Structs and methods for linear algebra.

This crate provides 3 main types of structs that implement basic arithmetic operations like + and -, and also pervide useful methods:

• Structs for handling vectors like Vec2 and Vec3.
• Structs for handling arrays of vectors with different types of memory orginization, like Vec3arr and Vec3box, for fixed length arrays and boxed arrays respectively.
• Structs for handling square matricies like Mat22 and Mat33.

Vectors can be easily initialized and used:

use lineq::vec3::Vec3;
let a : Vec3 = Vec3::UP;
let b : Vec3 = Vec3 { x: -1.0, y: 0.0, z: 0.0 };
assert_eq!(a.cross(b),Vec3::IN);

Arrays of vectors can be used to parallelize arithmetic:

use lineq::vec3::Vec3;
use lineq::vec3arr::Vec3arr;
//using a and b from last example:
let ab : Vec3arr<2> = Vec3arr([a,b]);
let cd : Vec3arr<2> = Vec3arr([Vec3::DOWN,Vec3::RIGHT]);
assert_eq!(ab + cd, Vec3arr([Vec3::ZERO,Vec3::ZERO]));

When adding arrays, the resulting type is deturmined by how structured the type is, so for example from most structured to least structured we have Vec3arr, then Vec3box, then Vec3win, then finally Vec3raw.

Only Vec2arr/Vec3arr and Vec2box/Vec3box have allocators, so when performing an operation that allocates (like +, -, *, /) one of the types needs to be a Vec2arr/Vec3arr or Vec2box/Vec3box.

Matricies are indexed like x1, y2, z3 ... where x, y, z are the rows and 1, 2, 3 are the columns:

use lineq::vec3::Vec3;
use lineq::mat33::Mat33;
//using a and b from first example:
let c : Vec3 = Vec3{ x: 1.0, y: -1.0, z: 2.0 };
let m1 : Mat33 = Mat33::augment(a,b,c);
let m2 : Mat33 = Mat33{
x1: 0.0, x2: -1.0, x3: 1.0,
y1: 1.0, y2: 0.0, y3: -1.0,
z1: 0.0, z2: 0.0, z3: 2.0 };
assert_eq!(m1,m2);