| 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: https://zenodo.org/records/15867279
Code: https://github.com/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! |