From 06fad94993987478f75c73a91fc23bc4403f1889 2017-03-01 13:19:29
From: Tom Bannink <tombannink@gmail.com>
Date: 2017-03-01 13:19:29
Subject: [PATCH] Add TODO list to mathematica notebook

---

diff --git a/cpp/showgraphs.m b/cpp/showgraphs.m
index f56096863698088e74f7f69514e24da3384a5a0a..aacc73ca46ba52499d44a4f91317698f98955a93 100644
--- a/cpp/showgraphs.m
+++ b/cpp/showgraphs.m
@@ -3,6 +3,25 @@
 Needs["ErrorBarPlots`"]
 
 
+(* ::Section:: *)
+(*TODO*)
+
+
+(* ::Text:: *)
+(*- Compute  Sum over i<j<k  of  (1-Exp[- d_i d_j / (2E)]) * (1 - Exp[-d_j d_k / (2E)]) * (1 - Exp[-d_k d_i / (2E)]) .*)
+(*   Only depends on degree sequence. Can be done with current data.*)
+(*- Do a single very long run to see if there are any weird things after many steps*)
+(*- Use different starting point for switch chain that is closer to uniform:*)
+(*   Do configuration model, starting with the vertex with highest degree and keeping track of a "forbidden list" meaning dont pair something that is not allowed*)
+(*   (a) How close is this to uniform ? At least w.r.t. the measure of #triangles*)
+(*   (b) How often does this procedure work/fail. Might still be faster to do switchings from Erdos-Gallai.*)
+(*- Improve runtime*)
+(*   (a) Don't remove/add edges from the std::vector. Simply replace them*)
+(*   (b) Better triangle counting? (I doubt it)*)
+(*   (c) Do not choose the three permutations with 1/3 probability: choose the "staying" one with a very low probability. Should still be a valid switch chain?*)
+(*- ?*)
+
+
 (* ::Section:: *)
 (*Visualize graphs*)
 
@@ -42,6 +61,7 @@ Export[NotebookDirectory[]<>"graphdata_merged_new.m",Join[mergedData,newData]]
 
 maxDegrees=Map[{#[[1]],Max[#[[3]]]}&,gsraw];
 maxDegrees=GatherBy[maxDegrees,{#[[1,2]]&,#[[1,1]]&}];
+(* maxDegrees[[ tau index, n index, run index,  ntau or dmax ]] *)
 
 
 Histogram[maxDegrees[[1,-1,All,2]],PlotRange->{{0,2000},{0,100}},AxesLabel->{"d_max","frequency"}]
@@ -73,10 +93,8 @@ 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 ]] *)
+averagesGrouped=GatherBy[averages,{#[[1,2]]&,#[[1,1]]&}];
+(* averagesGrouped[[ tau index, n index, run index , {ntau, tri, ds} ]] *)
 nlabels=Map["n = "<>ToString[#]&,averagesGrouped[[1,All,1,1,1]]];
 taulabels=Map["tau = "<>ToString[#]&,averagesGrouped[[All,1,1,1,2]]];
 averagesErrorBars=Map[
diff --git a/cpp/switchchain.cpp b/cpp/switchchain.cpp
index 6d840fc1665278004ccdb0ddb7c163f0defce46b..d46956ceedb2e54c40cc04157f92a297309c1731 100644
--- a/cpp/switchchain.cpp
+++ b/cpp/switchchain.cpp
@@ -84,7 +84,7 @@ int main() {
     outfile << '{';
     bool outputComma = false;
 
-    for (int numVertices = 2000; numVertices <= 10000; numVertices += 1000) {
+    for (int numVertices = 4000; numVertices <= 8000; numVertices += 1000) {
         for (float tau : tauValues) {
 
             DegreeSequence ds(numVertices);
@@ -118,7 +118,7 @@ int main() {
                           << numVertices << ", tau = " << tau << ". \t"
                           << std::flush;
 
-                constexpr int mixingTime = 15000;
+                constexpr int mixingTime = 20000;
                 constexpr int measureTime = 10000;
                 constexpr int measureSkip =
                     100; // Take a sample every 100 steps