How to use the norm function from mathjs

cryst.js

GitHub: stur86/crystcif-parse

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 *   @param  {Array} cell    Cartesian form cell (must be 3x3)
 *   @param  {bool}  radians If true, angles are returned in radians
 *   @return {Array}         Parameters in form [lengths, angles]
 */
function cellToCellpar(cell, radians) {
    var lengths = cell.map(function(c) { return mjs.norm(c); });
    var angles = [];
    for (var i = 0; i < 3; ++i) {
        var j = (i + 2) % 3;
        var k = (i + 1) % 3;

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gltf.controller.js

GitHub: MediaComem/smapshot-api

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const xyzCam = math.matrix([urCorner, ulCorner, lrCorner, llCorner]);

// Scale the model
// ---------------
const dif = math.subtract(urCorner, ulCorner);
const dist = math.norm(dif);

const maxSize = 100; // max size of the bigest side of the image
let ratio = maxSize / dist;
let scaledXYZ = math.multiply(xyzCam, ratio);

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jacobi.js

GitHub: DanielHernandezO/NumericalMethodsProject

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let err = tol + 1; 
   iteration.push([i,x[0],x[1],x[2]]);

while(err > tol && i<iter){
	let xi = mathjs.add(mathjs.multiply(T,x),C);
	err = mathjs.norm(mathjs.subtract(xi,x));
	x = xi;
	i=i+1;
	iteration.push([i,x[0],x[1],x[2],err]);
}

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gaussseidel.js

GitHub: DanielHernandezO/NumericalMethodsProject

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err = tol + 1; 
z = [i,x[0],x[1],x[2],err];
while(err > tol && i<iter){
	xi = mathjs.add(mathjs.multiply(T,x),C);
	console.log(xi);
	err = mathjs.norm(xi - x);
	x = xi;
}
i=i+1;
z[i][0]=i; 

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create_svg.cjs

GitHub: jannikluhn/ens-chain-letter

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for (let i = 1; i < points.length - 1; i++) {
  const middle = points[i];
  const prev = points[i - 1];
  const next = points[i + 1];
  const tangent = normalize(math.subtract(next, prev));
  const dPrev = math.norm(math.subtract(middle, prev));
  const dNext = math.norm(math.subtract(middle, next));
  const cPrev = math.subtract(middle, math.multiply(tangent, dPrev / 3));
  const cNext = math.add(middle, math.multiply(tangent, dNext / 3));
  controlPoints.push([cPrev, cNext]);

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+ 13 other calls in file

ImageSourceModel.js

GitHub: Billamw/Image-source-model

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//     return angle;
// }


// Other way to check if the reflectionpoint is valid
// function checkValidReflection(reflP=[], wall=[]) {
//     let area1 = 1/2 * math.norm(math.cross(math.subtract(wall[1], wall[0]), math.subtract(wall[3], wall[0])));
//     let area2 = 1/2 * math.norm(math.cross(math.subtract(wall[1], wall[2]), math.subtract(wall[3], wall[2])));
//     let area = area1 + area2;
//     // console.log(reflP)

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+ 19 other calls in file

embedding.js

GitHub: learning-openai/api-rest-node-openai

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const listSimilaties=[];
for (var i=0; i<listEmbeddings.length;i++) {

    let dotProduct = await math.dot(embeddingText, listEmbeddings[i].embedding);
    // let norm1 = math.norm(embeddingTet) 
    // let norm2 = math.norm(listEmbeddings[i].embedding) 
    // let similarity = dotProduct/(norm1*norm2)
    console.log(dotProduct)
    if(dotProduct>0.80){

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+ 7 other calls in file

optimization.js

GitHub: mljs/optimization

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var n = math.subset(math.size(p0), math.index(0));
var maxj = 10;
var big = 1e8;
var h = 1;
var P = math.zeros(maxj, n+1); // ojo con el tamaño
var len = math.norm(p0);
var y0 = f(p0);

if(len > 1e4) h = math.divide(len, 1e4);

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+ 9 other calls in file

neuron.js

GitHub: rbren/axon

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}

Neuron.prototype.sendUpdate = function() {
  var self = this;
  if (!self.io) return;
  var norm = M.norm(self.state.hidden);
  self.io.socket.emit('neuron', {
    id: self.id,
    transforms: self.getTransforms(),
    hidden: self.state.hidden._data,

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+ 13 other calls in file