120, 180, 240, 300, 480, 600, 900, 1200, 1440) ## labels <- formatC(at,format="g") ## ## Now do the minor ticks, at 1/10 of each power of 10 interval ## at.minor <- 2:9 * rep(c(10^c(1:max.pow)) / 10, each = length(2:9)) ## at.minor <- 1:10 * rep(c(10^c(1:max.pow)) / 10, each = length(1:10)) ## ##axis (yaxis.side, at = at.minor, tcl = -0.25, labels = FALSE, las=las) ## ##axis (yaxis.side, at = at.minor, labels = FALSE, tck=1, ## ## col='lightgray', lty='dotted', lwd=par("lwd")) ## } ## tck=1 draws the horizontal grid lines (grid() is seriously broken). axis(yaxis.side, at=at, labels=FALSE, tck=1, col='lightgray', lty='dotted', lwd=par("lwd")) axis(yaxis.side, at=at, labels=labels, las = las) mtext(ylab, yaxis.side, line = 2.2, las = 0, cex = par("cex") * par("cex.axis")) if (nrow(eafdiff)) { if (type == "area") { if (full.eaf) { # FIXME: eafplot.default is doing the same thing as # below, but with different defaults for (i in 1:length(col)) { poli <- points.steps(eafdiff[eafdiff[,3] == i, c(1,2), drop = FALSE]) poli <- rbind(c(best1(poli[,1]), extreme[2]), poli, c(extreme[1], best2(poli[,2])), extreme) polygon(poli[,1], poli[,2], border = NA, col = col[i]) } } else { eafdiff[,1] <- rm.inf(eafdiff[,1], extreme[1]) eafdiff[,2] <- rm.inf(eafdiff[,2], extreme[2]) polycol <- attr(eafdiff, "col") #print(unique(polycol)) #print(length(col)) ## The maximum value should also be painted. polycol[polycol > length(col)] <- length(col) polycol[polycol > length(col)] <- length(col) #print(eafdiff) #print(col[polycol]) polygon(eafdiff[,1], eafdiff[,2], border = NA, col = col[polycol]) } } else { ## The maximum value should also be painted. eafdiff[eafdiff[,3] > length(col), 3] <- length(col) eafdiff <- eafdiff[order(eafdiff[,3], decreasing = FALSE), , drop=FALSE] points(eafdiff[,1], eafdiff[,2], col = col[eafdiff[,3]], type = "p", pch=20) } } }), las = las, ...) lty <- c("solid", "dashed") lwd <- c(1) if (type == "area" && full.eaf) { col <- c("black", "black", "white") } else { col <- c("black") } ## Recycle values lwd <- rep(lwd, len = length(attsurfs)) lty <- rep(lty, len = length(attsurfs)) col <- rep(col, len = length(attsurfs)) for (k in seq_along(attsurfs)) { tmp <- attsurfs[[k]] lines(tmp[,1], tmp[,2], type = "s", lty = lty[k], lwd = lwd[k], col = col[k]) } mtext(title, 1, line=3.5, cex=par("cex.lab"), las = 0, font = 2) box() } #' Empirical attainment function differences #' #' Plot the differences between the empirical attainment functions of two #' data sets as a two-panel plot, where the left side shows the values of #' the left EAF minus the right EAF and the right side shows the #' differences in the other direction. #' #' @param data.left,data.right Data frames corresponding to the input data of #' left and right sides, respectively. Each data frame has at least three #' columns, the third one being the set of each point. See also #' \code{\link{read.data.sets}}. #' #' @param col A character vector of three colors for the magnitude of the #' differences of 0, 0.5, and 1. Intermediate colors are computed #' automatically given the value of \code{intervals}. #' #' @param intervals An integer or a character vector. The #' absolute range of the differences [0,1] is partitioned into the number #' of intervals provided. If an integer is provided, then labels for each #' interval are computed automatically. If a character vector is #' provided, its length is taken as the number of intervals. #' #' @param percentiles The percentiles of the EAF of each side that will be #' plotted as attainment surfaces. \code{NA} does not plot any. See #' \code{\link{eafplot.default}}. #' #' @param full.eaf Whether to plot the EAF of each side instead of the #' differences between the EAFs. #' #' @param type Whether the EAF differences are plotted as points #' (\samp{points}) or whether to color the areas that have at least a #' certain value (\samp{area}). #' #'@param legend.pos The position of the legend. See \code{\link{legend}}. #' #'@param title.left,title.right Title for left and right panels, respectively. #' #' @param xlim,ylim,cex,cex.lab,cex.axis Graphical parameters, see #' \code{\link{plot.default}}. #' #' @param maximise Whether the first and/or second objective correspond to #' a maximisation problem. #' #' @param grand.lines Whether to plot the grand-best and grand-worst #' attainment surfaces. #' #' @param left.panel.last,right.panel.last An expression to be evaluated after #' plotting has taken place on each panel (left or right). This can be useful #' for adding points or text to either panel. Note that this works by lazy #' evaluation: passing this argument from other \code{plot} methods may well #' not work since it may be evaluated too early. #' #' @param ... Other graphical parameters are passed down to #' \code{\link{plot.default}}. #' #' @details #' This function calculates the differences between the EAFs of two #' data sets, and plots on the left the differences in favour #' of the left data set, and on the right the differences in favour of #' the right data set. By default, it also plots the grand best and worst #' attainment surfaces, that is, the 0\% and 100\%-attainment surfaces #' over all data. This two surfaces delimit the area where differences #' may exist. In addition, it also plots the 50\%-attainment surface of #' each data set. #' #' With \code{type = "point"}, only the points where there is a change in #' the value of the EAF difference are plotted. This means that for areas #' where the EAF differences stays constant, the region will appear in #' white even if the value of the differences in that region is #' large. This explain "white holes" surrounded by black #' points. #' #' With \code{type = "area"}, the area where the EAF differences has a #' certain value is plotted. The idea for the algorithm to compute the #' areas was provided by Carlos M. Fonseca. The implementation uses R #' polygons, which some PDF viewers may have trouble rendering correctly #' (See #' \url{https://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-are-there-unwanted-borders}). Plots (should) look correct when printed. #' #' Large differences that appear when using \code{type = "points"} may #' seem to dissapear when using \code{type = "area"}. The explanation is #' the points size is independent of the axes range, therefore, the #' plotted points may seem to cover a much larger area than the actual #' number of points. On the other hand, the areas size is plotted with #' respect to the objective space, without any extra borders. If the #' range of an area becomes smaller than one-pixel, it won't be #' visible. As a consequence, zooming in or out certain regions of the plots #' does not change the apparent size of the points, whereas it affects #' considerably the apparent size of the areas. #' #' #' @return No return value. #' #' @seealso \code{\link{read.data.sets}}, \code{\link{eafplot}} #' #' @examples #' A1 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", "ALG_1_dat")) #' A2 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", "ALG_2_dat")) #' \donttest{# These take time #' eafdiffplot(A1, A2, full.eaf = TRUE) #' eafdiffplot(A1, A2, type = "area") #' eafdiffplot(A1, A2, type = "point") #' } #' # A more complex example #' a1 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", "wrots_l100w10_dat")) #' a2 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", "wrots_l10w100_dat")) #' DIFF <- eafdiffplot(a1, a2, col = c("white", "blue", "red"), intervals = 5, #' type = "point", #' title.left = expression("W-RoTS, " * lambda * "=" * 100 * ", " * omega * "=" * 10), #' title.right= expression("W-RoTS, " * lambda * "=" * 10 * #' ", " * omega * "=" * 100), #' right.panel.last={ abline(a = 0, b = 1, col = "red", lty = "dashed")}) #' DIFF$right[,3] <- -DIFF$right[,3] #' #' ## Save the values to a file. #' # write.table(rbind(DIFF$left,DIFF$right), #' # file = "wrots_l100w10_dat-wrots_l10w100_dat-diff.txt", #' # quote = FALSE, row.names = FALSE, col.names = FALSE) #' #' @keywords graphs #'@export eafdiffplot <- function(data.left, data.right, col = c("#FFFFFF", "#808080","#000000"), intervals = 5, percentiles = c(50), full.eaf = FALSE, type = "area", legend.pos = if (full.eaf) "bottomleft" else "topright", title.left = deparse(substitute(data.left)), title.right = deparse(substitute(data.right)), xlim = NULL, ylim = NULL, cex = par("cex"), cex.lab = par("cex.lab"), cex.axis = par("cex.axis"), maximise = c(FALSE, FALSE), grand.lines = TRUE, left.panel.last = NULL, right.panel.last = NULL, ...) { type <- match.arg (type, c("point", "area")) # FIXME: check that it is either an integer or a character vector. if (length(intervals) == 1) { intervals <- nintervals.labels(intervals) } if (length(col) != 3) { stop ("'col' must provide three colors (minimum, medium maximum)") } col <- colorRampPalette(col)(length(intervals)) title.left <- title.left title.right <- title.right maximise <- as.logical(maximise) data.left <- matrix.maximise(check.eaf.data(data.left), maximise) data.right <- matrix.maximise(check.eaf.data(data.right), maximise) attsurfs.left <- attsurfs.right <- list() if (!any(is.na(percentiles))) { attsurfs.left <- compute.eaf.as.list (data.left, percentiles) attsurfs.left <- lapply(attsurfs.left, matrix.maximise, maximise = maximise) attsurfs.right <- compute.eaf.as.list (data.right, percentiles) attsurfs.right <- lapply(attsurfs.right, matrix.maximise, maximise = maximise) } # FIXME: We do not need this for the full EAF. # Merge the data nruns.left <- max(data.left[,3]) data.combined <- data.right data.combined[,3] <- data.combined[,3] + nruns.left data.combined <- rbind(data.left, data.combined) def.par <- par(no.readonly = TRUE) # save default, for resetting... on.exit(par(def.par)) if (full.eaf) { DIFF <- list() if (type == "area") { lower.boundaries <- 0:(length(intervals)-1) * 100 / length(intervals) DIFF$left <- compute.eaf (data.left, percentiles = lower.boundaries) DIFF$right <- compute.eaf (data.right, percentiles = lower.boundaries) } else if (type == "point") { DIFF$left <- compute.eaf (data.left) DIFF$right <- compute.eaf (data.right) # Since plot.eafdiff.side uses floor to calculate the color, and # we want color[100] == color[99]. DIFF$left[DIFF$left[,3] == 100, 3] <- 99 DIFF$right[DIFF$right[,3] == 100, 3] <- 99 } # Convert percentile into color index DIFF$left[,3] <- DIFF$left[,3] * length(intervals) / 100 DIFF$right[,3] <- DIFF$right[,3] * length(intervals) / 100 #remove(data.left,data.right,data.combined) # Free memory? } else { if (type == "area") { DIFF <- compute.eafdiff.polygon (data.combined, intervals = length(intervals)) } else if (type == "point") { #remove(data.left,data.right) # Free memory? DIFF <- compute.eafdiff (data.combined, intervals = length(intervals)) #remove(data.combined) # Free memory? } } # FIXME: This can be avoided and just taken from the full EAF. grand.attsurf <- compute.eaf.as.list (data.combined, c(0, 100)) grand.best <- grand.attsurf[["0"]] grand.worst <- grand.attsurf[["100"]] if (!is.null(xlim) && maximise[1]) xlim <- -xlim if (!is.null(ylim) && maximise[2]) ylim <- -ylim if (is.null(xlim)) { xlim <- range(c(grand.best[,1], grand.worst[,1], range.finite(DIFF$left[,1]), range.finite(DIFF$right[,1]))) } if (is.null(ylim)) { ylim <- range(c(grand.best[,2], grand.worst[,2], range.finite(DIFF$left[,2]), range.finite(DIFF$right[,2]))) } if (maximise[1]) xlim <- range(-xlim) if (maximise[2]) ylim <- range(-ylim) grand.best <- matrix.maximise(grand.best, maximise) grand.worst <- matrix.maximise(grand.worst, maximise) DIFF$left <- matrix.maximise(DIFF$left,maximise) DIFF$right <- matrix.maximise(DIFF$right,maximise) # FIXME: This does not generate empty space between the two plots, but the # plots are not squared. layout(matrix(1:2, ncol=2, byrow=TRUE), respect=TRUE) bottommar <- 5 topmar <- 4 leftmar <- 4 rightmar <- 4 # FIXME: This generates empty spaces between the two plots. How to ensure # that the side-by-side plots are kept together? ## layout(matrix(1:2, ncol = 2)) ## par (pty = 's') # Force it to be square ## bottommar <- 5 ## topmar <- 4 ## leftmar <- 4 ## rightmar <- 4 # cex.axis is multiplied by cex, but cex.lab is not. par(cex = cex, cex.lab = cex.lab, cex.axis = cex.axis , mar = c(bottommar, leftmar, topmar, 0) , lab = c(10,5,7) , las = 0 ) if (grand.lines) { attsurfs <- c(list(grand.best), attsurfs.left, list(grand.worst)) } else { attsurfs <- attsurfs.left } .plot.eafdiff.side (DIFF$left, attsurfs = attsurfs, col = col, type = type, full.eaf = full.eaf, title = title.left, xlim = xlim, ylim = ylim, side = "left", maximise = maximise, ...) if (nchar(legend.pos) > 0 && !(legend.pos %in% c("no", "none"))) { legend(x = legend.pos, y = NULL, rev(intervals), rev(col), bg = "white", bty = "n", xjust=0, yjust=0, cex=0.9) } left.panel.last par(mar = c(bottommar, 0, topmar, rightmar)) if (grand.lines) { attsurfs <- c(list(grand.best), attsurfs.right, list(grand.worst)) } else { attsurfs <- attsurfs.right } .plot.eafdiff.side (DIFF$right, attsurfs = attsurfs, col = col, type = type, full.eaf = full.eaf, title = title.right, xlim = xlim, ylim = ylim, side = "right", maximise = maximise, ...) right.panel.last invisible(DIFF) } nintervals.labels <- function(n) { if (n < 2) stop ("number of intervals must be larger than 1") step <- round(1.0 / n, 4); if (step == 0) stop ("number of intervals is too large") intervals <- paste0("[0.0, ", step , ")") x <- step repeat { nx <- round(x + step, 4) if (nx >= 1) break intervals <- c(intervals, paste0("[", x, ", ", nx, ")")) x <- nx } return(c(intervals, paste0("[", x, ", 1.0]"))) } ### Local Variables: ### ess-indent-level: 2 ### ess-continued-statement-offset: 2 ### ess-brace-offset: 0 ### ess-expression-offset: 4 ### ess-else-offset: 0 ### ess-brace-imaginary-offset: 0 ### ess-continued-brace-offset: 0 ### ess-arg-function-offset: 2 ### ess-close-brace-offset: 0 ### indent-tabs-mode: nil ### ess-fancy-comments: nil ### End: eaf/R/common.R0000644000176200001440000000667213254545527012650 0ustar liggesusers ### Copyright (C) 2001-2006 Deepayan Sarkar ### Copyright (C) 2001-2005 Saikat DebRoy ### ### This file is part of the lattice package for R. ### It is made available under the terms of the GNU General Public ### License, version 2, or at your option, any later version, ### incorporated herein by reference. ### ### This program is distributed in the hope that it will be ### useful, but WITHOUT ANY WARRANTY; without even the implied ### warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR ### PURPOSE. See the GNU General Public License for more ### details. ### ### You should have received a copy of the GNU General Public ### License along with this program; if not, write to the Free ### Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, ### MA 02110-1301, USA .cupdate <- function(index, maxim) { ## This unexported function is used to handle arbitrary number of ## conditioning variables : every time it is called, it increments ## the "current" level of the conditioning variables suitably, ## i.e., it tries to increment the level of the 1st conditining ## variable (the one which varies fastest along panel order) and ## if it happens to be at its maximum (last) value, it sets it to ## the first value AND increments the "current" level of the 2nd ## (next) conditioning variable recursively. if(length(index)!=length(maxim)||length(maxim)<=0) stop("Inappropriate arguments") index[1] <- index[1] + 1 if (index[1] > maxim[1] && length(maxim) > 1) c(1, .cupdate(index[-1], maxim[-1])) else index } .check.layout <- function(layout, cond.max.level, skip = FALSE) { if (all(skip)) stop("skip cannot be all TRUE") number.of.cond <- length(cond.max.level) nplots <- prod(cond.max.level) if (!is.numeric(layout)) { layout <- c(0,1,1) if (number.of.cond == 1) layout[2] <- nplots else { layout[1] <- cond.max.level[1] layout[2] <- cond.max.level[2] } skip <- rep(skip, length.out = max(layout[1] * layout[2], layout[2])) plots.per.page <- length(skip) - length(skip[skip]) layout[3] <- ceiling(nplots/plots.per.page) # + 1 } else if (length(layout) == 1) stop("layout must have at least 2 elements") else if (length(layout) == 2) { if (all(is.na(layout))) stop("inadmissible value of layout") else if (all(layout < 1)) stop("at least one element of layout must be positive") else if (isTRUE(layout[2] == 0)) stop("inadmissible value of layout") if (is.na(layout[1])) layout[1] <- ceiling(nplots / layout[2]) if (is.na(layout[2])) layout[2] <- ceiling(nplots / layout[1]) skip <- rep(skip, length.out = max(layout[1] * layout[2], layout[2])) plots.per.page <- length(skip) - length(skip[skip]) layout[3] <- ceiling(nplots / plots.per.page) # + 1 } else if (length(layout)==3) { if(layout[1] < 0 || layout[2] < 1 || layout[3] < 1) stop("invalid value for layout") } layout } .compute.packet <- function(cond, levels) { id <- !(do.call("pmax", lapply(cond, is.na))) stopifnot(any(id)) for (i in seq_along(cond)) { var <- cond[[i]] id <- id & (as.numeric(var) == levels[i]) ## MARCO: Removed the possibility of numerical conditioning variables } id } eaf/R/eaf-package.R0000644000176200001440000002314313254745011013462 0ustar liggesusers#'Plots of the Empirical Attainment Function #' #' The empirical attainment function (EAF) describes the probabilistic #' distribution of the outcomes obtained by a stochastic algorithm in the #' objective space. This package implements plots of summary #' attainment surfaces and differences between the first-order #' EAFs. These plots may be used for exploring the performance of #' stochastic local search algorithms for biobjective optimization #' problems and help in identifying certain algorithmic behaviors in a #' graphical way. #' #' Functions: #' #'\tabular{rl}{ #'\code{\link{eafdiffplot}} \tab Empirical attainment function differences\cr #'\code{\link{eafplot}} \tab Plot the Empirical Attainment Function for two #'objectives\cr #'\code{\link{read.data.sets}}\tab Read several data.frame sets\cr #'} #' #'Data: #' #'\describe{ #'\item{\code{\link{gcp2x2}}}{ Metaheuristics for solving the Graph Vertex Coloring Problem} #'\item{\code{\link{HybridGA}}}{ Results of Hybrid GA on vanzyl and Richmond #'water networks} #'\item{\code{\link{SPEA2minstoptimeRichmond}}}{ Results of SPEA2 when minimising electrical cost and maximising the #'minimum idle time of pumps on Richmond water network} #'} #' #' Extras are available at \code{file.path(system.file(package="eaf"))}: #' #'\tabular{ll}{ #'\code{extdata} \tab External data sets (see \code{\link{read.data.sets}})\cr #'\code{scripts/eaf} \tab EAF command-line program\cr #'\code{scripts/eafplot}\tab Perl script to generate plots of attainment surfaces\cr #'\code{scripts/eafdiff}\tab Perl script to generate plots of EAF differences\cr #'} #' #' @import graphics grDevices stats #' #' @useDynLib eaf, .registration = TRUE #' #' @aliases eaf #' #' @author #' Maintainer: Manuel \enc{López-Ibáñez}{Lopez-Ibanez} #' \email{manuel.lopez-ibanez@ulb.ac.be} #' #' Contributors: Carlos Fonseca, Luis Paquete, Thomas \enc{Stützle}{Stuetzle}, #' Manuel \enc{López-Ibáñez}{Lopez-Ibanez} and Marco Chiarandini. #' #' @references #' V. Grunert da Fonseca, C. M. Fonseca, and A. O. Hall, \emph{Inferential #' performance assessment of stochastic optimisers and the attainment #' function}, in Evolutionary Multi-Criterion Optimization. First #' International Conference, EMO 2001 (E. Zitzler, K. Deb, L. Thiele, #' C. A. Coello Coello, and D. Corne, eds.), vol. 1993 of Lecture Notes #' in Computer Science, pp. 213-225, Berlin: Springer, 2001. #' #' V. Grunert da Fonseca and C. M. Fonseca, \emph{The attainment-function #' approach to stochastic multiobjective optimizer assessment and #' comparison}. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and #' M. Preuss, editors, Experimental Methods for the Analysis of #' Optimization Algorithms, pages 103-130, Springer, Berlin, Germany, #' 2010. #' #' M. \enc{López-Ibáñez}{Lopez-Ibanez}, L. Paquete, and T. \enc{Stützle}{Stuetzle}. \emph{Exploratory #' Analysis of Stochastic Local Search Algorithms in Biobjective #' Optimization}. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and #' M. Preuss, editors, Experimental Methods for the Analysis of #' Optimization Algorithms, pages 209–222. Springer, Berlin, Germany, #' 2010. doi: 10.1007/978-3-642-02538-9_9 #' #'@keywords package graphs #'@concept multivariate #'@concept optimize #'@concept time-quality algorithm profile #'@concept empirical attainment function #' #'@examples #'data(gcp2x2) #'tabucol<-subset(gcp2x2, alg!="TSinN1") #'tabucol$alg<-tabucol$alg[drop=TRUE] #'eafplot(time+best~run,data=tabucol,subset=tabucol$inst=="DSJC500.5") #' #'eafplot(time+best~run|inst,groups=alg,data=gcp2x2) #'eafplot(time+best~run|inst,groups=alg,data=gcp2x2, #' percentiles=c(0,50,100),include.extremes=TRUE, #' cex=1.4, lty=c(2,1,2),lwd=c(2,2,2), #' col=c("black","blue","grey50")) #' #'A1<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_1_dat")) #'A2<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_2_dat")) #'eafplot(A1,A2, percentiles=c(50)) #'eafplot(list(A1=A1, A2=A2), percentiles=c(50)) #'eafdiffplot(A1, A2) #'## Save to a PDF file #'# dev.copy2pdf(file="eaf.pdf", onefile=TRUE, width=5, height=4) #' "_PACKAGE" #> [1] "_PACKAGE" #' Results of Hybrid GA on vanzyl and Richmond water networks #' #' The data has the only goal of providing an example of use of eafplot. #' #'@format #' A list with two data frames, each of them with three columns, as #' produced by \code{\link{read.data.sets}}. #' \describe{ #' \item{\code{$vanzyl}}{data frame of results on vanzyl network} #' \item{\code{$richmond}}{data frame of results on Richmond #' network. The second column is filled with \code{NA}} #' } #' #'@source Manuel \enc{López-Ibáñez}{Lopez-Ibanez}. Operational Optimisation of Water Distribution #' Networks. PhD thesis, School of Engineering and the Built Environment, #' Edinburgh Napier University, UK, 2009. #' #' @examples #'data(HybridGA) #'print(HybridGA$vanzyl) #'print(HybridGA$richmond) #' "HybridGA" #'Results of SPEA2 when minimising electrical cost and maximising the #'minimum idle time of pumps on Richmond water network. #' #'The data has the only goal of providing an example of use of eafplot. #' #'@format #' A data frame as produced by \code{\link{read.data.sets}}. The second #' column measures time in seconds and corresponds to a maximisation problem. #' #' @source #' Manuel \enc{López-Ibáñez}{Lopez-Ibanez}. Operational Optimisation of Water Distribution #' Networks. PhD thesis, School of Engineering and the Built Environment, #' Edinburgh Napier University, UK, 2009. #' #'@examples #'data(HybridGA) #'data(SPEA2minstoptimeRichmond) #'SPEA2minstoptimeRichmond[,2] <- SPEA2minstoptimeRichmond[,2] / 60 #'eafplot (SPEA2minstoptimeRichmond, xlab = expression(C[E]), #' ylab = "Minimum idle time (minutes)", #' las = 1, log = "y", maximise = c(FALSE, TRUE)) #' "SPEA2minstoptimeRichmond" #' Results of SPEA2 with relative time-controlled triggers on Richmond water #' network. #' #' The data has the only goal of providing an example of use of eafplot. #' #'@format #' A data frame as produced by \code{\link{read.data.sets}}. #' #' @source #' Manuel \enc{López-Ibáñez}{Lopez-Ibanez}. Operational Optimisation of Water Distribution #' Networks. PhD thesis, School of Engineering and the Built Environment, #' Edinburgh Napier University, UK, 2009. #' #'@examples #'data(HybridGA) #'data(SPEA2relativeRichmond) #'eafplot (SPEA2relativeRichmond, percentiles = c(25, 50, 75), #' xlab = expression(C[E]), ylab = "Total switches", #' xlim = c(90, 140), ylim = c(0, 25), #' extra.points = HybridGA$richmond, extra.lty = "dashed", #' extra.legend = "Hybrid GA") #' "SPEA2relativeRichmond" #'Results of SPEA2 with relative time-controlled triggers on Vanzyl's #'water network. #' #'The data has the only goal of providing an example of use of eafplot. #' #'@format #' A data frame as produced by \code{\link{read.data.sets}}. #' #'@source #' Manuel \enc{López-Ibáñez}{Lopez-Ibanez}. Operational Optimisation of Water Distribution #' Networks. PhD thesis, School of Engineering and the Built Environment, #' Edinburgh Napier University, UK, 2009. #' #'@examples #'data(HybridGA) #'data(SPEA2relativeVanzyl) #'eafplot(SPEA2relativeVanzyl, percentiles = c(25, 50, 75), #' xlab = expression(C[E]), ylab = "Total switches", xlim = c(320, 400), #' extra.points = HybridGA$vanzyl, extra.legend = "Hybrid GA") #' "SPEA2relativeVanzyl" #' Metaheuristics for solving the Graph Vertex Coloring Problem #' #' Two metaheuristic algorithms, TabuCol (Hertz et al., 1987) and #' simulated annealing (Johnson et al., 1991), to find a good #' approximation of the chromatic number of two random graphs. The data #' here has the only goal of providing an example of use of eafplot for #' comparing algorithm performance with respect to both time and quality #' when modelled as two objectives in trade off. #' #' @format #' A data frame with 3133 observations on the following 6 variables. #' \describe{ #' \item{\code{alg}}{a factor with levels \code{SAKempeFI} and \code{TSinN1}} #' \item{\code{inst}}{a factor with levels \code{DSJC500.5} and #' \code{DSJC500.9}. Instances are taken from the DIMACS repository.} #' \item{\code{run}}{a numeric vector indicating the run to #' which the observation belong. } #' \item{\code{best}}{a numeric vector indicating the best solution in #' number of colors found in the corresponding run up to that time.} #' \item{\code{time}}{a numeric vector indicating the time since the #' beginning of the run for each observation. A rescaling is applied.} #' \item{\code{titer}}{a numeric vector indicating iteration number #' corresponding to the observations.} #' } #' #'@details #' Each algorithm was run 10 times per graph registering the time and #' iteration number at which a new best solution was found. A time limit #' corresponding to 500*10^5 total iterations of TabuCol was imposed. The #' time was then normalized on a scale from 0 to 1 to make it instance #' independent. #' #'@source M. Chiarandini (2005). Stochastic local search methods for highly #' constrained combinatorial optimisation problems. Ph.D. thesis, Computer #' Science Department, Darmstadt University of Technology, Darmstadt, #' Germany. page 138. #' #'@references #' A. Hertz and D. de Werra. Using Tabu Search Techniques for Graph #' Coloring. Computing, 1987, 39(4), 345-351. #' #' D.S. Johnson, C.R. Aragon, L.A. McGeoch and C. Schevon. Optimization #' by Simulated Annealing: An Experimental Evaluation; Part II, Graph #' Coloring and Number Partitioning. Operations Research, 1991, 39(3), #' 378-406 #' #'@examples #' data(gcp2x2) #' "gcp2x2" eaf/README.md0000644000176200001440000002020713254746103012272 0ustar liggesusersEAF
   differences between two bi-objective optimizers EAF differences between two variants of W-RoTS (color) **eaf**: Empirical Attainment Function (EAF) Tools ================================================================ [![CRAN Status](https://www.r-pkg.org/badges/version-last-release/eaf)](https://cran.r-project.org/package=eaf) [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/grand-total/eaf)](https://CRAN.R-project.org/package=eaf) [ [**Homepage**](http://lopez-ibanez.eu/eaftools) ] [ [**GitHub**](https://github.com/MLopez-Ibanez/eaf) ] **Maintainers:** [Manuel López-Ibáñez](http://lopez-ibanez.eu) **Contributors:** [Manuel López-Ibáñez](http://lopez-ibanez.eu), [Marco Chiarandini](http://www.imada.sdu.dk/~marco), [Carlos M. Fonseca](http://eden.dei.uc.pt/~cmfonsec), [Luís Paquete](http://eden.dei.uc.pt/~paquete), and [Thomas Stützle](http://iridia.ulb.ac.be/~stuetzle) --------------------------------------- Introduction ============ The empirical attainment function (EAF) describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. This R package implements plots of summary attainment surfaces and differences between the first-order EAFs. These plots may be used for exploring the performance of stochastic local search algorithms for biobjective optimization problems and help in identifying certain algorithmic behaviors in a graphical way. The corresponding [book chapter](http://lopez-ibanez.eu/eaftools.html#LopPaqStu09emaa) explains the use of these visualization tools and illustrate them with examples arising from practice. **Keywords**: empirical attainment function, summary attainment surfaces, EAF differences, multi-objective optimization, bi-objective optimization, performance measures, performance assessment, graphical analysis, visualization. **Relevant literature:** 1. Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. [Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization](http://dx.doi.org/10.1007/978-3-642-02538-9_9). In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, *Experimental Methods for the Analysis of Optimization Algorithms*, pages 209–222. Springer, Berlin, Germany, 2010.
(This chapter is also available in a slightly extended form as [Technical Report TR/IRIDIA/2009-015](http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-015r001.pdf)). [ [bibtex](./LopezIbanez_bib.html#LopPaqStu09emaa) | doi:[10.1007/978-3-642-02538-9_9](http://dx.doi.org/10.1007/978-3-642-02538-9_9) | [Presentation](./doc/gecco2010moworkshop.pdf) ] Download and installation ------------------------- The software is implemented as an R package, but the code for only computing the EAF is available as a C program, and it does not require installing R or any R packages. Just download the package source code, uncompress it, and look for the directory `inst/scripts/eaf`. This code can be used to implement your own visualizations instead of the visualizations provided by the eaf package. The visualization of the EAFs require installing the eaf package. Therefore, for making use of all the features, a basic knowledge of R is recommended. However, the eaf package contains two Perl scripts that allow to generate standard plots without any knowledge about R. See `inst/scripts/eafplot/` and `inst/scripts/eafdiff/` in the package source code. The scripts use the eaf package internally to generate the plots, and, hence, the eaf package must be installed and working. The first step before installing the eaf package is to [install R](https://cran.r-project.org/). Once R is installed in the system, there are two methods for installing the eaf package: 1. Install within R (automatic download, internet connection required). Invoke R, then ```r install.packages(c("modeltools", "eaf")) ``` 2. Download the package from CRAN (you may also need to download and install first the package modeltools), and invoke at the command-line: ```bash R CMD INSTALL ``` where `` is one of the three versions available: `.tar.gz` (Unix/BSD/GNU/Linux), `.tgz` (MacOS X), or `.zip` (Windows). Search the [R documentation](https://cran.r-project.org/faqs.html) if you need more help to install an R package on your system. If you wish to be notified of bugfixes and new versions, please subscribe to the [low-volume emo-list](https://lists.dei.uc.pt/mailman/listinfo/emo-list), where announcements will be made. [ [Download eaf package from CRAN](https://cran.r-project.org/package=eaf) ] [ [Download Reference manual](https://cran.r-project.org/package=eaf/eaf.pdf) ] [ [Development version (GitHub)](https://github.com/MLopez-Ibanez/eaf) ] Usage ----- Once the eaf package is installed, the following R commands will give more information: ```r library(eaf) ?eaf ?eafplot ?eafdiffplot ?read.data.sets example(eafplot) example(eafdiffplot) # This one takes some time ``` Apart from the main R package, the source code contains the following extras in the directory `inst/` (after installation, these files can be found at the directory printed by the R command `system.file(package="eaf")`): * `scripts/eaf` : This C program computes the empirical attainment function in 2 dimensions. It is NOT required by the other programs, but it is provided as a useful command-line utility. This version is based on the original code written by Carlos M. Fonseca available at http://www.tik.ee.ethz.ch/pisa/. Another version of the code that handles three dimensions is available at [Prof. Fonseca's website](http://eden.dei.uc.pt/~cmfonsec/software.html#aft). * `scripts/eafplot` : Perl script to plot summary attainment surfaces. * `scripts/eafdiff` : Perl script to plot the differences between the EAFs of two input sets. * `extdata/` : Examples of utilization of the above programs. These are discussed in the corresponding chapter [1]. For more information, consult the `README` files at each subdirectory. License -------- This software is Copyright (C) 2011 Carlos M. Fonseca, Luís Paquete, Thomas Stützle, Manuel López-Ibáñez and Marco Chiarandini. This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the [GNU General Public License](http://www.gnu.org/licenses/gpl.html) for more details. **IMPORTANT NOTE**: Please be aware that the fact that this program is released as Free Software does not excuse you from scientific propriety, which obligates you to give appropriate credit! If you write a scientific paper describing research that made substantive use of this program, it is your obligation as a scientist to (a) mention the fashion in which this software was used in the Methods section; (b) mention the algorithm in the References section. The appropriate citation is: Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. **Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization.** In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, *Experimental Methods for the Analysis of Optimization Algorithms*, pages 209–222. Springer, Berlin, Germany, 2010. doi: 10.1007/978-3-642-02538-9_9 Moreover, as a personal note, I would appreciate it if you would email `manuel.lopez-ibanez@manchester.ac.uk` with citations of papers referencing this work so I can mention them to my funding agent and tenure committee. eaf/MD50000644000176200001440000001071613255007653011330 0ustar liggesusers736424fd147394e0b6198b98e82c5afe *DESCRIPTION 3d94a7e9b2c7760cfdc60e48ce110e87 *NAMESPACE 47cab41bf49290fb63e943e8dc3f9d16 *NEWS 4272e81b0002468ee9438a3314025127 *R/calls.R 4f7d4b14d225e81992673331a0682ac6 *R/common.R 0edf15a23f31ce3a1fc58b9c6b9a2c5a *R/eaf-package.R aeefdea9b79695ceda24a9c9cd695820 *R/eaf.R 443f5e8b68b0722835bc626f3cc6424f *README.md 85bae51ecf771dbb34c15360c8f35aed *data/HybridGA.rda 8204f9c487a6fcf83e9bfe7df735cc08 *data/SPEA2minstoptimeRichmond.rda f7ae86083ff009c8f86665993c753428 *data/SPEA2relativeRichmond.rda 6a8def87a4e0571e7f10ee0e264e8897 *data/SPEA2relativeVanzyl.rda 096884252796983614f37d872868e5e8 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0ustar liggesusersPackage: eaf Type: Package Title: Plots of the Empirical Attainment Function Version: 1.8 Date: 2018-03-22 Authors@R: c(person("Manuel", "López-Ibáñez", role = c("aut", "cre"), email = "manuel.lopez-ibanez@manchester.ac.uk", comment = c(ORCID = "0000-0001-9974-1295")), person("Marco", "Chiarandini", role = "aut"), person("Carlos", "Fonseca", role = "aut"), person("Luis", "Paquete", role = "aut"), person("Thomas", "Stützle", role = "aut")) Description: Plots of the empirical attainment function for two objectives. Depends: R (>= 2.10.0) Imports: modeltools, graphics, grDevices, stats Suggests: testthat License: GPL (>= 2) BugReports: https://github.com/MLopez-Ibanez/eaf/issues URL: http://lopez-ibanez.eu/eaftools, https://github.com/MLopez-Ibanez/eaf LazyLoad: true LazyData: true Encoding: UTF-8 RoxygenNote: 6.0.1 NeedsCompilation: yes Packaged: 2018-03-22 15:44:12 UTC; manu Author: Manuel López-Ibáñez [aut, cre] (), Marco Chiarandini [aut], Carlos Fonseca [aut], Luis Paquete [aut], Thomas Stützle [aut] Maintainer: Manuel López-Ibáñez Repository: CRAN Date/Publication: 2018-03-22 20:18:51 UTC eaf/man/0000755000176200001440000000000013254747464011600 5ustar liggesuserseaf/man/eafs.Rd0000644000176200001440000000354313254545527013006 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf.R \name{eafs} \alias{eafs} \title{Exact computation of the EAF} \usage{ eafs(points, sets, groups = NULL, percentiles = NULL) } \arguments{ \item{points}{Either a matrix or a data frame of numerical values, where each row gives the coordinates of a point.} \item{sets}{A vector indicating which set each point belongs to.} \item{groups}{Indicates that the EAF must be computed separately for data belonging to different groups.} \item{percentiles}{The percentiles of the EAF of each side that will be plotted as attainment surfaces. \code{NA} does not plot any. See \code{\link{eafplot.default}}.} } \value{ A data frame (\code{data.frame}) containing the exact representation of EAF. The last column gives the percentile that corresponds to each point. If groups is not \code{NULL}, then an additional column indicates to which group the point belongs. } \description{ This function computes the EAF given a set of points and a vector \code{set} that indicates to which set each point belongs. } \note{ There are several examples of data sets in \code{file.path(system.file(package="eaf"),"extdata")}. } \examples{ eaf.path <- system.file(package="eaf") x <- read.data.sets(file.path(eaf.path, "extdata","example1_dat")) # Compute full EAF eafs(x[,1:2], x[,3]) # Compute only best, median and worst eafs(x[,1:2], x[,3], percentiles = c(0, 50, 100)) x <- read.data.sets(file.path(eaf.path,"extdata","spherical-250-10-3d.txt")) y <- read.data.sets(file.path(eaf.path,"extdata","uniform-250-10-3d.txt")) x <- data.frame(x, group = "spherical") x <- rbind(x, data.frame(y, group = "uniform")) # Compute only median separately for each group eafs(x[,1:3], sets = x[,4], groups = x[,5], percentiles = 50) } \seealso{ \code{\link{read.data.sets}} } \author{ Manuel \enc{López-Ibáñez}{Lopez-Ibanez} } eaf/man/eafdiffplot.Rd0000644000176200001440000001410213254567410014337 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf.R \name{eafdiffplot} \alias{eafdiffplot} \title{Empirical attainment function differences} \usage{ eafdiffplot(data.left, data.right, col = c("#FFFFFF", "#808080", "#000000"), intervals = 5, percentiles = c(50), full.eaf = FALSE, type = "area", legend.pos = if (full.eaf) "bottomleft" else "topright", title.left = deparse(substitute(data.left)), title.right = deparse(substitute(data.right)), xlim = NULL, ylim = NULL, cex = par("cex"), cex.lab = par("cex.lab"), cex.axis = par("cex.axis"), maximise = c(FALSE, FALSE), grand.lines = TRUE, left.panel.last = NULL, right.panel.last = NULL, ...) } \arguments{ \item{data.left, data.right}{Data frames corresponding to the input data of left and right sides, respectively. Each data frame has at least three columns, the third one being the set of each point. See also \code{\link{read.data.sets}}.} \item{col}{A character vector of three colors for the magnitude of the differences of 0, 0.5, and 1. Intermediate colors are computed automatically given the value of \code{intervals}.} \item{intervals}{An integer or a character vector. The absolute range of the differences [0,1] is partitioned into the number of intervals provided. If an integer is provided, then labels for each interval are computed automatically. If a character vector is provided, its length is taken as the number of intervals.} \item{percentiles}{The percentiles of the EAF of each side that will be plotted as attainment surfaces. \code{NA} does not plot any. See \code{\link{eafplot.default}}.} \item{full.eaf}{Whether to plot the EAF of each side instead of the differences between the EAFs.} \item{type}{Whether the EAF differences are plotted as points (\samp{points}) or whether to color the areas that have at least a certain value (\samp{area}).} \item{legend.pos}{The position of the legend. See \code{\link{legend}}.} \item{title.left, title.right}{Title for left and right panels, respectively.} \item{xlim, ylim, cex, cex.lab, cex.axis}{Graphical parameters, see \code{\link{plot.default}}.} \item{maximise}{Whether the first and/or second objective correspond to a maximisation problem.} \item{grand.lines}{Whether to plot the grand-best and grand-worst attainment surfaces.} \item{left.panel.last, right.panel.last}{An expression to be evaluated after plotting has taken place on each panel (left or right). This can be useful for adding points or text to either panel. Note that this works by lazy evaluation: passing this argument from other \code{plot} methods may well not work since it may be evaluated too early.} \item{...}{Other graphical parameters are passed down to \code{\link{plot.default}}.} } \value{ No return value. } \description{ Plot the differences between the empirical attainment functions of two data sets as a two-panel plot, where the left side shows the values of the left EAF minus the right EAF and the right side shows the differences in the other direction. } \details{ This function calculates the differences between the EAFs of two data sets, and plots on the left the differences in favour of the left data set, and on the right the differences in favour of the right data set. By default, it also plots the grand best and worst attainment surfaces, that is, the 0\% and 100\%-attainment surfaces over all data. This two surfaces delimit the area where differences may exist. In addition, it also plots the 50\%-attainment surface of each data set. With \code{type = "point"}, only the points where there is a change in the value of the EAF difference are plotted. This means that for areas where the EAF differences stays constant, the region will appear in white even if the value of the differences in that region is large. This explain "white holes" surrounded by black points. With \code{type = "area"}, the area where the EAF differences has a certain value is plotted. The idea for the algorithm to compute the areas was provided by Carlos M. Fonseca. The implementation uses R polygons, which some PDF viewers may have trouble rendering correctly (See \url{https://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-are-there-unwanted-borders}). Plots (should) look correct when printed. Large differences that appear when using \code{type = "points"} may seem to dissapear when using \code{type = "area"}. The explanation is the points size is independent of the axes range, therefore, the plotted points may seem to cover a much larger area than the actual number of points. On the other hand, the areas size is plotted with respect to the objective space, without any extra borders. If the range of an area becomes smaller than one-pixel, it won't be visible. As a consequence, zooming in or out certain regions of the plots does not change the apparent size of the points, whereas it affects considerably the apparent size of the areas. } \examples{ A1 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", "ALG_1_dat")) A2 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", "ALG_2_dat")) \donttest{# These take time eafdiffplot(A1, A2, full.eaf = TRUE) eafdiffplot(A1, A2, type = "area") eafdiffplot(A1, A2, type = "point") } # A more complex example a1 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", "wrots_l100w10_dat")) a2 <- read.data.sets(file.path(system.file(package="eaf"), "extdata", "wrots_l10w100_dat")) DIFF <- eafdiffplot(a1, a2, col = c("white", "blue", "red"), intervals = 5, type = "point", title.left = expression("W-RoTS, " * lambda * "=" * 100 * ", " * omega * "=" * 10), title.right= expression("W-RoTS, " * lambda * "=" * 10 * ", " * omega * "=" * 100), right.panel.last={ abline(a = 0, b = 1, col = "red", lty = "dashed")}) DIFF$right[,3] <- -DIFF$right[,3] ## Save the values to a file. # write.table(rbind(DIFF$left,DIFF$right), # file = "wrots_l100w10_dat-wrots_l10w100_dat-diff.txt", # quote = FALSE, row.names = FALSE, col.names = FALSE) } \seealso{ \code{\link{read.data.sets}}, \code{\link{eafplot}} } \keyword{graphs} eaf/man/SPEA2relativeRichmond.Rd0000644000176200001440000000174313254545527016122 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf-package.R \docType{data} \name{SPEA2relativeRichmond} \alias{SPEA2relativeRichmond} \title{Results of SPEA2 with relative time-controlled triggers on Richmond water network.} \format{A data frame as produced by \code{\link{read.data.sets}}.} \source{ Manuel \enc{López-Ibáñez}{Lopez-Ibanez}. Operational Optimisation of Water Distribution Networks. PhD thesis, School of Engineering and the Built Environment, Edinburgh Napier University, UK, 2009. } \usage{ SPEA2relativeRichmond } \description{ The data has the only goal of providing an example of use of eafplot. } \examples{ data(HybridGA) data(SPEA2relativeRichmond) eafplot (SPEA2relativeRichmond, percentiles = c(25, 50, 75), xlab = expression(C[E]), ylab = "Total switches", xlim = c(90, 140), ylim = c(0, 25), extra.points = HybridGA$richmond, extra.lty = "dashed", extra.legend = "Hybrid GA") } \keyword{datasets} eaf/man/eafplot.Rd0000644000176200001440000001372613254545527013526 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calls.R, R/eaf.R \name{eafplot} \alias{eafplot} \alias{eafplot.formula} \alias{eafplot.list} \alias{eafplot.data.frame} \alias{eafplot.default} \title{Plot the Empirical Attainment Function for two objectives generic} \usage{ eafplot(x, ...) \method{eafplot}{formula}(formula, data, groups = NULL, subset = NULL, ...) \method{eafplot}{list}(x, ...) \method{eafplot}{data.frame}(x, y = NULL, ...) \method{eafplot}{default}(x, sets = NULL, groups = NULL, percentiles = c(0, 50, 100), attsurfs = NULL, xlab = "objective 1", ylab = "objective 2", xlim = NULL, ylim = NULL, log = "", type = "point", col = NULL, lty = c("dashed", "solid", "solid", "solid", "dashed"), lwd = c(1.75), pch = NA, cex.pch = par("cex"), las = par("las"), legend.pos = "topright", legend.txt = NULL, extra.points = NULL, extra.legend = NULL, extra.pch = c(4:25), extra.lwd = 0.5, extra.lty = "dashed", extra.col = "black", maximise = c(FALSE, FALSE), xaxis.side = "below", yaxis.side = "left", axes = TRUE, ...) } \arguments{ \item{x}{Either a matrix of data values, or a data frame, or a list of data frames of exactly three columns.} \item{...}{Other graphical parameters to \code{\link{plot.default}}.} \item{formula}{A formula of the type: \code{time + cost ~ run | instance} will draw \code{time} on the x-axis and \code{cost} on the y-axis. If \code{instance} is present the plot is conditional to the instances.} \item{data}{Dataframe containing the fields mentioned in the formula and in groups.} \item{groups}{This may be used to plot profiles of different algorithms on the same plot.} \item{subset}{A vector indicating which rows of the data should be used. If left to default \code{NULL} all data in the data frame are used.} \item{y}{Either a matrix of data values, or a data frame.} \item{sets}{Vector indicating which set each point belongs to.} \item{percentiles}{Vector indicating which percentile should be plot. The default is to plot only the median attainment curve.} \item{attsurfs}{TODO} \item{xlab, ylab, xlim, ylim, log, col, lty, lwd, pch, cex.pch, las}{Graphical parameters, see \code{\link{plot.default}}.} \item{type}{string giving the type of plot desired. The following values are possible, \samp{points} and \samp{area}.} \item{legend.pos}{the position of the legend, see \code{\link{legend}}.} \item{legend.txt}{a character or expression vector to appear in the legend. If \code{NULL}, appropriate labels will be generated.} \item{extra.points}{A list of matrices or data.frames with two-columns. Each element of the list defines a set of points, or lines if one of the columns is \code{NA}.} \item{extra.legend}{A character vector providing labels for the groups of points.} \item{extra.pch, extra.lwd, extra.lty, extra.col}{Control the graphical aspect of the points. See \code{\link{points}} and \code{\link{lines}}.} \item{maximise}{Whether the first and/or second objective correspond to a maximisation problem.} \item{xaxis.side}{On which side that xaxis is drawn. Valid values are "below" and "above". See \code{\link{axis}}.} \item{yaxis.side}{On which side that yaxis is drawn. Valid values are "left" and "right". See \code{\link{axis}}.} \item{axes}{A logical value indicating whether both axes should be drawn on the plot.} } \value{ No value is returned. } \description{ Computes and plots the Empirical Attainment Function, either as attainment surfaces for certain percentiles or as points. } \details{ This function can be used to plot random sets of points like those obtained by different runs of biobjective stochastic optimization algorithms. An EAF curve represents the boundary separating points that are known to be attainable (that is, dominated in Pareto sense) in at least a fraction (quantile) of the runs from those that are not. The median EAF represents the curve where the fraction of attainable points is 50\%. In single objective optimization the function can be used to plot the profile of solution quality over time of a collection of runs of a stochastic optimizer. } \section{Methods (by class)}{ \itemize{ \item \code{formula}: Formula interface \item \code{list}: List interface for lists of data.frames \item \code{data.frame}: Data.frame interface \item \code{default}: Main function }} \examples{ data(gcp2x2) tabucol <- subset(gcp2x2, alg != "TSinN1") tabucol$alg <- tabucol$alg[drop=TRUE] eafplot(time+best~run,data=tabucol,subset=tabucol$inst=="DSJC500.5") \dontrun{ # These take time eafplot(time+best~run|inst,groups=alg,data=gcp2x2) eafplot(time+best~run|inst,groups=alg,data=gcp2x2, percentiles=c(0,50,100),include.extremes=TRUE, cex=1.4, lty=c(2,1,2),lwd=c(2,2,2), col=c("black","blue","grey50")) A1 <- read.data.sets(file.path(system.file(package = "eaf"), "extdata", "ALG_1_dat")) A2 <- read.data.sets(file.path(system.file(package = "eaf"), "extdata", "ALG_2_dat")) eafplot(A1, A2, percentiles = c(50)) eafplot(list(A1 = A1, A2 = A2), percentiles = c(50)) ## Save as a PDF file. # dev.copy2pdf(file = "eaf.pdf", onefile = TRUE, width = 5, height = 4) } ## Using extra.points \dontrun{ data(HybridGA) data(SPEA2relativeVanzyl) eafplot(SPEA2relativeVanzyl, percentiles = c(25, 50, 75), xlab = expression(C[E]), ylab = "Total switches", xlim = c(320, 400), extra.points = HybridGA$vanzyl, extra.legend = "Hybrid GA") data(SPEA2relativeRichmond) eafplot (SPEA2relativeRichmond, percentiles = c(25, 50, 75), xlab = expression(C[E]), ylab = "Total switches", xlim = c(90, 140), ylim = c(0, 25), extra.points = HybridGA$richmond, extra.lty = "dashed", extra.legend = "Hybrid GA") data(SPEA2minstoptimeRichmond) SPEA2minstoptimeRichmond[,2] <- SPEA2minstoptimeRichmond[,2] / 60 eafplot (SPEA2minstoptimeRichmond, xlab = expression(C[E]), ylab = "Minimum idle time (minutes)", las = 1, log = "y", maximise = c(FALSE, TRUE), main = "SPEA2 (Richmond)") } } \seealso{ \code{\link{read.data.sets}} \code{\link{eafdiffplot}} } \keyword{graphs} eaf/man/SPEA2minstoptimeRichmond.Rd0000644000176200001440000000211113254545527016645 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf-package.R \docType{data} \name{SPEA2minstoptimeRichmond} \alias{SPEA2minstoptimeRichmond} \title{Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network.} \format{A data frame as produced by \code{\link{read.data.sets}}. The second column measures time in seconds and corresponds to a maximisation problem.} \source{ Manuel \enc{López-Ibáñez}{Lopez-Ibanez}. Operational Optimisation of Water Distribution Networks. PhD thesis, School of Engineering and the Built Environment, Edinburgh Napier University, UK, 2009. } \usage{ SPEA2minstoptimeRichmond } \description{ The data has the only goal of providing an example of use of eafplot. } \examples{ data(HybridGA) data(SPEA2minstoptimeRichmond) SPEA2minstoptimeRichmond[,2] <- SPEA2minstoptimeRichmond[,2] / 60 eafplot (SPEA2minstoptimeRichmond, xlab = expression(C[E]), ylab = "Minimum idle time (minutes)", las = 1, log = "y", maximise = c(FALSE, TRUE)) } \keyword{datasets} eaf/man/gcp2x2.Rd0000644000176200001440000000443213254545527013173 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf-package.R \docType{data} \name{gcp2x2} \alias{gcp2x2} \title{Metaheuristics for solving the Graph Vertex Coloring Problem} \format{A data frame with 3133 observations on the following 6 variables. \describe{ \item{\code{alg}}{a factor with levels \code{SAKempeFI} and \code{TSinN1}} \item{\code{inst}}{a factor with levels \code{DSJC500.5} and \code{DSJC500.9}. Instances are taken from the DIMACS repository.} \item{\code{run}}{a numeric vector indicating the run to which the observation belong. } \item{\code{best}}{a numeric vector indicating the best solution in number of colors found in the corresponding run up to that time.} \item{\code{time}}{a numeric vector indicating the time since the beginning of the run for each observation. A rescaling is applied.} \item{\code{titer}}{a numeric vector indicating iteration number corresponding to the observations.} }} \source{ M. Chiarandini (2005). Stochastic local search methods for highly constrained combinatorial optimisation problems. Ph.D. thesis, Computer Science Department, Darmstadt University of Technology, Darmstadt, Germany. page 138. } \usage{ gcp2x2 } \description{ Two metaheuristic algorithms, TabuCol (Hertz et al., 1987) and simulated annealing (Johnson et al., 1991), to find a good approximation of the chromatic number of two random graphs. The data here has the only goal of providing an example of use of eafplot for comparing algorithm performance with respect to both time and quality when modelled as two objectives in trade off. } \details{ Each algorithm was run 10 times per graph registering the time and iteration number at which a new best solution was found. A time limit corresponding to 500*10^5 total iterations of TabuCol was imposed. The time was then normalized on a scale from 0 to 1 to make it instance independent. } \examples{ data(gcp2x2) } \references{ A. Hertz and D. de Werra. Using Tabu Search Techniques for Graph Coloring. Computing, 1987, 39(4), 345-351. D.S. Johnson, C.R. Aragon, L.A. McGeoch and C. Schevon. Optimization by Simulated Annealing: An Experimental Evaluation; Part II, Graph Coloring and Number Partitioning. Operations Research, 1991, 39(3), 378-406 } \keyword{datasets} eaf/man/HybridGA.Rd0000644000176200001440000000165313254545527013521 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf-package.R \docType{data} \name{HybridGA} \alias{HybridGA} \title{Results of Hybrid GA on vanzyl and Richmond water networks} \format{A list with two data frames, each of them with three columns, as produced by \code{\link{read.data.sets}}. \describe{ \item{\code{$vanzyl}}{data frame of results on vanzyl network} \item{\code{$richmond}}{data frame of results on Richmond network. The second column is filled with \code{NA}} }} \source{ Manuel \enc{López-Ibáñez}{Lopez-Ibanez}. Operational Optimisation of Water Distribution Networks. PhD thesis, School of Engineering and the Built Environment, Edinburgh Napier University, UK, 2009. } \usage{ HybridGA } \description{ The data has the only goal of providing an example of use of eafplot. } \examples{ data(HybridGA) print(HybridGA$vanzyl) print(HybridGA$richmond) } \keyword{datasets} eaf/man/SPEA2relativeVanzyl.Rd0000644000176200001440000000163713254545527015644 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf-package.R \docType{data} \name{SPEA2relativeVanzyl} \alias{SPEA2relativeVanzyl} \title{Results of SPEA2 with relative time-controlled triggers on Vanzyl's water network.} \format{A data frame as produced by \code{\link{read.data.sets}}.} \source{ Manuel \enc{López-Ibáñez}{Lopez-Ibanez}. Operational Optimisation of Water Distribution Networks. PhD thesis, School of Engineering and the Built Environment, Edinburgh Napier University, UK, 2009. } \usage{ SPEA2relativeVanzyl } \description{ The data has the only goal of providing an example of use of eafplot. } \examples{ data(HybridGA) data(SPEA2relativeVanzyl) eafplot(SPEA2relativeVanzyl, percentiles = c(25, 50, 75), xlab = expression(C[E]), ylab = "Total switches", xlim = c(320, 400), extra.points = HybridGA$vanzyl, extra.legend = "Hybrid GA") } \keyword{datasets} eaf/man/read.data.sets.Rd0000644000176200001440000000334313254545527014666 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf.R \name{read.data.sets} \alias{read.data.sets} \title{Read several data.frame sets} \usage{ read.data.sets(file, col.names) } \arguments{ \item{file}{Filename that contains the data. Each row of the table appears as one line of the file. If it does not contain an \emph{absolute} path, the file name is \emph{relative} to the current working directory, \code{getwd()}. Tilde-expansion is performed where supported.} \item{col.names}{Vector of optional names for the variables. The default is to use \samp{"V"} followed by the column number.} } \value{ A data frame (\code{data.frame}) containing a representation of the data in the file. An extra column \code{set} is added to indicate to which set each row belongs. } \description{ Reads a text file in table format and creates a data frame from it. The file may contain several sets, separated by empty lines. The function adds an additional column \code{set} to indicate to which set each row belongs. } \note{ There are several examples of data sets in \code{file.path(system.file(package="eaf"),"extdata")}. } \section{Warning}{ A known limitation is that the input file must use newline characters native to the host system, otherwise they will be, possibly silently, misinterpreted. In GNU/Linux the program \code{dos2unix} may be used to fix newline characters. } \examples{ A1<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_1_dat")) str(A1) A2<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_2_dat")) str(A2) } \seealso{ \code{\link{read.table}}, \code{\link{eafplot}}, \code{\link{eafdiffplot}} } \author{ Manuel \enc{López-Ibáñez}{Lopez-Ibanez} } \keyword{file} eaf/man/eaf-package.Rd0000644000176200001440000001025513254747464014216 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eaf-package.R \docType{package} \name{eaf-package} \alias{eaf-package} \alias{_PACKAGE} \alias{eaf} \title{Plots of the Empirical Attainment Function} \description{ The empirical attainment function (EAF) describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. This package implements plots of summary attainment surfaces and differences between the first-order EAFs. These plots may be used for exploring the performance of stochastic local search algorithms for biobjective optimization problems and help in identifying certain algorithmic behaviors in a graphical way. } \details{ Functions: \tabular{rl}{ \code{\link{eafdiffplot}} \tab Empirical attainment function differences\cr \code{\link{eafplot}} \tab Plot the Empirical Attainment Function for two objectives\cr \code{\link{read.data.sets}}\tab Read several data.frame sets\cr } Data: \describe{ \item{\code{\link{gcp2x2}}}{ Metaheuristics for solving the Graph Vertex Coloring Problem} \item{\code{\link{HybridGA}}}{ Results of Hybrid GA on vanzyl and Richmond water networks} \item{\code{\link{SPEA2minstoptimeRichmond}}}{ Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network} } Extras are available at \code{file.path(system.file(package="eaf"))}: \tabular{ll}{ \code{extdata} \tab External data sets (see \code{\link{read.data.sets}})\cr \code{scripts/eaf} \tab EAF command-line program\cr \code{scripts/eafplot}\tab Perl script to generate plots of attainment surfaces\cr \code{scripts/eafdiff}\tab Perl script to generate plots of EAF differences\cr } } \examples{ data(gcp2x2) tabucol<-subset(gcp2x2, alg!="TSinN1") tabucol$alg<-tabucol$alg[drop=TRUE] eafplot(time+best~run,data=tabucol,subset=tabucol$inst=="DSJC500.5") eafplot(time+best~run|inst,groups=alg,data=gcp2x2) eafplot(time+best~run|inst,groups=alg,data=gcp2x2, percentiles=c(0,50,100),include.extremes=TRUE, cex=1.4, lty=c(2,1,2),lwd=c(2,2,2), col=c("black","blue","grey50")) A1<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_1_dat")) A2<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_2_dat")) eafplot(A1,A2, percentiles=c(50)) eafplot(list(A1=A1, A2=A2), percentiles=c(50)) eafdiffplot(A1, A2) ## Save to a PDF file # dev.copy2pdf(file="eaf.pdf", onefile=TRUE, width=5, height=4) } \references{ V. Grunert da Fonseca, C. M. Fonseca, and A. O. Hall, \emph{Inferential performance assessment of stochastic optimisers and the attainment function}, in Evolutionary Multi-Criterion Optimization. First International Conference, EMO 2001 (E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, and D. Corne, eds.), vol. 1993 of Lecture Notes in Computer Science, pp. 213-225, Berlin: Springer, 2001. V. Grunert da Fonseca and C. M. Fonseca, \emph{The attainment-function approach to stochastic multiobjective optimizer assessment and comparison}. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 103-130, Springer, Berlin, Germany, 2010. M. \enc{López-Ibáñez}{Lopez-Ibanez}, L. Paquete, and T. \enc{Stützle}{Stuetzle}. \emph{Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization}. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 209–222. Springer, Berlin, Germany, 2010. doi: 10.1007/978-3-642-02538-9_9 } \seealso{ Useful links: \itemize{ \item \url{http://lopez-ibanez.eu/eaftools} \item \url{https://github.com/MLopez-Ibanez/eaf} \item Report bugs at \url{https://github.com/MLopez-Ibanez/eaf/issues} } } \author{ Maintainer: Manuel \enc{López-Ibáñez}{Lopez-Ibanez} \email{manuel.lopez-ibanez@ulb.ac.be} Contributors: Carlos Fonseca, Luis Paquete, Thomas \enc{Stützle}{Stuetzle}, Manuel \enc{López-Ibáñez}{Lopez-Ibanez} and Marco Chiarandini. } \concept{ multivariate optimize time-quality algorithm profile empirical attainment function } \keyword{graphs} \keyword{package}