'use strict';

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports.createAlgorithm02 = void 0;

var _factory = require("../../../utils/factory");

var _DimensionError = require("../../../error/DimensionError");

var name = 'algorithm02';
var dependencies = ['typed', 'equalScalar'];
var createAlgorithm02 =
/* #__PURE__ */
(0, _factory.factory)(name, dependencies, function (_ref) {
  var typed = _ref.typed,
      equalScalar = _ref.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
    var adata = denseMatrix._data;
    var asize = denseMatrix._size;
    var adt = denseMatrix._datatype; // sparse matrix arrays

    var bvalues = sparseMatrix._values;
    var bindex = sparseMatrix._index;
    var bptr = sparseMatrix._ptr;
    var bsize = sparseMatrix._size;
    var bdt = sparseMatrix._datatype; // validate dimensions

    if (asize.length !== bsize.length) {
      throw new _DimensionError.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]; // 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 (SparseMatrix)


    var cvalues = [];
    var cindex = [];
    var cptr = []; // loop columns in b

    for (var j = 0; j < columns; j++) {
      // update cptr
      cptr[j] = cindex.length; // values in column j

      for (var k0 = bptr[j], k1 = bptr[j + 1], k = k0; k < k1; k++) {
        // row
        var i = bindex[k]; // update C(i,j)

        var 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
    });
  };
});
exports.createAlgorithm02 = createAlgorithm02;