Module « scipy.stats.qmc »

Fonction scale - module scipy.stats.qmc

Signature de la fonction scale

def scale(sample: 'npt.ArrayLike', l_bounds: 'npt.ArrayLike', u_bounds: 'npt.ArrayLike', *, reverse: 'bool' = False) -> 'np.ndarray' 

Description

scale.__doc__

Sample scaling from unit hypercube to different bounds.

    To convert a sample from :math:`[0, 1)` to :math:`[a, b), b>a`,
    with :math:`a` the lower bounds and :math:`b` the upper bounds.
    The following transformation is used:

    .. math::

        (b - a) \cdot \text{sample} + a

    Parameters
    ----------
    sample : array_like (n, d)
        Sample to scale.
    l_bounds, u_bounds : array_like (d,)
        Lower and upper bounds (resp. :math:`a`, :math:`b`) of transformed
        data. If `reverse` is True, range of the original data to transform
        to the unit hypercube.
    reverse : bool, optional
        Reverse the transformation from different bounds to the unit hypercube.
        Default is False.

    Returns
    -------
    sample : array_like (n, d)
        Scaled sample.

    Examples
    --------
    Transform 3 samples in the unit hypercube to bounds:

    >>> from scipy.stats import qmc
    >>> l_bounds = [-2, 0]
    >>> u_bounds = [6, 5]
    >>> sample = [[0.5 , 0.75],
    ...           [0.5 , 0.5],
    ...           [0.75, 0.25]]
    >>> sample_scaled = qmc.scale(sample, l_bounds, u_bounds)
    >>> sample_scaled
    array([[2.  , 3.75],
           [2.  , 2.5 ],
           [4.  , 1.25]])

    And convert back to the unit hypercube:

    >>> sample_ = qmc.scale(sample_scaled, l_bounds, u_bounds, reverse=True)
    >>> sample_
    array([[0.5 , 0.75],
           [0.5 , 0.5 ],
           [0.75, 0.25]])