class_docstr: Hypervolume computation based on variant 3 of the algorithm in the paper:
  C. M. Fonseca, L. Paquete, and M. Lopez-Ibanez. An improved dimension-sweep
  algorithm for the hypervolume indicator. In IEEE Congress on Evolutionary
  Computation, pages 1157-1163, Vancouver, Canada, July 2006.
  
  Minimization is implicitly assumed here!
class_docstr_tok: ["Hypervolume","computation","based","on","variant","3","of","the","algorithm","in","the","paper",":","C.","M.","Fonseca",",","L.","Paquete",",","and","M.","Lopez-Ibanez",".","An","improved","dimension-sweep","algorithm","for","the","hypervolume","indicator",".","In","IEEE","Congress","on","Evolutionary","Computation",",","pages","1157-1163",",","Vancouver",",","Canada",",","July","2006",".","Minimization","is","implicitly","assumed","here","!"]
docstr: Sorts the list of nodes by the i-th value of the contained points.
docstr_tok: ["Sorts","the","list","of","nodes","by","the","i-th","value","of","the","contained","points","."]
code: def sortByDimension(self, nodes, i):
      """Sorts the list of nodes by the i-th value of the contained points."""
      # build a list of tuples of (point[i], node)
      decorated = [(node.cargo[i], node) for node in nodes]
      # sort by this value
      decorated.sort()
      # write back to original list
      nodes[:] = [node for (_, node) in decorated]
  
file_path: deap/deap/tools/_hypervolume/pyhv.py
imports: import warnings
  import numpy
label_desc: Use image node_id 5 for calling the _HyperVolume obj's underlying member method code with example usage: obj.sortByDimension(nodes, i) without return types