python的通用优先级队列

嗯, Queue.PriorityQueue ?回想一下,python不是强类型的,所以您可以保存任何您喜欢的东西:只需创建一个(priority,thing)元组就可以了。

我最终实现了 heapq ,添加dict以保持队列元素的唯一性。结果对于所有操作员都应该是相当有效的:

class PriorityQueueSet(object):

    """
    Combined priority queue and set data structure.

    Acts like a priority queue, except that its items are guaranteed to be
    unique. Provides O(1) membership test, O(log N) insertion and O(log N)
    removal of the smallest item.

    Important: the items of this data structure must be both comparable and
    hashable (i.e. must implement __cmp__ and __hash__). This is true of
    Python's built-in objects, but you should implement those methods if you
    want to use the data structure for custom objects.
    """

    def __init__(self, items=[]):
        """
        Create a new PriorityQueueSet.

        Arguments:
            items (list): An initial item list - it can be unsorted and
                non-unique. The data structure will be created in O(N).
        """
        self.set = dict((item, True) for item in items)
        self.heap = self.set.keys()
        heapq.heapify(self.heap)

    def has_item(self, item):
        """Check if ``item`` exists in the queue."""
        return item in self.set

    def pop_smallest(self):
        """Remove and return the smallest item from the queue."""
        smallest = heapq.heappop(self.heap)
        del self.set[smallest]
        return smallest

    def add(self, item):
        """Add ``item`` to the queue if doesn't already exist."""
        if item not in self.set:
            self.set[item] = True
            heapq.heappush(self.heap, item)

当使用优先级队列时,reduce key对于许多算法(dijkstra的算法,a*,optics)来说是必须的操作,我想知道为什么python的内置优先级队列不支持它。其他答案都不提供支持此功能的解决方案。

还支持reduce key操作的优先级队列是 this DanielStutzbach的实现对于我使用Python3.5非常有效。

from heapdict import heapdict

hd = heapdict()
hd["two"] = 2
hd["one"] = 1
obj = hd.popitem()
print("object:",obj[0])
print("priority:",obj[1])

# object: one
# priority: 1

我没用过,但你可以试试 PyHeap . 它是用C写的,所以希望它足够快。

你肯定HeapQ/PriorityQueue不够快吗?从一开始,也许值得和他们一起分析,看看这是否真的是你的性能瓶颈。

你看了吗 "Show Source" link 在HeapQ页面上?有一个例子,在使用一个具有(int,char)元组列表的堆作为优先级队列的过程中,还不到一半。

可以对非整数元素(元组)使用heapq

from heapq import *

heap = []
data = [(10,"ten"), (3,"three"), (5,"five"), (7,"seven"), (9, "nine"), (2,"two")]
for item in data:
    heappush(heap, item)
sorted = []
while heap:
    sorted.append(heappop(heap))
print sorted
data.sort()
print data == sorted

这是有效的,适用于字符串或任何类型的输入-:)

pq = []                         # list of entries arranged in a heap
entry_finder = {}               # mapping of tasks to entries
REMOVED = '<removed-task>'      # placeholder for a removed task
counter = itertools.count()     # unique sequence count

def add_task(task, priority=0):
    'Add a new task or update the priority of an existing task'
    if task in entry_finder:
        remove_task(task)
    count = next(counter)
    entry = [priority, count, task]
    entry_finder[task] = entry
    heappush(pq, entry)

def remove_task(task):
    'Mark an existing task as REMOVED.  Raise KeyError if not found.'
    entry = entry_finder.pop(task)
    entry[-1] = REMOVED

def pop_task():
    'Remove and return the lowest priority task. Raise KeyError if empty.'
    while pq:
        priority, count, task = heappop(pq)
        if task is not REMOVED:
            del entry_finder[task]
            return task
    raise KeyError('pop from an empty priority queue')

参考文献: http://docs.python.org/library/heapq.html

我有一个优先队列/fibonacci堆 https://pypi.python.org/pypi/fibonacci-heap-mod

不是很快(删除min时的大常数c,即o(c*logn))。但是find-min、insert、decrease和merge都是o(1)-low,这很懒惰。

如果对cpython的速度太慢,你可以尝试pypy、nuitka甚至cpython+numba:。

我可以使用 (priority, object) 就像查理·马丁建议的那样,或者只是执行 __cmp__ 为了我的目的。

如果您希望插入的对象按特定的规则进行优先级排序,我发现编写一个简单的 PriorityQueue 它接受一个键函数。您不必插入 (优先级,对象) 手动打褶,操作更自然。

所需行为的演示 :

>>> h = KeyHeap(sum)
>>> h.put([-1,1])
>>> h.put((-1,-2,-3))
>>> h.put({100})
>>> h.put([1,2,3])
>>> h.get()
(-1, -2, -3)
>>> h.get()
[-1, 1]
>>> h.get()
[1, 2, 3]
>>> h.get()
set([100])
>>> h.empty()
True
>>>
>>> k = KeyHeap(len)
>>> k.put('hello')
>>> k.put('stackoverflow')
>>> k.put('!')
>>> k.get()
'!'
>>> k.get()
'hello'
>>> k.get()
'stackoverflow'

Python 2代码

from Queue import PriorityQueue

class KeyHeap(PriorityQueue):
    def __init__(self, key, maxsize=0):            
        PriorityQueue.__init__(self, maxsize)
        self.key = key

    def put(self, x):
        PriorityQueue.put(self, (self.key(x), x))

    def get(self):
        return PriorityQueue.get(self)[1]

Python 3代码

from queue import PriorityQueue

class KeyHeap(PriorityQueue):
    def __init__(self, key, maxsize=0):            
        super().__init__(maxsize)
        self.key = key

    def put(self, x):
        super().put((self.key(x), x))

    def get(self):
        return super().get()[1]

显然,打电话来 put 威尔(应该!)如果试图插入键函数无法处理的对象,则会引发错误。