Module « scipy.stats.qmc »

Fonction update_discrepancy - module scipy.stats.qmc

Signature de la fonction update_discrepancy

def update_discrepancy(x_new: 'npt.ArrayLike', sample: 'npt.ArrayLike', initial_disc: 'DecimalNumber') -> 'float' 

Description

update_discrepancy.__doc__

Update the centered discrepancy with a new sample.

    Parameters
    ----------
    x_new : array_like (1, d)
        The new sample to add in `sample`.
    sample : array_like (n, d)
        The initial sample.
    initial_disc : float
        Centered discrepancy of the `sample`.

    Returns
    -------
    discrepancy : float
        Centered discrepancy of the sample composed of `x_new` and `sample`.

    Examples
    --------
    We can also compute iteratively the discrepancy by using
    ``iterative=True``.

    >>> from scipy.stats import qmc
    >>> space = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
    >>> l_bounds = [0.5, 0.5]
    >>> u_bounds = [6.5, 6.5]
    >>> space = qmc.scale(space, l_bounds, u_bounds, reverse=True)
    >>> disc_init = qmc.discrepancy(space[:-1], iterative=True)
    >>> disc_init
    0.04769081147119336
    >>> qmc.update_discrepancy(space[-1], space[:-1], disc_init)
    0.008142039609053513