def learn(N, k, loc=3, prng=None):
"""Simulate learning behavior"""
prng = process_prng(prng)
trials, prng = tlib.random(N, k, prng=prng)
trials, prng = tlib.jitter(trials, prng=prng)
l = trials.shape[0]
acc = np.zeros(l)
p = np.zeros(l)
conds = np.unique(trials)
for n in conds:
## Skip null trials
if (n == 0) | (n == '0'):
continue
p_n, prng = probability.learn(k, loc, prng=prng)
acc_n, prng = acclib.accuracy(p_n, prng=prng)
for t in enumerate(trials):
acc[trials == n] = acc_n
p[trials == n] = p_n
return trials, acc, p, prng
def random(N, k, prng=None):
"""Simulate random behavior
Parameters
----------
N : int
Number of conditions
k : int
Number of trials / condition
prng : RandomState object, None
Explicit passing of random state. None create
a new state
"""
prng = process_prng(prng)
trials, prng = tlib.random(N, k, prng=prng)
trials, prng = tlib.jitter(trials, prng=prng)
l = trials.shape[0]
acc = np.zeros(l)
p = np.zeros(l)
conds = np.unique(trials)
for n in conds:
## Skip null trials
if (n == 0) | (n == '0'):
continue
p_n, prng = probability.random(k, prng=prng)
acc_n, prng = acclib.accuracy(p_n, prng=prng)
for t in enumerate(trials):
acc[trials == n] = acc_n
p[trials == n] = p_n
return trials, acc, p, prng
