length.append(len(transformed_trial))
trials_data = trials_data[:TRIAL_LENGTH]
all_data[participant_id] = trials_data
count = np.zeros(shape=[6, 3, 6])
for trial_id, trial in enumerate(trials_data):
saver.save_data("step", participant_id, trial_id, len(trial))
time = 0
for step in trial:
time += step[3]
saver.save_data("time", participant_id, trial_id, time)
saver.save_data("normalized_time", participant_id, trial_id,
time / len(trial))
result = optimal_probability(participant_id,
trials_data,
is_simulate=False)
saver.save_trials_data("optimal", participant_id, result[0])
saver.save_trials_data("optimal_inner", participant_id,
result[1]["inner"])
saver.save_trials_data("optimal_outer", participant_id,
result[1]["outer"])
saver.save_trials_data("optimal_last", participant_id,
result[1]["last"])
if NEED_UPDATE_DATA_FRAME:
for trial in range(TRIAL_LENGTH):
block = trial // 36
timestep = trial % 36
optimal_data_frame = \
optimal_data_frame.append({"step": saver.get_trial_data("step", participant_id),
"reaction_time": saver.get_trial_data("time", participant_id),
continue
rawFile = open(path, "r")
reader = csv.DictReader(rawFile, delimiter="#")
trials_data = []
length = []
for row in reader:
trial = row["trial_data"]
if trial != "--":
transformed_trial = json.loads(trial)
trials_data.append(transformed_trial)
length.append(len(transformed_trial))
trials_data = trials_data[:TRIAL_LENGTH]
steps = []
for trial in trials_data:
steps.append(len(trial))
# all_reduction[alpha][tau][participant_id] = steps
result = optimal_probability(participant_id,
trials_data,
is_simulate=True,
is_randomized=randomized)
# all_reduction[alpha][tau][participant_id] = result[0]
# all_reduction[alpha][tau][participant_id] = result[1]["inner"]
# all_reduction[alpha][tau][participant_id] = result[1]["outer"]
all_reduction[alpha][tau][participant_id] = result[1]["last"]
with open("optimal_last_%s_randomized.pkl" % SIMULATE_METHOD, "wb") as f:
pickle.dump(all_reduction, f)