How to generate all the permutations of a multiset?

给你一囗甜甜゛ 提交于 2019-11-27 04:53:17
ruggero

Generating all the possible permutations and then discarding the repeated ones is highly inefficient. Various algorithms exist to directly generate the permutations of a multiset in lexicographical order or other kind of ordering. Takaoka's algorithm is a good example, but probably that of Aaron Williams is better

http://webhome.csc.uvic.ca/~haron/CoolMulti.pdf

moreover, it has been implemented in the R package ''multicool''.

Btw, if you just want the total number of distinct permutations, the answer is the Multinomial coefficient: e.g., if you have, say, n_a elements 'a', n_b elements 'b', n_c elements 'c', the total number of distinct permutations is (n_a+n_b+n_c)!/(n_a!n_b!n_c!)

There are O(1) (per permutation) algorithms for multiset permutation generation, for example, from Takaoka (with implementation)

This is my translation of the Takaoka multiset permutations algorithm into Python (available here and at repl.it):

def msp(items):
  '''Yield the permutations of `items` where items is either a list
  of integers representing the actual items or a list of hashable items.
  The output are the unique permutations of the items given as a list
  of integers 0, ..., n-1 that represent the n unique elements in
  `items`.

  Examples
  ========

  >>> for i in msp('xoxox'):
  ...   print(i)

  [1, 1, 1, 0, 0]
  [0, 1, 1, 1, 0]
  [1, 0, 1, 1, 0]
  [1, 1, 0, 1, 0]
  [0, 1, 1, 0, 1]
  [1, 0, 1, 0, 1]
  [0, 1, 0, 1, 1]
  [0, 0, 1, 1, 1]
  [1, 0, 0, 1, 1]
  [1, 1, 0, 0, 1]

  Reference: "An O(1) Time Algorithm for Generating Multiset Permutations", Tadao Takaoka
  https://pdfs.semanticscholar.org/83b2/6f222e8648a7a0599309a40af21837a0264b.pdf
  '''

  def visit(head):
      (rv, j) = ([], head)
      for i in range(N):
          (dat, j) = E[j]
          rv.append(dat)
      return rv

  u = list(set(items))
  E = list(reversed(sorted([u.index(i) for i in items])))
  N = len(E)
  # put E into linked-list format
  (val, nxt) = (0, 1)
  for i in range(N):
      E[i] = [E[i], i + 1]
  E[-1][nxt] = None
  head = 0
  afteri = N - 1
  i = afteri - 1
  yield visit(head)
  while E[afteri][nxt] is not None or E[afteri][val] < E[head][val]:
      j = E[afteri][nxt]  # added to algorithm for clarity
      if j is not None and E[i][val] >= E[j][val]:
          beforek = afteri
      else:
          beforek = i
      k = E[beforek][nxt]
      E[beforek][nxt] = E[k][nxt]
      E[k][nxt] = head
      if E[k][val] < E[head][val]:
          i = k
      afteri = E[i][nxt]
      head = k
      yield visit(head)

You can reduce your problem to enumerate all permutations of a list. The typcial permutation generation algorithm takes a list and don't check if elements are equal. So you only need to generate a list out of your multiset, and feed it to your permutation generating algorithm.

For example, you have the multiset {1,2,2}.

You transform it to the list [1,2,2].

And generate all permutations, for example in python:

import itertools as it
for i in it.permutations([1,2,2]):
   print i

And you will get the output

(1, 2, 2)
(1, 2, 2)
(2, 1, 2)
(2, 2, 1)
(2, 1, 2)
(2, 2, 1)

The problem is, that you get some permutations repeatedly. A simple solution would be just to filter them out:

import itertools as it
permset=set([i for i in it.permutations([1,2,2])])
for x in permset:
   print x

Output:

(1, 2, 2)
(2, 2, 1)
(2, 1, 2)
易学教程内所有资源均来自网络或用户发布的内容,如有违反法律规定的内容欢迎反馈
该文章没有解决你所遇到的问题?点击提问,说说你的问题,让更多的人一起探讨吧!