Python 包含用于最小堆的 heapq 模块,但我需要一个最大堆。我应该在 Python 中使用什么来实现最大堆?
最简单的方法是反转键的值并使用 heapq。例如,将 1000.0 变为 -1000.0,将 5.0 变为 -5.0。
您可以使用
import heapq
listForTree = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
heapq.heapify(listForTree) # for a min heap
heapq._heapify_max(listForTree) # for a maxheap!!
如果您想弹出元素,请使用:
heapq.heappop(minheap) # pop from minheap
heapq._heappop_max(maxheap) # pop from maxheap
_heapify_max
、_heappushpop_max
、_siftdown_max
和 _siftup_max
。
解决方案是在将值存储在堆中时取反,或者像这样反转对象比较:
import heapq
class MaxHeapObj(object):
def __init__(self, val): self.val = val
def __lt__(self, other): return self.val > other.val
def __eq__(self, other): return self.val == other.val
def __str__(self): return str(self.val)
最大堆示例:
maxh = []
heapq.heappush(maxh, MaxHeapObj(x))
x = maxh[0].val # fetch max value
x = heapq.heappop(maxh).val # pop max value
但是你必须记住包装和解包你的值,这需要知道你是在处理最小堆还是最大堆。
MinHeap、MaxHeap 类
为 MinHeap
和 MaxHeap
对象添加类可以简化您的代码:
class MinHeap(object):
def __init__(self): self.h = []
def heappush(self, x): heapq.heappush(self.h, x)
def heappop(self): return heapq.heappop(self.h)
def __getitem__(self, i): return self.h[i]
def __len__(self): return len(self.h)
class MaxHeap(MinHeap):
def heappush(self, x): heapq.heappush(self.h, MaxHeapObj(x))
def heappop(self): return heapq.heappop(self.h).val
def __getitem__(self, i): return self.h[i].val
示例用法:
minh = MinHeap()
maxh = MaxHeap()
# add some values
minh.heappush(12)
maxh.heappush(12)
minh.heappush(4)
maxh.heappush(4)
# fetch "top" values
print(minh[0], maxh[0]) # "4 12"
# fetch and remove "top" values
print(minh.heappop(), maxh.heappop()) # "4 12"
list
参数,在这种情况下我调用 heapq.heapify
并添加了一个 heapreplace
方法。
最简单理想的解决方案
将值乘以 -1
你去吧。现在所有最高的数字都是最低的,反之亦然。
请记住,当您弹出一个元素以将其与 -1 相乘以再次获得原始值时。
最简单的方法是将每个元素转换为负数,它将解决您的问题。
import heapq
heap = []
heapq.heappush(heap, 1*(-1))
heapq.heappush(heap, 10*(-1))
heapq.heappush(heap, 20*(-1))
print(heap)
输出将如下所示:
[-20, -1, -10]
我实现了 heapq 的最大堆版本并将其提交给 PyPI。 (heapq 模块 CPython 代码的微小变化。)
https://pypi.python.org/pypi/heapq_max/
https://github.com/he-zhe/heapq_max
安装
pip install heapq_max
用法
tl;dr:与 heapq 模块相同,除了向所有函数添加“_max”。
heap_max = [] # creates an empty heap
heappush_max(heap_max, item) # pushes a new item on the heap
item = heappop_max(heap_max) # pops the largest item from the heap
item = heap_max[0] # largest item on the heap without popping it
heapify_max(x) # transforms list into a heap, in-place, in linear time
item = heapreplace_max(heap_max, item) # pops and returns largest item, and
# adds new item; the heap size is unchanged
这是一个基于 heapq
的简单 MaxHeap
实现。虽然它只适用于数值。
import heapq
from typing import List
class MaxHeap:
def __init__(self):
self.data = []
def top(self):
return -self.data[0]
def push(self, val):
heapq.heappush(self.data, -val)
def pop(self):
return -heapq.heappop(self.data)
用法:
max_heap = MaxHeap()
max_heap.push(3)
max_heap.push(5)
max_heap.push(1)
print(max_heap.top()) # 5
我还需要使用最大堆,并且我正在处理整数,所以我只是将我需要的两个方法从 heap
包装起来,如下所示:
import heapq
def heappush(heap, item):
return heapq.heappush(heap, -item)
def heappop(heap):
return -heapq.heappop(heap)
然后我分别用 heappush()
和 heappop()
替换了我的 heapq.heappush()
和 heapq.heappop()
调用。
如果您要插入可比较但不类似于 int 的键,您可能会覆盖它们上的比较运算符(即 <= 变为 > 并且 > 变为 <=)。否则,您可以覆盖 heapq 模块中的 heapq._siftup (最后都是 Python 代码)。
# If available, use C implementation
) 之后有一些代码完全符合注释的描述。
扩展 int 类并覆盖 __lt__ 是其中一种方法。
import queue
class MyInt(int):
def __lt__(self, other):
return self > other
def main():
q = queue.PriorityQueue()
q.put(MyInt(10))
q.put(MyInt(5))
q.put(MyInt(1))
while not q.empty():
print (q.get())
if __name__ == "__main__":
main()
最好的办法:
from heapq import *
h = [5, 7, 9, 1, 3]
h_neg = [-i for i in h]
heapify(h_neg) # heapify
heappush(h_neg, -2) # push
print(-heappop(h_neg)) # pop
# 9
允许您选择任意数量的最大或最小项目
import heapq
heap = [23, 7, -4, 18, 23, 42, 37, 2, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2]
heapq.heapify(heap)
print(heapq.nlargest(3, heap)) # [42, 42, 37]
print(heapq.nsmallest(3, heap)) # [-4, -4, 2]
我创建了一个堆包装器,它反转值以创建一个最大堆,以及一个用于最小堆的包装器类,以使库更像 OOP。 Here 是要点。分为三类;堆(抽象类)、HeapMin 和 HeapMax。
方法:
isempty() -> bool; obvious
getroot() -> int; returns min/max
push() -> None; equivalent to heapq.heappush
pop() -> int; equivalent to heapq.heappop
view_min()/view_max() -> int; alias for getroot()
pushpop() -> int; equivalent to heapq.pushpop
为了详细说明 https://stackoverflow.com/a/59311063/1328979,这里有一个针对一般情况的完整记录、注释和测试的 Python 3 实现。
from __future__ import annotations # To allow "MinHeap.push -> MinHeap:"
from typing import Generic, List, Optional, TypeVar
from heapq import heapify, heappop, heappush, heapreplace
T = TypeVar('T')
class MinHeap(Generic[T]):
'''
MinHeap provides a nicer API around heapq's functionality.
As it is a minimum heap, the first element of the heap is always the
smallest.
>>> h = MinHeap([3, 1, 4, 2])
>>> h[0]
1
>>> h.peek()
1
>>> h.push(5) # N.B.: the array isn't always fully sorted.
[1, 2, 4, 3, 5]
>>> h.pop()
1
>>> h.pop()
2
>>> h.pop()
3
>>> h.push(3).push(2)
[2, 3, 4, 5]
>>> h.replace(1)
2
>>> h
[1, 3, 4, 5]
'''
def __init__(self, array: Optional[List[T]] = None):
if array is None:
array = []
heapify(array)
self.h = array
def push(self, x: T) -> MinHeap:
heappush(self.h, x)
return self # To allow chaining operations.
def peek(self) -> T:
return self.h[0]
def pop(self) -> T:
return heappop(self.h)
def replace(self, x: T) -> T:
return heapreplace(self.h, x)
def __getitem__(self, i) -> T:
return self.h[i]
def __len__(self) -> int:
return len(self.h)
def __str__(self) -> str:
return str(self.h)
def __repr__(self) -> str:
return str(self.h)
class Reverse(Generic[T]):
'''
Wrap around the provided object, reversing the comparison operators.
>>> 1 < 2
True
>>> Reverse(1) < Reverse(2)
False
>>> Reverse(2) < Reverse(1)
True
>>> Reverse(1) <= Reverse(2)
False
>>> Reverse(2) <= Reverse(1)
True
>>> Reverse(2) <= Reverse(2)
True
>>> Reverse(1) == Reverse(1)
True
>>> Reverse(2) > Reverse(1)
False
>>> Reverse(1) > Reverse(2)
True
>>> Reverse(2) >= Reverse(1)
False
>>> Reverse(1) >= Reverse(2)
True
>>> Reverse(1)
1
'''
def __init__(self, x: T) -> None:
self.x = x
def __lt__(self, other: Reverse) -> bool:
return other.x.__lt__(self.x)
def __le__(self, other: Reverse) -> bool:
return other.x.__le__(self.x)
def __eq__(self, other) -> bool:
return self.x == other.x
def __ne__(self, other: Reverse) -> bool:
return other.x.__ne__(self.x)
def __ge__(self, other: Reverse) -> bool:
return other.x.__ge__(self.x)
def __gt__(self, other: Reverse) -> bool:
return other.x.__gt__(self.x)
def __str__(self):
return str(self.x)
def __repr__(self):
return str(self.x)
class MaxHeap(MinHeap):
'''
MaxHeap provides an implement of a maximum-heap, as heapq does not provide
it. As it is a maximum heap, the first element of the heap is always the
largest. It achieves this by wrapping around elements with Reverse,
which reverses the comparison operations used by heapq.
>>> h = MaxHeap([3, 1, 4, 2])
>>> h[0]
4
>>> h.peek()
4
>>> h.push(5) # N.B.: the array isn't always fully sorted.
[5, 4, 3, 1, 2]
>>> h.pop()
5
>>> h.pop()
4
>>> h.pop()
3
>>> h.pop()
2
>>> h.push(3).push(2).push(4)
[4, 3, 2, 1]
>>> h.replace(1)
4
>>> h
[3, 1, 2, 1]
'''
def __init__(self, array: Optional[List[T]] = None):
if array is not None:
array = [Reverse(x) for x in array] # Wrap with Reverse.
super().__init__(array)
def push(self, x: T) -> MaxHeap:
super().push(Reverse(x))
return self
def peek(self) -> T:
return super().peek().x
def pop(self) -> T:
return super().pop().x
def replace(self, x: T) -> T:
return super().replace(Reverse(x)).x
if __name__ == '__main__':
import doctest
doctest.testmod()
https://gist.github.com/marccarre/577a55850998da02af3d4b7b98152cf4
heapq 模块拥有实现 maxheap 所需的一切。它只执行 max-heap 的 heappush 功能。我在下面演示了如何克服下面的问题⬇
在 heapq 模块中添加这个函数:
def _heappush_max(heap, item):
"""Push item onto heap, maintaining the heap invariant."""
heap.append(item)
_siftdown_max(heap, 0, len(heap)-1)
最后添加:
try:
from _heapq import _heappush_max
except ImportError:
pass
瞧!完成。
PS-去heapq函数。首先在您的编辑器中写入“import heapq”,然后右键单击“heapq”并选择转到定义。
如果您想使用最大堆获得最大的 K 元素,可以执行以下技巧:
nums= [3,2,1,5,6,4]
k = 2 #k being the kth largest element you want to get
heapq.heapify(nums)
temp = heapq.nlargest(k, nums)
return temp[-1]
nlargest
的实现和评论 -> github.com/python/cpython/blob/…
在python中有构建堆,但如果有人想像我一样自己构建它,我只想分享这个。我是python的新手,不要判断我是否犯了错误。算法正在运行,但关于效率我不知道
class Heap :
def __init__(self):
self.heap = []
self.size = 0
def add(self, heap):
self.heap = heap
self.size = len(self.heap)
def heappush(self, value):
self.heap.append(value)
self.size += 1
def heapify(self, heap ,index=0):
mid = int(self.size /2)
"""
if you want to travel great value from bottom to the top you need to repeat swaping by the hight of the tree
I don't how how can i get the height of the tree that's why i use sezi/2
you can find height by this formula
2^(x) = size+1 why 2^x because tree is growing exponentially
xln(2) = ln(size+1)
x = ln(size+1)/ln(2)
"""
for i in range(mid):
self.createTee(heap ,index)
return heap
def createTee(self, heap ,shiftindex):
"""
"""
"""
this pos reffer to the index of the parent only parent with children
(1)
(2) (3) here the size of list is 7/2 = 3
(4) (5) (6) (7) the number of parent is 3 but we use {2,1,0} in while loop
that why a put pos -1
"""
pos = int(self.size /2 ) -1
"""
this if you wanna sort this heap list we should swap max value in the root of the tree with the last
value in the list and if you wanna repeat this until sort all list you will need to prevent the func from
change what we already sorted I should decrease the size of the list that will heapify on it
"""
newsize = self.size - shiftindex
while pos >= 0 :
left_child = pos * 2 + 1
right_child = pos * 2 + 2
# this mean that left child is exist
if left_child < newsize:
if right_child < newsize:
# if the right child exit we wanna check if left child > rightchild
# if right child doesn't exist we can check that we will get error out of range
if heap[pos] < heap[left_child] and heap[left_child] > heap[right_child] :
heap[left_child] , heap[pos] = heap[pos], heap[left_child]
# here if the righ child doesn't exist
else:
if heap[pos] < heap[left_child] :
heap[left_child] , heap[pos] = heap[pos], heap[left_child]
# if the right child exist
if right_child < newsize :
if heap[pos] < heap[right_child] :
heap[right_child], heap[pos] = heap[pos], heap[right_child]
pos -= 1
return heap
def sort(self ):
k = 1
for i in range(self.size -1 ,0 ,-1):
"""
because this is max heap we swap root with last element in the list
"""
self.heap [0] , self.heap[i] = self.heap[i], self.heap[0]
self.heapify(self.heap ,k)
k+=1
return self.heap
h = Heap()
h.add([5,7,0,8,9,10,20,30,50,-1] )
h.heappush(-2)
print(" before heapify ")
print(h.heap)
print(" after heapify ")
print(h.heapify(h.heap,0))
print(" after sort ")
print(h.sort())
输出 :
在 heapify [5, 7, 0, 8, 9, 10, 20, 30, 50, -1, -2] 之前
堆后 [50, 30, 20, 8, 9, 10, 0, 7, 5, -1, -2]
排序后 [-2, -1, 0, 5, 7, 8, 9, 10, 20, 30, 50]
我希望你能理解我的代码。如果有什么你不明白的发表评论我会尽力帮助
arr = [3,4,5,1,2,3,0,7,8,90,67,31,2,5,567]
# max-heap sort will lead the array to assending order
def maxheap(arr,p):
for i in range(len(arr)-p):
if i > 0:
child = i
parent = (i+1)//2 - 1
while arr[child]> arr[parent] and child !=0:
arr[child], arr[parent] = arr[parent], arr[child]
child = parent
parent = (parent+1)//2 -1
def heapsort(arr):
for i in range(len(arr)):
maxheap(arr,i)
arr[0], arr[len(arr)-i-1]=arr[len(arr)-i-1],arr[0]
return arr
print(heapsort(arr))
尝试这个
heapq
没有提供相反的内容。heapq
提供的,而且没有好的替代方案。int
/float
的东西,您可以通过使用反向__lt__
运算符将它们包装在一个类中来反转顺序。