This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.


nilearn.datasets.fetch_mixed_gambles(n_subjects=1, data_dir=None, url=None, resume=True, return_raw_data=False, verbose=1)[source]#

Fetch Jimura “mixed gambles” dataset.

See [1].

n_subjectsint, optional

The number of subjects to load. If None is given, all the subjects are used. Default=1.

data_dirpathlib.Path or str, optional

Path where data should be downloaded. By default, files are downloaded in home directory.

urlstr, default=None

URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data).

resumebool, default=True

Whether to resume download of a partly-downloaded file.

verboseint, default=1

Verbosity level (0 means no message).

return_raw_databool, optional

If False, then the data will transformed into an (X, y) pair, suitable for machine learning routines. X is a list of n_subjects * 48 Nifti1Image objects (where 48 is the number of trials), and y is an array of shape (n_subjects * 48,). Default=False.


Dictionary-like object, the attributes of interest are:

  • ‘zmaps’: list of str Paths to realigned gain betamaps (one nifti per subject).

  • ‘gain’: list of Nifti1Image or None If make_Xy is True, this is a list of n_subjects * 48 Nifti1Image objects, else it is None.

  • ‘y’: ndarray of shape (n_subjects * 48,) or None If make_Xy is True, then this is a ndarray of shape (n_subjects * 48,), else it is None.


Examples using nilearn.datasets.fetch_mixed_gambles#

FREM on Jimura et al “mixed gambles” dataset

FREM on Jimura et al "mixed gambles" dataset