class_docstr: AbstractSolver base class for mystic optimizers.
      
class_docstr_tok: ["AbstractSolver","base","class","for","mystic","optimizers","."]
docstr: Generate Random Initial Points within given Bounds
  
  Args:
      min (list[float], default=None): lower bounds, list of length ``self.nDim``
      max (list[float], default=None): upper bounds, list of length ``self.nDim``
  
  Returns:
      None
  
  Notes:
      - each ``min[i]`` must be less than or equal to the corresponding ``max[i]``
          
code: def SetRandomInitialPoints(self, min=None, max=None):
      """Generate Random Initial Points within given Bounds
  
      Args:
          min (list[float], default=None): lower bounds, list of length ``self.nDim``
          max (list[float], default=None): upper bounds, list of length ``self.nDim``
  
      Returns:
          None
  
      Notes:
          - each ``min[i]`` must be less than or equal to the corresponding ``max[i]``
      """
      if min is None:
          min = self._defaultMin
      if max is None:
          max = self._defaultMax
      # if numpy.any(( asarray(min) > asarray(max) ),0):
      #    raise ValueError, "each min[i] must be <= the corresponding max[i]"
      if len(min) != self.nDim or len(max) != self.nDim:
          raise ValueError("bounds array must be length %s" % self.nDim)
      # when 'some' of the bounds are given as 'None', replace with default
      for i in range(len(min)):
          if min[i] is None:
              min[i] = self._defaultMin[0]
          if max[i] is None:
              max[i] = self._defaultMax[0]
      # generate random initial values
      for i in range(len(self.population)):
          for j in range(self.nDim):
              self.population[i][j] = random.uniform(min[j], max[j])
  
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 22 for calling the AbstractSolver obj's underlying member method code with example usage: obj.SetRandomInitialPoints(min, max) without return types