responses[i])
rt[i] = computeMeanRepresentativeSteps(step)[0]
model.state = convertStimulus(np.array(model.state))
model.action = np.array(model.action)
model.responses = np.array(model.responses)
model.reaction = np.array(model.reaction)
# -----------------------------------
# -----------------------------------
# HUMAN LEARNING
# -----------------------------------
human = HLearning(
dict({
'meg': ('../../PEPS_GoHaL/Beh_Model/', 48),
'fmri': ('../../fMRI', 39)
}))
# -----------------------------------
# -----------------------------------
# PARAMETERS + INITIALIZATION
# -----------------------------------
very_good_parameters = dict({
'noise': 0.0001,
'length': 10,
'alpha': 0.8,
'beta': 3.0,
'gamma': 0.4,
'threshold': 4.0,
'gain': 2.0
# -----------------------------------
if not sys.argv[1:]:
sys.stdout.write("Sorry: you must specify at least 1 argument")
sys.stdout.write("More help avalaible with -h or --help option")
sys.exit(0)
parser = OptionParser()
parser.add_option("-m", "--model", action="store", help="The name of the model to optimize", default=False)
parser.add_option("-t", "--time", action="store", help="Time of execution", default=False)
parser.add_option("-d", "--data", action="store", help="The data to fit", default=False)
parser.add_option("-o", "--output", action="store", help="Output directory", default=False)
(options, args) = parser.parse_args()
# -----------------------------------
# -----------------------------------
# HUMAN LEARNING
# -----------------------------------
human = HLearning(dict({'meg':('Beh/MEG/Beh_Model/',48), 'fmri':('Beh/fMRI',39)}))
# -----------------------------------
# -----------------------------------
# POSSIBLE MODELS
# -----------------------------------
models = dict({'bayesian':"BayesianWorkingMemory(['s1', 's2', 's3'], ['thumb', 'fore', 'midd', 'ring', 'little'], vars(options))",
'qlearning':"QLearning(['s1', 's2', 's3'], ['thumb', 'fore', 'midd', 'ring', 'little'], vars(options))",
'fusion':"FSelection(['s1', 's2', 's3'], ['thumb', 'fore', 'midd', 'ring', 'little'], vars(options))",
'kalman':"KalmanQLearning(['s1', 's2', 's3'], ['thumb', 'fore', 'midd', 'ring', 'little'], vars(options))",
'keramati':"KSelection(['s1', 's2', 's3'], ['thumb', 'fore', 'midd', 'ring', 'little'], vars(options))",
})
# -----------------------------------
# ------------------------------------
# CREATE DIRECTORY RESULTS