Lecture 15, Tue 05/23

Priority Queues, Heaps

Recorded Lecture: 5_23_23

Priority Queues

• In a queue structure, we can insert items from the back of the queue and remove items at the front of the queue
  • The order of elements in the queue were dictated by when items were inserted into the queue
• Priority Queues are similar to Queues EXCEPT:
  • We can insert items into the priority queue where an item has some value representing a priority, and items are ordered in the priority queue with respect to their priority value

Tree Properties

• Binary Tree: Any node may have at most two children
• Balanced Binary Tree: The left and right subtress of every node differ in height by no more than 1
• Complete Tree: A binary tree where every level of the tree, except the deepest, must contain as many nodes as possible. The deepest level must have all nodes to the left as possible

Heaps

• MaxHeap:
  • A complete tree where the value of a node is never less than the value of its children
  • Example:

• MinHeap:
  • A complete tree where the value of a node is never greater than the value of its children
  • Example:

• Heaps are an efficient way to implement a Priority Queue
  • The only element we care about when removing from the priority queue is the root of the heap (the min value for a minHeap and the max value for a maxHeap)
• Since binary heaps have the complete tree property, representing this with a Python List is used
  • Easier to represent the heap where the 0^th element in the list is meaningless
  • The root of the binary heap is at index 1
• A node’s index with respect to its parent and children can be generalized as:
  • A node’s parent index: node_index // 2
  • A node’s left child index: 2 * node_index
  • A node’s right child index: 2 * node_index + 1
• Example of Python List representation for MaxHeap example above:

Insertion into a MaxHeap Steps

1. insert the element in the first available location
   • Keeps the binary tree complete
2. While the element’s parent is less than the element
   • Swap the element with its parent

• Insertion is O(log n) (height of tree is log n)
• Inserting elements into a MaxHeap example:

• Note that MinHeap would be the same algorithm EXCEPT we swap while the element’s parent is greater than the element

Removing Max (root) element in a MaxHeap Steps

• Since heaps are used to implement priority queues, removing the root element is a commonly used operation

1. Copy the root element into a variable
2. Assign the last_element in the Python List to the root position
3. While new_root is less than one of its children
   • Swap the largest child with the new_root
4. Return the original root element

• Deletion is O(log n) (height of tree is log n)
• Removing elements from a MaxHeap example:

MinHeap Implementation (from textbook)

# MinHeap
class BinHeap:
	def __init__(self):
		self.heapList = [0]
		self.currentSize = 0

	def percUp(self,i):
		while i // 2 > 0:
			if self.heapList[i] < self.heapList[i // 2]:
				tmp = self.heapList[i // 2]
				self.heapList[i // 2] = self.heapList[i]
				self.heapList[i] = tmp
			i = i // 2

	def insert(self,k):
		self.heapList.append(k)
		self.currentSize = self.currentSize + 1
		self.percUp(self.currentSize)

	def percDown(self,i):
		while (i * 2) <= self.currentSize:
			mc = self.minChild(i)
			if self.heapList[i] > self.heapList[mc]:
				tmp = self.heapList[i]
				self.heapList[i] = self.heapList[mc]
				self.heapList[mc] = tmp
			i = mc

	def minChild(self,i):
		if i * 2 + 1 > self.currentSize:
			return i * 2
		else:
			if self.heapList[i*2] < self.heapList[i*2+1]:
				return i * 2
			else:
				return i * 2 + 1

	def delMin(self):
		retval = self.heapList[1]
		self.heapList[1] = self.heapList[self.currentSize]
		self.currentSize = self.currentSize - 1
		self.heapList.pop()
		self.percDown(1)
		return retval

# pytest
def test_BinHeap():
	bh = BinHeap()
	bh.insert(5)
	bh.insert(7)
	bh.insert(3)
	bh.insert(11)
	assert bh.delMin() == 3
	assert bh.delMin() == 5
	assert bh.delMin() == 7
	assert bh.delMin() == 11