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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) -> numpy.ndarray 

Description

help(scipy.stats.qmc.scale)

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]])

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