python-优先级队列

最新推荐文章于 2025-07-02 08:59:10 发布

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本文详细介绍了最大堆的数据结构实现，并基于此构建了一个优先级队列。通过具体的Python代码，展示了如何进行元素的添加与提取，维持堆的最大特性。

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# -*- coding:utf-8 -*-

class Array(object):
    def __init__(self,size=32):
        self._size = size
        self._items = [None] * size

    def __getitem__(self,index):
        return self._items[index]

    def __setitem__(self,index,value):
        self._items[index] = value

    def __len__(self):
        return self._size

    def clear(self,value=None):
        for i in range(self._items):
            self._items[i] = value

    def __iter__(self):
        for item in self._items:
            yield item


'''
    初始化堆结构
'''
class MaxHeap(object):
    def __init__(self,maxsize=None):
        self.maxsize = maxsize
        self._elements = Array(maxsize)
        self._count = 0

    def __len__(self):
        return self._count

    def add(self,value):
        if self._count >= self.maxsize:
            raise Exception('full')
        #将插入的值value给数组最后一值
        self._elements[self._count] = value
        #堆的容量加一
        self._count += 1
        self._siftup(self._count-1)

    def _siftup(self,ndx):      #递归交换直到满足最大堆特性
        if ndx > 0:
            #获取添加节点的父节点的下标值
            parent = int((ndx-1)/2)
            #如果添加节点的值大于父节点的值
            if self._elements[ndx] > self._elements[parent]:
                self._elements[ndx],self._elements[parent] = self._elements[parent],self._elements[ndx]
                self._siftup(parent)




    def extract(self):
        if self._count <= 0:
            raise Exception('empty')
        #获取根节点的值
        value = self._elements[0]
        #将堆最后一个叶子节点作为root节点
        self._elements[0] = self._elements[self._count]
        self._siftdown(0)
        return value

    def _siftdown(self,ndx):
        #获取左右孩子节点的下标
        left = 2 * ndx + 1
        right = 2 * ndx + 2
        #默认ndx为最大
        largest = ndx

        if(left < self._count and   #有左孩子
            self._elements[left] >= self._elements[largest] and
            self._elements[left] >= self._elements[right]):     #左孩子 > 右孩子
            largest = left

        elif right < self._count and self._elements[right] >= self._elements[largest]:
            largest = right

        if largest != ndx:
            self._elements[ndx],self._elements[largest] = self._elements[largest],self._elements[ndx]
            self._siftdown(largest)


def test_priority_queue():
    size = 5
    pq = PriorityQueue(size)
    pq.push(5,'purple')
    pq.push(0,'white')
    pq.push(3,'orange')
    pq.push(1,'black')

    res = []
    while not pq.is_empty():
        res.append(pq.pop())
    assert res == ['purple','orange','black','white']


class PriorityQueue(object):
    def __init__(self,maxsize=None):
        self.maxsize = maxsize
        self._maxheap = MaxHeap(maxsize)

    def push(self,priority,value):
        entry = (priority,value)    #push a tuple
        self._maxheap.add(entry)

    def pop(self,with_priority=False):
        entry = self._maxheap.extract()
        if with_priority:
            return entry            #return a tuple
        else:
            return entry[1]

    def is_empty(self):
        return len(self._maxheap) == 0