145 lines
5.5 KiB
Markdown
145 lines
5.5 KiB
Markdown
# Lecture 18 --- Trees, Part I
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## Review from Lecture 17
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- STL set container class (like STL map, but without the pairs!)
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- set iterators, insert, erase, find
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## Today’s Lecture
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- Binary trees, binary search trees
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- Implementation of ds_set class using binary search trees
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- In-order, pre-order, and post-order traversal
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## Overview: Lists vs Trees vs Graphs
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- Trees create a hierarchical organization of data, rather than the linear organization in linked lists (and arrays and vectors).
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- Binary search trees are the mechanism underlying maps & sets (and multimaps & multisets).
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- Mathematically speaking: A _graph_ is a set of vertices connected by edges. And a tree is a special graph that has no _cycles_. The edges that connect nodes in trees and graphs may be _directed_ or _undirected_.
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## 18.1 Definition: Binary Trees
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- A binary tree (strictly speaking, a “rooted binary
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tree”) is either empty or is a node that has
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pointers to two binary trees.
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- Here’s a picture of a binary tree storing integer
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values. In this figure, each large box indicates a
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tree node, with the top rectangle representing the
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value stored and the two lower boxes representing
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pointers. Pointers that are null are shown with a
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slash through the box.
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- The topmost node in the tree is called the root.
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- The pointers from each node are called left and
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right. The nodes they point to are referred to as
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that node’s (left and right) children.
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- The (sub)trees pointed to by the left and right
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pointers at any node are called the left subtree
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and right subtree of that node.
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- A node where both children pointers are null is
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called a leaf node.
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- A node’s parent is the unique node that points to
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it. Only the root has no parent.
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## 18.2 Definition: Binary Search Trees
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- A binary search tree (often abbreviated to
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BST) is a binary tree where at each node
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of the tree, the value stored at the node is
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– greater than or equal to all values
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stored in the left subtree, and
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– less than or equal to all values stored in
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the right subtree.
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- Here is a picture of a binary search tree
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storing string values.
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## 18.3 Definition: Balanced Trees
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- The number of nodes on each subtree of each node in a
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“balanced” tree is approximately the same. In order to
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be an exactly balanced binary tree, what must be true
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about the number of nodes in the tree?
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- In order to claim the performance advantages of trees, we must assume and ensure that our data structure
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remains approximately balanced. (You’ll see much more of this in Intro to Algorithms!)
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## 18.4 Exercise
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Consider the following values:
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4.5, 9.8, 3.5, 13.6, 19.2, 7.4, 11.7
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1. Draw a binary tree with these values that is NOT a binary search tree.
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2. Draw two different binary search trees with these values. Important note: This shows that the binary search
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tree structure for a given set of values is not unique!
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3. How many exactly balanced binary search trees exist with these numbers? How many exactly balanced
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binary trees exist with these numbers?
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## 18.5 Beginning our implementation of ds_set: The Tree Node Class
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- Here is the class definition for nodes in the tree. We will use this for the tree manipulation code we write.
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```cpp
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template <class T> class TreeNode {
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public:
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TreeNode() : left(NULL), right(NULL) {}
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TreeNode(const T& init) : value(init), left(NULL), right(NULL) {}
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T value;
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TreeNode* left;
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TreeNode* right;
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};
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```
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- Note: Sometimes a 3rd pointer — to the parent TreeNode — is added.
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## 18.6 Exercises
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1. Write a templated function to find the smallest value stored in a binary search tree whose root node is pointed
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to by p.
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2. Write a function to count the number of odd numbers stored in a binary tree (not necessarily a binary search
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tree) of integers. The function should accept a TreeNode<int> pointer as its sole argument and return an
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integer. Hint: think recursively!
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## 18.7 ds_set and Binary Search Tree Implementation
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- A partial implementation of a set using a binary search tree is provided in this [ds_set_starter.h](ds_set_starter.h). We will continue to study this implementation in Lab 10 & the next lecture.
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- The increment and decrement operations for iterators have been omitted from this implementation. Next week
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in lecture we will discuss a couple strategies for adding these operations.
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- We will use this as the basis both for understanding an initial selection of tree algorithms and for thinking
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about how standard library sets really work.
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## 18.8 ds_set: Class Overview
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- There is two auxiliary classes, TreeNode and tree_iterator. All three classes are templated.
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- The only member variables of the ds_set class are the root and the size (number of tree nodes).
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- The iterator class is declared internally, and is effectively a wrapper on the TreeNode pointers.
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– Note that operator* returns a const reference because the keys can’t change.
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– The increment and decrement operators are missing (we’ll fill this in next week in lecture!).
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- The main public member functions just call a private (and often recursive) member function (passing the root
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node) that does all of the work.
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- Because the class stores and manages dynamically allocated memory, a copy constructor, operator=, and
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destructor must be provided.
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## 18.9 Exercises
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1. Provide the implementation of the member function ds_set<T>::begin. This is essentially the problem of
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finding the node in the tree that stores the smallest value.
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2. Write a recursive version of the function find.
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