import heapq class PriorityQueue: def __init__(self): self.queue = [] self.index = 0 def push(self,item,priority): heapq.heappush(self.queue,(-priority,self.index,item)) self.index += 1 def pop(self): return heapq.heappop(self.queue)[-1] class Item: def __init__(self,name)...
给定一个优先级(Priority) 每次pop操作都会返回一个拥有最高优先级的项 代码如下: import heapq class PriorityQueue(object): def __init__(self): self._queue = [] #创建一个空列表用于存放队列 self._index = 0 #指针用于记录push的次序 def push(self, item, priority): """队列由(priority, index,...
import heapq class My_PriorityQueue(object): def __init__(self): self._queue = [] self._index = 0 def push(self, item, priority): """ 队列由 (priority, index, item) 形式组成 priority 增加 "-" 号是因为 heappush 默认是最小堆 index 是为了当两个对象的优先级一致时,按照插入顺序排列...
1fromdataclassesimportdataclass, field2fromtypingimportAny34@dataclass(order=True)5classPrioritizedItem:6priority: int7item: Any=field(compare=False) Queue对象 队列对象 (Queue,LifoQueue, 或者PriorityQueue) 提供下列描述的公共方法。 Queue.qsize() 返回队列的大致大小。注意,qsize() > 0 不保证后续的 ...
1fromdataclassesimportdataclass, field2fromtypingimportAny34@dataclass(order=True)5classPrioritizedItem:6priority: int7item: Any=field(compare=False) Queue对象 队列对象 (Queue,LifoQueue, 或者PriorityQueue) 提供下列描述的公共方法。 Queue.qsize() ...
import heapq class PriorityQueue: def __init__(self): self._queue = [] self._index =0 def push(self, item, priority): # 传入两个参数,一个是存放元素的数组,另一个是要存储的元素,这里是一个元组。 # 由于heap内部默认有小到大排,所以对priority取负数 heapq.heappush(self._queue, (-priority...
classPriorityQueue(Queue):'''VariantofQueue that retrieves open entriesinpriorityorder(lowest first).Entries are typically tuplesofthe form:(priority number,data).''' def_init(self,maxsize):self.queue=[]def_qsize(self,len=len):returnlen(self.queue)def_put(self,item,heappush=heapq.heappush...
class PriorityQueue: def __init__(self): self._queue = [] self._index = 0 def push(self, item, priority): heapq.heappush(self._queue, (-priority, self._index, item)) self._index += 1 def pop(self): return heapq.heappop(self._queue)[-1] ...
priority_queue 优先级队列之所以总能保证优先级最高的元素位于队头,是因为其底层采用堆数据结构存储结构。 priority_queue 底层采用vector或deque来存储元素的容器,而堆是一种数据结构,其本身无法存储数据,只能依附于某个存储介质,辅助其组织数据存储的先后次序。其次,priority_queue ...
if with_priority: return entry else: return entry[1] def is_empty(self): return len(self._maxheap) == 0 def test_priority_queue(): size = 5 pq = PriorityQueue(size) pq.push(5, 'purple') pq.push(0, 'white') pq.push(3, 'orange') ...