class_docstr: AbstractSolver base class for mystic optimizers.
      
class_docstr_tok: ["AbstractSolver","base","class","for","mystic","optimizers","."]
docstr: Generate Random Initial Points from Distribution (dist)
  
  Args:
      dist (Distribution, default=None): a mystic.math.Distribution instance
  
  Returns:
      None
  
  Notes:
      - if ``dist`` is None, use a uniform distribution in the interval ``[0, 1)``
          
code: def SetSampledInitialPoints(self, dist=None):
      """Generate Random Initial Points from Distribution (dist)
  
      Args:
          dist (Distribution, default=None): a mystic.math.Distribution instance
  
      Returns:
          None
  
      Notes:
          - if ``dist`` is None, use a uniform distribution in the interval ``[0, 1)``
      """
      from mystic.math import Distribution
  
      _dist = Distribution()
      if dist is None:
          dist = _dist
      elif type(_dist) not in dist.__class__.mro():
          dist = Distribution(dist)  # XXX: or throw error?
      for i in range(
          self.nPop
      ):  # FIXME: accept a list of Distributions
          self.population[i] = dist(self.nDim)
      return
  
imports: import random
  import numpy
  from numpy import inf, shape, asarray, absolute, asfarray, seterr
  from mystic.tools import wrap_function, wrap_nested, wrap_reducer
  from mystic.tools import wrap_bounds, wrap_penalty, reduced
  from klepto import isvalid, validate
  from collections.abc import Callable
label_desc: Use image node_id 24 for calling the AbstractSolver obj's underlying member method code with example usage: obj.SetSampledInitialPoints(dist) without return types