def run_trials(T0, alpha, out_avg_file):
beta = 1
m_initial = 1
max_time = 1100
trials = 30
solution_sets = []
best_s_sets = []
total_cpu_time = 0
for i in range(trials):
t = time()
(solution, best_s) = SA.simulated_annealing(Z[i], T0, alpha,
beta, m_initial, max_time)
total_cpu_time += (time() - t)
solution_sets.append(solution)
best_s_sets.append(best_s)
# Best vs. Cur Cost
with open(out_avg_file, 'w') as out_file:
for i in range(max_time):
total_cost_current = 0
total_cost_best = 0
for sol in range(trials):
total_cost_current += solution_sets[sol][i][1]
total_cost_best += solution_sets[sol][i][2]
iteration = solution_sets[sol][i][0]
avg_cost_current = total_cost_current / trials
avg_cost_best = total_cost_best / trials
out_file.write("%d, %f, %f\n" % (iteration,
avg_cost_current,
avg_cost_best))
# Avg and Std deviation
G = 1000
best_costs = numpy.zeros([trials, 1])
for sol in range(trials):
best_costs[sol] = solution_sets[sol][G][2]
avg_cost = numpy.mean(best_costs)
std_cost = numpy.std(best_costs)
print "Average best cost: %f, Std Dev: %f" % (avg_cost, std_cost)
# Avg CPU Time
avg_cpu_time = total_cpu_time / trials
print "Average CPU time: %f" % avg_cpu_time
def run_trials(T0, alpha, out_avg_file):
solution_sets = []
best_s_sets = []
total_cpu_time = time()
for i in range(trials):
(solution, best_s) = SA.simulated_annealing(Z[i], T0, alpha,
beta, m_initial, max_time)
solution_sets.append(solution)
best_s_sets.append(best_s)
total_cpu_time = time() - total_cpu_time
# Best vs. Cur Cost
with open(out_avg_file, 'w') as out_file:
for i in range(max_time):
total_cost_current = 0
total_cost_best = 0
for sol in range(trials):
total_cost_current += solution_sets[sol][i][1]
total_cost_best += solution_sets[sol][i][2]
iteration = solution_sets[sol][i][0]
avg_cost_current = total_cost_current / trials
avg_cost_best = total_cost_best / trials
out_file.write("%d, %f, %f\n" % (iteration,
avg_cost_current,
avg_cost_best))
# Avg and Std deviation
best_costs = numpy.zeros([trials, 1])
for sol in range(trials):
best_costs[sol] = solution_sets[sol][max_time - 1][2]
avg_cost = numpy.mean(best_costs)
std_cost = numpy.std(best_costs)
print("Average best cost: %f, Std Dev: %f" % (avg_cost, std_cost))
# Avg CPU Time
avg_cpu_time = total_cpu_time / trials
print("Average CPU time: %f" % avg_cpu_time)
with open("best_solutions_SA.txt", 'w') as output:
output.write('SA = [')
for best_cost in best_costs:
output.write(str(best_cost[0]) + ' ')
output.write('];')