アルゴリズムをシャッフル（高レナードスクランブルアルゴリズム）を記録

• 原理：最後の番号の数と前回交換の数N-1は、最後から二番目の前の交換と番号N-2などの数。

  • 達成するためのアルゴリズムをシャッフルパイソン

    import random
    lst = list(range(10))
    for i in reversed(range(len(lst))):
        j = random.randint(0,i)
        lst[i],lst[j] = lst[j],lst[i]
    print(lst)

• シャッフルアルゴリズムで使用するrandom.shuffle、ここに掲載のソースコード内のpython

      def shuffle(self, x, random=None):
          """Shuffle list x in place, and return None.
  
          Optional argument random is a 0-argument function returning a
          random float in [0.0, 1.0); if it is the default None, the
          standard random.random will be used.
  
          """
  
          if random is None:
              randbelow = self._randbelow
              for i in reversed(range(1, len(x))):
                  # pick an element in x[:i+1] with which to exchange x[i]
                  j = randbelow(i+1)
                  x[i], x[j] = x[j], x[i]
          else:
              _int = int
              for i in reversed(range(1, len(x))):
                  # pick an element in x[:i+1] with which to exchange x[i]
                  j = _int(random() * (i+1))
                  x[i], x[j] = x[j], x[i]
  
  
  

      def _randbelow(self, n, int=int, maxsize=1<<BPF, type=type,Method=_MethodType,BuiltinMethod=_BuiltinMethodType):
          "Return a random int in the range [0,n).  Raises ValueError if n==0."
  
          random = self.random
          getrandbits = self.getrandbits
          # Only call self.getrandbits if the original random() builtin method
          # has not been overridden or if a new getrandbits() was supplied.
          if type(random) is BuiltinMethod or type(getrandbits) is Method:
              k = n.bit_length()  # don't use (n-1) here because n can be 1
              r = getrandbits(k)          # 0 <= r < 2**k
              while r >= n:
                  r = getrandbits(k)
              return r
          # There's an overridden random() method but no new getrandbits() method,
          # so we can only use random() from here.
          if n >= maxsize:
              _warn("Underlying random() generator does not supply \n"
                  "enough bits to choose from a population range this large.\n"
                  "To remove the range limitation, add a getrandbits() method.")
              return int(random() * n)
          if n == 0:
              raise ValueError("Boundary cannot be zero")
          rem = maxsize % n
          limit = (maxsize - rem) / maxsize   # int(limit * maxsize) % n == 0
          r = random()
          while r >= limit:
              r = random()
          return int(r*maxsize) % n