print(
" - Plotting the cumulative rewards, and saving the plot to {} ..."
.format(savefig))
savefig = mainfig
evaluation.plotRegrets(envId, savefig=savefig,
moreAccurate=True) # XXX To save the figure
savefig = mainfig.replace('main', 'main_LessAccurate')
evaluation.plotRegrets(
envId, savefig=savefig,
moreAccurate=False) # XXX To save the figure
savefig = mainfig.replace('main', 'main_BestArmPulls')
print(
" - Plotting the probability of picking the best arm, and saving the plot to {} ..."
.format(savefig))
# --- Also plotting the probability of picking the best arm
evaluation.plotBestArmPulls(
envId, savefig=savefig) # XXX To save the figure
# if configuration['horizon'] >= 1000:
# savefig = mainfig.replace('main', 'main_semilogx')
# evaluation.plotRegrets(envId, savefig=savefig, semilogx=True) # XXX To save the figure
savefig = mainfig.replace('main', 'main_semilogy')
evaluation.plotRegrets(envId, savefig=savefig,
semilogy=True) # XXX To save the figure
if configuration['horizon'] >= 1000:
savefig = mainfig.replace('main', 'main_loglog')
evaluation.plotRegrets(envId, savefig=savefig,
loglog=True) # XXX To save the figure
if configuration['repetitions'] > 1:
if plotSTD:
savefig = savefig.replace('main', 'main_STD')
evaluation.plotRegrets(
envId,
evaluation.printLastRegrets(envId)
print("\nGiving the final ranking ...")
evaluation.printFinalRanking(envId)
print("\n\n- Plotting the last regrets...")
evaluation.plotLastRegrets(envId, boxplot=True)
print("\nGiving the mean and std running times ...")
evaluation.printRunningTimes(envId)
evaluation.plotRunningTimes(envId)
print("\nGiving the mean and std running times ...")
evaluation.printMemoryConsumption(envId)
evaluation.plotMemoryConsumption(envId)
print("\n\n- Plotting the mean reward...")
evaluation.plotRegrets(envId, meanReward=True)
print("\n\n- Plotting the regret...")
evaluation.plotRegrets(envId)
print("\n- Plotting the probability of picking the best arm of time...")
evaluation.plotBestArmPulls(envId)
print("\n- Plotting the histograms of regrets...")
evaluation.plotLastRegrets(envId, sharex=True, sharey=True)
# Done
print("Done for simulations example_of_main_singleplayer ...")
notify("Done for simulations example_of_main_singleplayer ...")