mathjs
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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
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JavaScript
'use strict';
var DimensionError = require('../../../error/DimensionError');
function factory(type, config, load, typed) {
var equalScalar = load(require('../../../function/relational/equalScalar'));
var SparseMatrix = type.SparseMatrix;
/**
* 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) = ┤
* └ 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
*/
var algorithm05 = function algorithm05(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(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 + ')');
} // 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 = avalues && bvalues ? [] : undefined;
var cindex = [];
var cptr = []; // matrix
var c = new SparseMatrix({
values: cvalues,
index: cindex,
ptr: cptr,
size: [rows, columns],
datatype: dt
}); // workspaces
var xa = cvalues ? [] : undefined;
var xb = cvalues ? [] : undefined; // marks indicating we have a value in x for a given column
var wa = [];
var wb = []; // vars
var i, j, k, k1; // loop columns
for (j = 0; j < columns; j++) {
// update cptr
cptr[j] = cindex.length; // columns mark
var mark = j + 1; // loop values A(:,j)
for (k = aptr[j], k1 = aptr[j + 1]; k < k1; k++) {
// row
i = aindex[k]; // push index
cindex.push(i); // update workspace
wa[i] = mark; // check we need to process values
if (xa) {
xa[i] = avalues[k];
}
} // loop values B(:,j)
for (k = bptr[j], k1 = bptr[j + 1]; k < k1; k++) {
// row
i = bindex[k]; // check row existed in A
if (wa[i] !== mark) {
// push index
cindex.push(i);
} // update workspace
wb[i] = mark; // check we need to process values
if (xb) {
xb[i] = bvalues[k];
}
} // check we need to process values (non pattern matrix)
if (cvalues) {
// initialize first index in j
k = cptr[j]; // loop index in j
while (k < cindex.length) {
// row
i = cindex[k]; // marks
var wai = wa[i];
var wbi = wb[i]; // check Aij or Bij are nonzero
if (wai === mark || wbi === mark) {
// matrix values @ i,j
var va = wai === mark ? xa[i] : zero;
var vb = wbi === mark ? xb[i] : zero; // Cij
var vc = cf(va, vb); // check for zero
if (!eq(vc, zero)) {
// push value
cvalues.push(vc); // 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 c;
};
return algorithm05;
}
exports.name = 'algorithm05';
exports.factory = factory;