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

scipy.stats.qmc.

class scipy.stats.qmc.Halton(d, *, scramble=True, optimization=None, rng=None)[source]#

Halton sequence.

Pseudo-random number generator that generalize the Van der Corput sequence for multiple dimensions. The Halton sequence uses the base-two Van der Corput sequence for the first dimension, base-three for its second and base-\(n\) for its n-dimension.

Parameters:

dint

Dimension of the parameter space.

scramblebool, optional

If True, use Owen scrambling. Otherwise no scrambling is done. Default is True.

optimization{None, “random-cd”, “lloyd”}, optional

Whether to use an optimization scheme to improve the quality after sampling. Note that this is a post-processing step that does not guarantee that all properties of the sample will be conserved. Default is None.

• random-cd: random permutations of coordinates to lower the centered discrepancy. The best sample based on the centered discrepancy is constantly updated. Centered discrepancy-based sampling shows better space-filling robustness toward 2D and 3D subprojections compared to using other discrepancy measures.
• lloyd: Perturb samples using a modified Lloyd-Max algorithm. The process converges to equally spaced samples.

Added in version 1.10.0.

rngnumpy.random.Generator, optional

Pseudorandom number generator state. When rng is None, a newnumpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator are passed to numpy.random.default_rng to instantiate a Generator.

Changed in version 1.15.0: As part of the SPEC-007transition from use of numpy.random.RandomState tonumpy.random.Generator, this keyword was changed from seed to_rng_. For an interim period, both keywords will continue to work, although only one may be specified at a time. After the interim period, function calls using the seed keyword will emit warnings. Following a deprecation period, the seed keyword will be removed.

Notes

The Halton sequence has severe striping artifacts for even modestly large dimensions. These can be ameliorated by scrambling. Scrambling also supports replication-based error estimates and extends applicability to unbounded integrands.

References

[1]

Halton, “On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals”, Numerische Mathematik, 1960.

Examples

Generate samples from a low discrepancy sequence of Halton.

from scipy.stats import qmc sampler = qmc.Halton(d=2, scramble=False) sample = sampler.random(n=5) sample array([[0. , 0. ], [0.5 , 0.33333333], [0.25 , 0.66666667], [0.75 , 0.11111111], [0.125 , 0.44444444]])

Compute the quality of the sample using the discrepancy criterion.

qmc.discrepancy(sample) 0.088893711419753

If some wants to continue an existing design, extra points can be obtained by calling again random. Alternatively, you can skip some points like:

_ = sampler.fast_forward(5) sample_continued = sampler.random(n=5) sample_continued array([[0.3125 , 0.37037037], [0.8125 , 0.7037037 ], [0.1875 , 0.14814815], [0.6875 , 0.48148148], [0.4375 , 0.81481481]])

Finally, samples can be scaled to bounds.

l_bounds = [0, 2] u_bounds = [10, 5] qmc.scale(sample_continued, l_bounds, u_bounds) array([[3.125 , 3.11111111], [8.125 , 4.11111111], [1.875 , 2.44444444], [6.875 , 3.44444444], [4.375 , 4.44444444]])

Methods