AI & MACHINE LEARNING PROGRAM • LEVEL 13 — CLUSTERING
Run One K-means Iteration with Python
Learn run one k-means iteration with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
[0, 0, 1, 1]
COMPLETE PYTHON PROGRAM
Complete Python implementation
from math import dist points = [[1,1],[2,1],[8,8],[9,8]] centroids = [[1,1],[9,8]] labels = [min(range(2), key=lambda i: dist(p,centroids[i])) for p in points] print(labels)
CURRENT STEP
Select Start to walk through the important lines.
SELECTED LINE
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EXPECTED OUTPUT FOR THE SAMPLE
[0, 0, 1, 1]
PROGRAM EXPLANATION
Algorithm and explanation
- Import the required standard-library tools and prepare the sample data for run one k-means iteration.
- Process the data step by step using K-means and Iteration.
- Display the result for run one k-means iteration and compare it with the documented sample output.
This example of run one k-means iteration processes the sample values in a controlled iteration. It demonstrates K-means and Iteration and prints a deterministic result that can be checked against the sample output.
EFFICIENCY
Time and space complexity
O(n · k · d)
O(n + k)
DEBUGGING CHECKLIST
Common mistakes
For run one k-means iteration, keep the data shape and value types consistent with K-means.
Keep every dependent statement inside the correct indented Python block.
Verify the final K-means and Iteration result against the sample before trying new data.