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mathjs

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Math.js is an extensive math library for JavaScript and Node.js. It features a flexible expression parser with support for symbolic computation, comes with a large set of built-in functions and constants, and offers an integrated solution to work with dif

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'use strict' const DimensionError = require('../../../error/DimensionError') function factory (type, config, load, typed) { const equalScalar = load(require('../../../function/relational/equalScalar')) const SparseMatrix = type.SparseMatrix /** * Iterates over SparseMatrix A and invokes the callback function f(Aij, Bij). * Callback function invoked NZA times, number of nonzero elements in A. * * * ┌ f(Aij, Bij) ; A(i,j) !== 0 * C(i,j) = ┤ * └ 0 ; otherwise * * * @param {Matrix} a The SparseMatrix instance (A) * @param {Matrix} b The SparseMatrix instance (B) * @param {Function} callback The f(Aij,Bij) operation to invoke * * @return {Matrix} SparseMatrix (C) * * see https://github.com/josdejong/mathjs/pull/346#issuecomment-97620294 */ const algorithm09 = function (a, b, callback) { // sparse matrix arrays const avalues = a._values const aindex = a._index const aptr = a._ptr const asize = a._size const adt = a._datatype // sparse matrix arrays const bvalues = b._values const bindex = b._index const bptr = b._ptr const bsize = b._size const bdt = b._datatype // validate dimensions if (asize.length !== bsize.length) { throw new DimensionError(asize.length, bsize.length) } // check rows & columns if (asize[0] !== bsize[0] || asize[1] !== bsize[1]) { throw new RangeError('Dimension mismatch. Matrix A (' + asize + ') must match Matrix B (' + bsize + ')') } // rows & columns const rows = asize[0] const columns = asize[1] // datatype let dt // equal signature to use let eq = equalScalar // zero value let zero = 0 // callback signature to use let cf = callback // process data types if (typeof adt === 'string' && adt === bdt) { // datatype dt = adt // find signature that matches (dt, dt) eq = typed.find(equalScalar, [dt, dt]) // convert 0 to the same datatype zero = typed.convert(0, dt) // callback cf = typed.find(callback, [dt, dt]) } // result arrays const cvalues = avalues && bvalues ? [] : undefined const cindex = [] const cptr = [] // matrix const c = new SparseMatrix({ values: cvalues, index: cindex, ptr: cptr, size: [rows, columns], datatype: dt }) // workspaces const x = cvalues ? [] : undefined // marks indicating we have a value in x for a given column const w = [] // vars let i, j, k, k0, k1 // loop columns for (j = 0; j < columns; j++) { // update cptr cptr[j] = cindex.length // column mark const mark = j + 1 // check we need to process values if (x) { // loop B(:,j) for (k0 = bptr[j], k1 = bptr[j + 1], k = k0; k < k1; k++) { // row i = bindex[k] // update workspace w[i] = mark x[i] = bvalues[k] } } // loop A(:,j) for (k0 = aptr[j], k1 = aptr[j + 1], k = k0; k < k1; k++) { // row i = aindex[k] // check we need to process values if (x) { // b value @ i,j const vb = w[i] === mark ? x[i] : zero // invoke f const vc = cf(avalues[k], vb) // check zero value if (!eq(vc, zero)) { // push index cindex.push(i) // push value cvalues.push(vc) } } else { // push index cindex.push(i) } } } // update cptr cptr[columns] = cindex.length // return sparse matrix return c } return algorithm09 } exports.name = 'algorithm09' exports.factory = factory