Lecture 5/13: Heaps

May 13, 2020

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Questions and Answers

Binary Heaps

CS 106B: Programming Abstractions

Spring 2020, Stanford University Computer Science Department

Lecturers: Chris Gregg and Julie Zelenski

A heap of candy sprinkles

Slide 2

Announcements

Mid-quarter assessments got started today and will be continuing on through the end of the weekend! Three important action items:
- Download BlueBook in advance of your assessment and use the Sample Exam Questions to test that you are able to successfully load a BlueBook exam file.
- Check your personal exam access time on the distribution portal - this should match the time that you expect to start your assessment. If it does not, please let Nick know ASAP.
- Have fun, enjoy yourself, and appreciate the learning experience!
Section is still taking place normally this week.
Assignment 5 will be released over the weekend.

Slide 3

Today's Goals:

Introduction to Binary Heaps
- A "tree" structure
- The Heap Property
- Parents have higher priority than children
- How to add and remove from a heap?
- Heaps can be used as a priority queue

Slide 4

Binary Heaps

A heap is a tree-based structure that satisfies the heap property:
- Parents have a higher priority than any of their children
There are two types of heaps: a min heap and a max heap.
A min-heap has the smallest element at the root, and a "higher priority" is a smaller number. Thus, 5 is a higher priority than 10.
```
          5
        /   \ 
      10      8     
     /  \    /  \
   12   11  14  13 
  /  \
22    43
```
A max-heap has the largest element at the root, and a "higher priority" is a larger number. Thus, 50 is a higher priority than 19:
```
         50
       /    \ 
     19      36
    /  \    /  \
  17    3  25   1 
 /  \
2    7
```
We will focus mostly on min-heaps, where the lower the number means a higher priority.

Slide 5

Binary Heaps

There are no implied orderings between siblings, so both of the trees below are min-heaps:
```
    5
  /   \           
10    12 

    5
  /   \           
12    10 
```

Slide 6

Which of the following are min-heaps?

          5
        /   \ 
      10      8     
     /  \    /  \
   12   85  14  13 
  /  \
22   11

         13 
        /   \ 
      19     36     
     /  \    /  \
   24   99  46  42 
  /  \
25   26

Answer: only the second one is a heap, because in the first one, the 11 is a child of the 12, and therefore it breaks the heap property that a parent has to be a higher priority than its children (remember: min-heaps designate higher priority as lower numbers).

Slide 7

Binary Heaps

Binary heaps are completely filled, with the exception of the bottom level. They are, therefore, complete binary trees:
- complete: all levels filled from left to right except the bottom
- binary: two children per node (parent)
Maximum number of nodes

Filled from left to right

                     5
               /            \ 
             10               8     
          /      \         /      \
        12        11      14       13 
       /  \      /  \    /  \     /   \
      22   43   __  __  __  __   __   __

Slide 8

How do you store a heap?

It turns out that an array works great for storing a binary heap
Later, we will see a much different approach to storing tree structures, but heaps (which also look like trees), the best solution is actually a simple array.
- The reason for this is because of the complete nature of the structure, with all levels filled from left to right.

Strangely, we will put the root at index 1 instead of index 0 (this makes the math work out a bit simpler). We leave index 0 empty, or use it for swap space, but it is not used for the heap itself.

                     5
               /            \ 
             10               8     
          /      \         /      \
        12        11      14       13 
       /  \      /  \    /  \     /   \
      22   43   __  __  __  __   __   __

Here is the array for the above heap:

Notice that the array follows the levels, 5, then 10, 8, then 12, 11, 14, 13, then 22, 43 – this is the way we fill a heap.
The array representation makes determining parents and children a matter of simple arithmetic:
- For an element at position i:
  - The left child is at 2 * i
  - The right child is at 2 * i + 1
  - The parent is at i / 2 (integer math)
- heapSize is the number of elements in the heap (for this heap, heapSize is 9.

Slide 9

Heap Operations

There are three important operations in a heap:
1. peek(): return the element with the highest priority (lowest number for a min-heap). Don't change the heap at all.
2. enqueue(e): insert an element e into the heap but retain the heap property! (we'll talk about this very soon)
3. dequeue(): remove the highest priority (smallest element for a min-heap) from the heap. This changes the heap, and we have to return it to a proper heap.

Slide 10

Heap operations: `peek()`

                       5
                 /            \ 
               10               8     
            /      \         /      \
          12        11      14       13 
         /  \      /  \    /  \     /   \
        22   43   __  __  __  __   __   __

peek():
- Just return the root! return heap[1]
- This is O(1) – yay, we love O(1)!

Slide 11

Heap operations: `enqueue(e)`

                       5
                 /            \ 
               10               8     
            /      \         /      \
          12        11      14       13 
         /  \      /  \    /  \     /   \
        22   43   __  __  __  __   __   __

How might we go about inserting into a binary heap?

enqueue(9) into the heap above?
We have to think about this a bit. The key is to understand how heaps are built: fill each level from left to right.

So, we start by putting the element as the left child of the 11. This probably destroys the heap property. We simply insert at heap[heapSize + 1]:

                   5
             /            \ 
           10               8     
        /      \         /      \
      12        11      14       13 
     /  \      /  \    /  \     /   \
    22   43   9   __  __  __   __   __

Now, to return the structure to a proper heap, we have to do an operation called "bubble up" – we take a look at the element we just added and compare it to its parent: in this case, we look at the 9 and its parent, 11. We see that this is not proper, so we swap them:
```
                   5
             /            \ 
           10               8     
        /      \         /      \
      12        9       14       13 
     /  \      /  \    /  \     /   \
    22   43   11  __  __  __   __   __
```

We might not yet be done! We need to repeat the process until the element we added is either larger than its parent, or at the root. So, we compare the 9 to its new parent, the 10 (how do we know that from the array? 9 is at index 5, and 5/2 == 2, so we look at index 2 and find 10). Because the 9 is smaller than the 10, we have to keep bubbling up! We swap them:
```
                   5
             /            \ 
            9               8     
        /      \         /      \
      12       10       14       13 
     /  \      /  \    /  \     /   \
    22   43   11  __  __  __   __   __
```

We must do one more comparison, between the 9 and its new parent, the 5. Because 5 is less than 9, we're done. If we look at the heap, we can see that it is proper again.
What is the complexity? O(log n) – yay!
- It turns out that from random inserts, the average complexity is actually O(1). See http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6312854 for details.
See the animation at http://www.cs.usfca.edu/~galles/visualization/Heap.html

Slide 12

Heap operations: `dequeue()`

How might we go about removing the highest priority (minimum)?

dequeue()

                   5
             /            \ 
            9               8     
        /      \         /      \
      12       10       14       11 
     /  \      /  \    /  \     /   \
    22   43   13  __  __  __   __   __

We know where the minimum always is – at the root. So, we know that we will remove that element to return it. This will always destroy the heap, because it has no root!

                       
             /            \ 
            9               8     
        /      \         /      \
      12       10       14       11 
     /  \      /  \    /  \     /   \
    22   43   13  __  __  __   __   __

Now, what do we know about the heap after we remove an element?
- We know it will be one element smaller. Where should that element come from?
- The last level, on the right! We know this will be an empty location at the end. So, what we do (and this may be counter-intuitive) is we take that element, and we put it at the root (the heap property will almost certainly be still destroyed):
```
                 13 
           /            \ 
          9               8     
      /      \         /      \
    12       10       14       11 
   /  \      /  \    /  \     /   \
  22   43   __  __  __  __   __   __
```

The next thing we do is a bubble down. We need to take the 13, and move it down.

To bubble down, you swap the element in question with its smaller child (we have to do this because if we swapped with the larger child, we might still have a bogus heap from the root!). This means we swap the 13 with the 8, which is its smallest child:

                   8 
             /            \ 
            9              13     
        /      \         /      \
      12       10       14       11 
     /  \      /  \    /  \     /   \
    22   43   __  __  __  __   __   __

But we're not necessarily done! We look at the 13 and its children, again, and notice that we have to swap with the smallest child, the 11:

                   8 
             /            \ 
            9              11     
        /      \         /      \
      12       10       14       13 
     /  \      /  \    /  \     /   \
    22   43   __  __  __  __   __   __

When we check again, we see that 13 has no more children, so we have completed the bubble down.
What is the complexity?
- O(log n) – yay!

Heaps are a great data structure, becuase all the operations are either O(1) or O(log n).

Slide 13

Priority Queues

Sometimes, we want to store data in a “prioritized way.”
Examples in real life:
- Emergency Room waiting rooms
- Professor Office Hours (what if a professor walks in? What about the department chair?)
- Getting on an airplane (First Class and families, then frequent flyers, then by row, etc.)
A priority queue stores elements according to their priority, and not in a particular order.
This is fundamentally different from other position-based data structures we have discussed.
There is no external notion of "position."

Slide 14

Priority queue functions

A priority queue, P, has three fundamental operations:
- enqueue(k,e): insert an element e with key k into P.
- dequeue(): removes the element with the highest priority key from P.
- peek(): return an element of P with the highest priority key (does not remove from queue).
Priority queues also have less fundamental operations:
- size(): returns the number of elements in P.
- isEmpty(): Boolean test if P is empty.
- clear(): empties the queue.
- peekPriority(): Returns the priority of the highest priority element (why might we want this?)
- changePriority(string value, int newPriority): Changes the priority of a value.
Priority queues are simpler than sequences: no need to worry about position (or insert(index, value), add(value) to append, get(index), etc.).
We only need one enqueue() and one dequeue() function

Slide 15

Priority Queue Example

Operation	Output	Priority Queue
enqueue(5,A)	-	{(5,A)}
enqueue(9,C)	-	{(5,A),(9,C)}
enqueue(3,B)	-	{(5,A),(9,C),(3,B)}
enqueue(7,D)	-	{(5,A),(9,C),(3,B),(7,D)}
peek()	B	{(5,A),(9,C),(3,B),(7,D)}
peekPriority()	3	{(5,A),(9,C),(3,B),(7,D)}
dequeue()	B	{(5,A),(9,C),(7,D)}
size()	3	{(5,A),(9,C),(7,D)}
peek()	A	{(5,A),(9,C),(7,D)}
dequeue()	A	{(9,C),(7,D)}
dequeue()	D	{(9,C)}
dequeue()	C	{}
dequeue()	error!	{}
isEmpty()	TRUE	{}

Slide 16

Building a heap from scratch

What is the best method for building a heap from scratch (buildHeap())
- Let's say we wanted to create a heap from: 14, 9, 13, 43, 10, 8, 11, 22, 12
We could insert each value in turn
- An insertion takes O(log n), and we have to insert n elements, so we would have a Big O of O(n log n)
There is actually a better way!
1. Insert all elements in the order they appear, without doing any bubbling.
2. Starting from the lowest completely filled level at the first node with children (e.g., at position n/2), bubble down each element (also O(n) to heapify the whole tree). In other words:
```
for (int i = heapSize() / 2; i > 0; i--) {
    bubbleDown(i);
}
```

                  14 
             /            \ 
            9              13     
        /      \         /      \
      43       10       8       11 
     /  \      /  \    /  \     /   \
    22   12   __  __  __  __   __   __

Start with the 43, which is at heapSize() / 2 (index 4)
- Bubbling it down means that the 43 swaps with the 12:

                  14 
             /            \ 
            9              13     
        /      \         /      \
      12       10       8       11 
     /  \      /  \    /  \     /   \
    22   43   __  __  __  __   __   __

Because we are looping backwards, the next index we look at is index 3, which holds the 13.
- Bubbling it down means that the 13 swaps with the 8:

                  14 
             /            \ 
            9               8     
        /      \         /      \
      12       10       13       11 
     /  \      /  \    /  \     /   \
    22   43   __  __  __  __   __   __

The next index we look at is 2, and the 9 does not need to be swapped, as it is already higher priority than its children.
Finally, we bubble-down the 14, which needs to first swap with the 8:

                   8 
             /            \ 
            9               14     
        /      \         /      \
      12       10       13       11 
     /  \      /  \    /  \     /   \
    22   43   __  __  __  __   __   __

We must keep bubbling down the 14, swapping it with the 11:

                   8 
             /            \ 
            9               11     
        /      \         /      \
      12       10       13       14 
     /  \      /  \    /  \     /   \
    22   43   __  __  __  __   __   __

Done!
What is the complexity?
- Not trivial to determine, but it turns out to be O(n) (not bad!)

Lecture 5/13: Heaps

Binary Heaps

CS 106B: Programming Abstractions

Spring 2020, Stanford University Computer Science Department

Lecturers: Chris Gregg and Julie Zelenski

Announcements

Today's Goals:

Binary Heaps

Binary Heaps

Which of the following are min-heaps?

Binary Heaps

How do you store a heap?

Heap Operations

Heap operations: peek()

Heap operations: enqueue(e)

Heap operations: dequeue()

Priority Queues

Priority queue functions

Priority Queue Example

Building a heap from scratch

Heap operations: `peek()`

Heap operations: `enqueue(e)`

Heap operations: `dequeue()`