AENC/switchchain Changeset - dfb75c7568d4 · Centrum Wiskunde & Informatica (CWI)

Changeset - dfb75c7568d4

Parent rev.

Child rev.

[Not reviewed]

0 2 0

Tom Bannink - 8 years ago 2017-02-27 17:09:19
tombannink@gmail.com

Improve Mathematica notebook

2 files changed with 18 insertions and 11 deletions:

cpp/showgraphs.m

cpp/switchchain.cpp

0 comments (0 inline, 0 general)

cpp/showgraphs.m

➞

Show inline comments

@@ @@ -28,50 +28,54 @@ Grid[Partition[gs,10],Frame->All] @@
 (*Data import and data merge*)
 gsraw=Import[NotebookDirectory[]<>"graphdata_merged.m"];
-newData=Import[NotebookDirectory[]<>"graphdata_tau_multi3.m"];
+newData=Import[NotebookDirectory[]<>"graphdata_tau_multi4.m"];
 mergedData=Import[NotebookDirectory[]<>"graphdata_merged.m"];
 Export[NotebookDirectory[]<>"graphdata_merged_new.m",Join[mergedData,newData]]
 (* ::Subsection:: *)
 (*Plot empirical distribution of maximum degree*)
 maxDegrees=Map[{#[[1]],Max[#[[3]]]}&,gsraw];
 maxDegrees=GatherBy[maxDegrees,{#[[1,2]]&,#[[1,1]]&}];
 Histogram[maxDegrees[[1,-1,All,2]],PlotRange->{{0,1100},{0,200}},AxesLabel->{"d_max","frequency"}]
 Histogram[maxDegrees[[2,-1,All,2]],PlotRange->{{0,1100},{0,200}},AxesLabel->{"d_max","frequency"}]
 Histogram[maxDegrees[[3,-1,All,2]],PlotRange->{{0,1100},{0,200}},AxesLabel->{"d_max","frequency"}]
 Histogram[maxDegrees[[1,-1,All,2]],PlotRange->{{0,2000},{0,100}},AxesLabel->{"d_max","frequency"}]
 Histogram[maxDegrees[[2,-1,All,2]],PlotRange->{{0,2000},{0,100}},AxesLabel->{"d_max","frequency"}]
 Histogram[maxDegrees[[3,-1,All,2]],PlotRange->{{0,2000},{0,100}},AxesLabel->{"d_max","frequency"}]
 (* ::Subsection:: *)
 (*Plot triangle count over "time" in Markov chain instances*)
 numPlots=10;
 minCount=Min[Map[Min[#[[2]]]&,gsraw[[1;;numPlots]]]];
 maxCount=Max[Map[Max[#[[2]]]&,gsraw[[1;;numPlots]]]];
 maxTime=Max[Map[Length[#[[2]]]&,gsraw[[1;;numPlots]]]];
 numPlots=15;
 selectedData=gsraw[[-numPlots-1;;-1]];
 minCount=Min[Map[Min[#[[2]]]&,selectedData]];
 maxCount=Max[Map[Max[#[[2]]]&,selectedData]];
 maxTime=Max[Map[Length[#[[2]]]&,selectedData]];
 skipPts=Round[maxTime/100]; (* Plotting every point is slow. Plot only once per `skipPts` timesteps *)
 coarseData=Map[#[[2,1;;-1;;skipPts]]&,gsraw[[1;;numPlots]]];
 labels=Map["{n,tau} = "<>ToString[#[[1]]]&,gsraw[[1;;numPlots]]];
 coarseData=Map[#[[2,1;;-1;;skipPts]]&,selectedData];
 labels=Map["{n,tau} = "<>ToString[#[[1]]]&,selectedData];
 ListPlot[coarseData,Joined->True,PlotRange->{minCount,maxCount},DataRange->{0,maxTime},PlotLegends->labels]
 (* Map[ListPlot[#,Joined->True,PlotRange\[Rule]{minCount,maxCount},DataRange\[Rule]{0,maxTime}]&,coarseData] *)
 (* ::Subsection:: *)
 (*Plot average #triangles vs n*)
 averages=Map[{#[[1]],Mean[#[[2,1;;-1]]]}&,gsraw];
 (* Sort by n *)
 averages=SortBy[averages,#[[1,1]]&];
 (* Split by n,tau *)
 averagesGrouped=GatherBy[averages,{#[[1,1]]&,#[[1,2]]&}];
 (* averagesGroupes[[ n index, tau index , run index, {ntau, tri, ds} ]] *)
 averagesGrouped=Transpose[averagesGrouped];
 (* averagesGrouped[[ tau index, n index, run index ]] *)
 nlabels=Map["n = "<>ToString[#]&,averagesGrouped[[1,All,1,1,1]]];
 taulabels=Map["tau = "<>ToString[#]&,averagesGrouped[[All,1,1,1,2]]];
@@ @@ -81,12 +85,15 @@ ErrorBar[StandardDeviation[#[[All,2]]]/Sqrt[Length[#]]] @@
 }&,averagesGrouped,{2}];
 ErrorListPlot[averagesErrorBars,Joined->True,PlotMarkers->Automatic,AxesLabel->{"n","\[LeftAngleBracket]triangles\[RightAngleBracket]"},PlotLegends->taulabels]
 ListLogPlot[averagesErrorBars[[All,All,1]],Joined->True,PlotMarkers->Automatic,AxesLabel->{"n","\[LeftAngleBracket]triangles\[RightAngleBracket]"},PlotLegends->taulabels]
 (* ::Subsection:: *)
 (*Plot #triangles distribution for specific (n,tau)*)
 histograms=Map[Histogram[#[[All,2]]]&,averagesGrouped,{2}];
 nlabels=Map["n = "<>ToString[#]&,averagesGrouped[[1,All,1,1,1]]];

cpp/switchchain.cpp

➞

Show inline comments

@@ @@ -81,13 +81,13 @@ int main() { @@
     Graph g;
     std::ofstream outfile("graphdata.m");
     outfile << '{';
     bool outputComma = false;
-    for (int numVertices = 200; numVertices <= 2000; numVertices += 200) {
+    for (int numVertices = 2000; numVertices <= 10000; numVertices += 1000) {
         for (float tau : tauValues) {
             DegreeSequence ds(numVertices);
             powerlaw_distribution degDist(tau, 2, numVertices);
             //std::poisson_distribution<> degDist(12);

0 comments (0 inline, 0 general)