mathjs/lib/type/matrix/utils/algorithm04.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';

var DimensionError = require('../../../error/DimensionError');

function factory(type, config, load, typed) {
  var equalScalar = load(require('../../../function/relational/equalScalar'));
  var SparseMatrix = type.SparseMatrix;
  /**
   * Iterates over SparseMatrix A and SparseMatrix B nonzero items and invokes the callback function f(Aij, Bij).
   * Callback function invoked MAX(NNZA, NNZB) times
   *
   *
   *          ┌  f(Aij, Bij)  ; A(i,j) !== 0 && B(i,j) !== 0
   * C(i,j) = ┤  A(i,j)       ; A(i,j) !== 0
   *          └  B(i,j)       ; B(i,j) !== 0
   *
   *
   * @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
   */

  var algorithm04 = function algorithm04(a, b, callback) {
    // sparse matrix arrays
    var avalues = a._values;
    var aindex = a._index;
    var aptr = a._ptr;
    var asize = a._size;
    var adt = a._datatype; // sparse matrix arrays

    var bvalues = b._values;
    var bindex = b._index;
    var bptr = b._ptr;
    var bsize = b._size;
    var 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


    var rows = asize[0];
    var columns = asize[1]; // datatype

    var dt; // equal signature to use

    var eq = equalScalar; // zero value

    var zero = 0; // callback signature to use

    var 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


    var cvalues = avalues && bvalues ? [] : undefined;
    var cindex = [];
    var cptr = []; // matrix

    var c = new SparseMatrix({
      values: cvalues,
      index: cindex,
      ptr: cptr,
      size: [rows, columns],
      datatype: dt
    }); // workspace

    var xa = avalues && bvalues ? [] : undefined;
    var xb = avalues && bvalues ? [] : undefined; // marks indicating we have a value in x for a given column

    var wa = [];
    var wb = []; // vars

    var i, j, k, k0, k1; // loop columns

    for (j = 0; j < columns; j++) {
      // update cptr
      cptr[j] = cindex.length; // columns mark

      var mark = j + 1; // loop A(:,j)

      for (k0 = aptr[j], k1 = aptr[j + 1], k = k0; k < k1; k++) {
        // row
        i = aindex[k]; // update c

        cindex.push(i); // update workspace

        wa[i] = mark; // check we need to process values

        if (xa) {
          xa[i] = avalues[k];
        }
      } // loop B(:,j)


      for (k0 = bptr[j], k1 = bptr[j + 1], k = k0; k < k1; k++) {
        // row
        i = bindex[k]; // check row exists in A

        if (wa[i] === mark) {
          // update record in xa @ i
          if (xa) {
            // invoke callback
            var v = cf(xa[i], bvalues[k]); // check for zero

            if (!eq(v, zero)) {
              // update workspace
              xa[i] = v;
            } else {
              // remove mark (index will be removed later)
              wa[i] = null;
            }
          }
        } else {
          // update c
          cindex.push(i); // update workspace

          wb[i] = mark; // check we need to process values

          if (xb) {
            xb[i] = bvalues[k];
          }
        }
      } // check we need to process values (non pattern matrix)


      if (xa && xb) {
        // initialize first index in j
        k = cptr[j]; // loop index in j

        while (k < cindex.length) {
          // row
          i = cindex[k]; // check workspace has value @ i

          if (wa[i] === mark) {
            // push value (Aij != 0 || (Aij != 0 && Bij != 0))
            cvalues[k] = xa[i]; // increment pointer

            k++;
          } else if (wb[i] === mark) {
            // push value (bij != 0)
            cvalues[k] = xb[i]; // increment pointer

            k++;
          } else {
            // remove index @ k
            cindex.splice(k, 1);
          }
        }
      }
    } // update cptr


    cptr[columns] = cindex.length; // return sparse matrix

    return c;
  };

  return algorithm04;
}

exports.name = 'algorithm04';
exports.factory = factory;