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#include <iostream>
#include "/home/tollie/Development/Maximilian/maximilian.h"
#include <vector>
#include <algorithm>
#include <chrono>

size_t amount = 1;
std::vector<maxiOsc> maxiObjects;

void process()

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for i, p in enumerate(self.population):             
    if elitism > 0 and i in n_fittest:
        pass

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self.disturbance_threshold *= decay

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envs = [gym.make(self.env_name) for _ in range(multiprocessing.cpu_count())]

amount_per_thread = int(np.floor(self.population_size / multiprocessing.cpu_count()))
left_over = self.population_size - amount_per_thread * multiprocessing.cpu_count()

fitnesses = np.zeros(len(self.population))

def get_weights_reward(begin, size, env):
    for i in range(begin, begin + size):
        fitnesses[i] = -self.get_reward(self.population[i], 

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swarms_best_index = np.argmin(fitnesses)
self.swarms_best_score = np.amin(fitnesses)
self.swarms_best = self.population[swarms_best_index]

if self.swarms_best_score <= self.all_time_best_score:
    self.all_time_best_score = self.swarms_best_score
    self.all_time_best = self.swarms_best

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if self.mode == 'original':
    if r[i][x] < self.disturbance_threshold:
        p[x] = np.random.normal(dev, mean)
    else:
        leader_rate = np.random.uniform(0.0, 1.0)
        update = self.swarms_best[x] - best_neighbour[x]
        p[x] = best_neighbour[x] + leader_rate * update

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elif self.mode == 'hybrid':
    if r[i][x] < self.disturbance_threshold:
        p[x] = np.random.normal(dev, mean)
    else:
        leader_rate = np.random.uniform(0.0, 1.0)
        update = (best_neighbour[x] + self.swarms_best[x]) / 2.0 - p[x]
        p[x] = p[x] + leader_rate * update  

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elif self.mode == 'n_fittest':
    if r[i][x] < self.disturbance_threshold:
        p[x] = np.random.normal(dev, mean)
    else:
        leader_rate = np.random.uniform(0.0, 1.0)
        update = np.average(self.population[n_fittest]) - best_neighbour[x]
        p[x] = best_neighbour[x] + leader_rate * update

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elif self.mode == 'random_gauss':    
    p[x] = np.random.normal(dev, mean)

elif self.mode == 'random_uniform':
    p[x] = np.random.sample()

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