319652fe08
Return a function that returns the second largest item in a binary search tree (i.e. BST). A BST is a tree where each node has no more than two children (i.e. one left child and one right child). All of the values in a BST's left subtree must be less than the value of the root node; all of the values in a BST's right subtree must be greater than the value of the root node; both left and right subtrees must also be BSTs themselves. I solved this problem thrice -- improving the performance profile each time. The final solution has a runtime complexity of O(n) and a spacetime complexity of O(1).
219 lines
5.6 KiB
TypeScript
219 lines
5.6 KiB
TypeScript
/*******************************************************************************
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* Setup
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******************************************************************************/
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interface BinaryTreeNode {
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value: number;
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left: BinaryTreeNode;
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right: BinaryTreeNode;
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}
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class BinaryTreeNode {
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constructor(value: number) {
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this.value = value;
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this.left = null;
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this.right = null;
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}
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insertLeft(value: number): BinaryTreeNode {
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this.left = new BinaryTreeNode(value);
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return this.left;
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}
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insertRight(value: number): BinaryTreeNode {
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this.right = new BinaryTreeNode(value);
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return this.right;
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}
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}
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/*******************************************************************************
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* First solution
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******************************************************************************/
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/**
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* I first solved this problem using O(n) space and O(n*log(n))
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* time. InterviewCake informs me that we can improve both the time and the
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* space performance.
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*/
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function findSecondLargest_first(node: BinaryTreeNode): number {
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const stack: Array<BinaryTreeNode> = [];
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const xs: Array<number> = [];
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stack.push(node);
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while (stack.length > 0) {
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const node = stack.pop()
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xs.push(node.value);
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if (node.left) {
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stack.push(node.left);
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}
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if (node.right) {
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stack.push(node.right);
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}
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}
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xs.sort();
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if (xs.length < 2) {
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throw new Error('Cannot find the second largest element in a BST with fewer than two elements.');
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} else {
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return xs[xs.length - 2];
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}
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}
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/*******************************************************************************
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* Second solution
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******************************************************************************/
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/**
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* My second solution accumulates a list of the values in the tree using an
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* in-order traversal. This reduces the runtime costs from O(n*log(n)) from the
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* previous solution to O(n). The memory cost is still O(n), which InterviewCake
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* informs me can be reduced to O(1).
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*/
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function findSecondLargest_second(node: BinaryTreeNode): number {
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const xs: Array<number> = accumulateInorder(node);
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if (xs.length < 2) {
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throw new Error('Cannot find the second largest element in a BST with fewer than two elements.');
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} else {
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return xs[xs.length - 2];
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}
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}
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/**
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* Returns an array containing the values of the tree, `node`, sorted in-order
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* (i.e. from smallest-to-largest).
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*/
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function accumulateInorder(node: BinaryTreeNode): Array<number> {
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let result = [];
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if (node.left) {
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result = result.concat(accumulateInorder(node.left));
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}
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result.push(node.value)
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if (node.right) {
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result = result.concat(accumulateInorder(node.right));
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}
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return result;
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}
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/*******************************************************************************
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* Third solution
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******************************************************************************/
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/**
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* Returns the largest number in a BST.
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*/
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function findLargest(node: BinaryTreeNode): number {
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let curr: BinaryTreeNode = node;
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while (curr.right) {
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curr = curr.right;
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}
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return curr.value;
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}
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/**
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* Returns the second largest number in a BST
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*/
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function findSecondLargest(node: BinaryTreeNode): number {
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let curr = node;
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let parent = null;
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while (curr.right) {
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parent = curr;
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curr = curr.right
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}
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if (curr.left) {
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return findLargest(curr.left);
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}
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else {
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return parent.value;
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}
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}
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// Tests
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let desc = 'full tree';
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let treeRoot = new BinaryTreeNode(50);
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let leftNode = treeRoot.insertLeft(30);
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leftNode.insertLeft(10);
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leftNode.insertRight(40);
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let rightNode = treeRoot.insertRight(70);
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rightNode.insertLeft(60);
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rightNode.insertRight(80);
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assertEquals(findSecondLargest(treeRoot), 70, desc);
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desc = 'largest has a left child';
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treeRoot = new BinaryTreeNode(50);
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leftNode = treeRoot.insertLeft(30);
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leftNode.insertLeft(10);
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leftNode.insertRight(40);
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rightNode = treeRoot.insertRight(70);
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rightNode.insertLeft(60);
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assertEquals(findSecondLargest(treeRoot), 60, desc);
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desc = 'largest has a left subtree';
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treeRoot = new BinaryTreeNode(50);
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leftNode = treeRoot.insertLeft(30);
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leftNode.insertLeft(10);
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leftNode.insertRight(40);
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rightNode = treeRoot.insertRight(70);
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leftNode = rightNode.insertLeft(60);
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leftNode.insertRight(65);
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leftNode = leftNode.insertLeft(55);
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leftNode.insertRight(58);
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assertEquals(findSecondLargest(treeRoot), 65, desc);
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desc = 'second largest is root node';
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treeRoot = new BinaryTreeNode(50);
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leftNode = treeRoot.insertLeft(30);
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leftNode.insertLeft(10);
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leftNode.insertRight(40);
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rightNode = treeRoot.insertRight(70);
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assertEquals(findSecondLargest(treeRoot), 50, desc);
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desc = 'descending linked list';
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treeRoot = new BinaryTreeNode(50);
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leftNode = treeRoot.insertLeft(40);
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leftNode = leftNode.insertLeft(30);
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leftNode = leftNode.insertLeft(20);
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leftNode = leftNode.insertLeft(10);
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assertEquals(findSecondLargest(treeRoot), 40, desc);
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desc = 'ascending linked list';
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treeRoot = new BinaryTreeNode(50);
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rightNode = treeRoot.insertRight(60);
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rightNode = rightNode.insertRight(70);
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rightNode = rightNode.insertRight(80);
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assertEquals(findSecondLargest(treeRoot), 70, desc);
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desc = 'one node tree';
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treeRoot = new BinaryTreeNode(50);
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assertThrowsError(() => findSecondLargest(treeRoot), desc);
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desc = 'when tree is empty';
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treeRoot = null;
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assertThrowsError(() => findSecondLargest(treeRoot), desc);
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function assertEquals(a, b, desc) {
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if (a === b) {
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console.log(`${desc} ... PASS`);
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} else {
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console.log(`${desc} ... FAIL: ${a} != ${b}`)
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}
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}
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function assertThrowsError(func, desc) {
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try {
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func();
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console.log(`${desc} ... FAIL`);
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} catch (e) {
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console.log(`${desc} ... PASS`);
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}
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}
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