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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JavaScript
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