parameters: (self, data_src=None, verify=True, *args, **kwargs)
docstring: Constructs a hypervolume object used for the computation of hypervolue and exclusive hypervolume.
  
      Object can be constructed from the population object, or from a fixed list/tuple of points
      Points within a fixed list must all be of equal size, of dimension larger than 1.
  
      USAGE:
              from PyGMO import *
              from PyGMO.util import *
              hv = hypervolume(pop)  # Constructs the hypervolume object from individual's fitness vectors
              hv = hypervolume([[1,1,2],[2,1,2],[2,2,3]])
              hv = hypervolume(((1,2), (3,0.5), (1.5, 1.5))
              hv = hypervolume(data_src = ((1,2),(2,3)), verify=False)
docstring_summary: Constructs a hypervolume object used for the computation of hypervolue and exclusive hypervolume.
docstring_tokens: Constructs a hypervolume object used for the computation of hypervolue and exclusive hypervolume .
function: def _hypervolume_ctor(self, data_src=None, verify=True, *args, **kwargs):
      """
      Constructs a hypervolume object used for the computation of hypervolue and exclusive hypervolume.
  
      Object can be constructed from the population object, or from a fixed list/tuple of points
      Points within a fixed list must all be of equal size, of dimension larger than 1.
  
      USAGE:
              from PyGMO import *
              from PyGMO.util import *
              hv = hypervolume(pop)  # Constructs the hypervolume object from individual's fitness vectors
              hv = hypervolume([[1,1,2],[2,1,2],[2,2,3]])
              hv = hypervolume(((1,2), (3,0.5), (1.5, 1.5))
              hv = hypervolume(data_src = ((1,2),(2,3)), verify=False)
      """
      if not data_src or len(args) > 0 or len(kwargs) > 0:
          raise _HypervolumeValidation.err_hv_ctor_args
  
      from PyGMO import population
      allowed_types = (population, list, tuple,)
      if not any(isinstance(data_src, T) for T in allowed_types):
          raise _HypervolumeValidation.err_hv_ctor_type
  
      args = []
      args.append(data_src)
      args.append(verify)
      try:
          return self._original_init(*args)
      except TypeError:
          raise _HypervolumeValidation.err_hv_ctor_type
function_tokens: def _hypervolume_ctor ( self , data_src = None , verify = True , * args , * * kwargs ) : if not data_src or len ( args ) > 0 or len ( kwargs ) > 0 : raise _HypervolumeValidation . err_hv_ctor_args from PyGMO import population allowed_types = ( population , list , tuple , ) if not any ( isinstance ( data_src , T ) for T in allowed_types ) : raise _HypervolumeValidation . err_hv_ctor_type args = [ ] args . append ( data_src ) args . append ( verify ) try : return self . _original_init ( * args ) except TypeError : raise _HypervolumeValidation . err_hv_ctor_type