# input("after while")
emo = np.random.random_integers(0,len(emos)-1)
# print("emo is: " + str(emos[emo]))
print("demo_filenames is: " + str(demo_filenames[emo]))
demos, starts, goals, modes, emos_ignore = rl.process_demos(demo_filenames[emo])
print("demos is:...")
vnorm_bins = rl.detect_bins(demos, starts, goals)
pweights_ = load_weights_numpy(pweight_filenames[emo])
print("pweights_ is: " + str(pweights_))
path, err_goal, termination_msg, path_duration = rl.create_path_from_policy(pweights_, start, goal, vnorm_bins, None)
paths = [path]
print("path is: "+ str(path))
path_filename = "exp" + str(subject) + "_"
rl.save_paths_to_file(paths, start, goal, emos[emo], "subject=" + str(subject), path_filename, i)
path_filenames.append(path_filename + str(i) + ".csv")
truth_file.write(str(i) + ":" + str(emos[emo]))
truth_file.write("\n")
i+=1
with open ("exp" + str(subject) + "_pathfile_names.txt", "w") as f:
for path_filename in path_filenames:
f.write(str(path_filename))
f.write("\n")
# input("checking...")
# print("pweights_ is: " + str(np.shape(pweights_)))
# print(pweights_)
###################################################################
print(pweights_) # generalized_start = [450,67] # generalized_goal = [86,396] # generalized_start = [250,167] # generalized_goal = [416,196] # generalized_start = [150,367] # generalized_goal = [379,112] generalized_start = [450,467] generalized_goal = [79,112] # generalized_start = [250,350] # generalized_goal = [150,225] # pweights = rl.SARSA(rweights_, generalized_start, generalized_goal, vnorm_bins, -1) # path, err_goal, termination_msg, path_duration = rl.create_path_from_policy(pweights, generalized_start, generalized_goal, vnorm_bins, None) path, err_goal, termination_msg, path_duration = rl.create_path_from_policy(pweights_, generalized_start, generalized_goal, vnorm_bins, None) paths = [path] rl.save_paths_to_file(paths, generalized_start, generalized_goal, "test_emo", "test_mode", "aaa", "na") # path, err_goal, termination_msg, path_duration = rl.create_path_from_policy(pweights_, start, goal, T)
