get_percent_different(eager_xstar_totals_lst, xstar_totals_lst), 2,
"Opt. Solution Improvement")
def to_percent(y, position):
# Ignore the passed in position. This has the effect of scaling the default
# tick locations.
s = str(100 * y)
# The percent symbol needs escaping in latex
if matplotlib.rcParams['text.usetex'] is True:
return s + r'$\%$'
else:
return s + '%'
plt.ylim(bottom=0)
formatter = FuncFormatter(to_percent)
# Set the formatter
plt.gca().yaxis.set_major_formatter(formatter)
plt.xticks(kNumRobotsList)
plt.xlabel("Agent Count")
plt.ylabel("Lazy vs Eager X* Mean Runtime Improvement")
#plt.yscale('log')
plot_helper.legend(loc=3)
plot_helper.save_fig("xstar_eager_vs_lazy")
plt.show()
save_data_to_file(
"xstar_totals_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation),
xstar_totals_lst)
save_data_to_file(
"mstar_totals_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation),
mstar_totals_lst)
else:
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file(
"xstar_firsts_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation))
xstar_totals_lst = read_saved_data_from_file(
"xstar_totals_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation))
mstar_totals_lst = read_saved_data_from_file(
"mstar_totals_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation))
plot_helper.plot_ci(kNumRobotsList, xstar_firsts_lst, 3, "X* First Solution")
plot_helper.plot_ci(kNumRobotsList, xstar_totals_lst, 3, "X* Opt. Solution")
plot_helper.plot_ci(kNumRobotsList, mstar_totals_lst, 3, "M* Solution")
plt.xticks(kNumRobotsList)
plt.xlabel("Agent Count")
plt.ylabel("Runtime (ms)")
plt.yscale('log')
plot_helper.legend(loc=2)
plot_helper.save_fig("xstar_vs_mstar_agents_vs_runtime")
plt.show()
xstar_totals_lst = []
if kRunNewTrials:
print("===Running new trials")
for inflation in kInflationValues:
seed = int(inflation * 100 * 53 + 17)
print("Inflation:", inflation)
print("X*")
xstar_first_exps, xstar_total_exps = run_xstar(num_robots, inflation)
xstar_firsts_lst.append(xstar_first_exps)
xstar_totals_lst.append(xstar_total_exps)
save_data_to_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_firsts_lst)
save_data_to_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_totals_lst)
else:
inflation = kInflationValues[-1]
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, kInflationValues[-1]))
xstar_totals_lst = read_saved_data_from_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, kInflationValues[-1]))
plot_helper.plot(kInflationValues, xstar_firsts_lst, 2, "X* First Solution")
plot_helper.plot(kInflationValues, xstar_totals_lst, 2, "X* Full Solution")
plt.ylim(bottom=0)
plt.xticks(kInflationValues)
plt.xlabel("Heuristic Inflation")
plt.ylabel("State Expansions")
#plt.yscale('log')
plot_helper.legend(loc=1)
plot_helper.save_fig("xstar_heuristic_inflation")
plt.show()
save_data_to_file(
"xstar_totals_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation),
xstar_totals_lst)
save_data_to_file(
"mstar_totals_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation),
mstar_totals_lst)
else:
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file(
"xstar_firsts_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation))
xstar_totals_lst = read_saved_data_from_file(
"xstar_totals_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation))
mstar_totals_lst = read_saved_data_from_file(
"mstar_totals_lst_ci_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation))
plot_helper.plot_ci(kNumRobotsList, xstar_totals_lst, 3, "X* Opt. Solution")
plot_helper.plot_ci(kNumRobotsList, xstar_firsts_lst, 3, "X* First Solution")
plot_helper.plot_ci(kNumRobotsList, mstar_totals_lst, 3, "M* Solution")
plt.xticks(kNumRobotsList)
plt.xlabel("Agent Count")
plt.ylabel("Runtime (ms)")
plt.yscale('log')
plot_helper.legend(loc=2)
plot_helper.save_fig("xstar_vs_mstar_agents_vs_runtime_random_field")
plt.show()
xstar_firsts_lst = []
xstar_totals_lst = []
if kRunNewTrials:
print("===Running new trials")
for num_robots in kAgentCount:
seed = int(num_robots * 100 * 53 + 17)
print("Agent Count:", num_robots)
print("X*")
xstar_first_exps, xstar_total_exps = run_xstar(num_robots)
xstar_firsts_lst.append(xstar_first_exps)
xstar_totals_lst.append(xstar_total_exps)
save_data_to_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_firsts_lst)
save_data_to_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_totals_lst)
else:
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation))
xstar_totals_lst = read_saved_data_from_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation))
plot_helper.plot(kAgentCount, xstar_firsts_lst, 2, "X* First Solution")
plot_helper.plot(kAgentCount, xstar_totals_lst, 2, "X* Opt. Solution")
plt.ylim(bottom=5)
plt.xticks(kAgentCount)
plt.xlabel("Agent Count")
plt.ylabel("Number of Expansions")
plt.yscale('log')
plot_helper.legend(loc=2)
plot_helper.save_fig("xstar_agent_sweep")
plt.show()
xstar_firsts_lst = []
xstar_totals_lst = []
if kRunNewTrials:
print("===Running new trials")
for radius in kRadiusValues:
seed = int(radius * 100 * 53 + 17)
print("radius:", radius)
print("X*")
xstar_first_exps, xstar_total_exps = run_xstar(num_robots, radius)
xstar_firsts_lst.append(xstar_first_exps)
xstar_totals_lst.append(xstar_total_exps)
save_data_to_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_firsts_lst)
save_data_to_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_totals_lst)
else:
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation))
xstar_totals_lst = read_saved_data_from_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation))
plot_helper.plot(kRadiusValues, xstar_firsts_lst, 2, "X* First Solution")
plot_helper.plot(kRadiusValues, xstar_totals_lst, 2, "X* Opt. Solution")
plt.ylim(bottom=0)
plt.xticks(kRadiusValues)
plt.xlabel("Radius Values")
plt.ylabel("State Expansions")
#plt.yscale('log')
plot_helper.legend(loc=4)
plot_helper.save_fig("xstar_radius_sweep")
plt.show()
plot_helper.plot(xs, final_percentage_iterations, 2, None)
def to_percent(y, position):
# Ignore the passed in position. This has the effect of scaling the default
# tick locations.
s = str(100 * y)
# The percent symbol needs escaping in latex
if matplotlib.rcParams['text.usetex'] is True:
return s + r'$\%$'
else:
return s + '%'
plt.ylim(bottom=0)
formatter = FuncFormatter(to_percent)
# Set the formatter
plt.gca().yaxis.set_major_formatter(formatter)
#plot_helper.plot(kAgentCount, xstar_totals_lst, 2, "X* Opt. Solution")
plt.xticks(xs)
plt.xlabel("Iteration Count")
plt.ylabel("Percentage of Total Computation Time")
#plt.yscale('log')
#plot_helper.legend(loc=2)
plot_helper.save_fig("xstar_computation_time_vs_iteration")
plt.show()