mathjs/lib/esm/type/matrix/utils/matAlgo01xDSid.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.js';
import { DimensionError } from '../../../error/DimensionError.js';
var name = 'matAlgo01xDSid';
var dependencies = ['typed'];
export var createMatAlgo01xDSid = /* #__PURE__ */factory(name, dependencies, _ref => {
  var {
    typed
  } = _ref;
  /**
   * 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) = ┤
   *          └  Dij          ; 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}                    DenseMatrix (C)
   *
   * see https://github.com/josdejong/mathjs/pull/346#issuecomment-97477571
   */
  return function algorithm1(denseMatrix, sparseMatrix, callback, inverse) {
    // dense matrix arrays
    var adata = denseMatrix._data;
    var asize = denseMatrix._size;
    var adt = denseMatrix._datatype || denseMatrix.getDataType();
    // sparse matrix arrays
    var bvalues = sparseMatrix._values;
    var bindex = sparseMatrix._index;
    var bptr = sparseMatrix._ptr;
    var bsize = sparseMatrix._size;
    var bdt = sparseMatrix._datatype || sparseMatrix._data === undefined ? sparseMatrix._datatype : sparseMatrix.getDataType();

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

    // process data types
    var dt = typeof adt === 'string' && adt !== 'mixed' && adt === bdt ? adt : undefined;
    // callback function
    var cf = dt ? typed.find(callback, [dt, dt]) : callback;

    // vars
    var i, j;

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

    // workspace
    var x = [];
    // marks indicating we have a value in x for a given column
    var w = [];

    // loop columns in b
    for (j = 0; j < columns; j++) {
      // column mark
      var mark = j + 1;
      // values in column j
      for (var k0 = bptr[j], k1 = bptr[j + 1], k = k0; k < k1; k++) {
        // row
        i = bindex[k];
        // update workspace
        x[i] = inverse ? cf(bvalues[k], adata[i][j]) : cf(adata[i][j], bvalues[k]);
        // mark i as updated
        w[i] = mark;
      }
      // loop rows
      for (i = 0; i < rows; i++) {
        // check row is in workspace
        if (w[i] === mark) {
          // c[i][j] was already calculated
          cdata[i][j] = x[i];
        } else {
          // item does not exist in S
          cdata[i][j] = adata[i][j];
        }
      }
    }

    // return dense matrix
    return denseMatrix.createDenseMatrix({
      data: cdata,
      size: [rows, columns],
      datatype: adt === denseMatrix._datatype && bdt === sparseMatrix._datatype ? dt : undefined
    });
  };
});