How to use the zeros function from mathjs
Find comprehensive JavaScript mathjs.zeros code examples handpicked from public code repositorys.
GitHub: larssbr/electricitymap
![larssbr profile picture](/larssbr.png?size=40)
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// Note that exports cancel out. // We wish to solve Ax = b var n = validCountries.length; var A = mathjs.sparse().resize([n, n]); var b = mathjs.zeros(n); validCountries.forEach(function (country, i) { A.set([i, i], country.totalProduction + country.totalImport); // Intern
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res |= (1 << qubit); }); return res; } var T = math.zeros([ 1 << totalQubits, 1 << totalQubits ]); for(var elrow = 0; elrow < U.length; elrow++) { var rowmask = getElMask(elrow);
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GitHub: zjohn77/retrieval
![zjohn77 profile picture](/zjohn77.png?size=40)
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* makes a mathjs sparse zero vector of the specified length. */ const math = require('mathjs'); module.exports = function(len_) { return math.zeros(len_, 1, 'sparse'); };
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return matrix; }; const makeAdjacencyMatrix = (nodes, n) => { // Graph const G = math.zeros(n, n, 'sparse'); // Column sum const D = math.zeros(n, n, 'sparse'); let index; let value;
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GitHub: asilvas/salient-maps
![asilvas profile picture](/asilvas.png?size=40)
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precomputeParameters(sigmac = 16) { let i; const [L_centroid, A_centroid, B_centroid] = meshgrid(this.lRange, this.aRange, this.bRange) this.uniquePixels = math.zeros(this.numberOfColors, 3).toArray(); let iIndex, iL, iA, iB; for (i = 0; i < this.numberOfColors; i++) { iIndex = this.indexMatrix[i]; iL = L_centroid[iIndex[0]][iIndex[1]][iIndex[2]];
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return [[]]; } n = n || 0; var dim = v.length + math.abs(n), mat = math.zeros(dim, dim), start = n > 0 ? [0, n] : [-n, 0], pos = start; v.forEach(function(val) {
GitHub: muntashir/3net.js
![muntashir profile picture](/muntashir.png?size=40)
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return math.dotMultiply(sigmoid(x), math.subtract(1, sigmoid(x))); } //Multiplies a column vector with a row vector because MathJS doesn't support tranposing 1 dimensional matrices function outerProduct(x, y) { var z = math.zeros([math.size(x)[0], math.size(y)[0]]); for (var i = 0; i < math.size(x)[0]; i += 1) { for (var j = 0; j < math.size(y)[0]; j += 1) { var r = x[i] * y[j]; z[i][j] = r;
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for (let index = 0; index < xList.length; index++) { const newX = xList[index]; const newY = yList[index]; const newZ = zList[index]; const newData = mathjs.zeros(width * width)._data; const newCenter = { x: newX, y: newY, z: newZ }; const startX = -floorHalf >> 1; const endX = floorHalf >> 1;
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b=b-A*x0; if (~arrayIsEmpty(beq)){ let g = math.reshape(beq); g = math.zeros(math.size(beq)); } paramsin[[2]]=b; paramsin[[4]]=beq;
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const numTags = dimensions[0]; var PageRankVector = mathjs.ones(numTags); // Initialize the PageRank vector to be a vector of 1/numTags PageRankVector = mathjs.multiply(PageRankVector, 1/numTags); // initialize the previous PageRank vector var previousPageRankVector = mathjs.zeros(numTags); // initialize the difference between the current PageRank vector and the previous PageRank vector var difference = mathjs.subtract(PageRankVector, previousPageRankVector); // initialize the number of iterations
GitHub: mljs/optimization
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var Npnt = y_dat.length;//length(y_dat); // number of data points var p_old = math.zeros(Npar,1); // previous set of parameters var y_old = math.zeros(Npnt,1); // previous model, y_old = y_hat(t;p_old) var X2 = 1e-2/eps; // a really big initial Chi-sq value var X2_old = 1e-2/eps; // a really big initial Chi-sq value var J = math.zeros(Npnt,Npar); /*var J = new Array(Npnt);//zeros(Npnt,Npar); // Jacobian matrix for(var i=0;i<Npnt;i++){ J[i] = new Array(Npar); }*/
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GitHub: nikitaiavdeev/BeamCalc
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if (s.type === 'Fixed') n += 2; else n++; }); const arr = math.zeros(n + 2, n + 2), ans = math.zeros(n + 2, 1); n = 0;
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GitHub: cblanken/aoc_2022
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/* Solve */ function solve1(data) { data = parseData(data); let max_dim = data.reduce((acc, pos) => math.max(...pos, acc), 0); let cubes = math.zeros(max_dim, max_dim, max_dim); let sa_total = 0; data.forEach(pos => {
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GitHub: mljs/optimization
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var counter = 0; while( (math.larger(y[high], math.add(y[low], epsilon)) && counter < maximumIterations) || counter < minimumIterations ) { var S = math.zeros(1, size); for(var i = 0; i < size + 1; ++i) { S = math.add(S, math.subset(vertices, math.index(i, [0, size]))); }
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GitHub: rbren/axon
![rbren profile picture](/rbren.png?size=40)
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history: { input: [], output: [], }, subscribers: [], hidden: M.zeros(dim.hidden), bias: M.random([dim.hidden], -1, 1), transforms: {}, }; this.log('CREATED', this.options);
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} function makePairingMatrix(data) { var map = makeTeamMapping(a); var n = map.length; var Mat = math.zeros(n, n, 'sparse'); for (i = 0; i < data.length; ++i) { var r1 = data[i][0]; var r2 = data[i][1];
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mathjs.evaluate is the most popular function in mathjs (87200 examples)