ActivityDrivenModel: Use the copula

Use the copula to generate correlated activities and reluctances.

Co-authored-by: Amrita Goswami <[email protected]>

include/connectivity.hpp

@@ -75,7 +75,7 @@ class TarjanConnectivityAlgo
                 {
                     lowest[v] = std::min( lowest[v], num[u] );
                 } // u not processed
-            }     // u has been visited
+            } // u has been visited
         }
 
         // Now v has been processed

include/models/ActivityDrivenModel.hpp

@@ -60,7 +60,7 @@ class ActivityDrivenModelAbstract : public Model<AgentT_>
         }
     }
 
-    void iteration() override{};
+    void iteration() override {};
 
 protected:
     NetworkT & network;
@@ -113,6 +113,8 @@ class ActivityDrivenModelAbstract : public Model<AgentT_>
         power_law_distribution<> dist_activity( eps, gamma );
         truncated_normal_distribution<> dist_reluctance( reluctance_mean, reluctance_sigma, reluctance_eps );
 
+        bivariate_gaussian_copula copula( covariance_factor, dist_activity, dist_reluctance );
+
         auto mean_activity = dist_activity.mean();
 
         // Initial conditions for the opinions, initialize to [-1,1]
@@ -121,18 +123,16 @@ class ActivityDrivenModelAbstract : public Model<AgentT_>
         {
             network.agents[i].data.opinion = dis_opinion( gen ); // Draw the opinion value
 
-            if( !mean_activities )
-            { // Draw from a power law distribution (1-gamma)/(1-eps^(1-gamma)) * a^(-gamma)
-                network.agents[i].data.activity = dist_activity( gen );
-            }
-            else
-            {
-                network.agents[i].data.activity = mean_activity;
-            }
+            auto res                        = copula( gen );
+            network.agents[i].data.activity = res[0];
 
             if( use_reluctances )
             {
-                network.agents[i].data.reluctance = dist_reluctance( gen );
+                network.agents[i].data.reluctance = res[1];
+            }
+            if( mean_activities )
+            {
+                network.agents[i].data.activity = mean_activity;
             }
         }
 

src/config_parser.cpp

@@ -253,8 +253,7 @@ void validate_settings( const SimulationOptions & options )
         {
             const std::string basic_deff_msg
                 = "The basic Deffuant model has not been implemented with multiple dimensions";
-            check(
-                name_and_var( model_settings.dim ), []( auto x ) { return x == 1; }, basic_deff_msg );
+            check( name_and_var( model_settings.dim ), []( auto x ) { return x == 1; }, basic_deff_msg );
         }
     }
 }

test/test_network.cpp

@@ -112,7 +112,7 @@ TEST_CASE( "Testing the network class" )
                 auto weight    = buffer_w[i_neighbour];
                 std::tuple<size_t, size_t, Network::WeightT> edge{
                     neighbour, i_agent, weight
-                };                                     // Note that i_agent and neighbour are flipped compared to before
+                }; // Note that i_agent and neighbour are flipped compared to before
                 REQUIRE( old_edges.contains( edge ) ); // can we find the transposed edge?
                 old_edges.extract( edge );             // extract the edge afterwards
             }