time_hist_ppt.append(time_hist)
dist_hist_ppt.append(best_hist)
print "Parallel Parallel Tempering, run ", i, " complete."
solution_spt, histories_spt = serial_parallel_tempering(
graph, distance, initial_Xs, initial_temps, iterr, changepath, nswaps, nbefore
)
best_hist, time_hist = hist_best(histories_spt[0])
time_hist_spt.append(time_hist)
dist_hist_spt.append(best_hist)
print "Serial Parallel Tempering, run ", i, " complete."
solution_sa, history_sa = simulated_annealing(
graph, distance, initial_Xs[0], initial_temps[0], nbefore, iterr, changepath, nswaps, reheat, cool
)
best_hist, time_hist = hist_best(history_sa)
time_hist_sa.append(time_hist)
dist_hist_sa.append(best_hist)
print "Simulated Annealing, run ", i, " complete."
# Take the mean of each column, save these outputs so they can be plotted and examined without re-running simulations
dist_hist_ppt = np.mean(dist_hist_ppt, 0)
time_hist_ppt = np.mean(time_hist_ppt, 0)
# Save results to plot in plotter.py
np.save("../SavedResults/time_hist_ppt.npy", time_hist_ppt)
np.save("../SavedResults/dist_hist_ppt.npy", dist_hist_ppt)
dist_hist_spt = np.mean(dist_hist_spt, 0)
initial_path = np.random.permutation(size)
print "Initial path: " + str(initial_path)
print "Initial path length: " + str(distance(graph, initial_path)) + "\n"
initial_temp = 2.
cool = 0.9
nbefore = 480
nswaps = 1
reheat = np.sqrt(5)
# Run simulated annealing
print "\nSimulated Annealing\n"
for iterr in [10**x for x in [3, 4, 5]]:
with Timer() as t:
solution, history = simulated_annealing(graph, distance,
initial_path,
initial_temp, nbefore,
iterr, changepath,
nswaps, reheat, cool)
print "Iterations: {:.2E}".format(iterr)
print "Calculated path: " + str(solution)
print "Calculated path length: " + str(distance(graph, solution))
print "Time: " + str(t.interval) + "\n"
# Reuse copy of initial path for all trials
nsystems = 4
initial_paths = []
for i in xrange(nsystems):
initial_paths.append(np.asarray(initial_path))
initial_temps = [1., np.sqrt(5), np.sqrt(5)**2, np.sqrt(5)**3]
print "Best path: " + str(bestpath)
print "Best path length: " + str(distance(graph, bestpath))
#Initial values, probably need to be tuned
initial_path = np.random.permutation(size)
initial_temp = 2.
cool = 0.9
reanneal = 100
nswaps = 3
# Run simulated annealing
print "\nSimulated Annealing\n"
for iterr in [10**x for x in [3,4,5]]:
with Timer() as t:
solution, history = simulated_annealing(graph, distance, initial_path,
initial_temp, cool, reanneal, iterr,
changepath, nswaps)
print "\nIterations: {:.2E}".format(iterr)
print "Calculated path: " + str(solution)
print "Calculated path length: " + str(distance(graph, solution))
print "Time: " + str(t.interval)
# Initial values for parallel tempering
nsystems = 3
initial_paths = [np.random.permutation(size) for i in xrange(nsystems)]
initial_temps = [1., 5., 10.]
nbefore = 100
# Run parallel tempering
print "\nParallel Tempering\n"
for iterr in [10**x for x in [3,4,5]]:
