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scipy.stats.qmc.

# Halton

class scipy.stats.qmc.Halton(d, *, scramble=True, optimization=None, rng=None, seed=None)

  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-\(p\) for its \(n\)-dimension, with \(p\) the \(n\)’th prime.

  Parameters:
    dint

      Dimension of the parameter space.

    scramblebool, optional

      If True, use random scrambling from [2]. 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.

    rng `numpy.random.Generator`, optional

      Pseudorandom number generator state. When rng is None, a new `numpy.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-007 transition from use of `numpy.random.RandomState` to `numpy.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.

  Methods

  `fast_forward`(n) | Fast-forward the sequence by n positions.
  `integers`(l_bounds, *[, u_bounds, n, ...]) | Draw n integers from l_bounds (inclusive) to u_bounds (exclusive), or if endpoint=True, l_bounds (inclusive) to u_bounds (inclusive).
  `random`([n, workers]) | Draw n in the half-open interval `[0, 1)`.
  `reset`() | Reset the engine to base state.

  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.

  [2]

  A. B. Owen. “A randomized Halton algorithm in R”, arXiv:1706.02808, 2017.

  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]])