6.9 KiB
heaps
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a heap is a binary tree with these properties:
- it must have all of its nodes in a specific order, and
- its shape must be complete (all the levels of the tree must be completely filled except maybe for the last one and the last level must have the left-most nodes filled, always).
- a max heap's root node must have all its children either greater than or equal to its children. a min heap is the opposite. duplicate values are allowed.
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since you always remove the root, insertion and deletion takes
O(log(n)). the maximum/minimum value in the heap can be obtained withO(1)time complexity. -
heaps can be represented with linked lists or queues (arrays) or binary trees.
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in the case of a tree to array heap:
- the parent node index is given by
n / 2 - the left children index is
2 * n - the right children index is
2 * n + 1 - a node is a leaf when its index
> n / 2
- the parent node index is given by
priority queues
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a priority queue is an abstract data type with the following properties:
- every item has a priority (usually an integer).
- an item with a high priority is dequeued before an item with low priority. 3. two items with an equal priority are dequeued based on their order in the queue.
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priority queues can be implemented with a stack, queue, or linked list data structures. however, heaps are the structure that guarantees both insertion and deletion to have time complexity
O(log N)(while maintaining get_max/get_min atO(1)).
min heaps
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a min heap is a complete binary tree where each node is smaller than its children (the root is the min element).
-
insert: - insert the element at the bottom, by finding the most rightmost node and checking its children: if left is empty, insert there, otherwise, insert on right. - then compare this node to each parent, exchanging them until the tree's properties are corret. -
extract_min: - first, remove/return the top and then replace the tree's top with its latest element (the bottom most rightmost). - then bubble down, swapping it with one of its children until the min-heap is properly restored - there is no need for order between right and left, so this operation would only takeO(log n)runtime. -
the code below is an example of an ad-hoc heap class in python.
class MinHeap:
def __init__(self, size):
self.heapsize = size
self.minheap = [0] * (size + 1)
self.realsize = 0
def add(self, element):
if self.realsize + 1 > self.heapsize:
print("Too many elements!")
return False
self.realsize += 1
self.minheap[self.realsize] = element
index = self.realsize
parent = index // 2
while self.minheap[index] < self.minheap[parent] and index > 1:
self.minheap[parent], self.minheap[index] = self.minheap[index], self.minheap[parent]
index = parent
parent = index // 2
def peek(self):
return self.minheap[1]
def pop(self):
if self.realsize < 1:
print("Heap is empty.")
return False
else:
remove_element = self.minheap[1]
self.minheap[1] = self.minheap[self.realsize]
self.realsize -= 1
index = 1
while index <= self.realsize // 2:
left_children = index * 2
right_children = (index * 2) + 1
if self.minheap[index] > self.minheap[left_children] or \
self.minheap[index] > self.minheap[right_children]:
if self.minheap[left_children] < self.minheap[right_children]:
self.minheap[left_children], self.minheap[index] = self.minheap[index], self.minheap[left_children]
index = left_children
else:
self.minheap[right_children], self.minheap[index] = self.minheap[index], self.minheap[right_children]
index = right_children
else:
break
return remove_element
def size(self):
return self.realsize
def __str__(self):
return str(self.minheap[1 : self.realsize + 1])
max heaps
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a max heap is a complete binary tree where each node is larger than its children (the root is the max element).
-
insert:- insert the element at the bottom, at the leftmost node.
- then compare the node to each parent, exchanging them until the tree's properties are correct.
-
extreact_max:- remove/return the top and then replace the tree's top with its bottom rightmost element.
- swap up until the max element is on the top.
-
the code below is an example of a max heap class built in python:
class MaxHeap:
def __init__(self, heapsize):
self.heapsize = heapsize
self.maxheap = [0] * (heapsize + 1)
self.realsize = 0
def add(self, element):
self.realsize += 1
if self.realsize > self.heapsize:
print("Too many elements!")
self.realsize -= 1
return False
self.maxheap[self.realsize] = element
index = self.realsize
parent = index // 2
while self.maxheap[index] > self.maxheap[parent] and index > 1:
self.maxheap[parent], self.maxheap[index] = self.maxheap[index], self.maxheap[parent]
index = parent
parent = index // 2
def peek(self):
return self.maxheap[1]
def pop(self):
if self.realsize < 1:
print("Heap is empty.")
return False
else:
remove_element = self.maxheap[1]
self.maxheap[1] = self.maxheap[self.realsize]
self.realsize -= 1
index = 1
while (index <= self.realsize // 2):
left_children = index * 2
right_children = (index * 2) + 1
if (self.maxheap[index] < self.maxheap[left_children] or self.maxheap[index] < self.maxheap[right_children]):
if self.maxheap[left_children] > self.maxheap[right_children]:
self.maxheap[left_children], self.maxheap[index] = self.maxheap[index], self.maxheap[left_children]
index = left_children
else:
self.maxheap[right_children], self.maxheap[index] = self.maxheap[index], self.maxheap[right_children]
index = right_children
else:
break
return remove_element
def size(self):
return self.realsize
def __str__(self):
return str(self.maxheap[1 : self.realsize + 1])