1 unstable release
0.1.0 | Dec 30, 2022 |
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#518 in Math
82KB
1.5K
SLoC
stack-algebra
A stack-allocated lightweight algebra library for bare-metal applications.
Overview
This crate provides a stack-allocated matrix type with constant size determined at compile time. The primary goal for this library is to be useful in building robotics applications in rust. This means several things:
- Target platform is often bare-metal
- Size of the matrices can usually be defined at compile-time
- Problem solving does not require large matrices or heavy optimization
- Users are not experts in rust but often familiar with scientific tools (e.g. python or matlab)
Implementing numerical algorithms in rust can be made much more productive and ergonomic
if simple abstractions and necessary algebra routines are available. This library is
a growing collection of addressing those needs. It is heavily based on
vectrix
for core implementations.
Install
Use cargo to add to your project (or add manually to your Cargo.toml
)
cargo add stack-algebra
Then import to your module by using
use stack_algebra::*; // or import just the items you need
Usage
-
matrix!
macro can be used to create a new matrix// 2-by-3 matrix let m = matrix![ 1.0, 2.0, 3.0; 4.0, 5.0, 6.0; // Semicolon here is optional ];
-
vector!
macro can be used to create a row/column vector// 1-by-3 row vector let r = vector![1.0, 2.0, 3.0]; // 3-by-1 column vector let c = vector![1.0; 2.0; 3.0]; // Vector to tuple conversion (for 3 or 4 element vectors) let (x, y, z) = r.into();
-
eye!
for creating square identity matrixlet m = eye!(2); let exp = matrix![ 1.0, 0.0; 0.0, 1.0 ]; assert_eq!(m, exp);
-
zeros!
for creating zero-valued matrixlet m = zeros!(2); // Square 2-by-2 matrix let exp = matrix![ 0.0, 0.0; 0.0, 0.0 ]; assert_eq!(m, exp); let m = zeros!(2,3); // 2-by-3 matrix let exp = matrix![ 0.0, 0.0, 0.0; 0.0, 0.0, 0.0 ]; assert_eq!(m, exp);
-
ones!
for creating matrix with 1.0s (same aszeros!
for usage) -
diag!
for creating a diagonal matrix with given entries (up to 6-by-6 size)let m = diag!(1.0, 2.0, 3.0); let exp = matrix![ 1.0, 0.0, 0.0; 0.0, 2.0, 0.0; 0.0, 0.0, 3.0 ]; assert_eq!(m, exp);
-
[i]
or[(r,c)]
to access individual elementslet m = matrix![ 1.0, 2.0, 3.0; 4.0, 5.0, 6.0 ]; assert_eq!(m[1], 4.0); // Using a single index assumes column-major order assert_eq!(m[(1,2)], 6.0);
-
*
,/
,+
,-
for matrix arithmaticslet m = matrix![ 1.0, 2.0; 3.0, 4.0 ]; let exp = matrix![ 2.0, 4.0; 6.0, 8.0 ]; assert_eq!(m + m, exp); // Add matrices let exp = matrix![ 2.0, 3.0; 4.0, 5.0 ]; assert_eq!(m + 1.0, exp); // Add scalar to matrix (note scalar has to be behind the operator)
-
.T()
for matrix transposelet m = matrix![ 1.0, 2.0; 3.0, 4.0 ]; let exp = matrix![ 1.0, 3.0; 2.0, 4.0 ]; assert_eq!(m.T(), exp);
-
.norm()
for computing theFrobenius norm
let m = matrix![ 1.0,-2.0; -3.0, 6.0; ]; assert_relative_eq!(m.norm(), 7.0710678, max_relative = 1e-6);
-
.trace()
for sum of diagonal elements of a sqaure matrixlet m = matrix![ 9.0, 8.0, 7.0; 6.0, 5.0, 4.0; 3.0, 2.0, 1.0; ]; assert_eq!(m.trace(), 15.0);
-
.det()
for determinant (only available for square matrix)let m = matrix![ 3.0, 7.0; 1.0, -4.0; ]; assert_eq!(m.det(), -19.0);
-
.inv()
for inverse of a matrix (for square invertible matrix)let m = matrix![ 6.0, 2.0, 3.0; 1.0, 1.0, 1.0; 0.0, 4.0, 9.0; ]; let exp = matrix![ 0.20833333, -0.25, -0.04166667; -0.375, 2.25, -0.125; 0.16666667, -1.0, 0.16666667; ]; assert_relative_eq!(m.inv().unwrap(), exp, max_relative = 1e-6);
License
This project is distributed under the terms of both the MIT license and the Apache License (Version 2.0).
See LICENSE-APACHE and LICENSE-MIT for details.
Dependencies
~2MB
~48K SLoC