MediaWiki API result
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"continue": {
"gapcontinue": "Robot_labs_around_the_World",
"continue": "gapcontinue||"
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"main": {
"*": "Subscribe to the mediawiki-api-announce mailing list at <https://lists.wikimedia.org/mailman/listinfo/mediawiki-api-announce> for notice of API deprecations and breaking changes."
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"revisions": {
"*": "Because \"rvslots\" was not specified, a legacy format has been used for the output. This format is deprecated, and in the future the new format will always be used."
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"1509": {
"pageid": 1509,
"ns": 0,
"title": "Repository of R scripts used by IRIDIA members",
"revisions": [
{
"contentformat": "text/x-wiki",
"contentmodel": "wikitext",
"*": "==Max's scripts==\n\n===Boxplot of solution values by algorithm===\n\n[[Image:Tai100a 1000.png]]\n\nI organize the data in the following way:\n\nin the file '''optimal_values.txt''' I put the best-known solution values for the problem instances I'm testing\n<pre>\ncat optimal_values.txt \n\ninstance optimum \nsko100a 152002 \nsko100e 149150 \ntai100a 21059006 \ntai100b 1185996137 \n</pre>\n\n\nin the files '''algorithm_factors_instance-size_cut-time.txt''' I record the history of the search of the algo for the instance\n<pre>\n head sequential_2-opt_100_8000.txt \n\nidalgo topo schema ls type cpu_id instance try best time iteration\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 155468 0.31 1\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 155390 0.31 1\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 155000 0.31 1\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 154934 0.64 2\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 154608 0.97 3\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 153958 1.27 4\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 153750 1.93 6\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 153720 1.93 6\nSEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 153634 2.57 8\n</pre>\n\nIn order to produce boxplots like the one above you can look at the R source used to produce it. \n\n\n<pre>\noptimal_values<-read.table(\"optimal_values_100.txt\",header=TRUE)\nresPIR2OPT<-read.table(\"parallel_independent_2-opt_100_100.txt\",header=TRUE)\nresSEQ2OPT<-read.table(\"sequential_2-opt_100_800.txt\",header=TRUE)\nresSEQ22OPT<-read.table(\"sequential2_2-opt_100_100.txt\",header=TRUE)\nresFC1x102OPT<-read.table(\"fc.1.x.10_2-opt_100_100.txt\",header=TRUE)\nresFC26102OPT<-read.table(\"fc.2.6.10_2-opt_100_100.txt\",header=TRUE)\nresFC27102OPT<-read.table(\"fc.2.7.10_2-opt_100_100.txt\",header=TRUE)\nresFC28102OPT<-read.table(\"fc.2.8.10_2-opt_100_100.txt\",header=TRUE)\nresFC29102OPT<-read.table(\"fc.2.9.10_2-opt_100_100.txt\",header=TRUE)\nresFC36102OPT<-read.table(\"fc.3.6.10_2-opt_100_100.txt\",header=TRUE)\nresFC37102OPT<-read.table(\"fc.3.7.10_2-opt_100_100.txt\",header=TRUE)\nresFC38102OPT<-read.table(\"fc.3.8.10_2-opt_100_100.txt\",header=TRUE)\nresFC39102OPT<-read.table(\"fc.3.9.10_2-opt_100_100.txt\",header=TRUE)\nresHC1x102OPT<-read.table(\"hc.1.x.10_2-opt_100_100.txt\",header=TRUE)\nresHC26102OPT<-read.table(\"hc.2.6.10_2-opt_100_100.txt\",header=TRUE)\nresHC27102OPT<-read.table(\"hc.2.7.10_2-opt_100_100.txt\",header=TRUE)\nresHC28102OPT<-read.table(\"hc.2.8.10_2-opt_100_100.txt\",header=TRUE)\nresHC29102OPT<-read.table(\"hc.2.9.10_2-opt_100_100.txt\",header=TRUE)\nresHC36102OPT<-read.table(\"hc.3.6.10_2-opt_100_100.txt\",header=TRUE)\nresHC37102OPT<-read.table(\"hc.3.7.10_2-opt_100_100.txt\",header=TRUE)\nresHC38102OPT<-read.table(\"hc.3.8.10_2-opt_100_100.txt\",header=TRUE)\nresHC39102OPT<-read.table(\"hc.3.9.10_2-opt_100_100.txt\",header=TRUE)\nresRW1x102OPT<-read.table(\"rw.1.x.10_2-opt_100_100.txt\",header=TRUE)\nresRW26102OPT<-read.table(\"rw.2.6.10_2-opt_100_100.txt\",header=TRUE)\nresRW27102OPT<-read.table(\"rw.2.7.10_2-opt_100_100.txt\",header=TRUE)\nresRW28102OPT<-read.table(\"rw.2.8.10_2-opt_100_100.txt\",header=TRUE)\nresRW29102OPT<-read.table(\"rw.2.9.10_2-opt_100_100.txt\",header=TRUE)\nresRW36102OPT<-read.table(\"rw.3.6.10_2-opt_100_100.txt\",header=TRUE)\nresRW37102OPT<-read.table(\"rw.3.7.10_2-opt_100_100.txt\",header=TRUE)\nresRW38102OPT<-read.table(\"rw.3.8.10_2-opt_100_100.txt\",header=TRUE)\nresRW39102OPT<-read.table(\"rw.3.9.10_2-opt_100_100.txt\",header=TRUE)\nresUR1x102OPT<-read.table(\"ur.1.x.10_2-opt_100_100.txt\",header=TRUE)\nresUR26102OPT<-read.table(\"ur.2.6.10_2-opt_100_100.txt\",header=TRUE)\nresUR27102OPT<-read.table(\"ur.2.7.10_2-opt_100_100.txt\",header=TRUE)\nresUR28102OPT<-read.table(\"ur.2.8.10_2-opt_100_100.txt\",header=TRUE)\nresUR29102OPT<-read.table(\"ur.2.9.10_2-opt_100_100.txt\",header=TRUE)\nresUR36102OPT<-read.table(\"ur.3.6.10_2-opt_100_100.txt\",header=TRUE)\nresUR37102OPT<-read.table(\"ur.3.7.10_2-opt_100_100.txt\",header=TRUE)\nresUR38102OPT<-read.table(\"ur.3.8.10_2-opt_100_100.txt\",header=TRUE)\nresUR39102OPT<-read.table(\"ur.3.9.10_2-opt_100_100.txt\",header=TRUE)\n\nres<-rbind(resFC1x102OPT,resFC26102OPT,resFC27102OPT,resFC28102OPT,resFC29102OPT,resFC36102OPT,\nresFC37102OPT,resFC38102OPT,resFC39102OPT,resRW1x102OPT,resRW26102OPT,resRW27102OPT,resRW28102OPT,\nresRW29102OPT,resRW36102OPT,resRW37102OPT,resRW38102OPT,resRW39102OPT,resHC1x102OPT,resHC26102OPT,\nresHC27102OPT,resHC28102OPT,resHC29102OPT,resHC36102OPT,resHC37102OPT,resHC38102OPT,resHC39102OPT,\nresUR1x102OPT,resUR26102OPT,resUR27102OPT,resUR28102OPT,resUR29102OPT,resUR36102OPT,resUR37102OPT,\nresUR38102OPT,resUR39102OPT,resPIR2OPT,resSEQ2OPT,resSEQ22OPT)\n\nlinstance<-levels(res$instance)\n\nres.split<-split(1:nrow(res), list(res$instance, res$try, res$idalgo), drop=TRUE)\n\nmin.list <- lapply(res.split, function(x){\n x[match(min(res$best[x]), res$best[x])]\n })\n\nmin.vector <- unlist(min.list)\n\nbestalgo<-res[min.vector,]\n\nbestalgo.split <- split(1:nrow(bestalgo), bestalgo$instance, drop=TRUE)\n\nfor (i in (1:length(bestalgo.split)))\n{\n bestalgo.vector <- unlist(bestalgo.split[i])\n bestalgo.temp <- bestalgo[bestalgo.vector,]\n l<-split(bestalgo.temp$best,bestalgo.temp$idalgo)\n\n epsfile=paste(linstance[i],\"_100_nolim.eps\",sep=\"\")\n postscript(file=epsfile,onefile=TRUE,horizontal=TRUE)\n par(mar=c(5,5,5,3),cex.axis=0.7,las=2,mgp=c(4, 1, 0))\n title_plot=paste(\"100 iterations - instance \",linstance[i],sep=\"\")\n boxplot(l,xlab=\"\",ylab=\"solution value\",names=c(levels(bestalgo$idalgo)),main=title_plot,\n yaxt=\"n\",ylim=c(optimal_values[optimal_values$instance==linstance[i],]$optimum,max(bestalgo.temp$best)))\n axis(2, seq(from=optimal_values[optimal_values$instance==linstance[i],]$optimum,to=max(bestalgo.temp$best),length.out=10))\n abline(h=optimal_values[optimal_values$instance==linstance[i],]$optimum)\n # draw an orizontal line at the y-level of the best-know solution value\n grid(nx=0, ny=55,col=\"gray5\")\n dev.off()\n}\n</pre>\n\n====Things to improve====\n\nUse a separate text file that contains all the id data on an algorithm (idalgo, topo, schema, ls, type) to reduce the size of the results text files (for the QAP experiments I obtained something like 500MB of text results files, half of which are redundant data)\n<pre>\nresult.txt should look like:\nidalgo cpu_id instance try best time iteration\n^^^^^^ ^^^^^^^^\nalgorithms.txt should look like:\nidalgo topo schema ls type number_of_cpus \n^^^^^\n\ninstances.txt should look like:\ninstance best-known-solution-value\n^^^^^^^^\n</pre>"
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"1511": {
"pageid": 1511,
"ns": 0,
"title": "Repository of scripts used by IRIDIA members",
"revisions": [
{
"contentformat": "text/x-wiki",
"contentmodel": "wikitext",
"*": "==Max's scripts==\n\nWhen testing different algorithms on some combinatorial optimization problem, I usually organize the data on my machine according to a same schema. Basically I create the following structure of directories to store sources, problem instances, output data, executables and scripts.\n<pre>\n+ main_project_folder\n|\n|-+ bin (contains the executables of the algorithms)\n|\n|-+ instances (contains the problem instances)\n|\n|-+ out (contains the outputs of the trial)\n| |\n| |-+ algorithm1\n| | |\n| | |-+ instance1\n| | | \n| | |-+ instance2\n| |\n| |-+ algorithm2\n| |\n| |-+ instance1\n| | \n| |-+ instance2\n|\n|-+ sh (contains the script to launch the experiments)\n|\n|-+ src (contains the sources of the algorithms)\n| |\n| |-+ algorithm1\n| | \n| |-+ algorithm2\n|\n|-+ analysis (contains the R script to boxplot the experimental data)\n</pre>\n\n\n\n===Boxplot of solution values by algorithm===\n\n[[Image:Tai100a 1000.png]]\n\nI organize the data in the following way:\n\nin the file '''optimal_values.txt''' I put the best-known solution values for the problem instances I'm testing\n<pre>\ninstance optimum \nsko100a 152002 \nsko100e 149150 \ntai100a 21059006 \ntai100b 1185996137 \n</pre>\n\n\nin the files '''algorithms.txt''' I put the algorithms details\n<pre>\nidalgo topo schema ls type number_of_cpus\nPIR-2opt PIR PIR 2 Par 2\nPIR-ts PIR PIR 3 Par 2\nSEQ-2opt SEQ SEQ 2 Par 2\nSEQ-ts SEQ SEQ 3 Par 2\nSEQ2-2opt SEQ SEQ 2 Par 2\nSEQ2-ts SEQ SEQ 3 Par 2\nHC.1.x.10-2opt HC 1.x.10 2 Par 2\nHC.2.6.10-2opt HC 2.6.10 2 Par 2\nHC.2.7.10-2opt HC 2.7.10 2 Par 2\n</pre>\n\n\nin the files '''algorithm_factors_instance-size_cut-time.txt''' I record the history of the search of the algo for the instance\n<pre>\nidalgo cpu_id instance try best time iteration\nHC.1.x.10-2opt 0 sko100a 1 155254 0.35 1\nHC.1.x.10-2opt 0 sko100a 1 154162 0.35 1\nHC.1.x.10-2opt 0 sko100a 1 154050 0.35 1\nHC.1.x.10-2opt 0 sko100a 1 153684 2.69 8\nHC.1.x.10-2opt 0 sko100a 1 153508 3.24 10\nHC.1.x.10-2opt 0 sko100a 1 153396 3.24 10\nHC.1.x.10-2opt 0 sko100a 1 153344 3.24 10\nHC.1.x.10-2opt 0 sko100a 1 153092 3.52 11\nHC.1.x.10-2opt 0 sko100a 1 153062 3.75 12\n</pre>\n\nIn order to produce boxplots like the one above you can look at the R source used to produce it. \n\n\n<pre>\noptimal_values<-read.table(\"optimal_values.txt\",header=TRUE)\n\nresPIR2OPT<-read.table(\"parallel_independent_2-opt_100_1000.txt\",header=TRUE)\nresSEQ2OPT<-read.table(\"sequential_2-opt_100_2000.txt\",header=TRUE)\nresSEQ22OPT<-read.table(\"sequential2_2-opt_100_1000.txt\",header=TRUE)\nresHC1x102OPT<-read.table(\"hc.1.x.10_2-opt_100_1000.txt\",header=TRUE)\nresHC26102OPT<-read.table(\"hc.2.6.10_2-opt_100_1000.txt\",header=TRUE)\nresHC27102OPT<-read.table(\"hc.2.7.10_2-opt_100_1000.txt\",header=TRUE)\nresHC28102OPT<-read.table(\"hc.2.8.10_2-opt_100_1000.txt\",header=TRUE)\nresHC29102OPT<-read.table(\"hc.2.9.10_2-opt_100_1000.txt\",header=TRUE)\nresHC36102OPT<-read.table(\"hc.3.6.10_2-opt_100_1000.txt\",header=TRUE)\nresHC37102OPT<-read.table(\"hc.3.7.10_2-opt_100_1000.txt\",header=TRUE)\nresHC38102OPT<-read.table(\"hc.3.8.10_2-opt_100_1000.txt\",header=TRUE)\nresHC39102OPT<-read.table(\"hc.3.9.10_2-opt_100_1000.txt\",header=TRUE)\nresRW1x102OPT<-read.table(\"rw.1.x.10_2-opt_100_1000.txt\",header=TRUE)\nresRW26102OPT<-read.table(\"rw.2.6.10_2-opt_100_1000.txt\",header=TRUE)\nresRW27102OPT<-read.table(\"rw.2.7.10_2-opt_100_1000.txt\",header=TRUE)\nresRW28102OPT<-read.table(\"rw.2.8.10_2-opt_100_1000.txt\",header=TRUE)\nresRW29102OPT<-read.table(\"rw.2.9.10_2-opt_100_1000.txt\",header=TRUE)\nresRW36102OPT<-read.table(\"rw.3.6.10_2-opt_100_1000.txt\",header=TRUE)\nresRW37102OPT<-read.table(\"rw.3.7.10_2-opt_100_1000.txt\",header=TRUE)\nresRW38102OPT<-read.table(\"rw.3.8.10_2-opt_100_1000.txt\",header=TRUE)\nresRW39102OPT<-read.table(\"rw.3.9.10_2-opt_100_1000.txt\",header=TRUE)\n\nres<-rbind(resRW1x102OPT,resRW26102OPT,resRW27102OPT,resRW28102OPT,resRW29102OPT,\nresRW36102OPT,resRW37102OPT,resRW38102OPT,resRW39102OPT,resHC1x102OPT,resHC26102OPT,\nresHC27102OPT,resHC28102OPT,resHC29102OPT,resHC36102OPT,resHC37102OPT,resHC38102OPT,\nresHC39102OPT,resPIR2OPT,resSEQ2OPT,resSEQ22OPT)\n\n\nlinstance<-levels(res$instance)\n\nres.split<-split(1:nrow(res), list(res$instance, res$try, res$idalgo), drop=TRUE)\n\nmin.list <- lapply(res.split, function(x){\n x[match(min(res$best[x]), res$best[x])]\n })\n\n# matches return the first among all the values with min best!!!\n# so is not the one with minimal time\n\nmin.vector <- unlist(min.list)\n\nbestalgo<-res[min.vector,]\n\nbestalgo.split <- split(1:nrow(bestalgo), bestalgo$instance, drop=TRUE)\n\nfor (i in (1:length(bestalgo.split)))\n{\n bestalgo.vector <- unlist(bestalgo.split[i])\n bestalgo.temp <- bestalgo[bestalgo.vector,]\n l<-split(as.double(bestalgo.temp$best),bestalgo.temp$idalgo)\n\n epsfile=paste(linstance[i],\"_1000.eps\",sep=\"\")\n postscript(file=epsfile,onefile=TRUE,horizontal=TRUE)\n par(mar=c(5,5,5,3),cex.axis=0.7,las=2,mgp=c(4, 1, 0))\n title_plot=paste(\"2 CPU - 1000 iterations - instance \",linstance[i],sep=\"\")\n boxplot(l,xlab=\"\",ylab=\"solution value\",names=c(levels(bestalgo$idalgo)),main=title_plot,\nyaxt=\"n\",ylim=c(min(min(bestalgo.temp$best),optimal_values[optimal_values$instance==linstance[i],]$optimum),\nmax(bestalgo.temp$best)))\n axis(2, seq(from=min(min(bestalgo.temp$best),optimal_values[optimal_values$instance==linstance[i],]$optimum),\nto=max(bestalgo.temp$best),length.out=10))\n abline(h=optimal_values[optimal_values$instance==linstance[i],]$optimum)\n grid(nx=0, ny=55,col=\"gray5\")\n dev.off()\n}\n</pre>"
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