Deffuant: Move initialize_agents from constructor

We now call initialize_agents for the Deffuant model out of the
constructor, so that we can pass it the dimensions for the
DeffuantVector model

Co-authored-by: Moritz Sallermann <[email protected]>

examples/DeffuantVector/conf.toml

@@ -0,0 +1,24 @@
+[simulation]
+model = "Deffuant"
+# rng_seed = 120 # Leaving this empty will pick a random seed
+
+[io]
+# n_output_network = 20 # Write the network every 20 iterations
+n_output_agents = 1 # Write the opinions of agents after every iteration
+print_progress = true # Print the iteration time ; if not set, then does not prints
+output_initial = true # Print the initial opinions and network file from step 0. If not set, this is true by default.
+start_output = 1 # Start writing out opinions and/or network files from this iteration. If not set, this is 1.
+
+[model]
+max_iterations = 1000 # If not set, max iterations is infinite
+
+[Deffuant]
+homophily_threshold = 0.2 # d in the paper; agents interact if difference in opinion is less than this value 
+mu = 0.5 # convergence parameter; similar to social interaction strength K (0,0.5] 
+use_network = false # If true, will use a square lattice Will throw if sqrt(n_agents) is not an integer
+binary_vector = true # If true, this will be the multi-dimensional binary vector Deffuant model
+dim = 5 # For the multi-dimensional binary vector Deffuant model, define the number of dimensions in each opinion vector 
+
+[network]
+number_of_agents = 1000
+connections_per_agent = 0

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/model_factory.hpp

@@ -77,4 +77,22 @@ inline auto create_model_deffuant( Network<AgentT> & network, const ModelVariant
     }
 }
 
+template<typename AgentT>
+inline auto
+create_model_deffuant_vector( Network<AgentT> & network, const ModelVariantT & model_settings, std::mt19937 & gen )
+{
+    if constexpr( std::is_same_v<AgentT, DeffuantModelVector::AgentT> )
+    {
+        auto deffuant_settings = std::get<Config::DeffuantSettings>( model_settings );
+        auto model             = std::make_unique<DeffuantModelVector>( deffuant_settings, network, gen );
+        model.initialize_agents();
+        return model;
+    }
+    else
+    {
+        throw std::runtime_error( "Incompatible agent and model type!" );
+        return std::unique_ptr<Model<AgentT>>{};
+    }
+}
+
 } // namespace Seldon::ModelFactory

include/models/DeffuantModel.hpp

@@ -41,8 +41,6 @@ class DeffuantModelAbstract : public Model<AgentT_>
             }
             network = NetworkGeneration::generate_square_lattice<AgentT>( n_edge );
         }
-
-        initialize_agents();
     }
 
     std::vector<std::size_t> select_interacting_agent_pair()

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
             }