mathjs/lib/cjs/type/matrix/utils/matAlgo11xS0s.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";

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports.createMatAlgo11xS0s = void 0;
var _factory = require("../../../utils/factory.js");
const name = 'matAlgo11xS0s';
const dependencies = ['typed', 'equalScalar'];
const createMatAlgo11xS0s = exports.createMatAlgo11xS0s = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => {
  let {
    typed,
    equalScalar
  } = _ref;
  /**
   * 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 matAlgo11xS0s(s, b, callback, inverse) {
    // sparse matrix arrays
    const avalues = s._values;
    const aindex = s._index;
    const aptr = s._ptr;
    const asize = s._size;
    const 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
    const rows = asize[0];
    const columns = asize[1];

    // datatype
    let dt;
    // equal signature to use
    let eq = equalScalar;
    // zero value
    let zero = 0;
    // callback signature to use
    let 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
    const cvalues = [];
    const cindex = [];
    const cptr = [];

    // loop columns
    for (let j = 0; j < columns; j++) {
      // initialize ptr
      cptr[j] = cindex.length;
      // values in j
      for (let k0 = aptr[j], k1 = aptr[j + 1], k = k0; k < k1; k++) {
        // row
        const i = aindex[k];
        // invoke callback
        const 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
    });
  };
});