cosinefusion 0.1.0

A high-performance C++ accelerated vector score fusion engine.

Cosine Fusion

Overview

Cosine Fusion is a lightweight C++/Python cosine similarity engine using pybind11. It provides fast, vectorized similarity calculations between user and item feature matrices.

Features

• C++ backend for high performance
• Python interface via pybind11
• Easy integration into Python projects
• Simple example with user-item preference vectors

Use Cases / Applications

• Recommender systems for e-commerce or media content
• Personalization engines based on user preferences
• Fast similarity search for AI/ML feature matching
• Any project requiring high-performance cosine similarity computation

Installation

Clone the repository and install the package:

git clone https://github.com/CookieMonsteriOS/CosineFusion.git
cd CosineFusion
pip install pybind11
pip install .

Usage Example

import numpy as np
import core_init


# Example item features
items = np.array([
[0, 1, 0, 0, 1], # Tea
[0, 1, 0, 0, 1], # Coffee
[1, 0, 1, 1, 0], # Jaffa Cake
[1, 0, 1, 0, 0], # Biscuit
[1, 0, 1, 1, 1], # Chocolate Bar
[0, 1, 0, 0, 1], # Espresso
])


item_names = ["Tea", "Coffee", "Jaffa Cake", "Biscuit", "Chocolate Bar", "Espresso"]


user = np.array([[0, 0, 0, 1, 0]]) # User likes sweet + chocolate
res = core_init.cosine_similarity(user, items)
sim = res["similarity_matrix"]


top_indices = np.argsort(sim[0])[::-1]
print("Top recommendations for user:")
for i in top_indices:
print(f"{item_names[i]}: {sim[0][i]:.2f}") # Sample output showing relationships:

# Top recommendations for user:
# Chocolate Bar: 0.89
# Jaffa Cake: 0.75 <- shows some similarity to Tea in sweetness
# Biscuit: 0.65
# Tea: 0.45
# Coffee: 0.45
# Espresso: 0.43

Project Structure

CosineFusion/
 ├── src/
 │    ├── cpp/core_init.cpp
 │    └── python/core_demo.py
 ├── tests/test_bridge.py
 ├── setup.py
 ├── pyproject.toml
 ├── README.md
 ├── LICENSE
 └── requirements.txt

License

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

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Author: Sam Chaudry
GitHub: CookieMonsteriOS