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
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)
model.reaction = center(model.reaction)
# model.weights = np.array(model.weights)
# model.p_wm =np.array(model.p_wm)
# model.p_rl =np.array(model.p_rl)
# # -----------------------------------
# -----------------------------------
# HUMAN LEARNING
# -----------------------------------
human = HLearning(dict({'meg':('../../PEPS_GoHaL/Beh_Model/',48), 'fmri':('../../fMRI',39)}))
# -----------------------------------
# -----------------------------------
# PARAMETERS + INITIALIZATION
# -----------------------------------
parameters = dict({'noise':0.0001,
'length':10,
'alpha':0.8,
'beta':3.0,
'gamma':0.5,
'threshold':4.0,
'sigma':0.5,
'gain':2.0})
# sys.stdout.write("More help avalaible with -h or --help option")
# sys.exit(0)
parser = OptionParser()
parser.add_option("-i", "--input", action="store", help="The name of the directory to load", default=False)
(options, args) = parser.parse_args()
# -----------------------------------
def center(x):
x = x-np.median(x)
x = x/(np.percentile(x, 75)-np.percentile(x, 25))
return x
# -----------------------------------
# HUMAN LEARNING
# -----------------------------------
human = HLearning(dict({'meg':('../../PEPS_GoHaL/Beh_Model/',42), 'fmri':('../../fMRI',39)}))
tmp = np.reshape(human.reaction['fmri'], (14, 4*39))
tmp = np.array(map(center, tmp))
human.reaction['fmri'] = np.reshape(tmp, (14*4, 39))
# -----------------------------------
# -----------------------------------
# LOADING DATA
# -----------------------------------
with open("beh_model.pickle", 'r') as handle:
data = pickle.load(handle)
# -----------------------------------
#order data
# -----------------------------------
hpcr = extractStimulusPresentation(human.responses['fmri'], human.stimulus['fmri'], human.action['fmri'], human.responses['fmri'])
(options, args) = parser.parse_args()
# -----------------------------------
def center(x):
x = x - np.median(x)
x = x / (np.percentile(x, 75) - np.percentile(x, 25))
return x
# -----------------------------------
# HUMAN LEARNING
# -----------------------------------
human = HLearning(
dict({
'meg': ('../../PEPS_GoHaL/Beh_Model/', 42),
'fmri': ('../../fMRI', 39)
}))
tmp = np.reshape(human.reaction['fmri'], (14, 4 * 39))
tmp = np.array(map(center, tmp))
human.reaction['fmri'] = np.reshape(tmp, (14 * 4, 39))
# -----------------------------------
# -----------------------------------
# LOADING DATA
# -----------------------------------
with open("beh_model.pickle", 'r') as handle:
data = pickle.load(handle)
# -----------------------------------
#order data
# -----------------------------------
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
