Hi 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.
Thank you for reading!