update_discrepancy — SciPy v1.15.2 Manual (original) (raw)

scipy.stats.qmc.

scipy.stats.qmc.update_discrepancy(x_new, sample, initial_disc)[source]#

Update the centered discrepancy with a new sample.

Parameters:

x_newarray_like (1, d)

The new sample to add in sample.

samplearray_like (n, d)

The initial sample.

initial_discfloat

Centered discrepancy of the sample.

Returns:

discrepancyfloat

Centered discrepancy of the sample composed of x_new and sample.

Examples

We can also compute iteratively the discrepancy by usingiterative=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 0.04769081147119336 qmc.update_discrepancy(space[-1], space[:-1], disc_init) 0.008142039609053513