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)