def gather_info(self):
"""Gather information from all the agents."""
diversity = np.ones(len(self.agents))
exploration = np.ones(len(self.agents))
foraging = np.ones(len(self.agents))
fittest = np.ones(len(self.agents))
for id in range(len(self.agents)):
diversity[id] = self.agents[id].diversity_fitness
exploration[id] = self.agents[id].exploration_fitness()
foraging[id] = self.agents[id].food_collected
fittest[id] = self.agents[id].individual[0].fitness
beta = self.agents[-1].beta
mean = Best(
self.pname, self.connect, self.sn, 1, 'MEAN', self.stepcnt,
beta, np.mean(fittest), np.mean(diversity), np.mean(exploration),
np.mean(foraging), "None"
)
mean.save()
std = Best(
self.pname, self.connect, self.sn, 1, 'STD', self.stepcnt, beta,
np.std(fittest), np.std(diversity), np.std(exploration),
np.std(foraging), "None"
)
std.save()
# Compute best agent for each fitness
self.best_agents(diversity, beta, "DIVERSE")
self.best_agents(exploration, beta, "EXPLORE")
self.best_agents(foraging, beta, "FORGE")
self.best_agents(fittest, beta, "OVERALL")
return np.argmax(foraging)
def best_agents(self, data, beta, header):
"""Find the best agents in each category."""
idx = np.argmax(data)
dfitness = self.agents[idx].diversity_fitness
ofitness = self.agents[idx].individual[0].fitness
ffitness = self.agents[idx].food_collected
efitness = self.agents[idx].exploration_fitness()
phenotype = self.agents[idx].individual[0].phenotype
best_agent = Best(self.pname, self.connect, self.sn, idx, header,
self.stepcnt, beta, ofitness, dfitness, efitness,
ffitness, phenotype)
best_agent.save()