bundles / scipy latest / scipy / stats / _qmc / update_discrepancy
function
scipy.stats._qmc:update_discrepancy
source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_qmc.py:461
Signature
def update_discrepancy(x_new: npt.ArrayLike, sample: npt.ArrayLike, initial_disc: DecimalNumber) → floatSummary
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: floatCentered discrepancy of the
sample.
Returns
discrepancy: floatCentered discrepancy of the sample composed of
x_newandsample.
Examples
We can also compute iteratively the discrepancy by using ``iterative=True``.import numpy as np 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 qmc.update_discrepancy(space[-1], space[:-1], disc_init)✓
0.008142039609053513
Aliases
scipy.stats._qmc.update_discrepancy