LinAlgKit

LinAlgKit Documentation

Welcome to the LinAlgKit docs. This project provides a comprehensive, Python-first linear algebra and deep learning math library built on NumPy.

Version: 0.2.1

GitHub

PyPI

Features

• 🔢 Matrix Operations - Full matrix algebra with decompositions
• 🧠 Deep Learning Functions - Activations, losses, normalization
• ⚡ High Performance - Numba JIT acceleration up to 13x faster
• 🐍 Pythonic API - Clean, intuitive interface

Quick Install

pip install LinAlgKit

For high-performance functions:

pip install LinAlgKit numba

Quick Start

import LinAlgKit as lk
import numpy as np

# Create matrices
A = lk.Matrix.from_numpy(np.array([[1.0, 2.0], [3.0, 4.0]]))

# Matrix operations
print(A.determinant())  # -2.0
print(A.T.to_numpy())   # Transpose

# Decompositions
L, U, P = A.lu()
Q, R = A.qr()

# Deep learning functions
x = np.random.randn(100, 10)
output = lk.relu(x)
probs = lk.softmax(x)
loss = lk.cross_entropy_loss(probs, targets)

Documentation

Section	Description
Getting Started	Installation, basic usage, examples
API Reference	Complete API documentation
Deep Learning	Activations, losses, normalization
Performance	Benchmarks and optimization
Release Notes	Version history and changelog

What’s New in v0.2.1

High-Performance fast Module

from LinAlgKit import fast

# Up to 13x faster with Numba JIT
loss = fast.fast_mse_loss(pred, target)
output = fast.fast_relu(x)

Performance Improvements

Function	Speedup
mae_loss	13.1x
mse_loss	12.0x
leaky_relu	4.4x
gelu	2.6x

In-Place Operations

A.add_(B)      # No memory allocation
A.mul_(2.0)    # Faster than A = A * 2

License

MIT License - see LICENSE

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