#0016

Assign Points To Nearest Centroid

MediumML22 min15 XP

Problem

Given data points X and centroids, return the index of the nearest centroid for each point using Euclidean distance.

Why This Matters

K-means alternates between assigning points to centroids and moving centroids. This problem teaches the first half clearly.

Function Signature

def assign_clusters(X, centroids):

Examples

Example 1

InputX = [[0,0], [9,9], [1,1]], centroids = [[0,0], [10,10]]

Outputarray([0, 1, 0])

The first and third points are closer to centroid 0; the second is closer to centroid 1.

Constraints

X is a 2D NumPy array.
centroids is a 2D NumPy array with the same feature count.
Return centroid indices as integers.

CodePython

import numpy as np

def assign_clusters(X, centroids):
    """
    Input example: X = [[0,0], [9,9], [1,1]], centroids = [[0,0], [10,10]]
    Return: array([0, 1, 0])
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return np.zeros(len(X), dtype=int)

import numpy as np

def assign_clusters(X, centroids):
    """
    Input example: X = [[0,0], [9,9], [1,1]], centroids = [[0,0], [10,10]]
    Return: array([0, 1, 0])
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return np.zeros(len(X), dtype=int)

Visible browser tests run here when available.

Testcases1 visible / 4 hidden categories

Two clear clusters

Inputthree points and two centroids

Expected[0, 1, 0]

Each point is assigned to the closest centroid.

Hidden Test Categories

One centroidThree centroidsOne-dimensional pointsTie behavior using argmin first index