Vecta
A computational maths library for ML, built from scratch in C++ with Python bindings. The long-term goal is to work chapter-by-chapter through Mathematics for Machine Learning (Deisenroth, Faisal, Ong), implementing every concept myself instead of reaching for a library.
Current state
The linear-algebra core is up and running as a header-only C++ template library, exposed to Python through a single pybind11 module:
Matrix— row-major dense matrix with numpy-style construction and indexingmatmul,add,scalar_mul— arithmetic (with dimension checking)transpose,identity/eye,is_symmetric
C++ test suites are planned but not written yet.
Stack
- C++17 header-only core (no external maths dependencies — everything implemented from scratch)
- pybind11 for Python bindings (fetched via CMake
FetchContent) - scikit-build-core + CMake for packaging
- uv for the Python venv and all package installs (no pip commands)
Layout
include/vecta/linalg/ C++ headers (header-only core)
matrix.hpp Matrix<T> class
core.hpp matmul, add, scalar_mul, transpose, identity, is_symmetric
src/bindings.cpp pybind11 binding code (module `_vecta`)
python/vecta/ Python package wrapper (imports `_vecta`)
examples/ scratch space for worked examples from the book
tests/cpp/ Catch2 tests (planned)
tests/python/ pytest tests (planned)
Dev workflow (uv-based, no pip)
Create the venv and install the package in editable mode:
uv venv
source .venv/bin/activate
uv pip install -e . --no-build-isolation
Rebuild + reinstall the Python module after changing C++ code:
uv pip install -e . --no-build-isolation
Install
pip install vecta-math
Usage
import vecta as vt
A = vt.array([[1, 2], [3, 4]]) # numpy-style construction
B = vt.zeros((2, 2)) # vt.ones((r, c)), vt.eye(n) too
A.shape # (2, 2)
A[0, 1] # read element; A[0, 1] = 9 writes (negative indices work)
A.T # transposed copy
C = vt.matmul(A, B) # dimension mismatch -> ValueError
D = vt.add(A, B)
E = vt.scalar_mul(A, 2.0)
print(vt.transpose(C))
print(vt.is_symmetric(A))
Roadmap
Follow the book chapter by chapter, folding each topic into include/vecta/<chapter>/,
src/bindings.cpp, and python/vecta/:
| Topic | Book chapter | Status |
|---|---|---|
| Linear Algebra | Ch 2 — Linear Algebra | Matrix + core ops |
| Analytic Geometry | Ch 3 — Analytic Geometry | not started |
| Matrix Decompositions | Ch 4 — Matrix Decompositions | not started |
| Vector Calculus | Ch 5 — Vector Calculus | not started |
| Probability and Distributions | Ch 6 — Probability and Distributions | not started |
| Continuous Optimization | Ch 7 — Continuous Optimization | not started |
| Linear Regression | Ch 9 — Linear Regression | not started |
| Dimensionality Reduction (PCA) | Ch 10 — Dimensionality Reduction | not started |
| Density Estimation (GMM) | Ch 11 — Density Estimation | not started |
| Classification (SVM) | Ch 12 — Classification | not started |
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- Download URL: vecta_math-0.0.3-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
- Upload date:
- Size: 109.5 kB
- Tags: CPython 3.9, manylinux: glibc 2.17+ i686
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.13.12
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