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Create matrices, run element-wise and linear-algebra operations, and factorize with SVD, EVD, LU, QR, Cholesky, and NIPALS — all from one package.\r\n\r\nHomepage: [https://matrixflow-js.github.io/matrixflow-js/](https://matrixflow-js.github.io/matrixflow-js/)\r\n\r\n## Features\r\n\r\n- Dense `Matrix` with Float64-backed storage and a full operator surface\r\n- Views that slice, transpose, or flip without copying data\r\n- `SymmetricMatrix` and `DistanceMatrix` for structured data\r\n- Decompositions: SVD, EVD, LU, QR, Cholesky, NIPALS\r\n- Solvers: inverse, pseudo-inverse, least squares\r\n- Statistics: mean, variance, covariance, correlation, center, scale\r\n- ESM, CommonJS, and a minified UMD build\r\n- TypeScript definitions included\r\n\r\n## Install\r\n\r\n```bash\r\nnpm install matrixflow-js\r\n```\r\n\r\n## Quick start\r\n\r\n### ES modules\r\n\r\n```js\r\nimport { Matrix } from 'matrixflow-js';\r\n\r\nconst matrix = Matrix.ones(5, 5);\r\n```\r\n\r\n### CommonJS\r\n\r\n```js\r\nconst { Matrix } = require('matrixflow-js');\r\n\r\nconst matrix = Matrix.ones(5, 5);\r\n```\r\n\r\n### Browser (CDN)\r\n\r\n```html\r\n\r\n\r\n```\r\n\r\n## Usage\r\n\r\n### Create matrices\r\n\r\n```js\r\nimport { Matrix } from 'matrixflow-js';\r\n\r\nconst A = new Matrix([\r\n [1, 1],\r\n [2, 2],\r\n]);\r\n\r\nconst zeros = Matrix.zeros(3, 2);\r\nconst ones = Matrix.ones(2, 3);\r\nconst identity = Matrix.eye(3, 4);\r\nconst diagonal = Matrix.diag([1, 2, 3]);\r\n```\r\n\r\n### Arithmetic\r\n\r\n```js\r\nimport { Matrix } from 'matrixflow-js';\r\n\r\nconst A = new Matrix([\r\n [1, 1],\r\n [2, 2],\r\n]);\r\nconst B = new Matrix([\r\n [3, 3],\r\n [1, 1],\r\n]);\r\n\r\nMatrix.add(A, B); // [[4, 4], [3, 3]]\r\nMatrix.sub(A, B); // [[-2, -2], [1, 1]]\r\nA.mmul(B); // matrix product\r\nMatrix.mul(A, 10); // scalar multiply\r\nMatrix.div(A, 10); // scalar divide\r\nMatrix.max(A, B);\r\nMatrix.min(A, B);\r\n```\r\n\r\nIn-place variants mutate the receiver:\r\n\r\n```js\r\nconst C = B.clone();\r\nC.add(A);\r\nC.mul(10);\r\n```\r\n\r\n### Math functions\r\n\r\n```js\r\nconst A = new Matrix([\r\n [1, 1],\r\n [-1, -1],\r\n]);\r\n\r\nMatrix.exp(A);\r\nMatrix.cos(A);\r\nMatrix.abs(A);\r\nA.clone().abs(); // in place\r\n```\r\n\r\nAvailable: `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`.\r\n\r\n### Inspect and reshape\r\n\r\n```js\r\nA.rows;\r\nA.columns;\r\nA.size;\r\nA.get(0, 0);\r\nA.set(1, 0, 10);\r\nA.diag();\r\nA.mean();\r\nA.prod();\r\nA.norm();\r\nA.transpose();\r\nA.isSquare();\r\nA.isSymmetric();\r\n```\r\n\r\n### Rows, columns, and concatenation\r\n\r\n```js\r\nconst M = new Matrix([\r\n [1, 2, 3],\r\n [4, 5, 6],\r\n]);\r\n\r\nconst sumOf = (vector) => vector.reduce((total, value) => total + value, 0);\r\n\r\nM.applyAlongAxis(sumOf, 'row'); // [6, 15]\r\nM.applyAlongAxis(sumOf, 'column'); // [5, 7, 9]\r\n\r\nM.concat([[7, 8, 9]]);\r\nM.concat(Matrix.columnVector([7, 8]), 'column');\r\n```\r\n\r\n## Linear algebra\r\n\r\n```js\r\nimport {\r\n Matrix,\r\n inverse,\r\n solve,\r\n linearDependencies,\r\n QrDecomposition,\r\n LuDecomposition,\r\n CholeskyDecomposition,\r\n EigenvalueDecomposition,\r\n SingularValueDecomposition,\r\n} from 'matrixflow-js';\r\n```\r\n\r\n### Inverse and pseudo-inverse\r\n\r\n```js\r\nconst A = new Matrix([\r\n [2, 3, 5],\r\n [4, 1, 6],\r\n [1, 3, 0],\r\n]);\r\n\r\nconst inverseA = inverse(A);\r\nA.mmul(inverseA); // ~ identity\r\n\r\nconst singular = new Matrix([\r\n [1, 2, 3],\r\n [4, 5, 6],\r\n [7, 8, 9],\r\n]);\r\ninverse(singular, true); // SVD-based inverse\r\n\r\nconst tall = new Matrix([\r\n [1, 2],\r\n [3, 4],\r\n [5, 6],\r\n]);\r\ntall.pseudoInverse();\r\n```\r\n\r\n### Least squares\r\n\r\nSolve `A · x = B`:\r\n\r\n```js\r\nconst A = new Matrix([\r\n [3, 1],\r\n [4.25, 1],\r\n [5.5, 1],\r\n [8, 1],\r\n]);\r\nconst B = Matrix.columnVector([4.5, 4.25, 5.5, 5.5]);\r\nconst x = solve(A, B);\r\n```\r\n\r\nPass `true` as the third argument to use SVD when `A` is singular.\r\n\r\n### Decompositions\r\n\r\n```js\r\nconst A = new Matrix([\r\n [2, 3, 5],\r\n [4, 1, 6],\r\n [1, 3, 0],\r\n]);\r\n\r\nconst QR = new QrDecomposition(A);\r\nQR.orthogonalMatrix;\r\nQR.upperTriangularMatrix;\r\n\r\nconst LU = new LuDecomposition(A);\r\nLU.lowerTriangularMatrix;\r\nLU.upperTriangularMatrix;\r\nLU.pivotPermutationVector;\r\n\r\nconst cholesky = new CholeskyDecomposition(A);\r\ncholesky.lowerTriangularMatrix;\r\n\r\nconst evd = new EigenvalueDecomposition(A);\r\nevd.realEigenvalues;\r\nevd.imaginaryEigenvalues;\r\nevd.eigenvectorMatrix;\r\n\r\nconst svd = new SingularValueDecomposition(A);\r\nsvd.diagonal;\r\nsvd.leftSingularVectors;\r\nsvd.rightSingularVectors;\r\n```\r\n\r\n### Linear dependencies\r\n\r\n```js\r\nconst A = new Matrix([\r\n [2, 0, 0, 1],\r\n [0, 1, 6, 0],\r\n [0, 3, 0, 1],\r\n [0, 0, 1, 0],\r\n [0, 1, 2, 0],\r\n]);\r\n\r\nlinearDependencies(A);\r\n```\r\n\r\n## Documentation\r\n\r\nFull homepage, live playground, and API overview:\r\n\r\n**[https://matrixflow-js.github.io/matrixflow-js/](https://matrixflow-js.github.io/matrixflow-js/)**\r\n\r\nThe homepage deploys automatically on every push to `main`. In the GitHub repository, set **Settings → Pages → Source** to **GitHub Actions**.\r\n\r\nTypeScript definitions ship with the package (`matrix.d.ts`).\r\n\r\n## License\r\n\r\n[MIT](./LICENSE) © LinNianPing ([lin.n.ping@proton.me](mailto:lin.n.ping@proton.me))\r\n","readmeFilename":"README.md"}