semvec/pypi-packages huggingface dataset:
boron-haren 0.1.3
HAREN++: Hybrid Associative Retrieval Engine with Nullified Worst-Case
Requires Python: >=3.6

# HAREN++: Hybrid Associative Retrieval Engine with Nullified Worst-Case

HAREN++ is a high-performance hybrid search algorithm that redefines practical search efficiency across both sorted and unsorted arrays. It combines direct memory indexing, adaptive hashing, predictive segment regression, and sub-logarithmic fallback strategies to achieve constant-time average search performance and compressed worst-case complexity.

Unlike traditional search methods such as linear search (O(N)) and binary search (O(log N)), HAREN++ delivers near-constant search performance even in datasets containing millions of elements. It is designed for real-time applications where ultra-fast retrieval is essential.

## Key Features
- **O(1) Average-Case Lookup:** Memory-accelerated direct indexing.
- **O(log log N) Worst-Case Search:** Fast fallback using anchor tree search.
- **Sorted and Unsorted Arrays:** Supports mixed datasets without performance loss.
- **Predictive Indexing:** Uses lightweight learned segment models to guide fast searches.
- **Benchmark Proven:** Up to 25,000Ã— faster than linear search and up to 20Ã— faster than binary search on million-element datasets.
- **Layered Search Strategy:** Combines direct map, adaptive hashing, predictive regression, and a fallback anchor tree.

## Installation

```bash
pip install boron-haren
harenpp 0.1.1
HAREN++: Hybrid Associative Retrieval Engine with Nullified Worst-Case
Requires Python: >=3.6

# HAREN++: Hybrid Associative Retrieval Engine with Nullified Worst-Case

HAREN++ is a high-performance hybrid search algorithm that redefines practical search efficiency across both sorted and unsorted arrays. It combines direct memory indexing, adaptive hashing, predictive segment regression, and sub-logarithmic fallback strategies to achieve constant-time average search performance and compressed worst-case complexity.

Unlike traditional search methods such as linear search (O(N)) and binary search (O(log N)), HAREN++ delivers near-constant search performance even in datasets containing millions of elements. It is designed for real-time applications where ultra-fast retrieval is essential.

## Key Features
- **O(1) Average-Case Lookup:** Memory-accelerated direct indexing.
- **O(log log N) Worst-Case Search:** Fast fallback using anchor tree search.
- **Sorted and Unsorted Arrays:** Supports mixed datasets without performance loss.
- **Predictive Indexing:** Uses lightweight learned segment models to guide fast searches.
- **Benchmark Proven:** Up to 25,000Ã— faster than linear search and up to 20Ã— faster than binary search on million-element datasets.
- **Layered Search Strategy:** Combines direct map, adaptive hashing, predictive regression, and a fallback anchor tree.

## Installation

```bash
pip install harenpp
qchem 0.54.0
A Quantum Chemistry Package for Python. Used and Created by the UW Formula Nano Team to design a Nano Car for the 2026/7 Nano Car Race

QChem is a project used for Quantum Chemistry Simulations and Data Anaylsis/Visualization, developed by the Formula Nano student design team at the University of Waterloo.

Usage:

To use the package at the moment, you need to install Poetry and then install the dependencies contained in the pushed lock file. This ensures consistency among users. Ideally work should be done in Jupyter Notebooks or Python Scripts within the notebooks folder. This will allow you to import the package, allowing access to any submodules (e.g., "from qchem import utils").

Development:

Work on seperate branches for changes and submit PR's to main; squash commits when appropriate to do so. When rebasing, squashing, or changing history in any way use --force-with-lease instead of --force as an extra caution and a stale checker.

## How to use poetry:
To start, you will need to have poetry installed. DO NOT do this through pip, it needs to be installed into it's own virtual environment. 
On Windows (Powershell), the recommended command is `(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | py -`. On Linux, macOS and Windows (WSL), the recommended command is `curl -sSL https://install.python-poetry.org | python3 -`. See more at https://python-poetry.org/docs/#installing-with-the-official-installer. If you already have it installed, you can uninstall/reinstall it to be safe, or just try your luck that it was installed correctly originally. 

When entering the project, run `poetry shell` to enter a virtual environment, then run `poetry install`. This environment is built with dependencies from the lock file, which ensures consistency across users.