algebrax 0.5.0

AlgebraX - Algebraic Primitives for Sparse Data Structures in Python

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AlgebraX treats Python's native dict as a first-class sparse algebraic object, unifying linear algebra, graph algorithms, formal language theory, signal transforms, and information metrics under a single polymorphic framework.

Key Features

• ⚡ Zero Heavy Dependencies: Pure Python core requiring no C++ build steps. Includes native bidirectional converters between sparse dict mappings and dense multidimensional arrays.
• 🔄 Polymorphic Semiring Computing: By swapping the algebraic semiring $(\oplus, \otimes)$, the exact same matrix algorithms compute standard linear algebra, tropical shortest path latencies, or symbolic rule provenance.
• 🌌 Sparse Multidimensional Tensors: Arbitrary nested mappings behave as infinite-dimensional sparse tensors, tries, and lattices (AlgebraicTrie) with custom key operators.
• 🔬 Interactive Desktop GUI: Repo includes DearPyGui with 12 interactive modules, dynamic texture previews, force-directed graph canvases, and signal transforms.

Installation

# Using uv (recommended)
uv add algebrax

# Using pip
pip install algebrax

5-Minute Quickstart

By changing the semiring parameter in matrix.dot, you can transform standard linear matrix multiplication into shortest-path solvers or symbolic rule derivation tracking:

from algebrax.matrix import dot
from algebrax.semiring import ProvenanceSemiring, StandardSemiring, TropicalSemiring

# Define a Sparse Graph Adjacency / Distance Matrix
graph = {
    0: {1: 2.0, 2: 10.0},
    1: {2: 3.0},
}

# 1. Standard Linear Matrix Multiplication (+, *)
linear_mult = dot(graph, graph, semiring=StandardSemiring())
print('Linear Combination (0->2):', linear_mult[0][2])
# Output: 30.0

# 2. Tropical Shortest Path (min, +)
shortest_path = dot(graph, graph, semiring=TropicalSemiring())
print('Shortest Path Cost (0->1->2):', shortest_path[0][2])
# Output: 5.0

# 3. Symbolic Provenance Rule Tracking
provenance_graph = {
    0: {1: {('rule_A',): 1}, 2: {('rule_C',): 1}},
    1: {2: {('rule_B',): 1}},
}
provenance_mult = dot(provenance_graph, provenance_graph, semiring=ProvenanceSemiring())
print('Symbolic Derivation Polynomial:', provenance_mult[0][2])
# Output: {('rule_A', 'rule_B'): 1}

Use Case Recipes & Jupyter Notebooks

The recipes/ directory contains standalone CLI scripts and matching interactive .ipynb notebooks for 19 real-world scenarios:

Run any recipe using uv:

uv run recipes/image_processing.py

Graphical Desktop Laboratory

Launch the interactive DearPyGui laboratory application featuring 12 interactive modules, live image convolution texture previews, force-directed graph canvases, signal transforms, and information theory calculators:

uv run recipes/lab.py

Documentation

Comprehensive documentation is hosted online and structured into 3 Diátaxis pillars:

• 🚀 Start: Installation, quickstart, and core philosophy.
• 📖 Tutorials: In-depth guides for Semirings, Tries, Transforms, Graphs, and Benchmarks.
• 🍳 Recipes & GUI Lab: Real-world use cases and laboratory documentation.

License

Distributed under the MIT License. See LICENSE for more information.