Skip to content
Snippets Groups Projects
Commit fa413f94 authored by Matthieu Oberon's avatar Matthieu Oberon
Browse files

update results

parent 422aefda
No related branches found
No related tags found
No related merge requests found
import matplotlib.pyplot as plt
import numpy as np
###########################################################################
## Compare 2 different setups
###########################################################################
cost_opti = []
iter_list = []
iter = 1
......@@ -55,3 +59,27 @@ plt.ylabel("Temps d'exécution(s)")
plt.title("Temps d'exécution de la routine d'optimisation\npour un problème 256*256*256 avec 10 générations de 64 fourmis.")
plt.legend()
plt.show()
###########################################################################
## Test stability of the code
###########################################################################
for i in range(1, 6):
throughputs = []
iter_list = []
iter = 1
with open(f"cost_opti_16np_32ants_400iter_{i}.txt", 'r') as file:
for line in file:
cost = float(line.strip("\n"))
throughputs.append(cost)
iter_list.append(iter)
iter +=1
plt.plot(iter_list, throughputs, '*-', label=f"Batch {i}")
plt.title("Résulats de plusiseurs batchs pour 400 générations de 32 fourmis sur 16 process.")
plt.xlabel("Iteration")
plt.ylabel("Throughput optimal (MPoint/s)")
plt.legend()
plt.show()
\ No newline at end of file
results/linear_scalability_of_problem.png

52.3 KiB | W: | H:

results/linear_scalability_of_problem.png

52.3 KiB | W: | H:

results/linear_scalability_of_problem.png
results/linear_scalability_of_problem.png
results/linear_scalability_of_problem.png
results/linear_scalability_of_problem.png
  • 2-up
  • Swipe
  • Onion skin
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment