"use strict";

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports.createAlgorithm11 = void 0;

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

var name = 'algorithm11';
var dependencies = ['typed', 'equalScalar'];
var createAlgorithm11 = /* #__PURE__ */(0, _factory.factory)(name, dependencies, function (_ref) {
  var typed = _ref.typed,
      equalScalar = _ref.equalScalar;

  /**
   * Iterates over SparseMatrix S nonzero items and invokes the callback function f(Sij, b).
   * Callback function invoked NZ times (number of nonzero items in S).
   *
   *
   *          ┌  f(Sij, b)  ; S(i,j) !== 0
   * C(i,j) = ┤
   *          └  0          ; otherwise
   *
   *
   * @param {Matrix}   s                 The SparseMatrix instance (S)
   * @param {Scalar}   b                 The Scalar value
   * @param {Function} callback          The f(Aij,b) operation to invoke
   * @param {boolean}  inverse           A true value indicates callback should be invoked f(b,Sij)
   *
   * @return {Matrix}                    SparseMatrix (C)
   *
   * https://github.com/josdejong/mathjs/pull/346#issuecomment-97626813
   */
  return function algorithm11(s, b, callback, inverse) {
    // sparse matrix arrays
    var avalues = s._values;
    var aindex = s._index;
    var aptr = s._ptr;
    var asize = s._size;
    var adt = s._datatype; // sparse matrix cannot be a Pattern matrix

    if (!avalues) {
      throw new Error('Cannot perform operation on Pattern Sparse Matrix and Scalar value');
    } // 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') {
      // 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); // convert b to the same datatype

      b = typed.convert(b, dt); // callback

      cf = typed.find(callback, [dt, dt]);
    } // result arrays


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

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

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

        var v = inverse ? cf(b, avalues[k]) : cf(avalues[k], b); // check value is zero

        if (!eq(v, zero)) {
          // push index & value
          cindex.push(i);
          cvalues.push(v);
        }
      }
    } // update ptr


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

    return s.createSparseMatrix({
      values: cvalues,
      index: cindex,
      ptr: cptr,
      size: [rows, columns],
      datatype: dt
    });
  };
});
exports.createAlgorithm11 = createAlgorithm11;