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python计算最小优先级队列代码分享

发布时间：2024-04-26 22:35:11 来源：互联网

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# -*- coding: utf-8 -*-
class Heap(object):
 @classmethod
 def parent(cls, i):
 """父结点下标"""
 return int((i - 1) >> 1);
 @classmethod
 def left(cls, i):
 """左儿子下标"""
 return (i << 1) + 1;
 @classmethod
 def right(cls, i):
 """右儿子下标"""
 return (i << 1) + 2;
class MinPriorityQueue(list, Heap):
 @classmethod
 def min_heapify(cls, A, i, heap_size):
 """最小堆化A[i]为根的子树"""
 l, r = cls.left(i), cls.right(i)
 if l < heap_size and A[l] < A[i]:
 least = l
 else:
 least = i
 if r < heap_size and A[r] < A[least]:
 least = r
 if least != i:
 A[i], A[least] = A[least], A[i]
 cls.min_heapify(A, least, heap_size)
 def minimum(self):
 """返回最小元素，伪码如下：
 HEAP-MINIMUM(A)
 1 return A[1]
 T(n) = O(1)
 """
 return self[0]
 def extract_min(self):
 """去除并返回最小元素，伪码如下：
 HEAP-EXTRACT-MIN(A)
 1 if heap-size[A] < 1
 2 then error "heap underflow"
 3 min ← A[1]
 4 A[1] ← A[heap-size[A]] // 尾元素放到第一位
 5 heap-size[A] ← heap-size[A] - 1 // 减小heap-size[A]
 6 MIN-HEAPIFY(A, 1) // 保持最小堆性质
 7 return min
 T(n) = θ(lgn)
 """
 heap_size = len(self)
 assert heap_size > 0, "heap underflow"
 val = self[0]
 tail = heap_size - 1
 self[0] = self[tail]
 self.min_heapify(self, 0, tail)
 self.pop(tail)
 return val
 def decrease_key(self, i, key):
 """将i处的值减少到key，伪码如下：
 HEAP-DECREASE-KEY(A, i, key)
 1 if key > A[i]
 2 then error "new key is larger than current key"
 3 A[i] ← key
 4 while i > 1 and A[PARENT(i)] > A[i] // 不是根结点且父结点更大时
 5 do exchange A[i] ↔ A[PARENT(i)] // 交换两元素
 6 i ← PARENT(i) // 指向父结点位置
 T(n) = θ(lgn)
 """
 val = self[i]
 assert key <= val, "new key is larger than current key"
 self[i] = key
 parent = self.parent
 while i > 0 and self[parent(i)] > self[i]:
 self[i], self[parent(i)] = self[parent(i)], self[i]
 i = parent(i)
 def insert(self, key):
 """将key插入A，伪码如下：
 MIN-HEAP-INSERT(A, key)
 1 heap-size[A] ← heap-size[A] + 1 // 对元素个数增加
 2 A[heap-size[A]] ← +∞ // 初始新增加元素为+∞
 3 HEAP-DECREASE-KEY(A, heap-size[A], key) // 将新增元素减少到key
 T(n) = θ(lgn)
 """
 self.append(float('inf'))
 self.decrease_key(len(self) - 1, key)
if __name__ == '__main__':
 import random
 keys = range(10)
 random.shuffle(keys)
 print(keys)
 queue = MinPriorityQueue() # 插入方式建最小堆
 for i in keys:
 queue.insert(i)
 print(queue)
 print('*' * 30)
 for i in range(len(queue)):
 val = i % 3
 if val == 0:
 val = queue.extract_min() # 去除并返回最小元素
 elif val == 1:
 val = queue.minimum() # 返回最小元素
 else:
 val = queue[1] - 10
 queue.decrease_key(1, val) # queue[1]减少10
 print(queue, val)
 print([queue.extract_min() for i in range(len(queue))])