transformative-harmonization 0.2.2

A Python library for Transformative Harmonization, a novel similarity metric

Transformative Harmonization A Python library for Transformative Harmonization, a novel mathematical operation designed by Zayn. This operation combines numbers, vectors, or matrices in a context-aware, harmonious manner, offering a new similarity metric for data science and machine learning applications. Features

Harmonization of numbers, vectors, and matrices with customizable context parameters. Optimized for machine learning tasks (clustering, classification, recommendation systems). Comprehensive mathematical properties (commutativity, continuity) and unit tests. Extensive documentation with practical examples. MIT licensed for open-source use.

Installation Install via pip: pip install transformative-harmonization

Published on PyPI: https://pypi.org/project/transformative-harmonization/

Quick Start Basic Harmonization (Numbers) from transformative_harmonization import TransformativeHarmonization

th = TransformativeHarmonization(context={'weight': 1.0, 'scale': 1.0}) result = th.harmonize(3, 4) print(result) # Output: ~3.50

Vector Harmonization (Machine Learning) import numpy as np from transformative_harmonization import VectorHarmonization

vh = VectorHarmonization(context={'weight': 1.0, 'scale': 1.0}) v1 = np.array([1, 0]) v2 = np.array([0, 1]) result = vh.harmonize(v1, v2) print(result) # Output: Harmonized vector

Custom Harmonization from transformative_harmonization import CustomHarmonization

def custom_harmony(a, b, context): return context['scale'] * (a + b) / 2 ch = CustomHarmonization(custom_harmony, context={'scale': 2.0}) result = ch.harmonize(3, 5) print(result) # Output: 8.0

Running Tests To run the unit tests: pip install numpy scikit-learn python -m unittest discover tests

Documentation Full documentation is available at Read the Docs. Mathematical Properties

Commutative: Yes (H(a, b) = H(b, a)). Associative: No (H(H(a, b), c) ≠ H(a, H(b, c))). Continuous: Yes for all inputs except a + b = 0. Contextual: Adjustable via weight and scale parameters.

Contributing Contributions are welcome! Please:

Fork the repository. Create

a feature branch.3. Submit a pull request with clear documentation and tests. See CONTRIBUTING.md for details. License This project is licensed under the MIT License. See the LICENSE file for details. Contact For questions or feedback, please open an issue on GitHub or contact Zayn.