当前位置: 首页>>代码示例>>Python>>正文


Python Bunch.zmaps方法代码示例

本文整理汇总了Python中sklearn.datasets.base.Bunch.zmaps方法的典型用法代码示例。如果您正苦于以下问题:Python Bunch.zmaps方法的具体用法?Python Bunch.zmaps怎么用?Python Bunch.zmaps使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在sklearn.datasets.base.Bunch的用法示例。


在下文中一共展示了Bunch.zmaps方法的1个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: fetch_mixed_gambles

# 需要导入模块: from sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import zmaps [as 别名]
def fetch_mixed_gambles(n_subjects=1, data_dir=None, url=None, resume=True, return_raw_data=False, verbose=0):
    """Fetch Jimura "mixed gambles" dataset.

    Parameters
    ----------
    n_subjects: int, optional (default 1)
        The number of subjects to load. If None is given, all the
        subjects are used.

    data_dir: string, optional (default None)
        Path of the data directory. Used to force data storage in a specified
        location. Default: None.

    url: string, optional (default None)
        Override download URL. Used for test only (or if you setup a mirror of
        the data).

    resume: bool, optional (default True)
        If true, try resuming download if possible.

    verbose: int, optional (default 0)
        Defines the level of verbosity of the output.

    return_raw_data: bool, optional (default True)
        If false, then the data will transformed into and (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,).

    smooth: float, or list of 3 floats, optional (default 0.)
        Size of smoothing kernel to apply to the loaded zmaps.

    Returns
    -------
    data: Bunch
        Dictionary-like object, the interest attributes are :
        'zmaps': string list
            Paths to realigned gain betamaps (one nifti per subject).
        'gain': ..
            If make_Xy is true, this is a list of n_subjects * 48
            Nifti1Image objects, else it is None.
        'y': array of shape (n_subjects * 48,) or None
            If make_Xy is true, then this is an array of shape
            (n_subjects * 48,), else it is None.

    References
    ----------
    [1] K. Jimura and R. Poldrack, "Analyses of regional-average activation
        and multivoxel pattern information tell complementary stories",
        Neuropsychologia, vol. 50, page 544, 2012
    """
    if n_subjects > 16:
        warnings.warn("Warning: there are only 16 subjects!")
        n_subjects = 16
    if url is None:
        url = "https://www.nitrc.org/frs/download.php/7229/" "jimura_poldrack_2012_zmaps.zip"
    opts = dict(uncompress=True)
    files = [("zmaps%ssub%03i_zmaps.nii.gz" % (os.sep, (j + 1)), url, opts) for j in range(n_subjects)]
    data_dir = _get_dataset_dir("jimura_poldrack_2012_zmaps", data_dir=data_dir)
    zmap_fnames = _fetch_files(data_dir, files, resume=resume, verbose=verbose)
    data = Bunch(zmaps=zmap_fnames)
    if not return_raw_data:
        X, y, mask_img = _load_mixed_gambles(map(nibabel.load, data.zmaps))
        data.zmaps, data.gain, data.mask_img = X, y, mask_img
    return data
开发者ID:LisaLeroi,项目名称:nilearn,代码行数:67,代码来源:func.py


注:本文中的sklearn.datasets.base.Bunch.zmaps方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。