"use strict";

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports.createAlgorithm08 = void 0;

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

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

var name = 'algorithm08';
var dependencies = ['typed', 'equalScalar'];
var createAlgorithm08 = /* #__PURE__ */(0, _factory.factory)(name, dependencies, function (_ref) {
  var typed = _ref.typed,
      equalScalar = _ref.equalScalar;

  /**
   * 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
   *          └  0            ; otherwise
   *
   *
   * @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
   */
  return function algorithm08(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.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 (!avalues || !bvalues) {
      throw new Error('Cannot perform operation on Pattern Sparse Matrices');
    } // 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 = [];
    var cindex = [];
    var cptr = []; // workspace

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

    var w = []; // vars

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

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

      var mark = j + 1; // loop values in a

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

        w[i] = mark; // set value

        x[i] = avalues[k]; // add index

        cindex.push(i);
      } // loop values in b


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

        if (w[i] === mark) {
          // evaluate callback
          x[i] = cf(x[i], bvalues[k]);
        }
      } // initialize first index in j


      k = cptr[j]; // loop index in j

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

        var v = x[i]; // check for zero value

        if (!eq(v, zero)) {
          // push value
          cvalues.push(v); // increment pointer

          k++;
        } else {
          // remove value @ i, do not increment pointer
          cindex.splice(k, 1);
        }
      }
    } // update cptr


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

    return a.createSparseMatrix({
      values: cvalues,
      index: cindex,
      ptr: cptr,
      size: [rows, columns],
      datatype: dt
    });
  };
});
exports.createAlgorithm08 = createAlgorithm08;