mathjs/src/type/matrix/utils/algorithm02.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

import { factory } from '../../../utils/factory'
import { DimensionError } from '../../../error/DimensionError'

const name = 'algorithm02'
const dependencies = ['typed', 'equalScalar']

export const createAlgorithm02 = /* #__PURE__ */ factory(name, dependencies, ({ typed, equalScalar }) => {
  /**
   * Iterates over SparseMatrix nonzero items and invokes the callback function f(Dij, Sij).
   * Callback function invoked NNZ times (number of nonzero items in SparseMatrix).
   *
   *
   *          ┌  f(Dij, Sij)  ; S(i,j) !== 0
   * C(i,j) = ┤
   *          └  0            ; otherwise
   *
   *
   * @param {Matrix}   denseMatrix       The DenseMatrix instance (D)
   * @param {Matrix}   sparseMatrix      The SparseMatrix instance (S)
   * @param {Function} callback          The f(Dij,Sij) operation to invoke, where Dij = DenseMatrix(i,j) and Sij = SparseMatrix(i,j)
   * @param {boolean}  inverse           A true value indicates callback should be invoked f(Sij,Dij)
   *
   * @return {Matrix}                    SparseMatrix (C)
   *
   * see https://github.com/josdejong/mathjs/pull/346#issuecomment-97477571
   */
  return function algorithm02 (denseMatrix, sparseMatrix, callback, inverse) {
    // dense matrix arrays
    const adata = denseMatrix._data
    const asize = denseMatrix._size
    const adt = denseMatrix._datatype
    // sparse matrix arrays
    const bvalues = sparseMatrix._values
    const bindex = sparseMatrix._index
    const bptr = sparseMatrix._ptr
    const bsize = sparseMatrix._size
    const bdt = sparseMatrix._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 + ')') }

    // sparse matrix cannot be a Pattern matrix
    if (!bvalues) { throw new Error('Cannot perform operation on Dense Matrix and Pattern Sparse Matrix') }

    // 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 (SparseMatrix)
    const cvalues = []
    const cindex = []
    const cptr = []

    // loop columns in b
    for (let j = 0; j < columns; j++) {
      // update cptr
      cptr[j] = cindex.length
      // values in column j
      for (let k0 = bptr[j], k1 = bptr[j + 1], k = k0; k < k1; k++) {
        // row
        const i = bindex[k]
        // update C(i,j)
        const cij = inverse ? cf(bvalues[k], adata[i][j]) : cf(adata[i][j], bvalues[k])
        // check for nonzero
        if (!eq(cij, zero)) {
          // push i & v
          cindex.push(i)
          cvalues.push(cij)
        }
      }
    }
    // update cptr
    cptr[columns] = cindex.length

    // return sparse matrix
    return sparseMatrix.createSparseMatrix({
      values: cvalues,
      index: cindex,
      ptr: cptr,
      size: [rows, columns],
      datatype: dt
    })
  }
})