I need to modify a Binary Search Tree that I created to assure that it is balanced. I only need to modify the add and remove methods, according to my instructions. Here's what I currently have:
package proj;
public class BinarySearchTree<T extends Comparable<T>>{
public static void main(String[] args) {
BinarySearchTree<Integer> tree = new BinarySearchTree<Integer>();
tree.add(5);
tree.add(1);
tree.add(2);
tree.add(6);
}
private Node<T> root;
private int size;
String inorder = "";
String preorder = "";
public BinarySearchTree(){
root = null;
size = 0;
}
//adds a new item to the queue
public void add(T obj) {
Node<T> n = new Node<T>(obj);
if( root == null ) {
root = n;
} else {
add( root, n );
}
size++;
}
private void add(Node<T> subtree, Node<T> n) {
if( subtree.getValue().compareTo(n.getValue()) > 0 ) {
if( subtree.getLeftChild() == null ) {
subtree.setLeftChild(n);
n.setParent(subtree);
} else {
add( subtree.getLeftChild(), n );
}
} else {
if( subtree.getRightChild() == null ) {
subtree.setRightChild(n);
n.setParent(subtree);
} else {
add( subtree.getRightChild(), n );
}
}
}
//returns the head of the queue
public T peek(){
Node<T> current = root;
while(current.getLeftChild() != null){
current = current.getLeftChild();
}
return current.getValue();
}
//removes the head of the queue and returns it
public T remove(){
if(root == null){
return null;
}
Node<T> current = root;
while(current.getLeftChild() != null){
current = current.getLeftChild();
}
if( current.getParent() == null ) {
root = current.getRightChild();
if(root != null){
root.setParent(null);
}
} else {
current.getParent().setLeftChild(current.getRightChild());
if(current.getRightChild() != null){
current.getRightChild().setParent(current.getParent());
}
}
size--;
return current.getValue();
}
//returns the position of an element in the queue, or -1 if it is not found
public int search(T searchItem){
String tempOrdered = inorder(root);
for(int i = 0; i<tempOrdered.length(); i++){
if(String.valueOf(tempOrdered.charAt(i)).equals(searchItem.toString())){
return i;
}
}
return -1;
}
//returns number of nodes in the tree
//returns the total number of elements in the queue
public int getSize(){
return size;
}
public String inorder() {
inorder = "";
if( root == null )
return inorder;
return inorder(root);
}
//returns an in-order, comma-separated string of every element in the queue
private String inorder(Node<T> n){
if(n.getLeftChild() != null){
inorder(n.getLeftChild());
}
inorder += n.getValue();
if(n.getRightChild() != null){
inorder(n.getRightChild());
}
return inorder;
}
public String preorder() {
preorder = "";
if( root == null )
return preorder;
return preorder(root);
}
//returns a pre-ordered, comma-separated string of every element in the queue
private String preorder(Node<T> n){
开发者_C百科 preorder+= n.getValue();
if(n.getLeftChild() != null){
preorder(n.getLeftChild());
}
if(n.getRightChild() != null){
preorder(n.getRightChild());
}
return preorder;
}
//returns the height of the tree; returns -1 if the tree is empty
public int height(Node<T> n){
if(n == null){
return -1;
}
return Math.max(height(n.getLeftChild()), height(n.getRightChild()))+ 1;
}
//returns the root node
public Node<T> getRoot(){
return root;
}
}
I'm not looking for someone to walk me through this assignment - simply looking for some advice as to how I should go about doing this so that I don't break the code half way in. I'm guessing that I'll need to do something to the effect of checking the balance factor of the tree each time something is added or removed, then reconstruct the tree or 'rotate' when it's unbalanced.
Thanks for any given advice in advance. :) Appreciate all the tips.
-Chris
The AVL tree article on Wikipedia gives all you need to implement this kind of self-balanced tree (I especially like the picture showing rotations needed for rebalancing). Basically you need to implement left and right tree rotation and use it in your add
and remove
methods according to the rules given in the article.
If you are more adventurous, try implementing a red-black tree. A good description with pseudo code can be found in Introduction to Algorithms.
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