feat:node-modules
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71
node_modules/mathjs/lib/esm/function/set/setDifference.js
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71
node_modules/mathjs/lib/esm/function/set/setDifference.js
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import { flatten, generalize, identify } from '../../utils/array.js';
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import { factory } from '../../utils/factory.js';
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var name = 'setDifference';
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var dependencies = ['typed', 'size', 'subset', 'compareNatural', 'Index', 'DenseMatrix'];
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export var createSetDifference = /* #__PURE__ */factory(name, dependencies, _ref => {
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var {
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typed,
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size,
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subset,
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compareNatural,
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Index,
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DenseMatrix
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} = _ref;
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/**
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* Create the difference of two (multi)sets: every element of set1, that is not the element of set2.
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* Multi-dimension arrays will be converted to single-dimension arrays before the operation.
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*
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* Syntax:
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*
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* math.setDifference(set1, set2)
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*
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* Examples:
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*
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* math.setDifference([1, 2, 3, 4], [3, 4, 5, 6]) // returns [1, 2]
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* math.setDifference([[1, 2], [3, 4]], [[3, 4], [5, 6]]) // returns [1, 2]
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*
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* See also:
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*
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* setUnion, setIntersect, setSymDifference
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*
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* @param {Array | Matrix} a1 A (multi)set
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* @param {Array | Matrix} a2 A (multi)set
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* @return {Array | Matrix} The difference of two (multi)sets
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*/
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return typed(name, {
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'Array | Matrix, Array | Matrix': function Array__Matrix_Array__Matrix(a1, a2) {
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var result;
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if (subset(size(a1), new Index(0)) === 0) {
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// empty-anything=empty
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result = [];
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} else if (subset(size(a2), new Index(0)) === 0) {
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// anything-empty=anything
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return flatten(a1.toArray());
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} else {
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var b1 = identify(flatten(Array.isArray(a1) ? a1 : a1.toArray()).sort(compareNatural));
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var b2 = identify(flatten(Array.isArray(a2) ? a2 : a2.toArray()).sort(compareNatural));
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result = [];
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var inb2;
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for (var i = 0; i < b1.length; i++) {
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inb2 = false;
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for (var j = 0; j < b2.length; j++) {
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if (compareNatural(b1[i].value, b2[j].value) === 0 && b1[i].identifier === b2[j].identifier) {
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// the identifier is always a decimal int
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inb2 = true;
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break;
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}
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}
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if (!inb2) {
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result.push(b1[i]);
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}
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}
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}
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// return an array, if both inputs were arrays
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if (Array.isArray(a1) && Array.isArray(a2)) {
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return generalize(result);
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}
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// return a matrix otherwise
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return new DenseMatrix(generalize(result));
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}
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});
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});
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