bsfit.nodes.models.utils.get_logl¶
- bsfit.nodes.models.utils.get_logl(fit_p: numpy.ndarray, params: dict, stim_mean: pandas.core.series.Series, data: pandas.core.series.Series)[source]¶
calculate the log(likelihood) of the data given the model
- Parameters
fit_p (np.ndarray) – the model fit 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 (e.g., motion direction)
data (pd.Series) – stimulus feature estimates to fit
- Returns
-log(likelihood) of data given model
- Return type
(float)