bsfit.nodes.models.utils.simulate_dataset¶
- bsfit.nodes.models.utils.simulate_dataset(fit_p: numpy.ndarray, params: Dict[str, any], stim_mean: pandas.core.series.Series, granularity: str, **kwargs: dict)[source]¶
“”get model predictions
- Parameters
fit_p (np.ndarray) – model free parameters
params (dict) –
the parameters:
params: { "task": { "fixed_params": the task fixed parameters }, "model": { "fixed_params": the model fixed parameters "init_params": the model initial parameters }
stim_mean (pd.Series) – stimulus features
stim_estimate (pd.Series) – stimulus feature estimates
granularity (str) –
- "trial": prediction are stochastic choices sampled from the model generative probability density - "mean": prediction statistics (mean and std calculated from the model generative probability density)
- kwargs:
n_repeats (int): repeat count per condition, if granularity=”trial”
- Returns
simulation output
- Return type
(dict)