mljs / matrix

Matrix manipulation and computation library

ml-matrix

Matrix manipulation and computation library.

Maintained by Zakodium

Installation

$ npm install ml-matrix

Usage

As an ES module

import { Matrix } from 'ml-matrix';

const matrix = Matrix.ones(5, 5);

As a CommonJS module

const { Matrix } = require('ml-matrix');

const matrix = Matrix.ones(5, 5);

API Documentation

Examples

Standard operations

const { Matrix } = require('ml-matrix');

var A = new Matrix([
  [1, 1],
  [2, 2],
]);

var B = new Matrix([
  [3, 3],
  [1, 1],
]);

var C = new Matrix([
  [3, 3],
  [1, 1],
]);

Operations

const addition       = Matrix.add(A, B);   // addition       = Matrix [[4, 4], [3, 3], rows: 2, columns: 2]
const subtraction    = Matrix.sub(A, B);   // subtraction    = Matrix [[-2, -2], [1, 1], rows: 2, columns: 2]
const multiplication = A.mmul(B);          // multiplication = Matrix [[4, 4], [8, 8], rows: 2, columns: 2]
const mulByNumber    = Matrix.mul(A, 10);  // mulByNumber    = Matrix [[10, 10], [20, 20], rows: 2, columns: 2]
const divByNumber    = Matrix.div(A, 10);  // divByNumber    = Matrix [[0.1, 0.1], [0.2, 0.2], rows: 2, columns: 2]
const modulo         = Matrix.mod(B, 2);   // modulo         = Matrix [[1, 1], [1, 1], rows: 2, columns: 2]
const maxMatrix      = Matrix.max(A, B);   // max            = Matrix [[3, 3], [2, 2], rows: 2, columns: 2]
const minMatrix      = Matrix.min(A, B);   // max            = Matrix [[1, 1], [1, 1], rows: 2, columns: 2]

Inplace Operations

C.add(A);   // => C = C + A
C.sub(A);   // => C = C - A
C.mul(10);  // => C = 10 * C
C.div(10);  // => C = C / 10
C.mod(2);   // => C = C % 2

Math Operations

// Standard Math operations: (abs, cos, round, etc.)
var A = new Matrix([
  [ 1,  1],
  [-1, -1],
]);

var exponential = Matrix.exp(A);  // exponential = Matrix [[Math.exp(1), Math.exp(1)], [Math.exp(-1), Math.exp(-1)], rows: 2, columns: 2].
var cosinus     = Matrix.cos(A);  // cosinus     = Matrix [[Math.cos(1), Math.cos(1)], [Math.cos(-1), Math.cos(-1)], rows: 2, columns: 2].
var absolute    = Matrix.abs(A);  // absolute    = Matrix [[1, 1], [1, 1], rows: 2, columns: 2].
// Note: you can do it inplace too as A.abs()

Available Methods:

abs, acos, acosh, asin, asinh, atan, atanh, cbrt, ceil, clz32, cos, cosh, exp, expm1, floor, fround, log, log1p, log10, log2, round, sign, sin, sinh, sqrt, tan, tanh, trunc

Manipulation of the matrix

// remember: A = Matrix [[1, 1], [-1, -1], rows: 2, columns: 2]

var numberRows     = A.rows;             // A has 2 rows
var numberCols     = A.columns;          // A has 2 columns
var firstValue     = A.get(0, 0);        // get(rows, columns)
var numberElements = A.size;             // 2 * 2 = 4 elements
var isRow          = A.isRowVector();    // false because A has more than 1 row
var isColumn       = A.isColumnVector(); // false because A has more than 1 column
var isSquare       = A.isSquare();       // true, because A is 2 * 2 matrix
var isSym          = A.isSymmetric();    // false, because A is not symmetric
A.set(1, 0, 10);                         // A = Matrix [[1, 1], [10, -1], rows: 2, columns: 2]. We have changed the second row and the first column
var diag           = A.diag();           // diag = [1, -1] (values in the diagonal)
var m              = A.mean();           // m = 2.75
var product        = A.prod();           // product = -10 (product of all values of the matrix)
var norm           = A.norm();           // norm = 10.14889156509222 (Frobenius norm of the matrix)
var transpose      = A.transpose();      // transpose = Matrix [[1, 10], [1, -1], rows: 2, columns: 2]

Instantiation of matrix

var z = Matrix.zeros(3, 2); // z = Matrix [[0, 0], [0, 0], [0, 0], rows: 3, columns: 2]
var z = Matrix.ones(2, 3);  // z = Matrix [[1, 1, 1], [1, 1, 1], rows: 2, columns: 3]
var z = Matrix.eye(3, 4);   // z = Matrix [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], rows: 3, columns: 4]. there are 1 only in the diagonal

Maths

const {
  Matrix,
  inverse,
  solve,
  linearDependencies,
  QrDecomposition,
  LuDecomposition,
  CholeskyDecomposition,
  EigenvalueDecomposition,
} = require('ml-matrix');

Inverse and Pseudo-inverse

var A = new Matrix([
  [2, 3, 5],
  [4, 1, 6],
  [1, 3, 0],
]);

var inverseA = inverse(A);
var B = A.mmul(inverseA); // B = A * inverse(A), so B ~= Identity


// if A is singular, you can use SVD :
var A = new Matrix([
  [1, 2, 3],
  [4, 5, 6],
  [7, 8, 9],
]); 
// A is singular, so the standard computation of inverse won't work (you can test if you don't trust me^^)

var inverseA = inverse(A, (useSVD = true)); // inverseA is only an approximation of the inverse, by using the Singular Values Decomposition
var B = A.mmul(inverseA); // B = A * inverse(A), but inverse(A) is only an approximation, so B doesn't really be identity.

// if you want the pseudo-inverse of a matrix :
var A = new Matrix([
  [1, 2],
  [3, 4],
  [5, 6],
]);

var pseudoInverseA = A.pseudoInverse();
var B = A.mmul(pseudoInverseA).mmul(A); // with pseudo inverse, A*pseudo-inverse(A)*A ~= A. It's the case here

Least square

Least square is the following problem: We search for x, such that A.x = B (A, x and B are matrix or vectors). Below, how to solve least square with our function

// If A is non singular :
var A = new Matrix([
  [3,    1],
  [4.25, 1],
  [5.5,  1],
  [8,    1],
]);

var B = Matrix.columnVector([4.5, 4.25, 5.5, 5.5]);
var x = solve(A, B);
var error = Matrix.sub(B, A.mmul(x)); // The error enables to evaluate the solution x found.

// If A is non singular :
var A = new Matrix([
  [1, 2, 3],
  [4, 5, 6],
  [7, 8, 9],
]);

var B = Matrix.columnVector([8, 20, 32]);
var x = solve(A, B, (useSVD = true)); // there are many solutions. x can be [1, 2, 1].transpose(), or [1.33, 1.33, 1.33].transpose(), etc.
var error = Matrix.sub(B, A.mmul(x)); // The error enables to evaluate the solution x found.

Decompositions

QR Decomposition

var A = new Matrix([
  [2, 3, 5],
  [4, 1, 6],
  [1, 3, 0],
]);

var QR = new QrDecomposition(A);
var Q = QR.orthogonalMatrix;
var R = QR.upperTriangularMatrix;
// So you have the QR decomposition. If you multiply Q by R, you'll see that A = Q.R, with Q orthogonal and R upper triangular

LU Decomposition

var A = new Matrix([
  [2, 3, 5],
  [4, 1, 6],
  [1, 3, 0],
]);

var LU = new LuDecomposition(A);
var L = LU.lowerTriangularMatrix;
var U = LU.upperTriangularMatrix;
var P = LU.pivotPermutationVector;
// So you have the LU decomposition. P includes the permutation of the matrix. Here P = [1, 2, 0], i.e the first row of LU is the second row of A, the second row of LU is the third row of A and the third row of LU is the first row of A.

Cholesky Decomposition

var A = new Matrix([
  [2, 3, 5],
  [4, 1, 6],
  [1, 3, 0],
]);

var cholesky = new CholeskyDecomposition(A);
var L = cholesky.lowerTriangularMatrix;

Eigenvalues & eigenvectors

var A = new Matrix([
  [2, 3, 5],
  [4, 1, 6],
  [1, 3, 0],
]);

var e = new EigenvalueDecomposition(A);
var real = e.realEigenvalues;
var imaginary = e.imaginaryEigenvalues;
var vectors = e.eigenvectorMatrix;

Linear dependencies

var A = new Matrix([
  [2, 0, 0, 1],
  [0, 1, 6, 0],
  [0, 3, 0, 1],
  [0, 0, 1, 0],
  [0, 1, 2, 0],
]);

var dependencies = linearDependencies(A);
// dependencies is a matrix with the dependencies of the rows. When we look row by row, we see that the first row is [0, 0, 0, 0, 0], so it means that the first row is independent, and the second row is [ 0, 0, 0, 4, 1 ], i.e the second row = 4 times the 4th row + the 5th row.

License

MIT

matrix's Issues

add concept of matrix view

We need several changes:

• create a base class that will implement all the methods
• make Matrix extend this class
• create subclasses of this base class that allow to work on a part of a matrix
  • transpose view
  • submatrix view
  • row / column view
  • flip views
  • sorted views
  • [stride views](https://dst.lbl.gov/ACSSoftware/colt/api/cern/colt/matrix/DoubleMatrix2D.html#viewStrides%28int, int%29)

allow setRow and setColumn to work with vectors

use case: mat1.setColumn(mat2.getColumnView(1))

eye and diag

I think eye and diag functions should be compatible with the Matlab versions, which are not necessary for square matrices.
B = eye(3,4)
B =
1 0 0 0
0 1 0 0
0 0 1 0
diag(B)
ans =
1
1
1
C = diag(diag(B))
C =
1 0 0
0 1 0
0 0 1

Improve speed

https://github.com/mljs/matrix/blob/master/src/matrix.js#L186-L190

Adding an intermediate variable:
row=result[i]
Should improve a lot the speed.

QR Decomposition with column pivoting

My goal is to implement an equivalent to the LINEST function in excel. The goal is to calculate a mulitple linear regression for matrices that are potentially not full rank. My (limited) understanding is that this requires doing "column pivoting" in the implementation of QR decomposition. Does ml-matrix support this algorithm? Currently it seems that QrDecomposition.solve fails when the matrix is not full rank.

Add methods isEchelonForm and isReducedEchelonForm

Implement a fast algorithm that checks if the matrix is echelonForm or reducedEchelonForm

https://en.wikipedia.org/wiki/Row_echelon_form

Add methods:

• isEchelonForm( options = {kind='row', threshold=Number.EPSILON}) (kind is row or column, threshold is the Math.abs under (or equal) which the value is considered as 0), return true / false
• isReducedEchelonForm( options = {kind='row'', threshold=Number.EPSILON}) (kind is row or column), return true / false

Add corresponding testcases

The documentation should be updated

Concatenate horizontally 2 matrix

Is there a method to concatenate horizontally or vertically 2 matrix ?
I don't find anything, so I have to use that :

function concatMatrix(A, B, direction='H'){
  var result = A;
  if(direction == 'H'){
    for(var i = 0; i < B.columns; i++){
      result = result.addColumnVector(B.getColumn(i));
    }
  }
  else{
    for(var i = 0; i < B.rows; i++){
      result = result.addRowVector(B.getRow(i));
    }
  }
  return result;
}

Large dataset caught in an infinite loop

I'm using these two data sets in MLR, I noticed it causes the while loop on line 48 to never resolve (p never equals anything lower than 93).

Apologies in advance I'm not as familiar with the calculations in the module but I thought the data set being used would be helpful in debugging

import { default as MLR, } from 'ml-regression-multivariate-linear';
const train_y_matrix = [
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 129.01 ],
  [ 424.1 ],
  [ 699.64 ],
  [ 895.08 ],
  [ 377.87999999999994 ],
  [ 413.43 ],
  [ 222.88 ],
  [ 397.96000000000004 ],
  [ 326.04 ],
  [ 360.24 ],
  [ 385.87 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 264.9 ],
  [ 288.08 ],
  [ 478.11999999999995 ],
  [ 400.59000000000003 ],
  [ 415.93 ],
  [ 277.91999999999996 ],
  [ 313.98 ],
  [ 330.68 ],
  [ 654.43 ],
  [ 525.38 ],
  [ 151.26999999999998 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 268.09999999999997 ],
  [ 545.1700000000001 ],
  [ 489.99000000000007 ],
  [ 632.1700000000001 ],
  [ 359.96999999999997 ],
  [ 482.75 ],
  [ 480.03 ],
  [ 646.92 ],
  [ 1165.3699999999997 ],
  [ 1299.77 ],
  [ 498.5 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 12 ],
  [ 437.01 ],
  [ 906.0299999999999 ],
  [ 742.33 ],
  [ 558.04 ],
  [ 567.25 ],
  [ 483.09 ],
  [ 565.5 ],
  [ 780.5599999999998 ],
  [ 1223.1100000000001 ],
  [ 1302.8 ],
  [ 496.96000000000004 ],
  [ 13 ],
  [ 0 ],
  [ 850.39 ],
  [ 0 ],
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  [ 0 ],
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  [ 0 ],
  [ 0 ],
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  [ 0 ],
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  [ 0 ],
  [ 234.44 ],
  [ 670.45 ],
  [ 1010.87 ],
  [ 991.52 ],
  [ 497.44 ],
  [ 470.82 ],
  [ 528.78 ],
  [ 947.8099999999997 ],
  [ 1522.76 ],
  [ 1522.88 ],
  [ 493.74 ],
  [ 13 ],
  [ 0 ],
  [ 533.48 ],
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  [ 0 ],
  [ 0 ],
  [ 323.15999999999997 ],
  [ 833.71 ],
  [ 808.5699999999999 ],
  [ 780.0099999999999 ],
  [ 676.33 ],
  [ 386.90999999999997 ],
  [ 425.16999999999996 ],
  [ 627.6300000000001 ],
  [ 1196.27 ],
  [ 1378.0799999999997 ],
  [ 588.5999999999999 ],
  [ 0 ],
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  [ 363.90000000000003 ],
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  [ 690.4999999999998 ],
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  [ 410.43 ],
  [ 568.6700000000001 ],
  [ 840.25 ],
  [ 1507.1999999999998 ],
  [ 1762.2300000000005 ],
  [ 795.6 ],
  [ 13 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ],
  [ 0 ]
];


const train_x_matrix = [
  [ 304.0233333333351, 129.01, 43.00333333333333, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 304.0233333333351, 553.11, 184.37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 304.0233333333351, 1252.75, 417.5833333333333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 304.71273809523996, 2018.8200000000002, 672.94, 244.82999999999998, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 305.24095238095424, 1972.6000000000001, 657.5333333333334, 512.8399999999999, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 306.470654761907, 1686.39, 562.13, 906.2299999999999, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 306.77696428571653, 1014.19, 338.06333333333333, 946.54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 309.7448214285739, 1034.27, 344.75666666666666, 876.4799999999999, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 308.98982142857386, 946.8799999999999, 315.6266666666666, 286.59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 309.7872023809548, 1084.24, 361.41333333333336, 356.84000000000003, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 312.0727380952404, 1072.1499999999999, 357.38333333333327, 781.93, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 314.7548214285738, 746.1099999999999, 248.7033333333333, 776.63, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
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  [ 356.79035714286135, 2570.8299999999995, 856.9433333333332, 1837.19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 355.9877380952422, 808.6, 269.53333333333336, 1627.39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 356.6092261904805, 13, 4.333333333333333, 900.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 356.53184523809955, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 372.0725000000046, 0, 0, 2610.83, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 372.0725000000046, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 372.0725000000046, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 372.0725000000046, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 372.0725000000046, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
  [ 372.0725000000046, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ]
];
const Model = new MLR(train_x_matrix, train_y_matrix, { statistics: false, }); // causes infinite loop at line 25:       const beta = new Matrix.SVD(x, { autoTranspose: true }).solve(y);
console.log('never gets here because of infinite loop');

Sort out the d.ts, check it and add it to the library.

declare module "ml-matrix" {
  type NRows = number | Array<any> | Matrix;
  class BaseView extends Matrix {}
  class MatrixColumnView extends BaseView {
    set(rowIndex: number, columnIndex: number, value: any): MatrixColumnView;
    get(rowIndex: number): number;
  }
  class MatrixColumnSelectionView extends BaseView {
    set(
      rowIndex: number,
      columnIndex: number,
      value: any
    ): MatrixColumnSelectionView;
    get(rowIndex: number, columnIndex: number): number;
  }
  class MatrixFlipColumnView extends BaseView {
    set(
      rowIndex: number,
      columnIndex: number,
      value: any
    ): MatrixFlipColumnView;
    get(rowIndex: number, columnIndex: number): number;
  }
  class MatrixFlipRowView extends BaseView {
    set(rowIndex: number, columnIndex: number, value: any): MatrixFlipRowView;
    get(rowIndex: number, columnIndex: number): number;
  }
  class MatrixRowView extends BaseView {
    set(rowIndex: number, columnIndex: number, value: any): MatrixRowView;
    get(rowIndex: number, columnIndex: number): number;
  }
  class MatrixRowSelectionView extends BaseView {
    set(
      rowIndex: number,
      columnIndex: number,
      value: any
    ): MatrixRowSelectionView;
    get(rowIndex: number, columnIndex: number): number;
  }
  class MatrixSelectionView extends BaseView {
    set(rowIndex: number, columnIndex: number, value: any): MatrixSelectionView;
    get(rowIndex: number, columnIndex: number): number;
  }
  class MatrixSubView extends BaseView {
    set(rowIndex: number, columnIndex: number, value: any): MatrixSubView;
    get(rowIndex: number, columnIndex: number): number;
  }
  class MatrixTransposeView extends BaseView {
    set(rowIndex: number, columnIndex: number, value: any): MatrixTransposeView;
    get(rowIndex: number, columnIndex: number): number;
  }
  class Matrix {
    readonly size: number;
    constructor(nRows: NRows, nColumns: number);
    static from1DArray(
      newRows: number,
      newColumns: number,
      newData: Array<any>
    ): Matrix;
    apply(callback: Function): Matrix;
    to1DArray<T = any>(): Array<T>;
    to2DArray<T = any>(): Array<Array<T>>;
    isRowVector(): boolean;
    isColumnVector(): boolean;
    isVector(): boolean;
    isSquare(): boolean;
    isSymmetric(): boolean;
    set(rowIndex: number, columnIndex: number, value: number): Matrix;
    get(rowIndex: number, columnIndex: number): number;
    repeat(rowRep: number, colRep: number): Matrix;
    fill(value: number): Matrix;
    neg(): Matrix;
    getRow<T = any>(index: number): Array<T>;
    getRowVector(index: number): Matrix;
    setRow(index: number, array: Array<any> | Matrix[]): Matrix;
    swapRows(row1: number, row2: number): Matrix;
    getColumn(index: number): Array<any>;
    getColumnVector(index: number): Matrix;
    setColumn(index: number, array: Array<any> | Matrix[]): Matrix;
    swapColumns(column1: number, column2: number): Matrix;
    addRowVector(vector: Array<any> | Matrix[]): Matrix;
    subRowVector(vector: Array<any> | Matrix[]): Matrix;
    mulRowVector(vector: Array<any> | Matrix[]): Matrix;
    divRowVector(vector: Array<any> | Matrix[]): Matrix;
    addColumnVector(vector: Array<any> | Matrix[]): Matrix;
    subColumnVector(vector: Array<any> | Matrix[]): Matrix;
    mulColumnVector(vector: Array<any> | Matrix[]): Matrix;
    divColumnVector(vector: Array<any> | Matrix[]): Matrix;
    mulRow(index: number, value: number): Matrix;
    mulColumn(index: number, value: number): Matrix;
    max(): number;
    maxIndex(): Array<any>;
    min(): number;
    minIndex(): Array<any>;
    maxRow(row: number): number;
    maxRowIndex(row: number): Array<any>;
    minRow(row: number): number;
    minRowIndex(row: number): Array<any>;
    maxColumn(column: number): number;
    maxColumnIndex(column: number): Array<any>;
    minColumn(column: number): number;
    minColumnIndex(column: number): Array<number>;
    diag(): Array<any>;
    sum(by: "row" | "column"): Matrix | number;
    mean(): number;
    prod(): number;
    norm(type: "frobenius" | "max"): number;
    cumulativeSum(): Matrix;
    dot(vector2: Matrix): number;
    mmul(other: Matrix): Matrix;
    strassen2x2(other: Matrix): Matrix;
    strassen3x3(other: Matrix): Matrix;
    mmulStrassen(y: Matrix): Matrix;
    scaleRows(min: number, max: number): Matrix;
    scaleColumns(min: number, max: number): Matrix;
    kroneckerProduct(other: Matrix): Matrix;
    transpose(): Matrix;
    sortRows(compareFunction: Function): Matrix;
    sortColumns(compareFunction: Function): Matrix;
    subMatrix(
      startRow: number,
      endRow: number,
      startColumn: number,
      endColumn: number
    ): Matrix;
    subMatrixRow(
      indices: Array<any>,
      startColumn: number,
      endColumn: number
    ): Matrix;
    subMatrixColumn(
      indices: Array<any>,
      startRow: number,
      endRow: number
    ): Matrix;
    setSubMatrix(
      matrix: Matrix | Array<any>,
      startRow: number,
      startColumn: number
    ): Matrix;
    selection(rowIndices: Array<number>, columnIndices: Array<number>): Matrix;
    trace(): number;
    transposeView(): MatrixTransposeView;
    rowView(row: number): MatrixRowView;
    columnView(column: number): MatrixColumnView;
    flipRowView(): MatrixFlipRowView;
    flipColumnView(): MatrixFlipColumnView;
    subMatrixView(
      startRow: number,
      endRow: number,
      startColumn: number,
      endColumn: number
    ): MatrixSubView;
    selectionView(
      rowIndices: Array<number>,
      columnIndices: Array<number>
    ): MatrixSelectionView;
    rowSelectionView(rowIndices: Array<number>): MatrixRowSelectionView;
    columnSelectionView(
      columnIndices: Array<number>
    ): MatrixColumnSelectionView;
    det(): number;
    pseudoInverse(threshold: number): Matrix;
    clone(): Matrix;
    static rowVector<T = any>(newData: Array<T>): Matrix;
    static columnVector<T = any>(newData: Array<T>): Matrix;
    static empty(rows: number, columns: number): Matrix;
    static zeros(rows: number, columns: number): Matrix;
    static ones(rows: number, columns: number): Matrix;
    static rand(rows: number, columns: number, rng: Function): Matrix;
    static randInt(
      rows: number,
      columns: number,
      maxValue: number,
      rng: Function
    ): Matrix;
    static eye(rows: number, columns: number, value: number): Matrix;
    static diag(data: Array<number>, rows: number, columns: number): Matrix;
    static min(matrix1: Matrix, matrix2: Matrix): Matrix;
    static max(matrix1: Matrix, matrix2: Matrix): Matrix;
    static checkMatrix(value: any): Matrix;
    static isMatrix(value: any): value is Matrix;
  }
  export default Matrix;
}

Trouble Integrating with webpack

Cool library! It's so fast!

I used the eigenvalue decomposition function from ml-matrix as part of a library that I developed in node, and it worked really well there. I'm trying to bring it over to the browser and I've been having trouble importing it in a webpack build. It's totally the case that most webpack bugs tend to be due to the configurer, but I saw you recently were flipping some stuff around on exports and wanted to double checked.

Specifically my bug is that webpack throws somewhat opaquely on in base.js like so

base.js:4 Uncaught TypeError: __webpack_require__.i(...) is not a function
    at Object.<anonymous> (base.js:4)
    at __webpack_require__ (bootstrap 240ca1a7412a4a4ff7b3:659)
    at fn (bootstrap 240ca1a7412a4a4ff7b3:83)
    at Object.<anonymous> (sub.js:28)
    at __webpack_require__ (bootstrap 240ca1a7412a4a4ff7b3:659)
    at fn (bootstrap 240ca1a7412a4a4ff7b3:83)
    at Object.<anonymous> (number.js:3)
    at __webpack_require__ (bootstrap 240ca1a7412a4a4ff7b3:659)
    at fn (bootstrap 240ca1a7412a4a4ff7b3:83)
    at Object.<anonymous> (ascending.js:3)
(anonymous) @ base.js:4
__webpack_require__ @ bootstrap 240ca1a7412a4a4ff7b3:659
fn @ bootstrap 240ca1a7412a4a4ff7b3:83
(anonymous) @ sub.js:28
__webpack_require__ @ bootstrap 240ca1a7412a4a4ff7b3:659
fn @ bootstrap 240ca1a7412a4a4ff7b3:83
(anonymous) @ number.js:3
__webpack_require__ @ bootstrap 240ca1a7412a4a4ff7b3:659
fn @ bootstrap 240ca1a7412a4a4ff7b3:83
(anonymous) @ ascending.js:3
__webpack_require__ @ bootstrap 240ca1a7412a4a4ff7b3:659
fn @ bootstrap 240ca1a7412a4a4ff7b3:83
(anonymous) @ event.js:73
__webpack_require__ @ bootstrap 240ca1a7412a4a4ff7b3:659
fn @ bootstrap 240ca1a7412a4a4ff7b3:83
(anonymous) @ k-shape.js:4
__webpack_require__ @ bootstrap 240ca1a7412a4a4ff7b3:659
fn @ bootstrap 240ca1a7412a4a4ff7b3:83
(anonymous) @ app.js:4
__webpack_require__ @ bootstrap 240ca1a7412a4a4ff7b3:659
fn @ bootstrap 240ca1a7412a4a4ff7b3:83
(anonymous) @ log-apply-result.js:30
__webpack_require__ @ bootstrap 240ca1a7412a4a4ff7b3:659
module.exports @ bootstrap 240ca1a7412a4a4ff7b3:708
(anonymous) @ bootstrap 240ca1a7412a4a4ff7b3:708
client?e36c:38 

Feel free to close if you think the error is on my end

sum method by row or column

It would be nice to have a parameter at the sum method that allows to get the sum at each row or column, for example:

// assuming that this is a Matrix instance
var a = [[3, 3], [2, 2]];
a.sum('rows'); // [[3], [2]]
a.sum('columns'); //[[5, 5]] 

matrix.addColumn() available in node, but not in browser

I'm trying to use a library built on top of this library (https://github.com/mljs/regression-multivariate-linear) and getting errors (mljs/regression-multivariate-linear#9).

So I created a new create-react-app app and a node.js app and imported ml-matrix to both. In the browser, addColumn is not a method on an instance of the matrix. In node.js, it is.

Browser (Chrome and Safari, Mac):

import Matrix from 'ml-matrix'
const matrix = Matrix.ones(5, 5)
console.log('matrix: ', matrix)  // [1, 1, 1, 1, 1, rows: 5, columns: 5]
console.log('matrix.addColumn: ', matrix.addColumn)  // Uncaught TypeError: matrix.addColumn is not a function

Node.js (version 11)

const { Matrix } = require('ml-matrix')
const matrix = Matrix.ones(5, 5)
console.log('matrix: ', matrix)
matrix:  Matrix [
  [ 1, 1, 1, 1, 1 ],
  [ 1, 1, 1, 1, 1 ],
  [ 1, 1, 1, 1, 1 ],
  [ 1, 1, 1, 1, 1 ],
  [ 1, 1, 1, 1, 1 ],
  rows: 5,
  columns: 5 ]

console.log('matrix.addColumn: ', matrix.addColumn(new Array(matrix.length).fill(0)))
matrix.addColumn:  Matrix [
  [ 1, 1, 1, 1, 1, 0 ],
  [ 1, 1, 1, 1, 1, 0 ],
  [ 1, 1, 1, 1, 1, 0 ],
  [ 1, 1, 1, 1, 1, 0 ],
  [ 1, 1, 1, 1, 1, 0 ],
  rows: 5,
  columns: 6 ]

This may be related to this ticket #69 but it's a different error so I opened a new ticket.

I looked at the rollup config: the output is called matrix.js, which was last updated about a year ago. Also, .gitignore excludes /matrix.js, maybe the compiled file isn't being committed (but works on the developers machines who are compiling this repo). Not sure - just a guess. Anybody have suggestions?

Add .toString method to Matrix instances

Hi!
At first i'd like to say that this is a really cool and handy library, Very complete, very intuitive in usage. Nice idea with this chaining vs. statics pattern.

We all know, however, that despite modern browsers' and IDEs' great debugging capabilites, good ol' console.log is still a weapon of choice in JavaScript development. So if you have some time left for the next release:
could you add a .toString() method to your Matrix class for nicely formatted output? This would be really great!

Thanks a lot and take care.

Using transpose and transposeView with mmul

Using:

var Suite = require('benchmark').Suite;
var Matrix = require('ml-matrix').Matrix;

var n = 400;

var A1 = Matrix.rand(n, n);
var A2 = Matrix.rand(n, n);

var suite = new Suite();

suite
    .add('transpose mmul', function () {
        A1.transpose().mmul(A2);
    })
    .add('transposeView mmul', function () {
        A1.transposeView().mmul(A2);
    })
    .on('cycle', function (event) {
        console.log(String(event.target));
    })
    .on('complete', function() {
        console.log('Fastest is ' + this.filter('fastest').map('name'));
    })
    .run();

I got this output on node 7 and 8:

transpose mmul x 13.44 ops/sec ±0.45% (37 runs sampled)
transposeView mmul x 3.99 ops/sec ±0.18% (14 runs sampled)
Fastest is transpose mmul

Should be faster with transposeView, no @targos @maasencioh?, but using the sum for example, the transposeView works ok

suite
    .add('transpose sum', function () {
        A1.transpose().sum(A2);
    })
    .add('transposeView sum', function () {
        A1.transposeView().sum(A2);
    })
    .on('cycle', function (event) {
        console.log(String(event.target));
    })
    .on('complete', function() {
        console.log('Fastest is ' + this.filter('fastest').map('name'));
    })
    .run();

I got this:

transpose sum x 567 ops/sec ±0.56% (90 runs sampled)
transposeView sum x 1,864 ops/sec ±0.45% (95 runs sampled)
Fastest is transposeView sum

Behavior of mmulStrassen

mmulStrassen does not compute a matrix multiplication, as its name implies.

new Matrix([[1, 2]]).mmulStrassen(new Matrix([[1,2],[3,4]])).to2DArray()
// returns [[7, 10], [0,0]]

The caller gets back a multiplication result, zero padded to a square matrix. I believe this is done intentionally in embed, see comment here. It's unclear why the returned result wouldn't be trimmed to the correct dimensions (perhaps it made the recursion easier to implement?).

While this may not be unintentional, I think it's misleading at best; it's not documented anywhere I could find.

TypedArrays and Other Functions

Hi!

Thanks for this great library, something that JavaScript desperately needs. I am looking to contribute to it in my free time.

Here are some improvements and new functions I could think of:

1. Matrix.det(). Even though ML operations (in my limited experience) do not seem to need this, it would be good to have it for completeness and generalization
2. Use of TypedArrays to store data. For example, one can create a subclass that was TypedMatrix which would implement the main get, set, etc functions.

Do you think it would be a good addition and are there any open issues that could use some coding?

Zaf

Generalize in-place methods

make a list of operators, template functions and add them to the prototype automatically

Norm

I don't find any function which compute the norm of a matrix (e.g Frobenius norm). Does it exists or not already ?

Implement NNMF

A C example that could be used as web assembly but requires an external library for mathematical operations.

https://www.cs.virginia.edu/~jdl/nmf/

Release v1.0.0

cc @mljs/collaborators

I think I resolved all the issues for a v1.0.0 release. I will still work a bit to add a few testcases and a minimal documentation before publishing.

In the mean time, you can test the prerelease version by installing it with npm install ml-matrix@next
I you find a bug, or you think something is missing for this release, please create an issue or answer here. I will publish the package in 24 hours.

first K eigenvalues?

in EigenvalueDecomposition it would be nice to be able to retrieve only the first k eigenvalues & associated vectors, if it could save time and memory. I have no idea how to do it :)

Currently it's difficult to go beyond a 500x500 matrix.

dot problem

Matrix(23) dot Matrix(32) should be Matrix(2*2),but throw a error;

now I fixed like this:

/**
 * 
 * @param {Matrix} x 
 */
const origin_dot = Matrix.prototype.dot;
Matrix.prototype.dot = function(x){
  const {rows:r1,columns : c1} = this;
  const {rows:r2,columns : c2} = x;
  if(r1 === r2){
    return origin_dot.call(this,x);
  }
  if(c1!==r2){
    throw new Error('行列不对应');
  }
  const result = new Matrix(r1,c2);
  let i = 0;
  while(i < r1){
    let j = 0;
    const sma = [];
    const r = this.getRow(i);
    while(j < c2){
      const c = x.getColumn(j);
      let count = 0;
      r.forEach((n,i)=>count+=n*c[i]);
      sma.push(count);
      j++
    }
    result.setRow(i,new Matrix([sma]));
    i++;
  }
  return result;
}

new Matrix(
[
    [1,1,1],
    [2,2,2]
]
).dot(new Matrix([[1,1],[2,2],[3,3]]))
// return:
// Matrix {data: Array(2), rows: 2, columns: 2}
// data:(2) [Float64Array(2), Float64Array(2)]
// 0:Float64Array(2) [6, 6]
// 1:Float64Array(2) [12, 12]

does not support below ES6?

there are codes that are unsupported in previous Node.js (ex, 0.12).
such as class statements.

Is this intended?

Error in result

when performing the matrix calculation
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]

The wrong result is returned.

correlation() modifies input matrix

Similarly to #97 correlation(X) changes X. That makes sense with abs or other transformations, but what's the motivation behind mutating X when computing a statistic? Thanks!

const { Matrix, correlation } = require('ml-matrix')
const matrix = new Matrix([[1,2,3], [4,3,6], [7,1,9]])
const corr = correlation(matrix)
console.log(matrix.to2DArray())

produces [[-1, 0, -1], [0, 1, 0], [1, -1, 1]]

Should abandon this project?

mathjs + numeric.js are great tools that can work together and cover all the functionalities of this library with a lot of support.

add option to return a new matrix in all inplace methods

supported version

Hello, is the supported node version >=2.0.0?

Because this test is failing for that version, so maybe it's >2.0.0

abs() does not return a new matrix, modifies existing object

I ran into an interesting bug, where calling .abs() on a matrix modified it rather than returning a new matrix. Maybe this should be documented better.

add fill value option for Matrix.eye

Could be used to optimize:
https://github.com/mljs/regression/blob/71d7e0ada9da04d5ad7e242abad4c6e8380f73f7/src/regression/kernel-ridge-regression.js#L36-L36

Error build jupyterlab with ml-matrix

Hello,
I got following error while using ml-matrix with a JupyterLab extension.

ERROR in vendors~main.1a6067ba633abf1df8f1.js from Terser Unexpected token: punc ()) [./node_modules/ml-matrix/src/util.js:44,0][vendors~main.1a6067ba633abf1df8f1.js:800393,4]

Do you have any idea where does this error come from?

stale branches

there's two stale branches:

• onedtests: I don't understand what was the intended test to do?
• svd-problem: there's any problem in svd? we are already using it

Add one vector to matrix

@targos Maybe it is stupid question i don't find there is a function to add ones vector at start position of Matrix object.

add pow in the list of functions

sum over row or column

How to sum per row or column ?

get vector from a given column or row

How to get the vector in a matrix object ?

getVector should return a Matrix instance by default

implement tnt-nn

There is a very efficient and fast nnls called tnt-nn
https://github.com/ProfMyre/tnt-nn
is it possible to implement it ?

SVD? Sparse?

Can this do SVD on sparse representations?

LU decomposition subMatrixRow

I'm getting this error trying the new matrix in the curve-fitting project:

/Users/acastillo/git/curve-fitting/node_modules/ml-matrix/src/dc/lu.js:144
X = value.subMatrixRow(this.pivotVector, 0, count - 1),
^
TypeError: value.subMatrixRow is not a function

Implement Gaussian elimination

https://en.wikipedia.org/wiki/Gaussian_elimination#Computational_efficiency

add custom inspect function

SVD sign error

I'm trying to calculate SVD values for known matrices, while comparing results with MATLAB and php output for the same matrix. Everything is the same except from sign.

Here's an example using this code:

var uval = new SingularValueDecomposition(mymatrix,{autoTranspose:true}) var U = new Matrix(uval.U); console.log(U)

and this matrix:
[[2,0,2],[0,1,7],[0,0,0]]

The expected result is this:

[0.2898, -0.9571, 0.0000], [0.9571, 0.2898, 0.0000], [0.0000, 0.0000, 1.0000]

but the function actually returns this:

[ 0.2898, 0.9571, 0 ], [ 0.9571, -0.2898, 0 ], [ 0, 0, 1 ]
What's wrong with these signs? Am I missing something?

Cannot add a matrix and a 1x1 matrix

Adding a matrix and a scalar works as expected, but adding a matrix and a 1x1 matrix throws RangeError('Matrices dimensions must be equal').

It would be convenient if the two were treated as equivalent; I don't know whether the current behavior is a bug or a deliberate design decision.

This works:

const { Matrix } = require('ml-matrix');
var A = new Matrix([[1, 1], [2, 2]]);
A.add(1)

But this doesn't:

const { Matrix } = require('ml-matrix');
var A = new Matrix([[1, 1], [2, 2]]);
A.add(Matrix.ones(1,1))

mmul function

Hello,

In the case when the columns and the rows are not equal in mmul function, you should throw an error not a console.warn().

Thanks.

add setSubmatrix

SVD output gives unanticipated left and right singular matrices.

I'd like to start by saying I'm not as much of a mathematician so this may be a non-issue and is simply my lack of understanding of linear algebra. However, I have cross-referenced the code and output for SVDs very thoroughly against other implementations in other languages so this discrepancy sticks out to me.

Given a matrix M:

M = [
  [5, 5, 5],
  [5, 5, 5],
  [5, 5, 5]
]

Running new SingularValueDecomposition returns the following left and right matrices:

0: Float64Array(3) [-0.5773502691896257, -5.551115123125783e-17, 0.8164965809277261]
1: Float64Array(3) [-0.5773502691896258, -0.7071067811865475, -0.40824829046386285]
2: Float64Array(3) [-0.5773502691896258, 0.7071067811865476, -0.40824829046386285]

0: Float64Array(3) [-0.5773502691896257, -0, 0.8164965809277261]
1: Float64Array(3) [-0.5773502691896258, 0.7071067811865475, -0.40824829046386296]
2: Float64Array(3) [-0.5773502691896258, -0.7071067811865476, -0.40824829046386296]

The second and third columns of the resultant left and right matrices are flipped - any further operations on these will result in very strange issues.

QR decomposition / orthogonalMatrix throws

The example in documentation throws:

 Cannot read property '2' of undefined

It looks like there was some refactor and the line on matrix.js:3294 refers to qr as array:

   for (k = columns - 1; k >= 0; k--) {
      for (i = 0; i < rows; i++) {
        X.set(i, k, 0);
      }
      X.set(k, k, 1);
      for (j = k; j < columns; j++) {
        if (qr.get(k, k) !== 0) {
          s = 0;
          for (i = k; i < rows; i++) {
            s += qr.get(i, k) * X.get(i, j);
          }

>>>>          s = -s / qr[k][k];

          for (i = k; i < rows; i++) {
            X.set(i, j, X.get(i, j) + s * qr.get(i, k));
          }
        }
      }
    }
    return X;
  }
}

Support Empty Matrices

None of the constructors, nor any of the remove* methods allow a matrix to become 0x0. e.g.

const m = new Matrix([[0]]);
m.removeRow(0);
// RangeError: A matrix cannot have less than one row

const m = new Matrix(0, 0);
// TypeError: First argument must be a positive number or an array

Is there a reason not to support empty matrices?