def setUp(self):
self.basic = ddm.Model(dx=.005,
dt=.01,
T_dur=2,
drift=ddm.DriftConstant(drift=.4),
noise=ddm.NoiseConstant(noise=1),
bound=ddm.BoundConstant(B=1))
class NoiseCond(ddm.Noise):
name = "Noise with a condition"
required_conditions = ['cond']
required_parameters = []
def get_noise(self, conditions, **kwargs):
return conditions["cond"]
self.withcond = ddm.Model(noise=NoiseCond())
class FancyBounds(ddm.Bound):
name = "Increasing/decreasing bounds"
required_conditions = []
required_parameters = []
def get_bound(self, conditions, t, **kwargs):
if t <= 1:
return 1 + t
if t > 1:
return 2 / t
self.bound = ddm.Model(bound=FancyBounds())
def test_ICPoint_collapsing_bounds(self):
m = ddm.Model(name='ICPoint_BCollapsingLin_test',
drift=ddm.DriftConstant(drift=2),
noise=ddm.NoiseConstant(noise=1.5),
bound=ddm.BoundCollapsingLinear(B=1, t=0.5),
IC=ddm.ICPoint(x0=-.25))
_modeltest_numerical_vs_analytical(m,
method="implicit",
max_diff=.3,
mean_diff=.2,
prob_diff=.05)
def test_ICPoint(self):
"""Arbitrary pointwise initial condition"""
m = ddm.Model(name='ICPoint_test',
drift=ddm.DriftConstant(drift=2),
noise=ddm.NoiseConstant(noise=1.5),
bound=ddm.BoundConstant(B=1),
IC=ddm.ICPoint(x0=-.25))
_modeltest_numerical_vs_analytical(m,
method="implicit",
max_diff=.3,
mean_diff=.2,
prob_diff=.05)
def setUp(self):
self.basic = ddm.Model(dx=.005,
dt=.01,
T_dur=2,
drift=ddm.DriftConstant(drift=.4),
noise=ddm.NoiseConstant(noise=1),
bound=ddm.BoundConstant(B=1))
class NoiseCond(ddm.Noise):
name = "Noise with a condition"
required_conditions = ['cond']
required_parameters = []
def get_noise(self, conditions, **kwargs):
return conditions["cond"]
self.withcond = ddm.Model(noise=NoiseCond())
def __init__(self, ndt='gaussian'):
overlay = (ddm.OverlayNonDecisionUniform(nondectime=ddm.Fittable(minval=0, maxval=0.5),
halfwidth=ddm.Fittable(minval=0.001, maxval=0.3))
if ndt=='uniform' else
self.OverlayNonDecisionGaussian(nondectime=ddm.Fittable(minval=0, maxval=0.6),
ndsigma=ddm.Fittable(minval=0.001, maxval=0.3)))
self.model = ddm.Model(name='5 TTA- and d-dependent drift and bounds and uniformly distributed nondecision time',
drift=self.DriftTtaDistance(alpha=ddm.Fittable(minval=0.1, maxval=3),
beta=ddm.Fittable(minval=0, maxval=1),
theta=ddm.Fittable(minval=4, maxval=40)),
noise=ddm.NoiseConstant(noise=1),
bound=self.BoundCollapsingTta(b_0=ddm.Fittable(minval=0.5, maxval=5),
k=ddm.Fittable(minval=0.1, maxval=2),
tta_crit=ddm.Fittable(minval=3, maxval=6)),
overlay=overlay,
T_dur=self.T_dur)
def test_double_fit(self):
"""Fit different parameters in the same (or a different) model using a single Fittable object"""
class NoiseDouble(ddm.Noise):
name = "time-varying noise"
required_parameters = ["noise1", "noise2"]
def get_noise(self, **kwargs):
if np.random.rand() > .5:
return self.noise1
else:
return self.noise2
class NoiseConstantButNot(ddm.Noise
): # To avoid the numerical simulations
name = "almost noise constant"
required_parameters = ["noise"]
def get_noise(self, **kwargs):
return self.noise
# Generate data
m = ddm.Model(name="DDM",
drift=ddm.DriftConstant(drift=1),
noise=ddm.NoiseConstant(noise=1.7))
s = m.solve_numerical(
) # Solving analytical and then fitting numerical may give a bias
sample = s.resample(10000)
mone = ddm.fit_model(
sample,
drift=ddm.DriftConstant(drift=1),
noise=NoiseConstantButNot(noise=ddm.Fittable(minval=.5, maxval=3)))
sigs = ddm.Fittable(minval=.5, maxval=3)
msam = ddm.fit_model(sample,
drift=ddm.DriftConstant(drift=1),
noise=NoiseDouble(noise1=sigs, noise2=sigs))
assert msam._noisedep.noise1 == msam._noisedep.noise2, "Fitting to be the same failed"
assert abs(msam._noisedep.noise1 -
mone._noisedep.noise) < 0.1 * mone._noisedep.noise
