Solving Leetcode Interviews in Seconds with AI: Top K Frequent Elements
Introduction
In this blog post, we will explore how to solve the LeetCode problem "347" 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
Given an integer array nums and an integer k, return the k most frequent elements. You may return the answer in any order. Example 1: Input: nums = [1,1,1,2,2,3], k = 2 Output: [1,2] Example 2: Input: nums = [1], k = 1 Output: [1] Constraints: 1 <= nums.length <= 105 -104 <= nums[i] <= 104 k is in the range [1, the number of unique elements in the array]. It is guaranteed that the answer is unique. Follow up: Your algorithm's time complexity must be better than O(n log n), where n is the array's size.
Explanation
Here's a breakdown of the approach and the Python code for finding the k most frequent elements in an array:
- Frequency Counting: Use a hash map (dictionary in Python) to count the frequency of each element in the input array.
- Bucket Sort (Radix Sort Idea): Create a "bucket" array (list in Python) where the index represents the frequency and the value at that index is a list of elements with that frequency. This avoids full sorting and is more efficient.
Retrieve Top k: Iterate through the bucket array in reverse order (highest frequency to lowest) and add elements to the result until we have k elements.
Runtime Complexity: O(n), Storage Complexity: O(n)
Code
def topKFrequent(nums, k):
"""
Finds the k most frequent elements in an array.
Args:
nums: The input integer array.
k: The number of most frequent elements to return.
Returns:
A list of the k most frequent elements.
"""
# 1. Frequency Counting
freq_map = {}
for num in nums:
freq_map[num] = freq_map.get(num, 0) + 1
# 2. Bucket Sort
bucket = [[] for _ in range(len(nums) + 1)] # Initialize empty buckets
for num, freq in freq_map.items():
bucket[freq].append(num)
# 3. Retrieve Top k
result = []
for i in range(len(bucket) - 1, 0, -1):
for num in bucket[i]:
result.append(num)
if len(result) == k:
return result
return result # Should not reach here as the problem guarantees a unique answer