class_docstr: This is a one-shot optimization method, hopefully better than random search
  by ensuring more uniformity.
  
  Parameters
  ----------
  sampler: str
      Choice of the sampler among "Halton", "Hammersley" and "LHS".
  scrambled: bool
      Adds scrambling to the search; much better in high dimension and rarely worse
      than the original search.
  middle_point: bool
      enforces that the first suggested point (ask) is zero.
  cauchy: bool
      use Cauchy inverse distribution instead of Gaussian when fitting points to real space
      (instead of box).
  scale: float or "random"
      scalar for multiplying the suggested point values.
  rescaled: bool or str
      rescales the sampling pattern to reach the boundaries and/or applies automatic rescaling.
  recommendation_rule: str
      "average_of_best" or "pessimistic"; "pessimistic" is the default and implies selecting the pessimistic best.
  
  Notes
  -----
  - Halton is a low quality sampling method when the dimension is high; it is usually better
    to use Halton with scrambling.
  - When the budget is known in advance, it is also better to replace Halton by Hammersley.
    Basically the key difference with Halton is adding one coordinate evenly spaced
    (the discrepancy is better).
    budget, low discrepancy sequences (e.g. scrambled Hammersley) have a better discrepancy.
  - Reference: Halton 1964: Algorithm 247: Radical-inverse quasi-random point sequence, ACM, p. 701.
    adds scrambling to the Halton search; much better in high dimension and rarely worse
    than the original Halton search.
  - About Latin Hypercube Sampling (LHS):
    Though partially incremental versions exist, this implementation needs the budget in advance.
    This can be great in terms of discrepancy when the budget is not very high.
class_docstr_tok: ["This","is","a","one-shot","optimization","method",",","hopefully","better","than","random","search","by","ensuring","more","uniformity",".","Parameters","--","--","--","--","--","sampler",":","str","Choice","of","the","sampler","among","``","Halton","''",",","``","Hammersley","''","and","``","LHS","''",".","scrambled",":","bool","Adds","scrambling","to","the","search",";","much","better","in","high","dimension","and","rarely","worse","than","the","original","search",".","middle_point",":","bool","enforces","that","the","first","suggested","point","(","ask",")","is","zero",".","cauchy",":","bool","use","Cauchy","inverse","distribution","instead","of","Gaussian","when","fitting","points","to","real","space","(","instead","of","box",")",".","scale",":","float","or","``","random","''","scalar","for","multiplying","the","suggested","point","values",".","rescaled",":","bool","or","str","rescales","the","sampling","pattern","to","reach","the","boundaries","and\/or","applies","automatic","rescaling",".","recommendation_rule",":","str","``","average_of_best","''","or","``","pessimistic","''",";","``","pessimistic","''","is","the","default","and","implies","selecting","the","pessimistic","best",".","Notes","--","--","-","-","Halton","is","a","low","quality","sampling","method","when","the","dimension","is","high",";","it","is","usually","better","to","use","Halton","with","scrambling",".","-","When","the","budget","is","known","in","advance",",","it","is","also","better","to","replace","Halton","by","Hammersley",".","Basically","the","key","difference","with","Halton","is","adding","one","coordinate","evenly","spaced","(","the","discrepancy","is","better",")",".","budget",",","low","discrepancy","sequences","(","e.g",".","scrambled","Hammersley",")","have","a","better","discrepancy",".","-","Reference",":","Halton","1964",":","Algorithm","247",":","Radical-inverse","quasi-random","point","sequence",",","ACM",",","p.","701.","adds","scrambling","to","the","Halton","search",";","much","better","in","high","dimension","and","rarely","worse","than","the","original","Halton","search",".","-","About","Latin","Hypercube","Sampling","(","LHS",")",":","Though","partially","incremental","versions","exist",",","this","implementation","needs","the","budget","in","advance",".","This","can","be","great","in","terms","of","discrepancy","when","the","budget","is","not","very","high","."]
code: def __init__(
      self,
      *,
      sampler: str = "Halton",
      scrambled: bool = False,
      middle_point: bool = False,
      opposition_mode: tp.Optional[str] = None,
      cauchy: bool = False,
      autorescale: tp.Union[bool, str] = False,
      scale: float = 1.0,
      rescaled: bool = False,
      recommendation_rule: str = "pessimistic",
  ) -> None:
      super().__init__(_SamplingSearch, locals())
  
imports: import copy
  import numpy
  from scipy.spatial import ConvexHull
  import nevergrad.common.typing
  from nevergrad.parametrization import parameter
  from nevergrad.parametrization import transforms
  from .None import sequences
  from .None import base
  from .base import IntOrParameter
  from .None import utils
label_desc: Use image node_id 1 to create a new SamplingSearch object from inherited base classes: base with example: obj = SamplingSearch()