Introduction

In this blog post, we will explore how to solve the LeetCode problem "406" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like Chatmagic, we can generate solutions quickly and efficiently - helping you pass the interviews and get the job offer without having to study for months.

Problem Statement

You are given an array of people, people, which are the attributes of some people in a queue (not necessarily in order). Each people[i] = [hi, ki] represents the ith person of height hi with exactly ki other people in front who have a height greater than or equal to hi. Reconstruct and return the queue that is represented by the input array people. The returned queue should be formatted as an array queue, where queue[j] = [hj, kj] is the attributes of the jth person in the queue (queue[0] is the person at the front of the queue). Example 1: Input: people = [[7,0],[4,4],[7,1],[5,0],[6,1],[5,2]] Output: [[5,0],[7,0],[5,2],[6,1],[4,4],[7,1]] Explanation: Person 0 has height 5 with no other people taller or the same height in front. Person 1 has height 7 with no other people taller or the same height in front. Person 2 has height 5 with two persons taller or the same height in front, which is person 0 and 1. Person 3 has height 6 with one person taller or the same height in front, which is person 1. Person 4 has height 4 with four people taller or the same height in front, which are people 0, 1, 2, and 3. Person 5 has height 7 with one person taller or the same height in front, which is person 1. Hence [[5,0],[7,0],[5,2],[6,1],[4,4],[7,1]] is the reconstructed queue. Example 2: Input: people = [[6,0],[5,0],[4,0],[3,2],[2,2],[1,4]] Output: [[4,0],[5,0],[2,2],[3,2],[1,4],[6,0]] Constraints: 1 <= people.length <= 2000 0 <= hi <= 106 0 <= ki < people.length It is guaranteed that the queue can be reconstructed.

Explanation

Here's the breakdown of the solution:

• Sort by Height (Descending) and K (Ascending): Sort the people array first by height in descending order and then by the number of people in front (k) in ascending order. This ensures that taller people are processed first. Among people of the same height, those with fewer people in front are placed earlier.

• Insert at Kth Position: Iterate through the sorted array and insert each person at the index specified by their k value in the output queue. Because taller people are added first, the insertions will maintain the 'k' constraint.

• Data structure choice: Using a list to represent the queue allows for efficient insertion at arbitrary positions.

• Time & Space Complexity:

  • Time Complexity: O(N log N) for sorting + O(N^2) for insertions, which simplifies to O(N^2) because sorting happens only once.
  • Space Complexity: O(N) for the output queue.

Code

    def reconstruct_queue(people):
    """
    Reconstructs the queue based on the given people array.

    Args:
        people: A list of lists, where each inner list represents a person
                with [height, k] attributes.

    Returns:
        A list of lists representing the reconstructed queue.
    """

    people.sort(key=lambda x: (-x[0], x[1]))  # Sort by height (descending) and k (ascending)
    queue = []
    for person in people:
        queue.insert(person[1], person)  # Insert at the kth position

    return queue