def run_simulation(setup, savefile):
"""
A helper function that will run the simulation.
:param setup: An object containing all variables that will be used in the simulation.
:param savefile: The file where we want to save the results to.
:return: averaged payoffs per generation, number of times the target was reached per generation
and the averaged contributions per round per generation (GxR matrix)
"""
generations_avg_payoffs = []
generations_targets_reached = []
generations_rounds_contributions_counts = []
generations_behaviors_counts = []
generation = Generation(setup)
for i in range(setup.num_generations):
setup.risk += setup.risk_cont
print("Generation " + str(i))
targets_reached, avg_payoff, rounds_contributions_counts, behaviors_counts = generation.play(
)
generations_avg_payoffs.append(avg_payoff)
generations_targets_reached.append(np.sum(targets_reached))
generations_rounds_contributions_counts.append(
rounds_contributions_counts.tolist())
generations_behaviors_counts.append(behaviors_counts.tolist())
print("Targets reached: ", np.sum(targets_reached))
print("With an average payoff of " + str(avg_payoff))
print("Behavior counts: ")
print(behaviors_counts)
print("Rounds contributions counts: ")
print(rounds_contributions_counts)
generation.evolve()
# If we are investigating the robustness of strategies we return the number of generations it has survived.
if setup.strategy is not None:
if generation.frequency < setup.population_size / 2:
return i
# If we are investigating the robustness of strategies we return the number of generations it has survived.
if setup.strategy is not None:
return setup.num_generations
print(generations_targets_reached)
# We place everything inside a dictionary and then in a data frame.
results = {
"avg_payoffs": generations_avg_payoffs,
"targets_reached": generations_targets_reached,
"rounds_contributions_counts": generations_rounds_contributions_counts,
"behaviors_counts": generations_behaviors_counts
}
results_df = pd.DataFrame(results)
# We write the results to a file.
results_df.to_csv(savefile,
sep=',',
mode='w',
header=True,
index=False,
encoding="ascii")
