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AI & MACHINE LEARNING PROGRAM • LEVEL 13 — CLUSTERING

Assign a Point to a Centroid with Python

Learn assign a point to a centroid with python with a short, executable Python example.

IntermediateK-meansCluster assignment

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
0

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-13-nearest-centroid.py
Open in compiler
from math import dist
point = [2,2]
centroids = [[0,0],[5,5]]
print(min(range(len(centroids)), key=lambda i: dist(point,centroids[i])))

GUIDED CODE TOUR • NOT LIVE EXECUTION

Study the program line by line

Use the real compiler button above to run and debug with different inputs.

CURRENT STEP

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SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

0
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Import the required standard-library tools and prepare the sample data for assign a point to a centroid.
  2. Apply K-means and Cluster assignment to compute the required result.
  3. Display the result for assign a point to a centroid and compare it with the documented sample output.

This example of assign a point to a centroid computes the result directly from the prepared sample data. It demonstrates K-means and Cluster assignment and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(k · d)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For assign a point to a centroid, keep the data shape and value types consistent with K-means.

Check this

Apply K-means in the same order shown by the algorithm; changing the order can change the result.

Check this

Verify the final K-means and Cluster assignment result against the sample before trying new data.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.