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
import { factory } from '../../../utils/factory.js';
var name = 'matAlgo11xS0s';
var dependencies = ['typed', 'equalScalar'];
export var createMatAlgo11xS0s = /* #__PURE__ */factory(name, dependencies, _ref => {
var {
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
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
});
};
});