Hello everyone,
Over the past few months, I’ve been working on a new library and research paper that unify structure-preserving matrix transformations within a high-dimensional framework (hypersphere and hypercubes).
Today I’m excited to share: MatrixTransformer—a Python library and paper built around a 16-dimensional decision hypercube that enables smooth, interpretable transitions between matrix types like
- Symmetric
- Hermitian
- Toeplitz
- Positive Definite
- Diagonal
- Sparse
- …and many more
It is a lightweight, structure-preserving transformer designed to operate directly in 2D and nD matrix space, focusing on:
- Symbolic & geometric planning
- Matrix-space transitions (like high-dimensional grid reasoning)
- Reversible transformation logic
- Compatible with standard Python + NumPy
It simulates transformations without traditional training—more akin to procedural cognition than deep nets.
What’s Inside:
- A unified interface for transforming matrices while preserving structure
- Interpolation paths between matrix classes (balancing energy & structure)
- Benchmark scripts from the paper
- Extensible design—add your own matrix rules/types
- Use cases in ML regularization and quantum-inspired computation
Links:
Paper: MatrixTransformer
Code: GitHub - fikayoAy/MatrixTransformer
Related: [quantum_accel]—a quantum-inspired framework evolved with the MatrixTransformer framework link: fikayoAy/quantum_accel
If you’re working in machine learning, numerical methods, symbolic AI, or quantum simulation, I’d love your feedback.
Feel free to open issues, contribute, or share ideas.
Thanks for reading!