#!/usr/bin/env python
# -*- coding: utf-8 -*-
import heapq
import copy
import datetime
import random
def get_max_heap(heap, size, root): # 在堆中做结构调整使得父节点的值大于子节点
left = 2 * root + 1
right = left + 1
larger = root
if left < size and heap[larger] < heap[left]: # 保证最大值不会被重新排序
larger = left
if right < size and heap[larger] < heap[right]: # 保证最大值不会被重新排序
larger = right
if larger != root: # 如果做了堆调整则larger的值等于左节点或者右节点的，这个时候做对调值操作
heap[larger], heap[root] = heap[root], heap[larger]
get_max_heap(heap, size, larger)
def build_heap(heap):
# 构造一个堆，将堆中所有数据重新排序
for index in xrange(len(heap) / 2 - 1, -1, -1): # 从第一个非叶子节点开始
get_max_heap(heap, len(heap), index)
def sort(heap):
build_heap(heap) # 获得一个大顶堆
for index in xrange(len(heap) - 1, -1, -1):
heap[0], heap[index] = heap[index], heap[0] # 将最大值调到最后
get_max_heap(heap, index, 0) # size递减，保证最大值不会被重新排序
return heap
if __name__ == '__main__':
# a = eval(raw_input('请输入一个待排序列表\n'))
a = [random.randint(1, 2000) for i in range(1000)]
b = copy.deepcopy(a)
b_begin = datetime.datetime.now()
sort(b)
b_end = datetime.datetime.now()
print 'my method use %s' % (b_end - b_begin).total_seconds()
c = copy.deepcopy(a)
c_begin = datetime.datetime.now()
heapq.heapify(c)
c_end = datetime.datetime.now()
print 'inner method use %s' % (c_end - c_begin).total_seconds()
——————————————————————————————
my method use 0.011
inner method use 0.001
#可以看到，我们实现的排序算法在时间上不如内置的heapq.heapify()
