matt
JavaScript DSL for Matrices
Matt
Matt is a JavaScript DSL for Matrices. Determinate, transpose, and invert immutable, ES6ready matrices with ease.
Matt is available on NPM.
npm install matt
Matt is pretty intuitive to use if already familiar with common matrix operations and transforms. It looks like this, required in Node.js:
var Matt = require'matt';var Matrix = MattMatrix;var assert = require'assert'; // liststyle (1D Array) var A = 3 3 1 2 3 4 5 6 7 8 9; // tablestyle (2D Array) var B = 1 2 3 4 5 6 7 8 9; assertAequalsB;assertAtransposetransposeequalsA;assertequalAtrace Btrace;
Also see Seth, my other mathematical DSL for Set Theory.
Features
Matt exposes one core Matrix
ES6 written and ready class with tons of matrix methods. Methods include get
, set
, getRow
, getColumn
, getDiagonal
, getRightDiagonal
, trace
, rightTrace
, add
, subtract
, multiply
, joinHorizontal
, joinVertical
, clone
, map
, fmap
, forEach
, reduce
, scale
, transpose
, identity
, submatrix
, minor
, cofactor
, cofactorMatrix
, invert
, determinant
, isSquare
, equals
, toArray
, toTable
, and toString
.
All methods are tested and throw appropriate errors when the operation is impossible. For example, determinant
will throw when called on a nonsquare matrix.
Matrices are immutable. All methods that mutate a matrix will return a new matrix. This means variables will not be overwritten with new data and operations can be chained and composed more functionally.
Documentation
The method signatures of the Matrix class are listed.
constructor(rows Number, cols Number, elements [Any]) Matrixget(row Number, col Number) Anyset(row Number, col Number, value Any) MatrixgetRow(row Number) [Any]getColumn(col Number) [Any]getDiagonal(<offset Number = 0>) [Any]getRightDiagonal(<offset Number = 0>) [Any]trace() NumberrightTrace() Numberadd(matrix Matrix, <reduce Function(elementA Any, elementB Any) Any>) Matrixsubtract(matrix Matrix, <reduce Function(elementA Any, elementB Any) Any>) Matrixmultiply(matrix Matrix, <reduce Function(elementA Any, elementB Any) Any>) MatrixjoinHorizontal(matrix Matrix) MatrixjoinVertical(matrix Matrix) Matrixclone() Matrixmap(fn Function(element Any, row Number, col Number, matrix Matrix)) Matrixfmap(fn Function(element Any, row Number, col Number, matrix Matrix)) MatrixforEach(fn Function(element Any, row Number, col Number, matrix Matrix)) voidreduce(fn Function(acc Any, value Any, row Number, col Number, matrix Matrix), <memo Any = M(0,0)>) Matrixscale(num Number) Matrixtranspose() Matrixidentity() Matrixsubmatrix(topLeftRow Number, topLeftCol Number, bottomRightRow Number, bottomRightCol Number) Matrixminor(row Number, col Number) Matrixcofactor(row Number, col Number) NumbercofactorMatrix() Matrixinvert() Matrixdeterminant() NumberisSquare() booleanequals(matrix Matrix) booleantoArray() [Any]toTable() [[Any]]toString() String
For complete documentation, please refer to the tests as they double as documentation quite well. In the matt
directory run:
npm install && npm run build && npm test
Performance
Every effort has been and will be made to keep Matt performant. As JavaScript is a higherlevel language, Matt's performance will not rival the C family's any time soon. However with V8 optimizations, new primitive data types, and projects such as asm.js, performance will get better over time.
Matt was designed to be ready to embrace these changes. Unlike most matrix implementations, Matt deals with onedimensional instead of twodimensional arrays. This decision was made for multiple reasons.

JavaScript arrays are not really arrays, just objects with some arraylike methods. Thus, there is really not much reason to deal with arrays in arrays for the sake of performance.

Since V8 and others optimize for array methods, Matt uses builtin functions, initializes arrays where possible, and runs for loops wherever possible to gain those speed boosts. Arrays also enjoy faster lookup than tables (2D arrays) while being less messy.

Typing. It is faster and easier to write
2, 2, [1, 2, 3, 4]
than[[1,2],[3,4]]
; more with larger and nested matrices. It is also better for the new ES6 destructuring syntax we all will be using soon enough.
These decisions and details are really only important to contributors. The bottomline is: expect Matt to be just as fast as any other JavaScript matrix library (I know, not setting the bar very high) and to continue seeing more improvements as they become possible in the language.
The places where performance is worst is the same as all other implementations: determinants and inversions when N
(i.e. N * N
matrices) is large and anything when N
is very, very, very large.
Contributing
I have never taken a class on advanced linear algebra or matrices (heck I'm still in high school). I did read the Wikipedia page a few dozen times. This is just an interest of mine that I saw was lacking in implementation in the opensource JavaScript community at large, so I wanted to attempt to fill the gap.
If you are a professional linear algebraist or even an amateur like me, if there is a bug please open an issue. If there is a feature this DSL should have, more common operations or methods, please point me to them or be hardcore and send me the pull request.
Contributions are incredibly welcome as long as they are standardly applicable and pass the tests (or break bad ones). Tests are written in Mocha and assertions are done with the Node.js core assert
module.
# generating sourcenpm run build# running testsnpm test
Follow me on Twitter for updates or just for the lolz and please check out my other repositories if I have earned it. I thank you for reading.