class_docstr: This class implements the differential evolution solver
  
  Parameters
  ----------
  func : callable
      The objective function to be minimized.  Must be in the form
      ``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
      and ``args`` is a  tuple of any additional fixed parameters needed to
      completely specify the function.
  bounds : sequence or `Bounds`
      Bounds for variables.  There are two ways to specify the bounds:
      1. Instance of `Bounds` class.
      2. ``(min, max)`` pairs for each element in ``x``, defining the finite
      lower and upper bounds for the optimizing argument of `func`. It is
      required to have ``len(bounds) == len(x)``. ``len(bounds)`` is used
      to determine the number of parameters in ``x``.
  args : tuple, optional
      Any additional fixed parameters needed to
      completely specify the objective function.
  strategy : str, optional
      The differential evolution strategy to use. Should be one of:
  
          - 'best1bin'
          - 'best1exp'
          - 'rand1exp'
          - 'randtobest1exp'
          - 'currenttobest1exp'
          - 'best2exp'
          - 'rand2exp'
          - 'randtobest1bin'
          - 'currenttobest1bin'
          - 'best2bin'
          - 'rand2bin'
          - 'rand1bin'
  
      The default is 'best1bin'
  
  maxiter : int, optional
      The maximum number of generations over which the entire population is
      evolved. The maximum number of function evaluations (with no polishing)
      is: ``(maxiter + 1) * popsize * len(x)``
  popsize : int, optional
      A multiplier for setting the total population size. The population has
      ``popsize * len(x)`` individuals. This keyword is overridden if an
      initial population is supplied via the `init` keyword. When using
      ``init='sobol'`` the population size is calculated as the next power
      of 2 after ``popsize * len(x)``.
  tol : float, optional
      Relative tolerance for convergence, the solving stops when
      ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
      where and `atol` and `tol` are the absolute and relative tolerance
      respectively.
  mutation : float or tuple(float, float), optional
      The mutation constant. In the literature this is also known as
      differential weight, being denoted by F.
      If specified as a float it should be in the range [0, 2].
      If specified as a tuple ``(min, max)`` dithering is employed. Dithering
      randomly changes the mutation constant on a generation by generation
      basis. The mutation constant for that generation is taken from
      U[min, max). Dithering can help speed convergence significantly.
      Increasing the mutation constant increases the search radius, but will
      slow down convergence.
  recombination : float, optional
      The recombination constant, should be in the range [0, 1]. In the
      literature this is also known as the crossover probability, being
      denoted by CR. Increasing this value allows a larger number of mutants
      to progress into the next generation, but at the risk of population
      stability.
  seed : {None, int, `numpy.random.Generator`,
          `numpy.random.RandomState`}, optional
  
      If `seed` is None (or `np.random`), the `numpy.random.RandomState`
      singleton is used.
      If `seed` is an int, a new ``RandomState`` instance is used,
      seeded with `seed`.
      If `seed` is already a ``Generator`` or ``RandomState`` instance then
      that instance is used.
      Specify `seed` for repeatable minimizations.
  disp : bool, optional
      Prints the evaluated `func` at every iteration.
  callback : callable, `callback(xk, convergence=val)`, optional
      A function to follow the progress of the minimization. ``xk`` is
      the current value of ``x0``. ``val`` represents the fractional
      value of the population convergence. When ``val`` is greater than one
      the function halts. If callback returns `True`, then the minimization
      is halted (any polishing is still carried out).
  polish : bool, optional
      If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
      method is used to polish the best population member at the end, which
      can improve the minimization slightly. If a constrained problem is
      being studied then the `trust-constr` method is used instead.
  maxfun : int, optional
      Set the maximum number of function evaluations. However, it probably
      makes more sense to set `maxiter` instead.
  init : str or array-like, optional
      Specify which type of population initialization is performed. Should be
      one of:
  
          - 'latinhypercube'
          - 'sobol'
          - 'halton'
          - 'random'
          - array specifying the initial population. The array should have
            shape ``(M, len(x))``, where M is the total population size and
            len(x) is the number of parameters.
            `init` is clipped to `bounds` before use.
  
      The default is 'latinhypercube'. Latin Hypercube sampling tries to
      maximize coverage of the available parameter space.
  
      'sobol' and 'halton' are superior alternatives and maximize even more
      the parameter space. 'sobol' will enforce an initial population
      size which is calculated as the next power of 2 after
      ``popsize * len(x)``. 'halton' has no requirements but is a bit less
      efficient. See `scipy.stats.qmc` for more details.
  
      'random' initializes the population randomly - this has the drawback
      that clustering can occur, preventing the whole of parameter space
      being covered. Use of an array to specify a population could be used,
      for example, to create a tight bunch of initial guesses in an location
      where the solution is known to exist, thereby reducing time for
      convergence.
  atol : float, optional
      Absolute tolerance for convergence, the solving stops when
      ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
      where and `atol` and `tol` are the absolute and relative tolerance
      respectively.
  updating : {'immediate', 'deferred'}, optional
      If `immediate` the best solution vector is continuously updated within
      a single generation. This can lead to faster convergence as trial
      vectors can take advantage of continuous improvements in the best
      solution.
      With `deferred` the best solution vector is updated once per
      generation. Only `deferred` is compatible with parallelization, and the
      `workers` keyword can over-ride this option.
  workers : int or map-like callable, optional
      If `workers` is an int the population is subdivided into `workers`
      sections and evaluated in parallel
      (uses `multiprocessing.Pool <multiprocessing>`).
      Supply `-1` to use all cores available to the Process.
      Alternatively supply a map-like callable, such as
      `multiprocessing.Pool.map` for evaluating the population in parallel.
      This evaluation is carried out as ``workers(func, iterable)``.
      This option will override the `updating` keyword to
      `updating='deferred'` if `workers != 1`.
      Requires that `func` be pickleable.
  constraints : {NonLinearConstraint, LinearConstraint, Bounds}
      Constraints on the solver, over and above those applied by the `bounds`
      kwd. Uses the approach by Lampinen.
  x0 : None or array-like, optional
      Provides an initial guess to the minimization. Once the population has
      been initialized this vector replaces the first (best) member. This
      replacement is done even if `init` is given an initial population.
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docstr: best1bin, best1exp
docstr_tok: ["best1bin",",","best1exp"]
code: def _best1(self, samples):
      """best1bin, best1exp"""
      r0, r1 = samples[:2]
      return self.population[0] + self.scale * (
          self.population[r0] - self.population[r1]
      )
  
imports: import warnings
  import numpy
  from scipy.optimize import OptimizeResult, minimize
  from scipy.optimize.optimize import _status_message
  from scipy._lib._util import check_random_state, MapWrapper
  from scipy.optimize._constraints import Bounds, new_bounds_to_old, NonlinearConstraint, LinearConstraint
  from scipy.sparse import issparse
label_desc: Use image node_id 23 for calling the DifferentialEvolutionSolver obj's underlying member method code with example usage: obj._best1(samples) and returns: unknown