def test_get_set_parameters_functions(self):
"""Test get_parameters, set_parameters, and get_parameter_names"""
p1 = ddm.Fittable(minval=0, maxval=1)
p2 = ddm.Fittable(minval=.3, maxval=.9, default=.4)
m = ddm.Model(drift=ddm.DriftConstant(drift=p1), noise=ddm.NoiseLinear(noise=p2, x=.2, t=p1))
print(m.get_model_parameters())
assert all(id(a) == id(b) for a,b in zip(m.get_model_parameters(), [p1, p2]))
assert all(a == b for a,b in zip(m.get_model_parameter_names(), ["drift/t", "noise"]))
m.set_model_parameters(m.get_model_parameters())
assert all(id(a) == id(b) for a,b in zip(m.get_model_parameters(), [p1, p2]))
m.set_model_parameters([.5, .5])
assert all(a == b for a,b in zip(m.get_model_parameters(), [.5, .5]))
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
def test_fit_with_condition(self):
"""A simple one-parameter fit with conditions"""
m = self.cond_m
s = self.cond_s
mfit = ddm.Model(drift=self.DriftCond(
param=ddm.Fittable(minval=.1, maxval=3)))
ddm.fit_adjust_model(model=mfit, sample=s)
# Within 10%
if SHOW_PLOTS:
mfit.name = "Fitted solution"
sfit = mfit.solve()
plot_compare_solutions(s, sfit)
plt.show()
_verify_param_match("drift", "param", m, mfit)
def test_fit_drift(self):
"""A simple one-parameter fit"""
m = ddm.Model(name="DDM", drift=ddm.DriftConstant(drift=2))
s = m.solve()
sample = s.resample(10000)
mfit = ddm.Model(
name="DDM",
drift=ddm.DriftConstant(drift=ddm.Fittable(minval=0, maxval=10)))
ddm.fit_adjust_model(model=mfit, sample=sample)
# Within 10%
if SHOW_PLOTS:
mfit.name = "Fitted solution"
sfit = mfit.solve()
plot_compare_solutions(s, sfit)
plt.show()
_verify_param_match("drift", "drift", m, mfit)
FakePointModel.solve_numerical = FakePointModel.solve
FakePointModel.solve_numerical_cn = FakePointModel.solve
FakePointModel.solve_numerical_implicit = FakePointModel.solve
FakePointModel.solve_numerical_explicit = FakePointModel.solve
m = FakePointModel()
s = m.solve()
so = OverlayNonDecisionUniform(nondectime=-.05, st=.1).apply(s)
so2 = OverlayNonDecisionUniform2(nondectime=-.05, st=.1).apply(s)
assert np.all(so.corr == so2.corr)
so = OverlayNonDecisionGamma(shape=4, scale=.01, nondectime=.1).apply(s)
print(list(so.corr))
print(np.sum(so.corr))
#m = FakePointModel(overlay=OverlayNonDecisionGamma(shape=ddm.Fittable(minval=1, maxval=3), scale=ddm.Fittable(minval=0, maxval=.2), nondectime=ddm.Fittable(minval=0, maxval=1)))
m = FakePointModel(overlay=OverlayNonDecisionGaussian(
ndsigma=ddm.Fittable(minval=0, maxval=1),
nondectime=ddm.Fittable(minval=0, maxval=1)))
#m = FakePointModel(overlay=OverlayNonDecisionUniform(st=ddm.Fittable(minval=0, maxval=1), nondectime=ddm.Fittable(minval=0, maxval=1)))
import ddm.plot
ddm.plot.model_gui(
m, ddm.Sample(np.asarray([1, 1.1, 1.2, 1.3, 1.4, 1.5]), np.asarray([1])))
from ddm import ICGaussian
r = ICPointRew(x0=.99)
#print(r.get_IC(np.linspace(-1, 1, 201), dx=.01, conditions={"highreward": 1}))
traj = model.simulate_trial(conditions=conditions,
rk4=rk4,
seed=seed,
cutoff=True)
set_dip_type(model, diptype)
return traj
# END overlay
#################### Example usage ####################
if __name__ == "__main__":
# BEGIN demo
DIPTYPE = 1 # Change to 1, 2, or 3 depending on which model you want
snr = ddm.Fittable(minval=0.5, maxval=20, default=9.243318909157688)
leak = ddm.Fittable(minval=-10, maxval=30, default=9.46411355874963)
x0 = ddm.Fittable(minval=-.5, maxval=.5, default=0.1294632585920082)
leaktargramp = ddm.Fittable(minval=0, maxval=3, default=0)
noise = ddm.Fittable(minval=.2, maxval=2, default=1.1520906498077081)
t1 = ddm.Fittable(minval=0, maxval=1, default=0.34905555600815663)
t1slope = ddm.Fittable(minval=0, maxval=3, default=1.9643425020687162)
dipstart = ddm.Fittable(minval=-.4, maxval=0, default=-.2)
dipstop = ddm.Fittable(minval=0, maxval=.5, default=.05)
nondectime = ddm.Fittable(minval=0, maxval=.3, default=.1)
detect = ddm.Fittable(minval=2, maxval=50, default=10)
diptype = DIPTYPE
dipparam = ddm.Fittable(minval=0, maxval=50) if diptype == 2 else 0
pmixturecoef = ddm.Fittable(minval=0, maxval=.2, default=.03)
rate = ddm.Fittable(minval=.1, maxval=10, default=1)
