K-Means Assignment Step
MediumMachine LearningMathArray
Description
Given a list of points and a list of cluster centroids (both lists of equal-length coordinate vectors), perform one k-means assignment step: return an array where each entry is the index of the centroid closest to the corresponding point by Euclidean distance. Break ties toward the lower centroid index.
Examples
Input:
[[1,1],[2,2],[8,8],[9,9]], [[0,0],[10,10]]Output:
[0,0,1,1]Explanation:
Each point is labeled with the index of the closest centroid measured by Euclidean distance, breaking any tie toward the lower index.
Input:
[[1,2],[3,4],[10,10]], [[2,3],[9,9]]Output:
[0,0,1]Explanation:
Each point is labeled with the index of the closest centroid measured by Euclidean distance, breaking any tie toward the lower index.
Input:
[[0,0],[5,5]], [[0,0],[5,5]]Output:
[0,1]Explanation:
Each point is labeled with the index of the closest centroid measured by Euclidean distance, breaking any tie toward the lower index.
Constraints
- •
1 ≤ points, centroids ≤ 10³ - •
All vectors share the same dimension