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)