def main(args):
filename = args.file
num_ants = args.ants
iterations = args.iterations
pher = 0.5
a = args.alpha
b = args.beta
decay = args.decay
seed = args.seed
stats = args.stats
num_points, p_median, points = read_input(filename)
aco = AntColony(num_ants,
iterations,
pher,
a,
b,
decay,
seed=seed,
stat_file=stats)
aco.set_data(num_points, p_median, points)
solution = aco.run()
print('Best distance: ' + str(solution))
beta = 2 # Visibility factor
e = .5 # Evaporation rate
el = 2 # Amount of elite ants
# Matrices
am = get_adjacency_matrix(M)
dist_m = fill_dis_matr(M) # Distances matrix
pheromones = np.ones((M, M))
print_matrix(am, M, "Adjacency Matrix")
print_matrix(pheromones, M, "Pheromones Matrix")
plot_graph(am, dist_m)
full_search_result = full_search.find_best_way(dist_m)
ac = AntColony(distances=dist_m,
n_ants=M,
n_best=2,
n_iterations=100,
decay=0.5,
alpha=1,
beta=1)
aco_result = ac.run()
def main():
pass
if __name__ == '__main__':
main()
end_num = 90
for line in sys.stdin:
line = line.strip()
dic = line
dic = json.loads(dic) #we make dictionary from string
all_ants = float(dic['n_ants'])
distances = np.asarray(dic['distances'])
pheromones = np.asarray(dic['pheromones'])
upper_bond = int(math.ceil(all_ants/batch_size))
for splits_number in range(upper_bond):
dic2 = dic.copy()
if batch_size*(1+splits_number) < all_ants:
dic2['n_ants'] = batch_size
dic2['n_best'] = dic2['n_ants']
else:
dic2['n_ants'] = int(all_ants - splits_number*batch_size)
dic2['n_best'] = dic2['n_ants']
ant_colony = AntColony(distances, dic2['n_ants'], dic2['n_iterations']-1, dic2['decay'], dic2['alpha'],
dic2['beta'], pheromones)
shortest_path, new_pheromone = ant_colony.run(start_num, end_num)
dic2['distances'] = dic['distances']
dic2['pheromones'] = dic['pheromones']
print('%s\t%s' % (shortest_path, json.dumps(dic2)))
#!/usr/bin/env python
from aco import AntColony
import numpy as np
import json
import sys
start_num = 1
end_num = 90
input_copy_filename = "../data/input500_10_800.txt"
file_input_copy = open(input_copy_filename, "r")
for input_copy in file_input_copy:
dic = input_copy.strip()
dic = json.loads(dic)
dic['distances'] = np.asarray(dic['distances'])
ant_colony = AntColony(dic['distances'], dic['n_ants'], dic['n_iterations'],
dic['decay'], dic['alpha'], dic['beta'])
optimal_path = ant_colony.run(start_num, end_num)
print(optimal_path[1])