def __init__(self,discretisation,reward_type,load_saved = False):
self.disc = discretisation#discretisation model
if load_saved == True:
self.feature_f = fn.pickle_loader("saved/feature_f")
self.transition = Transition()
self.transition = fn.pickle_loader("saved/transition_f")
else:
self.buildTransitionFunction(1)
self.buildFeatureFunction()
fn.pickle_saver(self.feature_f,"saved/feature_f")
fn.pickle_saver(self.transition,"saved/transition_f")
self.choose_reward_function(reward_type)
self.reward_f_initial = self.buildRewardFunction()
self.reward_f = self.buildRewardFunction()
def __init__(self,discretisation,reward_type,load_saved = False):
self.disc = discretisation#discretisation model
if load_saved == True:
self.feature_f = fn.pickle_loader("saved/feature_f")
self.transition = Transition()
self.transition = fn.pickle_loader("saved/transition_f")
else:
self.buildTransitionFunction(1)
self.buildFeatureFunction()
fn.pickle_saver(self.feature_f,"saved/feature_f")
fn.pickle_saver(self.transition,"saved/transition_f")
self.choose_reward_function(reward_type)
self.reward_f_initial = self.buildRewardFunction()
self.reward_f = self.buildRewardFunction()
def plot_results(experiment_name, directory,number_of_runs): all_results = fn.pickle_loader(directory+"/"+experiment_name+".pkl") print len(all_results[0]) results_f = average_and_std_type(all_results,0) results_n = average_and_std_type(all_results,1) results_s = average_and_std_type(all_results,2) results_plot_individual_means(results_f,results_n,results_s,directory+"/"+experiment_name,num_of_runs=number_of_runs)
def plot_results(experiment_name, directory, number_of_runs):
all_results = fn.pickle_loader(directory + "/" + experiment_name + ".pkl")
print len(all_results[0])
results_f = average_and_std_type(all_results, 0)
results_n = average_and_std_type(all_results, 1)
results_s = average_and_std_type(all_results, 2)
results_plot_individual_means(results_f,
results_n,
results_s,
directory + "/" + experiment_name,
num_of_runs=number_of_runs)
