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    Matt is a JavaScript DSL for Matrices. Determinate, transpose, and invert immutable, ES6-ready 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 = Matt.Matrix;
    var assert = require('assert');
    // list-style (1D Array) 
    var A = new Matrix(3, 3, [
        1, 2, 3,
        4, 5, 6,
        7, 8, 9
    // table-style (2D Array) 
    var B = new Matrix([
        [1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]
    assert.equal(A.trace(), B.trace());

    Also see Seth, my other mathematical DSL for Set Theory.


    Matt exposes one core Matrix ES6 written and ready class with tons of matrix methods. Methods include get, set, getRow, setRow, getColumn, setColumn, 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 non-square 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.


    The method signatures of the Matrix class are listed.

    constructor(rows Number, cols Number, elements [Any]) Matrix
    get(row Number, col Number) Any
    set(row Number, col Number, value Any) Matrix
    getRow(row Number) [Any]
    setRow(row Number, elements [Any]) Matrix
    getColumn(col Number) [Any]
    setColumn(col Number, elements [Any]) Matrix
    getDiagonal(<offset Number = 0>) [Any]
    getRightDiagonal(<offset Number = 0>) [Any]
    trace() Number
    rightTrace() Number
    add(matrix Matrix, <reduce Function(elementA Any, elementB Any) Any>) Matrix
    subtract(matrix Matrix, <reduce Function(elementA Any, elementB Any) Any>) Matrix
    multiply(matrix Matrix, <reduce Function(elementA Any, elementB Any) Any>) Matrix
    joinHorizontal(matrix Matrix) Matrix
    joinVertical(matrix Matrix) Matrix
    clone() Matrix
    map(fn Function(element Any, row Number, col Number, matrix Matrix)) Matrix
    fmap(fn Function(element Any, row Number, col Number, matrix Matrix)) Matrix
    forEach(fn Function(element Any, row Number, col Number, matrix Matrix)) void
    reduce(fn Function(acc Any, value Any, row Number, col Number, matrix Matrix), <memo Any = M(0,0)>) Matrix
    scale(num Number) Matrix
    transpose() Matrix
    identity() Matrix
    submatrix(topLeftRow Number, topLeftCol Number, bottomRightRow Number, bottomRightCol Number) Matrix
    minor(row Number, col Number) Matrix
    cofactor(row Number, col Number) Number
    cofactorMatrix() Matrix
    invert() Matrix
    determinant() Number
    isSquare() boolean
    equals(matrix Matrix) boolean
    toArray() [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


    Every effort has been and will be made to keep Matt performant. As JavaScript is a higher-level 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 one-dimensional instead of two-dimensional arrays. This decision was made for multiple reasons.

    • JavaScript arrays are not really arrays, just objects with some array-like 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 built-in 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 bottom-line 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.


    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 open-source 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 source
    npm run build
    # running tests
    npm 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.


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