[Avida-SVN] r1852 - branches/dkdev/source/main

[dknoester at myxo.css.msu.edu]

Author: dknoester
Date: 2007-07-26 21:30:38 -0400 (Thu, 26 Jul 2007)
New Revision: 1852

Modified:
   branches/dkdev/source/main/cPopulation.cc
   branches/dkdev/source/main/cStats.cc
Log:
Max cpl

Modified: branches/dkdev/source/main/cPopulation.cc
===================================================================
--- branches/dkdev/source/main/cPopulation.cc	2007-07-25 22:27:44 UTC (rev 1851)
+++ branches/dkdev/source/main/cPopulation.cc	2007-07-27 01:30:38 UTC (rev 1852)
@@ -1031,7 +1031,9 @@
         // Max clustering coefficient is... 1, occuring when all possible edges are present.
         // Let's try 0.5 first, scaled to 0-100.
         clustering_coeff *= 100.0;
+        // 057-clustering, 058-clustering2
         //double target_clustering_coeff=50.0;
+        // 061-clustering3
         double target_clustering_coeff=25.0;
         
         // What we need here is an exponentially-decreasing function of target to both
@@ -1073,7 +1075,10 @@
         double max_cpl = world_x * world_y/num_demes * 0.75;
         
         m_world->GetStats().TopologyCPL(cpl);
-        source_germline_merit = pow(max_cpl - cpl + 1, 2);
+        // 059-cpl, 060-cpl2:
+        // source_germline_merit = pow(max_cpl - cpl + 1, 2);
+        // 062-cpl3:
+        source_germline_merit = pow(cpl, 2);
         break;
       }
 //case 5: {

Modified: branches/dkdev/source/main/cStats.cc
===================================================================
--- branches/dkdev/source/main/cStats.cc	2007-07-25 22:27:44 UTC (rev 1851)
+++ branches/dkdev/source/main/cStats.cc	2007-07-27 01:30:38 UTC (rev 1852)
@@ -900,14 +900,14 @@
 	df.WriteComment( "Topology data\n" );
 	df.WriteTimeStamp();
 	df.Write(GetUpdate(), "update" );
-  df.Write(_topo_edges.Average(), "mean edges in all constructed networks");
-  df.Write(_topo_edges_repl.Average(), "mean edges in networks that caused replication");
-  df.Write(_topo_connected, "count of connected networks");
-  df.Write(_topo_meanlsp.Average(), "mean mean longest shortest-path");
-  df.Write(_topo_maxlsp.Average(), "mean max longest shortest-path");
-  df.Write(_topo_maxlsp_frac.Average(), "mean (mean lsp / max lsp)");
-  df.Write(_topo_clustering_coeff.Average(), "mean clustering coefficient");
-  df.Write(_topo_cpl.Average(), "mean characteristic path length");
+  df.Write(_topo_edges.Average(), "mean edges in all constructed networks [edges]");
+  df.Write(_topo_edges_repl.Average(), "mean edges in networks that caused replication [repl_edges]");
+  df.Write(_topo_connected, "count of connected networks [conn]");
+  df.Write(_topo_meanlsp.Average(), "mean mean longest shortest-path [mean_lsp]");
+  df.Write(_topo_maxlsp.Average(), "mean max longest shortest-path [max_lsp]");
+  df.Write(_topo_maxlsp_frac.Average(), "mean (mean lsp / max lsp) [lsp_ratio]");
+  df.Write(_topo_clustering_coeff.Average(), "mean clustering coefficient [cluster]");
+  df.Write(_topo_cpl.Average(), "mean characteristic path length [cpl]");
 	df.Endl();
   
   _topo_connected = 0;
@@ -1011,11 +1011,12 @@
   df.WriteComment("Deme replication data");
   df.WriteTimeStamp();
   df.Write(GetUpdate(), "update");
-  df.Write(_deme_repl_count, "replication count");
-  df.Write(std::min(_deme_repl_count/(double)m_world->GetPopulation().GetNumDemes(), 1.0), "replication probability");
-  df.Write(_deme_age.Average(), "mean age of replicated demes");
+  df.Write(_deme_repl_count, "replication count [count]");
+  df.Write(std::min(_deme_repl_count/(double)m_world->GetPopulation().GetNumDemes(), 1.0), 
+           "replication probability [prob]");
+  df.Write(_deme_age.Average(), "mean age of replicated demes [age]");
   if(m_world->GetConfig().DEMES_USE_GERMLINE.Get() && m_world->GetConfig().DEMES_HAVE_MERIT.Get()) {
-    df.Write(_deme_merit.Average(), "mean merit of replicated demes");
+    df.Write(_deme_merit.Average(), "mean merit of replicated demes [merit]");
   }
   df.Endl();