def create_default_params(parnames):
pars = parameters.parameter_container()
for pname in parnames:
if pname in [
'diffstd', 'critstd', 'dnoisestd', 'cnoisestd', 'dnoisestd',
'dirstd', 'ndtspread', 'noisestd', 'intstd'
]:
pars.add_param(pname, 0, 1, parameters.exponential())
elif pname == 'bound':
pars.add_param(pname, 0, 1,
parameters.gaussprob(width=0.5, shift=0.5))
elif pname in ['bias', 'lapseprob', 'lapsetoprob']:
pars.add_param(pname, 0, 1, parameters.gaussprob())
elif pname in ['cpsqrtkappa']:
pars.add_param(pname, 0, 1, parameters.zero())
else:
pars.add_param(pname, 0, 1)
return pars
# normalising constant of the uniform distribution over the data space in
# which samples are accepted as defined by distfun and epsilon; for
# response_dist this is 2*epsilon for the Euclidean distance between true
# and sampled RT
veps = 2 * epsilon
#%% inference
for mname in models:
# generate time-stamped file name
fname = datetime.datetime.now().strftime('infres_'+mname+'_%Y%m%d%H%M')
# prepare common model parameters
pars = pyEPABC.parameters.parameter_container()
pars.add_param('ndtmean', -1, 1)
pars.add_param('ndtspread', -1.5, 1, exponential())
pars.add_param('noisestd', 4, 1.5, exponential())
pars.add_param('bound', 0, 1, gaussprob(0.5, 0.5))
pars.add_param('prior', 0, 0.5, gaussprob())
pars.add_param('lapseprob', -1, 1, gaussprob())
pars.add_param('lapsetoprob', 0, 1, gaussprob())
# model-specific preparations
if mname == 'basic':
pass
elif mname == 'collapse':
pars.add_param('bshape', np.log(1.4), 1.5, exponential())
pars.add_param('bstretch', 0, 1, gaussprob())
else:
raise ValueError('unknown model requested')
dt,
data.tarDir.unique() / 180 * np.pi,
data.critDir / 180 * np.pi,
maxrt=data.dropna().RT.max() + dt,
toresponse=helpers.toresponse,
choices=choices)
errorm = model.correct != data.response
ind = data.response != 0
assert np.all(
data[ind].error == errorm[ind]), ("Model and data don't agree "
"about which trials are error trials!")
#%% infer
pars = pyEPABC.parameters.parameter_container()
pars.add_param('noisestd', 0, 1.2, exponential())
#pars.add_param('intstd', 0, 1.2, exponential())
model.intstd = 0.2
pars.add_param('bound', 0, 1, gaussprob(width=0.5, shift=0.5))
pars.add_param('bias', 0, .2, gaussprob())
pars.add_param('ndtmean', -2, 1)
pars.add_param('ndtspread', np.log(0.2), 1, exponential())
pars.add_param('lapseprob', -1.65, 1, gaussprob()) # median approx at 0.05
pars.add_param('lapsetoprob', 0, 1, gaussprob())
pars.plot_param_dist()
simfun = lambda data, dind, parsamples: model.gen_distances_with_params(
data[0], data[1], dind, pars.transform(parsamples), pars.names)
epsilon = 0.05
import matplotlib.pyplot as plt
#%% define parameters and their prior
ndtdist = 'uniform'
modelname = 'base'
use_liks = True
pars = parameters.parameter_container()
if modelname == 'base':
fix = {'bound': 0.7}
if use_liks:
fix['noisestd'] = 0
if 'noisestd' not in fix.keys():
pars.add_param('noisestd', 0, 1.2, exponential())
if 'intstd' not in fix.keys():
pars.add_param('intstd', 0, 1.2, exponential())
elif modelname == 'diff':
fix = {'bound': 0.7, 'diffstd': 1000, 'cnoisestd': 1e-12, 'cpsqrtkappa': 0}
if 'diffstd' not in fix.keys():
pars.add_param('diffstd', 0, 10, exponential())
if 'cpsqrtkappa' not in fix.keys():
pars.add_param('cpsqrtkappa', 1, 1, zero())
if 'critstd' not in fix.keys():
pars.add_param('critstd', 0, 1, exponential())
if 'cnoisestd' not in fix.keys():
pars.add_param('cnoisestd', 0, 1, exponential())
if 'dnoisestd' not in fix.keys():