Introduction

In this blog post, we will explore how to solve the LeetCode problem "2028" 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 have observations of n + m 6-sided dice rolls with each face numbered from 1 to 6. n of the observations went missing, and you only have the observations of m rolls. Fortunately, you have also calculated the average value of the n + m rolls. You are given an integer array rolls of length m where rolls[i] is the value of the ith observation. You are also given the two integers mean and n. Return an array of length n containing the missing observations such that the average value of the n + m rolls is exactly mean. If there are multiple valid answers, return any of them. If no such array exists, return an empty array. The average value of a set of k numbers is the sum of the numbers divided by k. Note that mean is an integer, so the sum of the n + m rolls should be divisible by n + m. Example 1: Input: rolls = [3,2,4,3], mean = 4, n = 2 Output: [6,6] Explanation: The mean of all n + m rolls is (3 + 2 + 4 + 3 + 6 + 6) / 6 = 4. Example 2: Input: rolls = [1,5,6], mean = 3, n = 4 Output: [2,3,2,2] Explanation: The mean of all n + m rolls is (1 + 5 + 6 + 2 + 3 + 2 + 2) / 7 = 3. Example 3: Input: rolls = [1,2,3,4], mean = 6, n = 4 Output: [] Explanation: It is impossible for the mean to be 6 no matter what the 4 missing rolls are. Constraints: m == rolls.length 1 <= n, m <= 105 1 <= rolls[i], mean <= 6

Explanation

Here's the breakdown of the solution:

• Calculate the required sum: Determine the total sum needed for all n + m rolls to achieve the target mean.
• Calculate the sum of existing rolls: Sum the values in the rolls array. Subtract this sum from the required sum to find the total value needed from the missing n rolls.

• Distribute the missing value: Divide the required sum by n. If the result is not within the range [1, 6], no solution exists. Otherwise, distribute the sum as evenly as possible among the n rolls, ensuring each roll's value is between 1 and 6.

• Runtime Complexity: O(m + n), where m is the length of the rolls array and n is the number of missing rolls.

• Storage Complexity: O(n) for storing the result array.

Code

    def missingRolls(rolls, mean, n):
    m = len(rolls)
    total_sum = mean * (n + m)
    rolls_sum = sum(rolls)
    missing_sum = total_sum - rolls_sum

    if missing_sum < n or missing_sum > 6 * n:
        return []

    base = missing_sum // n
    remainder = missing_sum % n
    result = [base] * n
    for i in range(remainder):
        result[i] += 1

    return result