# scipy.stats.qmc.LatinHypercube¶

class scipy.stats.qmc.LatinHypercube(d, *, centered=False, seed=None)[source]

Latin hypercube sampling (LHS).

A Latin hypercube sample  generates $$n$$ points in $$[0,1)^{d}$$. Each univariate marginal distribution is stratified, placing exactly one point in $$[j/n, (j+1)/n)$$ for $$j=0,1,...,n-1$$. They are still applicable when $$n << d$$. LHS is extremely effective on integrands that are nearly additive . LHS on $$n$$ points never has more variance than plain MC on $$n-1$$ points . There is a central limit theorem for LHS , but not necessarily for optimized LHS.

Parameters
dint

Dimension of the parameter space.

centeredbool, optional

Center the point within the multi-dimensional grid. Default is False.

seed{None, int, numpy.random.Generator}, optional

If seed is None the numpy.random.Generator singleton is used. If seed is an int, a new Generator instance is used, seeded with seed. If seed is already a Generator instance then that instance is used.

References

1

Mckay et al., “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code”, Technometrics, 1979.

2

M. Stein, “Large sample properties of simulations using Latin hypercube sampling.” Technometrics 29, no. 2: 143-151, 1987.

3

A. B. Owen, “Monte Carlo variance of scrambled net quadrature.” SIAM Journal on Numerical Analysis 34, no. 5: 1884-1910, 1997

4

Loh, W.-L. “On Latin hypercube sampling.” The annals of statistics 24, no. 5: 2058-2080, 1996.

Examples

Generate samples from a Latin hypercube generator.

>>> from scipy.stats import qmc
>>> sampler = qmc.LatinHypercube(d=2)
>>> sample = sampler.random(n=5)
>>> sample
array([[0.1545328 , 0.53664833],  # random
[0.84052691, 0.06474907],
[0.52177809, 0.93343721],
[0.68033825, 0.36265316],
[0.26544879, 0.61163943]])


Compute the quality of the sample using the discrepancy criterion.

>>> qmc.discrepancy(sample)
0.019558034794794565  # random


Finally, samples can be scaled to bounds.

>>> l_bounds = [0, 2]
>>> u_bounds = [10, 5]
>>> qmc.scale(sample, l_bounds, u_bounds)
array([[1.54532796, 3.609945  ],  # random
[8.40526909, 2.1942472 ],
[5.2177809 , 4.80031164],
[6.80338249, 3.08795949],
[2.65448791, 3.83491828]])


Methods

 Fast-forward the sequence by n positions. random([n]) Draw n in the half-open interval [0, 1). Reset the engine to base state.