re-search

This repo is a small, educational playground for comparing search algorithms and talking about Big O. It includes JavaScript examples as the primary learning path, plus a readable Python version for cross-checking ideas.

What is in here

• JavaScript search experiments: search.js builds sorted arrays of even numbers, runs linear search and binary search, and records timing results so you can compare behavior at different input sizes.
• Python search experiments: search.py mirrors the JavaScript logic in Python so you can read the same ideas in a second syntax.
• Notebook walkthrough: search.ipynb breaks the code into small sections with explanations and plots so you can see time and iteration counts.
• Plot helper: plot_results.py renders the timing results as horizontal bar charts and labels each bar with the search index and iteration count.

Learning goals

• See the difference between linear search ( O(n) ) and binary search ( O(log n) ).
• Observe how input size affects runtime.
• Connect iteration counts to algorithmic complexity.
• Learn how to measure performance and visualize results.

How to run (JavaScript)

1. Open search.js.
2. Run it with Node:

node search.js

How to run (Python)

1. Open search.py or search.ipynb.
2. Run the script:

python search.py

1. Or open the notebook and run cells in order.

Notes for trainees

• The arrays contain only even numbers. When we add 1 to a target, it becomes odd and is guaranteed to be missing. This is a simple way to test "not found" cases.
• Timing very small arrays can be noisy because overhead and scheduling can dominate the real work.
• The Python code is included for readability, not as a separate learning track.