class_docstr: Creates `Hammersley` sequence samples.
  
  The Hammersley set is equivalent to the Halton sequence, except for one
  dimension is replaced with a regular grid. It is not recommended to
  generate a Hammersley sequence with more than 10 dimension.
  
  For ``dim == 1`` the sequence falls back to Van Der Corput sequence.
  
  References
  ----------
  T-T. Wong, W-S. Luk, and P-A. Heng, "Sampling with Hammersley and Halton
  Points," Journal of Graphics Tools, vol. 2, no. 2, 1997, pp. 9 - 24.
  
  Parameters
  ----------
  min_skip : int, default=-1
      Minimum skipped seed number. When `min_skip != max_skip` and
      both are > -1, a random number is picked.
  max_skip : int, default=-1
      Maximum skipped seed number. When `min_skip != max_skip` and
      both are > -1, a random number is picked.
  primes : tuple, default=None
      The (non-)prime base to calculate values along each axis. If
      empty, growing prime values starting from 2 will be used.
docstr: Creates samples from Hammersly set.
  
  Parameters
  ----------
  dimensions : list, shape (n_dims,)
      List of search space dimensions.
      Each search dimension can be defined either as
  
      - a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer`
        dimensions),
      - a `(lower_bound, upper_bound, "prior")` tuple (for `Real`
        dimensions),
      - as a list of categories (for `Categorical` dimensions), or
      - an instance of a `Dimension` object (`Real`, `Integer` or
        `Categorical`).
  n_samples : int
      The order of the Hammersley sequence.
      Defines the number of samples.
  random_state : int, RandomState instance, or None (default)
      Set random state to something other than None for reproducible
      results.
  
  Returns
  -------
  np.array, shape=(n_dim, n_samples)
      Hammersley set.
code: def generate(self, dimensions, n_samples, random_state=None):
      """Creates samples from Hammersly set.
  
      Parameters
      ----------
      dimensions : list, shape (n_dims,)
          List of search space dimensions.
          Each search dimension can be defined either as
  
          - a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer`
            dimensions),
          - a `(lower_bound, upper_bound, "prior")` tuple (for `Real`
            dimensions),
          - as a list of categories (for `Categorical` dimensions), or
          - an instance of a `Dimension` object (`Real`, `Integer` or
            `Categorical`).
      n_samples : int
          The order of the Hammersley sequence.
          Defines the number of samples.
      random_state : int, RandomState instance, or None (default)
          Set random state to something other than None for reproducible
          results.
  
      Returns
      -------
      np.array, shape=(n_dim, n_samples)
          Hammersley set.
  
      """
      rng = check_random_state(random_state)
      halton = Halton(
          min_skip=self.min_skip,
          max_skip=self.max_skip,
          primes=self.primes,
      )
      space = Space(dimensions)
      n_dim = space.n_dims
      transformer = space.get_transformer()
      space.set_transformer("normalize")
      if n_dim == 1:
          out = halton.generate(dimensions, n_samples, random_state=rng)
      else:
          out = np.empty((n_dim, n_samples), dtype=float)
          out[: n_dim - 1] = np.array(
              halton.generate(
                  [
                      (0.0, 1.0),
                  ]
                  * (n_dim - 1),
                  n_samples,
                  random_state=rng,
              )
          ).T
  
          out[n_dim - 1] = np.linspace(0, 1, n_samples + 1)[:-1]
          out = space.inverse_transform(out.T)
      space.set_transformer(transformer)
      return out
  
file_path: guildai/guild/_skopt/sampler/hammersly.py
imports: import numpy
  from .halton import Halton
  from ..space import Space
  from .base import InitialPointGenerator
  from sklearn.utils import check_random_state
label_desc: Use image node_id 2 for calling the Hammersly obj's underlying member method code with example usage: obj.generate(dimensions, n_samples, random_state) and returns: out