TinyMatrix

TinyMatrix is a minimal, dependency-free Python library that provides a small Matrix type with basic linear-algebra-like operations: construction, element-wise arithmetic, matrix multiplication, transpose, indexing/slicing, and a few static constructors (identity, zeroes, ones).

Designed for learning and small scripts where a tiny and readable matrix helper is preferred over a large dependency.

Install

Install editable from the project root for development:

python -m pip install -e .

Or install normally (if packaged):

python -m pip install .

Quick usage

from tinymatrix import Matrix

# Create a matrix
A = Matrix(matrix=[[1, 2], [3, 4]])

# Arithmetic
B = Matrix(matrix=[[5, 6], [7, 8]])
C = A + B  # element-wise addition

# Scalar multiply
D = A * 2

# Matrix multiplication
E = A @ Matrix(matrix=[[1], [2]])

# Transpose
T = A.T

# Indexing / slicing
val = A[0, 1]
sub = A[0:1, :]

# Static constructors
I = Matrix.identity(3)
Z = Matrix.zeroes(2, 3)
O = Matrix.ones(2, 2)

Refer to tests/test_matrix.py for additional examples and expected behavior.

Running tests

From the project root run:

pytest -q

License

Licensed under the MIT License. See the LICENSE file for full text.

🗺️ Roadmap / TODO

Phase 1: Foundation & Quality

• Add full type hints for all public APIs
• Add optional dtype support (float/int/Decimal/complex)
• Add stricter input and type validation
• Improve slice/index edge-case tests
• Add GitHub Actions for lint, test, and build
• Add code coverage tooling + badge

Phase 2: Core Matrix Operations

• Add .row(i), .col(j), .flatten() helpers
• Add .apply(func) for element-wise operations
• Add broadcasting support (1×N and M×1 vectors)
• Add random matrix constructors (uniform, normal)
• Improve printing for large matrices (ellipsis, alignment)
• Add .reshape(), .squeeze(), .expand_dims()
• Add element-wise functions (abs, sqrt, exp, log, sin, cos, etc.)
• Add reduction operations (sum, mean, std, min, max) with axis support
• Add .diagonal(), .trace() operations

Phase 3: Linear Algebra Core

• Implement determinant calculation
• Implement matrix inverse
• Implement matrix rank
• Implement matrix norm (Frobenius, spectral, etc.)
• Add matrix power (A ** n)
• Add Kronecker product
• Add Hadamard (element-wise) product operator

Phase 4: Decompositions & Solvers

• Add LU decomposition
• Add QR decomposition
• Add Cholesky decomposition
• Add SVD (Singular Value Decomposition)
• Add eigenvalue/eigenvector computation
• Add linear system solver Ax = b
• Add least-squares solver

Phase 5: Performance & Optimization

• Optimize matrix multiplication (Strassen or blocked algorithms)
• Add optional NumPy fallback mode for performance comparisons
• Add lazy evaluation for chained operations (optional)
• Profile and optimize hotspots
• Add benchmarking utilities and performance docs

Phase 6: Advanced Features

• Add sparse matrix support (CSR/CSC formats)
• Add immutability mode (frozen=True)
• Add matrix views (no-copy slicing where possible)
• Add .copy() and copy-on-write semantics
• Add stacking operations (vstack, hstack, block)
• Add advanced indexing (fancy indexing, boolean masks)
• Add matrix condition number calculation
• Add pseudo-inverse (Moore-Penrose)

Phase 7: Documentation & Polish

• Expand README with comprehensive examples
• Add Sphinx docs site
• Add example notebooks (Jupyter)
• Add comparison guide: TinyMatrix vs NumPy
• Add migration guide for NumPy users
• Complete PyPI metadata
• Add mathematical notation in docstrings
• Create logo and branding

Stretch Goals

• Add matrix calculus operations (gradient, Jacobian, Hessian)
• Add graph/network matrices (adjacency, Laplacian)
• Add special matrix generators (Toeplitz, Hankel, Vandermonde)
• Add matrix equation solvers (Sylvester, Lyapunov)
• Add tensor product operations
• Consider Cython/C extensions for critical paths (while keeping pure-Python as default)