def __init__(self, n, alphas, TR=2, ISI=2, prng=None):
try:
Exp.__init__(self, TR=2, ISI=2, prng=None)
except AttributeError:
pass
self.prng = process_prng(prng)
# Simulate a learning task and acc.
ncond = 1
trials, acc, p, self.prng = simBehave.behave.learn(ncond, n, 3, True, self.prng)
# And hand the results off of self
self.trials = np.array(trials)
self.durations = np.array([1] * len(self.trials))
# Or add them to the data attr
self.data["ncond"] = ncond
self.data["acc"] = acc
self.data["p"] = p
# Run the RL sim, and save the results keying (partly)
# off of alpha an alpha counter
for ii, alpha in enumerate(alphas):
v_dict, rpe_dict = rl.reinforce.b_delta(acc, trials, alpha)
values = rl.misc.unpack(v_dict, trials)
rpes = rl.misc.unpack(rpe_dict, trials)
self.data["value" + str(ii)] = values
self.data["rpe" + str(ii)] = rpes
self.data["alphas"] = alphas
# Create random data too.
self.data["rand"] = self.prng.rand(n)
def __init__(self, n, TR=2, ISI=2, prng=None):
try:
Exp.__init__(self, TR=2, ISI=2, prng=None)
except AttributeError:
pass
self.prng = process_prng(prng)
self.trials = np.array([0,]*n + [1,]*n)
self.durations = [1, ] * len(self.trials)
self.prng.shuffle(self.trials)
def __init__(self, n, TR=2, ISI=2, prng=None):
try:
Exp.__init__(self, TR=2, ISI=2, prng=None)
except AttributeError:
pass
self.prng = process_prng(prng)
# event_random(N,k,mult=1)
self.trials, self.prng = event_random(2, n, 1, self.prng)
self.trials = np.array(self.trials)
self.durations = [1, ] * len(self.trials)
def __init__(self, n, n_cond=2, TR=2, durations=None,):
try:
Exp.__init__(self, TR=TR, ISI=2, prng=None)
except AttributeError:
pass
self.trials, self.prng = event_random(n_cond, n, 1, self.prng)
self.trials = np.array(self.trials)
if durations != None:
start, stop = durations[0], durations[1]
self.durations = [random_integers(start, stop) for
_ in self.trials]
else:
self.durations = [1, ] * len(self.trials)
def __init__(self, n, behave='learn', TR=2, ISI=2, prng=None):
try:
Exp.__init__(self, TR=2, ISI=2, prng=None)
except AttributeError:
pass
self.prng = process_prng(prng)
n_cond = 1
n_trials_cond = n
trials = []
acc = []
p = []
if behave == 'learn':
trials, acc, p, self.prng = simBehave.behave.learn(
n_cond, n_trials_cond, 3, True, self.prng)
elif behave == 'random':
trials, acc, p, self.prng = simBehave.behave.random(
n_cond,n_trials_cond, 3, self.prng)
else:
raise ValueError(
'{0} is not known. Try learn or random.'.format(behave))
# Find best RL learning parameters for the behavoiral data
# then generate the final data and unpack it into a list.
best_rl_pars, best_logL = rl.fit.ml_delta(acc, trials, 0.05)
v_dict, rpe_dict = rl.reinforce.b_delta(acc, trials, best_rl_pars[0])
values = rl.misc.unpack(v_dict, trials)
rpes = rl.misc.unpack(rpe_dict, trials)
# Store the results in appropriate places
self.trials = np.array(trials)
self.durations = np.array([1, ] * len(self.trials))
self.data['acc'] = acc
self.data['p'] = p
self.data['best_logL'] = best_logL
self.data['best_rl_pars'] = best_rl_pars
self.data['value'] = values
self.data['rpe'] = rpes
self.data['rand'] = self.prng.rand(len(self.trials))
def __init__(
self,
n,
n_cond=2,
TR=2,
durations=None,
):
try:
Exp.__init__(self, TR=TR, ISI=2, prng=None)
except AttributeError:
pass
self.trials, self.prng = event_random(n_cond, n, 1, self.prng)
self.trials = np.array(self.trials)
if durations != None:
start, stop = durations[0], durations[1]
self.durations = [
random_integers(start, stop) for _ in self.trials
]
else:
self.durations = [
1,
] * len(self.trials)
