class_docstr: Compute the Hamming distance between the two or more sequences.
  The Hamming distance is the number of differing items in ordered sequences.
  
  http://www.sial.iias.spb.su/files/386-386-1-PB.pdf
  https://github.com/Yomguithereal/talisman/blob/master/src/metrics/mlipns.js
class_docstr_tok: ["Compute","the","Hamming","distance","between","the","two","or","more","sequences",".","The","Hamming","distance","is","the","number","of","differing","items","in","ordered","sequences",".","http",":","\/\/www.sial.iias.spb.su\/files\/386-386-1-PB.pdf","https",":","\/\/github.com\/Yomguithereal\/talisman\/blob\/master\/src\/metrics\/mlipns.js"]
code: def __init__(
      self,
      threshold: float = 0.25,
      maxmismatches: int = 2,
      qval: int = 1,
      external: bool = True,
  ) -> None:
      self.qval = qval
      self.threshold = threshold
      self.maxmismatches = maxmismatches
      self.external = external
  
imports: from __future__ import annotations
  from collections import defaultdict
  from itertools import zip_longest
  from typing import Any, Sequence, TypeVar
  from .base import Base, BaseSimilarity
  from .types import SimFunc, TestFunc
label_desc: Use image node_id 1 to create a new MLIPNS object from inherited base classes: _BaseSimilarity with example: obj = MLIPNS(threshold, maxmismatches, qval, external)