ActivityDrivenModel: Generation of reluctances

ActivityDrivenModel::generate_agents_from_power_law(), now also generates the reluctances from a truncated normal distribution. Optionally they can be correlated with the activities. TODO: parsing of the config file to control these options Co-authored-by: Amrita Goswami <[email protected]>
seldon-code · Mar 16, 2024 · c8520b9 · c8520b9
1 parent 1b7cee5
commit c8520b9
Show file tree

Hide file tree

Showing 7 changed files with 32 additions and 24 deletions.
diff --git a/include/models/ActivityDrivenModel.hpp b/include/models/ActivityDrivenModel.hpp
@@ -118,6 +118,12 @@ class ActivityDrivenModel : public Model<Agent<ActivityAgentData>>
 
     double convergence_tol = 1e-12; // TODO: ??
 
+    bool use_reluctances     = false;
+    double reluctance_mean   = 1.0;
+    double reluctance_sigma  = 0.25;
+    double reluctance_eps    = 0.01;
+    double covariance_factor = 0.0;
+
     // bot @TODO: less hacky
 
     size_t n_bots                     = 0; // The first n_bots agents are bots

diff --git a/include/simulation.hpp b/include/simulation.hpp
@@ -85,8 +85,7 @@ class Simulation : public SimulationInterface
             auto degroot_settings = std::get<Config::DeGrootSettings>( options.model_settings );
 
             // DeGroot specific parameters
-            model = [&]()
-            {
+            model = [&]() {
                 auto model             = std::make_unique<DeGrootModel>( network );
                 model->max_iterations  = degroot_settings.max_iterations;
                 model->convergence_tol = degroot_settings.convergence_tol;
@@ -102,8 +101,7 @@ class Simulation : public SimulationInterface
         {
             auto activitydriven_settings = std::get<Config::ActivityDrivenSettings>( options.model_settings );
 
-            model = [&]()
-            {
+            model = [&]() {
                 auto model             = std::make_unique<ActivityDrivenModel>( network, gen );
                 model->dt              = activitydriven_settings.dt;
                 model->m               = activitydriven_settings.m;

diff --git a/include/util/math.hpp b/include/util/math.hpp
@@ -162,7 +162,7 @@ class truncated_normal_distribution
     template<typename Generator>
     ScalarT operator()( Generator & gen )
     {
-        for( int i = 0; i < max_tries; i++ )
+        for( size_t i = 0; i < max_tries; i++ )
         {
             auto sample = normal_dist( gen );
             if( sample > eps )

diff --git a/src/config_parser.cpp b/src/config_parser.cpp
@@ -76,24 +76,21 @@ SimulationOptions parse_config_file( std::string_view config_file_path )
         // bot
         model_settings.n_bots = tbl["ActivityDriven"]["n_bots"].value_or<size_t>( 0 );
 
-        auto push_back_bot_array = [&]( auto toml_node, auto & options_array, auto default_value )
-        {
+        auto push_back_bot_array = [&]( auto toml_node, auto & options_array, auto default_value ) {
             if( toml_node.is_array() )
             {
                 toml::array * toml_arr = toml_node.as_array();
 
-                toml_arr->for_each(
-                    [&]( auto && elem )
+                toml_arr->for_each( [&]( auto && elem ) {
+                    if( elem.is_integer() )
+                    {
+                        options_array.push_back( elem.as_integer()->get() );
+                    }
+                    else if( elem.is_floating_point() )
                     {
-                        if( elem.is_integer() )
-                        {
-                            options_array.push_back( elem.as_integer()->get() );
-                        }
-                        else if( elem.is_floating_point() )
-                        {
-                            options_array.push_back( elem.as_floating_point()->get() );
-                        }
-                    } );
+                        options_array.push_back( elem.as_floating_point()->get() );
+                    }
+                } );
             }
             else
             {

diff --git a/src/models/ActivityDrivenModel.cpp b/src/models/ActivityDrivenModel.cpp
@@ -38,6 +38,8 @@ void ActivityDrivenModel::get_agents_from_power_law()
 {
     std::uniform_real_distribution<> dis_opinion( -1, 1 ); // Opinion initial values
     power_law_distribution<> dist_activity( eps, gamma );
+    truncated_normal_distribution<> dist_reluctance( reluctance_mean, reluctance_sigma, reluctance_eps );
+
     auto mean_activity = dist_activity.mean();
 
     // Initial conditions for the opinions, initialize to [-1,1]
@@ -54,6 +56,14 @@ void ActivityDrivenModel::get_agents_from_power_law()
         {
             network.agents[i].data.activity = mean_activity;
         }
+
+        if( use_reluctances )
+        {
+            network.agents[i].data.reluctance = dist_reluctance( gen );
+            auto a                            = network.agents[i].data.activity;
+            network.agents[i].data.activity += covariance_factor * network.agents[i].data.reluctance;
+            network.agents[i].data.reluctance += covariance_factor * a;
+        }
     }
 
     if( bot_present() )
@@ -145,8 +155,7 @@ void ActivityDrivenModel::update_network_mean()
         contact_prob_list[idx_agent] = weights; // set to zero
     }
 
-    auto probability_helper = []( double omega, size_t m )
-    {
+    auto probability_helper = []( double omega, size_t m ) {
         double p = 0;
         for( size_t i = 1; i <= m; i++ )
             p += ( std::pow( -omega, i + 1 ) + omega ) / ( omega + 1 );

diff --git a/test/test_activity.cpp b/test/test_activity.cpp
@@ -167,8 +167,7 @@ TEST_CASE( "Test the meanfield activity driven model with 10 agents", "[activity
     auto mean_activity = dist.mean();
 
     // Calculate the critical controversialness
-    auto set_opinions_and_run = [&]( bool above_critical_controversialness )
-    {
+    auto set_opinions_and_run = [&]( bool above_critical_controversialness ) {
         auto simulation            = Simulation<AgentT>( options, std::nullopt, std::nullopt );
         auto initial_opinion_delta = 0.1; // Set the initial opinion in the interval [-delta, delta]
 

diff --git a/test/test_sampling.cpp b/test/test_sampling.cpp
@@ -98,8 +98,7 @@ TEST_CASE( "Testing sampling functions" )
 
         std::vector<size_t> histogram( n, 0 ); // Count how often each element occurs amongst all samples
 
-        auto weight_callback = []( size_t idx )
-        {
+        auto weight_callback = []( size_t idx ) {
             if( ( idx == ignore_idx ) || ( idx == ignore_idx2 ) )
             {
                 return 0.0;