/algo-grokking_algorithm_2nd_ed

Primary LanguagePython

Algo: Grokking Algorithm 2nd Edition

This repository contains my code implementations and notes while working through the book "Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People (2nd Edition)" by Aditya Bhargava.

Features

  • Visual Complexity Analysis

    • Real-time plotting of algorithm performance
    • Comparison with theoretical complexity curves (O(log n), O(n), O(n log n), O(n²))
    • Interactive visualization using matplotlib
    • Logarithmic scaling for better pattern visibility

  • Comprehensive Testing Framework

    • Parallel test execution
    • Automatic complexity detection
    • Support for both numeric and string inputs
    • Configurable timeout settings
    • Detailed performance reporting

  • Search Algorithms

    • Binary Search (O(log n))
    • Simple Search (O(n))
    • Type-safe implementations
    • Support for both numbers and strings

Project Structure

algo_grokking_algorithm_2nd_ed/
├── ch01_introduction_to_algorithms/
│   ├── 01a_simple_search.py   # Simple linear search implementation
│   └── 01b_binary_search.py   # Binary search implementation
└── utils/
    ├── input_generator.py     # Test data generation utilities
    └── test_utils.py         # Testing and analysis framework

Setup

  1. Make sure you have Python 3.12+ installed
  2. Install Poetry (dependency management tool)
  3. Clone this repository
  4. Run poetry install to install dependencies
  5. Run poetry shell to activate the virtual environment

Running the Code

Running Search Algorithm Tests

To run a specific search algorithm with visualization:

poetry run python -m algo_grokking_algorithm_2nd_ed.ch01_introduction_to_algorithms.01b_binary_search

This will:

  1. Run the algorithm against various input sizes
  2. Display a real-time plot of performance characteristics
  3. Compare actual performance with theoretical complexity curves
  4. Show detailed test results in the console

Dependencies

  • Python 3.12+
  • Poetry for dependency management
  • Key packages:
    • matplotlib: For complexity visualization
    • numpy: For numerical computations
    • pytest: For testing (coming soon)

Features in Development

  • More search algorithm implementations
  • Additional visualization options
  • Comprehensive test suite
  • Performance benchmarking tools
  • Interactive algorithm demonstrations

Contributing

Feel free to open issues or submit pull requests if you have suggestions for improvements or find any bugs.

License

This project is licensed under the MIT License - see the LICENSE file for details.