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.- >>> 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