#include "models/ActivityDrivenModel.hpp"

#include "network.hpp"

#include "util/math.hpp"

#include <cstddef>

#include <random>

#include <vector>

namespace Seldon

{

template<>

void ActivityDrivenModelAbstract<ActivityAgent>::iteration()

{

Model<AgentT>::iteration();

update_network();

// Integrate the ODE using 4th order Runge-Kutta

// k_1 = hf(x_n,y_n)

get_euler_slopes( k1_buffer, [this]( size_t i ) { return network.agents[i].data.opinion; } );

// k_2 = hf(x_n+1/2h,y_n+1/2k_1)

get_euler_slopes(

k2_buffer, [this]( size_t i ) { return network.agents[i].data.opinion + 0.5 * dt * this->k1_buffer[i]; } );

// k_3 = hf(x_n+1/2h,y_n+1/2k_2)

get_euler_slopes(

k3_buffer, [this]( size_t i ) { return network.agents[i].data.opinion + 0.5 * dt * this->k2_buffer[i]; } );

// k_4 = hf(x_n+h,y_n+k_3)

get_euler_slopes(

k4_buffer, [this]( size_t i ) { return network.agents[i].data.opinion + dt * this->k3_buffer[i]; } );

// Update the agent opinions

for( size_t idx_agent = 0; idx_agent < network.n_agents(); ++idx_agent )

{

// y_(n+1) = y_n+1/6k_1+1/3k_2+1/3k_3+1/6k_4+O(h^5)

network.agents[idx_agent].data.opinion

+= dt

* ( k1_buffer[idx_agent] + 2 * k2_buffer[idx_agent] + 2 * k3_buffer[idx_agent] + k4_buffer[idx_agent] )

/ 6.0;

}

if( bot_present() )

{

for( size_t bot_idx = 0; bot_idx < n_bots; bot_idx++ )

{

network.agents[bot_idx].data.opinion = bot_opinion[bot_idx];

}

}

}

template class ActivityDrivenModelAbstract<ActivityAgent>;

} // namespace Seldon