mathjs/src/type/matrix/utils/algorithm07.js

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

'use strict'

const DimensionError = require('../../../error/DimensionError')

function factory (type, config, load, typed) {
  const DenseMatrix = type.DenseMatrix

  /**
   * Iterates over SparseMatrix A and SparseMatrix B items (zero and nonzero) and invokes the callback function f(Aij, Bij).
   * Callback function invoked MxN times.
   *
   * C(i,j) = f(Aij, Bij)
   *
   * @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}                    DenseMatrix (C)
   *
   * see https://github.com/josdejong/mathjs/pull/346#issuecomment-97620294
   */
  const algorithm07 = function (a, b, callback) {
    // sparse matrix arrays
    const asize = a._size
    const adt = a._datatype
    // sparse matrix arrays
    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
    // zero value
    let zero = 0
    // callback signature to use
    let cf = callback

    // process data types
    if (typeof adt === 'string' && adt === bdt) {
      // datatype
      dt = adt
      // convert 0 to the same datatype
      zero = typed.convert(0, dt)
      // callback
      cf = typed.find(callback, [dt, dt])
    }

    // vars
    let i, j

    // result arrays
    const cdata = []
    // initialize c
    for (i = 0; i < rows; i++) { cdata[i] = [] }

    // matrix
    const c = new DenseMatrix({
      data: cdata,
      size: [rows, columns],
      datatype: dt
    })

    // workspaces
    const xa = []
    const xb = []
    // marks indicating we have a value in x for a given column
    const wa = []
    const wb = []

    // loop columns
    for (j = 0; j < columns; j++) {
      // columns mark
      const mark = j + 1
      // scatter the values of A(:,j) into workspace
      _scatter(a, j, wa, xa, mark)
      // scatter the values of B(:,j) into workspace
      _scatter(b, j, wb, xb, mark)
      // loop rows
      for (i = 0; i < rows; i++) {
        // matrix values @ i,j
        const va = wa[i] === mark ? xa[i] : zero
        const vb = wb[i] === mark ? xb[i] : zero
        // invoke callback
        cdata[i][j] = cf(va, vb)
      }
    }

    // return sparse matrix
    return c
  }

  function _scatter (m, j, w, x, mark) {
    // a arrays
    const values = m._values
    const index = m._index
    const ptr = m._ptr
    // loop values in column j
    for (let k = ptr[j], k1 = ptr[j + 1]; k < k1; k++) {
      // row
      const i = index[k]
      // update workspace
      w[i] = mark
      x[i] = values[k]
    }
  }

  return algorithm07
}

exports.name = 'algorithm07'
exports.factory = factory