scipy.stats.qmc.update_discrepancy#
- 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 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