Crate vectora[−][src]

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A vector computation library

Contents

About

Vectora is a library for n-dimensional vector computation with real and complex scalar types. The main library entry point is the Vector struct. Please see the Gettting Started guide for a detailed library overview with examples.

Safety Guarantee

The current default distribution does not contain unsafe code blocks.

Versioning

This project uses semantic versioning and is currently in a pre-v1.0 stage of development. The public API should not be considered stable across release versions at this time.

Minimum Rust Version Compatibility Policy

This project parameterizes generics by constants and relies on the constant generics feature support that was stabilized in Rust v1.51. The minimum supported rustc version is believed to be v1.51.0.

Source

The source files are available at https://github.com/chrissimpkins/vectora.

Changes

Please see the CHANGELOG.md document in the source repository.

Issues

The issue tracker is available on the GitHub repository. Don’t be shy. Please report any issues that you identify so that we can address them.

Contributing

Contributions are welcomed. Developer documentation is available in the source repository README.

Submit your source or documentation changes as a GitHub pull request on the source repository.

License

Vectora is released under the Apache License v2.0. Please review the full text of the license for details.

Getting Started

See the Vector page for detailed API documentation of the main library entry point.

The following section provides an overview of common tasks and will get you up and running with the library quickly.

Add Vectora to Your Project

Import the vectora library in the [dependencies] section of your Cargo.toml file:

[dependencies]
vectora = "0.3.0"

The examples below assume the following Vector struct import in your Rust source files:

use vectora::Vector;

Numeric Type Support

This library supports computation with real and complex scalar number types.

Integers

Support is available for the following primitive integer data types:

Note: overflowing integer arithmetic uses the default Rust standard library approach of panics in debug builds and twos complement wrapping in release builds. You will not encounter undefined behavior with either build type, but this approach may not be what you want. Avoid these operator overloads if your use case requires support for integer overflows/underflows, and you prefer to handle it differently.

Floating Point Numbers

Support is available for the following primitive IEEE 754-2008 floating point types:

Complex Numbers

A 2-Vector can be used as a complex number with real and imaginary integer or floating point parts at indices 0 and 1.

The Vector type also supports collections of complex numbers as represented by the num::Complex type.

Note: This guide will not provide detailed examples with num::Complex data in order to remain as concise as possible. With the notable exception of some floating point only Vector methods, most of the public API supports the num::Complex type. These areas should be evident in the Vector API descriptions and source trait bounds. num::Complex support should be available when a general num::Num trait bound is used in implementations. In these cases, you can replace the integer or floating point numbers in the following examples with num::Complex types.

Initialization

A Vector can have mutable values, but it cannot grow in length. The dimension length is fixed at instantiation, and all fields are initialized at instantiation.

Zero Vector

Use the Vector::zero method to initialize a Vector with zero values of the respective numeric type:

let v_zero_int: Vector<i32, 3> = Vector::zero();
let v_zero_float: Vector<f64, 2> = Vector::zero();

// Note: the following complex number example requires an import of the `num::Complex` type!
use num::Complex;

let v_zero_complex: Vector<Complex<f64>, 2> = Vector::zero();

With Predefined Data in Other Types

The recommended approach is to use Vector::from with an array of ordered data when possible:

// example three dimensional f64 Vector
let v: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);

// example two dimensional i32 Vector
let v: Vector<i32, 2> = Vector::from([4, -5]);

// with num crate Complex numbers
// Note: the following complex number example requires an import of the `num::Complex` type!
use num::Complex;

let v: Vector<Complex<f64>, 2> = Vector::from([Complex::new(1.0, 2.0), Complex::new(3.0, 4.0)]);

// with a library type alias
use vectora::types::vector::Vector3dF64;

let v: Vector3dF64 = Vector::from([1.0, 2.0, 3.0]);

or use one of the alternate initialization approaches with data in iterator, array, slice, or Vec types:

// from an iterator over an array or Vec with collect
let v: Vector<i32, 3> = [1, 2, 3].into_iter().collect();
let v: Vector<f64, 2> = vec![1.0, 2.0].into_iter().collect();

// from a slice with try_from
let arr = [1, 2, 3];
let vec = vec![1.0, 2.0, 3.0];
let v: Vector<i32, 3> = Vector::try_from(&arr[..]).unwrap();
let v: Vector<f64, 3> = Vector::try_from(&vec[..]).unwrap();

// from a Vec with try_from
let vec = vec![1, 2, 3];
let v: Vector<i32, 3> = Vector::try_from(&vec).unwrap();

Please see the API docs for the approach to overflows and underflows with the FromIterator trait implementation that supports the collect approach.

Access and Assignment with Indexing

Use zero-based indices for access and assignment:

Access

let v1: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);

let x = v1[0];
let y = v1[1];
let z = v1[2];

Attempts to access items beyond the length of the Vector panic:

ⓘ
// panics!
let _ = v1[10];

Assignment

let mut v1_m: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);

v1_m[0] = 10.0;
v1_m[1] = 20.0;
v1_m[2] = 30.0;

Attempts to assign to items beyond the length of the Vector panic:

ⓘ
// panics!
v1_m[10] = 100.0;

See the Vector::get and Vector::get_mut method documentation for getters that perform bounds checks and do not panic.

Slicing

Coerce to a read-only slice of the Vector:

let v = Vector::<i32, 3>::from([1, 2, 3]);
let v_slice = &v[0..2];

assert_eq!(v_slice, [1, 2]);

Partial Equivalence Testing

Partial equivalence relation support is available with the == operator for integer, floating point, and num::Complex numeric types.

Integer types

Vector of real integer values:

let v1: Vector<i32, 3> = Vector::from([10, 50, 100]);
let v2: Vector<i32, 3> = Vector::from([5*2, 25+25, 10_i32.pow(2)]);

assert!(v1 == v2);

Vector of num::Complex numbers with integer real and imaginary parts:

use num::Complex;

let v1: Vector<Complex<i32>, 2> = Vector::from([Complex::new(1, 2), Complex::new(3, 4)]);
let v2: Vector<Complex<i32>, 2> = Vector::from([Complex::new(1, 2), Complex::new(3, 4)]);

assert!(v1 == v2);

Float types

We compare floating point types with the approx crate relative epsilon equivalence relation implementation by default. This includes fixed definitions of the epsilon and max relative difference values. See the section below for customization options with methods.

Why a different strategy for floats?

Some floating point numbers are not considered equivalent due to floating point precision:

ⓘ
// panics!
assert!(0.15_f64 + 0.15_f64 == 0.1_f64 + 0.2_f64);

You likely want these floating point sums to compare as approximately equivalent.

With the Vector type, they do.

Vector of floating point values:

let v1: Vector<f64, 1> = Vector::from([0.15 + 0.15]);
let v2: Vector<f64, 1> = Vector::from([0.1 + 0.2]);

assert!(v1 == v2);

Vector of num::Complex numbers with floating point real and imaginary parts:

use num::Complex;

let v1: Vector<Complex<f64>, 2> = Vector::from([Complex::new(0.15 + 0.15, 2.0), Complex::new(3.0, 4.0)]);
let v2: Vector<Complex<f64>, 2> = Vector::from([Complex::new(0.1 + 0.2, 2.0), Complex::new(3.0, 4.0)]);

assert!(v1 == v2);

assert_eq! and assert_ne! macro assertions use the same partial equivalence approach, as you’ll note throughout these docs.

You can implement the same equivalence relation approach for float types that are not contained in a Vector with the approx crate relative_eq!, relative_ne!, assert_relative_eq!, and assert_relative_ne! macros.

Custom equivalence relations for floating point types

The library also provides method support for absolute, relative, and units in last place (ULPs) approximate floating point equivalence relations. These methods allow custom epsilon, max relative, and max ULPs difference tolerances to define relations when float data are near and far apart. You must call the method to use them. It is not possible to modify the default approach used in the == operator overload.

See the API documentation for Vector implementations of the approx crate AbsDiffEq, RelativeEq, UlpsEq traits in the links below:

Absolute difference equivalence relation

The absolute difference equivalence relation approach supports custom epsilon tolerance definitions.

Relative difference equivalence relation

The relative difference equivalence relation approach supports custom epsilon (data that are near) and max relative difference (data that are far apart) tolerance definitions.

Units in Last Place (ULPs) difference equivalence relation

The ULPs difference equivalence relation approach supports custom epsilon (data that are near) and max ULPs difference (data that are far apart) tolerance definitions.

Iteration and Loops

Over immutable scalar references

let v: Vector<i32, 3> = Vector::from([-1, 2, 3]);
let mut iter = v.iter();

assert_eq!(iter.next(), Some(&-1));
assert_eq!(iter.next(), Some(&2));
assert_eq!(iter.next(), Some(&3));
assert_eq!(iter.next(), None);

The syntax for a loop over this type:

for x in &v {
    // do things
}

Over mutable scalar references

let mut v: Vector<i32, 3> = Vector::from([-1, 2, 3]);
let mut iter = v.iter_mut();

assert_eq!(iter.next(), Some(&mut -1));
assert_eq!(iter.next(), Some(&mut 2));
assert_eq!(iter.next(), Some(&mut 3));
assert_eq!(iter.next(), None);

The syntax for a loop over this type:

for x in &mut v {
    // do things
}

Over mutable scalar values

let v: Vector<i32, 3> = Vector::from([-1, 2, 3]);
let mut iter = v.into_iter();

assert_eq!(iter.next(), Some(-1));
assert_eq!(iter.next(), Some(2));
assert_eq!(iter.next(), Some(3));
assert_eq!(iter.next(), None);

The syntax for a loop over this type:

for x in v {
    // do things
}

Vector Arithmetic

Use operator overloads for vector arithmetic:

Unary Negation

The unary negation operator yields the additive inverse Vector:

let v: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);

assert_eq!(-v, Vector::from([-1.0, -2.0, -3.0]));
assert_eq!(v + -v, Vector::<f64, 3>::zero());

Vector Addition

let v1: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);
let v2: Vector<f64, 3> = Vector::from([4.0, 5.0, 6.0]);

let v3 = v1 + v2;

assert_eq!(v3, Vector::from([5.0, 7.0, 9.0]));

Vector Subtraction

let v1: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);
let v2: Vector<f64, 3> = Vector::from([4.0, 5.0, 6.0]);

let v3 = v2 - v1;

assert_eq!(v3, Vector::from([3.0, 3.0, 3.0]));

Scalar Multiplication

let v1: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);
let v2: Vector<f64, 3> = Vector::from([4.0, 5.0, 6.0]);

let v3 = v1 * 10.0;
let v4 = v2 * -1.0;

assert_eq!(v3, Vector::from([10.0, 20.0, 30.0]));
assert_eq!(v4, Vector::from([-4.0, -5.0, -6.0]));
Scalar multiplication with num::Complex numbers

Vector of num::Complex types support multiplication with real and complex numbers.

num::Complex * real
use num::Complex;

let v: Vector<Complex<i32>, 2> = Vector::from([Complex::new(1, 2), Complex::new(3, 4)]);

assert_eq!(v * 10, Vector::<Complex<i32>, 2>::from([Complex::new(10, 20), Complex::new(30, 40)]));
num::Complex * num::Complex
use num::Complex;

let v: Vector<Complex<f64>, 2> = Vector::from([Complex::new(3.0, 2.0), Complex::new(-3.0, -2.0)]);
let c: Complex<f64> = Complex::new(1.0, 7.0);

assert_eq!(v * c, Vector::from([Complex::new(-11.0, 23.0), Complex::new(11.0, -23.0)]));

Methods for Vector Operations

Method support is available for common vector calculations. Examples of some frequently used operations are shown below:

Dot product

use approx::assert_relative_eq;

let v1: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);
let v2: Vector<f64, 3> = Vector::from([4.0, 5.0, 6.0]);

let dot_prod = v1.dot(&v2);

assert_relative_eq!(dot_prod, 32.0);

[ API docs ]

Vector Magnitude

use approx::assert_relative_eq;

let v1: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);

let m = v1.magnitude();

assert_relative_eq!(m, 3.7416573867739413);

[ API docs ]

Vector Distance

use approx::assert_relative_eq;

let v1: Vector<f64, 2> = Vector::from([2.0, 2.0]);
let v2: Vector<f64, 2> = Vector::from([4.0, 4.0]);

assert_relative_eq!(v1.distance(&v2), 8.0_f64.sqrt());
assert_relative_eq!(v1.distance(&v1), 0.0_f64);

[ API docs ]

Opposite Vector

use approx::assert_relative_eq;

let v: Vector<f64, 3> = Vector::from([2.0, 2.0, 2.0]);

assert_eq!(v.opposite(), Vector::from([-2.0, -2.0, -2.0]));
assert_relative_eq!(v.opposite().magnitude(), v.magnitude());

[ API docs ]

Normalization

use approx::assert_relative_eq;

let v1: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);

let unit_vector = v1.normalize();

assert_relative_eq!(unit_vector.magnitude(), 1.0);

[ API docs ]

Linear Interpolation

 let v1: Vector<f64, 3> = Vector::from([1.0, 2.0, 3.0]);
 let v2: Vector<f64, 3> = Vector::from([4.0, 5.0, 6.0]);

 let v3 = v1.lerp(&v2, 0.5).unwrap();

 assert_eq!(v3, Vector::from([2.5, 3.5, 4.5]));

[ API docs ]

Closure Mapping

let v1: Vector<f64, 3> = Vector::from([-1.0, 2.0, 3.0]);

let v3 = v1.map_closure(|x| x.powi(2));

assert_eq!(v3, Vector::from([1.0, 4.0, 9.0]));

[ API docs ]

Function Mapping

 let v1: Vector<f64, 3> = Vector::from([-1.0, 2.0, 3.0]);

 fn square(x: f64) -> f64 {
     x.powi(2)
 }

 let v3 = v1.map_fn(square);

 assert_eq!(v3, Vector::from([1.0, 4.0, 9.0]));

[ API docs ]

Many of these methods have mutable alternates that edit the Vector data in place instead of allocating a new Vector. The mutable methods are prefixed with mut_*.

See the Vector method implementations docs for the complete list of supported methods and additional examples.

Working with Rust Standard Library Types

Casts to commonly used Rust standard library data collection types are straightforward.
Note that some of these type casts support mutable Vector owned data references, allowing you to use standard library type operations to change the Vector data.

array Representations

Immutable:

let v: Vector<i32, 3> = Vector::from([-1, 2, 3]);

assert_eq!(v.as_array(), &[-1, 2, 3]);
assert_eq!(v.to_array(), [-1, 2, 3]);

Mutable:

let mut v: Vector<i32, 3> = Vector::from([-1, 2, 3]);

let m_arr = v.as_mut_array();

assert_eq!(m_arr, &mut [-1, 2, 3]);

m_arr[0] = -10;

assert_eq!(m_arr, &mut [-10, 2, 3]);
assert_eq!(v, Vector::from([-10, 2, 3]));

slice Representations

Immutable:

let v: Vector<i32, 3> = Vector::from([-1, 2, 3]);

assert_eq!(v.as_slice(), &[-1, 2, 3][..]);

Mutable:

let mut v: Vector<i32, 3> = Vector::from([-1, 2, 3]);

let m_sli = v.as_mut_slice();

assert_eq!(m_sli, &mut [-1, 2, 3][..]);

m_sli[0] = -10;

assert_eq!(m_sli, &mut [-10, 2, 3]);
assert_eq!(v, Vector::from([-10, 2, 3]));

Vec Representations

Casts to Vec always allocate a new Vec with copied data.

let v: Vector<i32, 3> = Vector::from([-1, 2, 3]);

assert_eq!(v.to_vec(), vec![-1, 2, 3]);

See the Initialization section for documentation of the syntax to instantiate a Vector from a standard library Vec type.

Re-exports

pub use types::vector::Vector;

Modules

errors

Error types.

types

Library data types.