AENC/switchchain Changeset - 9c4226491043 · Centrum Wiskunde & Informatica (CWI)

Changeset - 9c4226491043

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Tom Bannink - 8 years ago 2017-05-22 17:08:04
tombannink@gmail.com

Split gcm-initial-triangles notebook

3 files changed with 97 insertions and 10 deletions:

cpp/switchchain_initialtris.cpp

triangle_analysis.m

triangle_gcm_initial_analysis.m

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cpp/switchchain_initialtris.cpp

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@@ @@ -223,6 +223,9 @@ int main() { @@
+                    }
+                }
                 std::cout << "Running n = " << numVertices << ", tau = " << tau
                           << "." << std::flush;
                 //
                 // Test the GCM1 and GCM2 success rate
                 //
@@ @@ -250,9 +253,6 @@ int main() { @@
                     return 1;
+                }
                 std::cout << "Running n = " << numVertices << ", tau = " << tau
                           << ". \t" << std::flush;
                 int mixingTime = (32.0f - 20.0f * (tau - 2.0f)) * numVertices;
                 constexpr int measurements = 20;
                 constexpr int measureSkip = 200;
@@ @@ -261,6 +261,8 @@ int main() { @@
                 long long trianglesTotal = 0;
                 std::cout << "  .. \t" << std::flush;
                 for (int i = 0; i < mixingTime; ++i) {
                     if (chain.doMove())
                         ++movesDone;
@@ @@ -269,7 +271,7 @@ int main() { @@
                     for (int j = 0; j < measureSkip; ++j)
                         if (chain.doMove())
                             ++movesDone;
                     trianglesTotal = chain.g.countTriangles();
+                    trianglesTotal += chain.g.countTriangles();
+                }
                 std::cout << movesDone << '/' << mixingTime + measurements * measureSkip

triangle_analysis.m

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  (* Stats for a single run at every (n,tau) *)
 timeWindow=Round[Length[gdata[[1,1,1,2]]]/10];
 skipPts=Max[1,Round[timeWindow/100]];
 getSingleStats[runs_]:=Module[{run,avg,stddev},
     run=runs[[1]]; (* Select some run *)
     avg=N[Mean[run[[2,-timeWindow;;-1]]]];
@@ @@ -234,7 +235,8 @@ getSingleStats[runs_]:=Module[{run,avg,stddev}, @@
     stddev/avg,
     (run[[2,1]])/avg, (* EG starting point *)
     Map[N[#/avg]&,run[[4]]],  (* GCM1 counts *)
     Map[N[#/avg]&,run[[5]]] (* GCM2 counts *)
     Map[N[#/avg]&,run[[5]]],  (* GCM2 counts *)
     (run[[2,-timeWindow;;-1;;skipPts]])/avg  (* counts in uniform distribution *)
+    }
+]
 singleStats=Map[getSingleStats,gdata,{2}];
@@ @@ -242,7 +244,8 @@ singleStats=Map[getSingleStats,gdata,{2}]; @@
 (* Yellow: GCM1 (take new highest everytime *)
 (* Blue: GCM2 (finish highest first, more similar to EG) *)
 histogramsSingle=Map[Histogram[{#[[4]],#[[5]]},PlotRange->{{0,5},Automatic},ImageSize->300,PlotLabel->"ErdosGallai="<>ToString[NumberForm[#[[3]],3]]<>"\[Cross]average. stddev="<>ToString[NumberForm[#[[2]],3]]<>"\[Cross]average"]&,singleStats,{2}];
 (* Green: Actual uniform distribution *)
 histogramsSingle=Map[Histogram[{#[[4]],#[[5]],#[[6]]},PlotRange->{{0,3},Automatic},ImageSize->300,PlotLabel->"ErdosGallai="<>ToString[NumberForm[#[[3]],3]]<>"\[Cross]average"]&,singleStats,{2}];
 TableForm[histogramsSingle,TableHeadings->{taulabels,nlabels}]
  (* Consider all runs *)
 timeWindow=Round[Length[gdata[[1,1,1,2]]]/10];
 skipPts=Max[1,Round[timeWindow/100]];
 getAverage[run_]:=Module[{avg,stddev},
     avg=N[Mean[run[[2,-timeWindow;;-1]]]];
+    {
     Mean[run[[4]]]/avg,(* GCM1 counts *)
     Mean[run[[5]]]/avg, (* GCM2 counts *)
     (run[[2,1]])/avg (* EG starting point *)
+    (run[[2,1]])/avg, (* EG starting point *)
+    }
+]
 getTotalStats[runs_]:=Transpose[Map[getAverage,runs]];
@@ @@ -263,15 +267,13 @@ totalStats=Map[getTotalStats,gdata,{2}]; @@
 (* Yellow: GCM1 (take new highest everytime *)
-(* Blue: GCM2 (finish\[AliasDelimiter] highest first, more similar to EG) *)
 (* Blue: GCM2 (finish highest first, more similar to EG) *)
 histogramsTotal=Map[Histogram[#,{0.1},PlotRange->{{0,5},Automatic},ImageSize->300]&,totalStats,{2}];
 TableForm[histogramsTotal,TableHeadings->{taulabels,nlabels}]
 (* ::Subsection:: *)
 (*GCM1 vs GCM2 success rates*)

triangle_gcm_initial_analysis.m

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@@ new file 100644 @@
 (* ::Package:: *)
 Needs["ErrorBarPlots`"]
 (* ::Section:: *)
 (*Data import*)
 gsraw=Import[NotebookDirectory[]<>"data/graphdata_initialtris.m"];
 (* gsraw=SortBy[gsraw,{#[[1,1]]&,#[[1,2]]&}]; (* Sort by n and then by tau. The {} forces a *stable* sort because otherwise Mathematica sorts also on triangle count and other things. *) *)
 gdata=GatherBy[gsraw,{#[[1,2]]&,#[[1,1]]&}];
 (* Data format: *)
 (* gdata[[ tau index, n index, run index , datatype index ]] *)
 (* datatype index:
 : {n,tau}
 : avgtriangles when mixed
 : {GCM1 starting triangle counts summed, GCM1 number of successes}
 : {GCM2 starting triangle counts summed, GCM2 number of successes}
 *)
 nlabels=Map["n = "<>ToString[#]&,gdata[[1,All,1,1,1]]];
 taulabels=Map["tau = "<>ToString[#]&,gdata[[All,1,1,1,2]]];
 (* ::Section:: *)
 (*Greedy configuration model*)
 (* ::Subsection:: *)
 (*Distribution of initial #triangles for GCM1,GCM2,EG compared to average #triangles.*)
  (* Consider all runs *)
 getIt[x_,avg_]:=If[x[[2]]>=5, x[[1]]/x[[2]]/avg,-0.5]
 getAverage[run_]:=Module[{avg},
     avg=20*run[[2]];
     If[avg>0,
     { getIt[run[[3]],avg], getIt[run[[4]],avg] }
     , {3,3}]
+]
 getTotalStats[runs_]:=Transpose[Map[getAverage,runs]];
 totalStats=Map[getTotalStats,gdata,{2}];
 (* Yellow: GCM1 (take new highest everytime *)
 (* Blue: GCM2 (finish highest first, more similar to EG) *)
 histogramsTotal=Map[Histogram[#,{0.05},PlotRange->{{-0.5,2},Automatic},ImageSize->300,AxesOrigin->{0,0}]&,totalStats,{2}];
 TableForm[histogramsTotal,TableHeadings->{taulabels,nlabels}]
 (* ::Subsubsection:: *)
 (*Exporting plots*)
 tauIndices={2,5,8};
 nIndices={2};
 makeHistogram[datasets_,n_,tau_]:=Histogram[datasets,{0.05},PlotRange->{{-0.5,1.5},Automatic},ImageSize->300,AxesOrigin->{0,0}];
 histogramsTotal=Map[makeHistogram[#,1000,tau]&,totalStats[[tauIndices,nIndices]],{2}];
 TableForm[histogramsTotal,TableHeadings->{taulabels[[tauIndices]],nlabels[[nIndices]]}]
 (* ::Subsection:: *)
 (*GCM1 vs GCM2 success rates*)
 (* gdata[[ tau index, n index, run index , {ntau, #tris, ds, greedyTriangles} ]] *)
 successrates=Map[{#[[3,2]],#[[4,2]]}&,gdata,{3}];
 successrates=Map[Transpose,successrates,{2}];
 successratesDelta=Map[#[[3,2]]-#[[4,2]]&,gdata,{3}];
 rateHistograms=Map[Histogram[#,{10},PlotRange->{{0,100},Automatic}]&,successrates,{2}];
 TableForm[rateHistograms,TableHeadings->{taulabels,nlabels}]
 rateHistograms=Map[Histogram[#,{10},PlotRange->{{-100,100},Automatic}]&,successratesDelta,{2}];
 TableForm[rateHistograms,TableHeadings->{taulabels,nlabels}]
 (*TableForm[Transpose[rateHistograms],TableHeadings->{nlabels,taulabels}]*)

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