How to use the random function from mathjs

_server.js

GitHub: coder0987/Taroky

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this.layersWeights = [20,1000,1000];
this.layersBias = [20,1000];
this.outputWeights = [14,1000];
this.outputBias = [14];

this.inputWeights   = math.random(math.matrix([this.inputWeightsSize[0], this.inputWeightsSize[1]])); // 2k x 1k
this.layersWeights  = math.random(math.matrix([this.layersWeights[0], this.layersWeights[1], this.layersWeights[2]])); // 20 x 1k x 1k
this.layersBias     = math.random(math.matrix([this.layersBias[0], this.layersBias[1]])); // 20 x 1k x 1
this.outputWeights  = math.random(math.matrix([this.outputWeights[1], this.outputWeights[0]])); // 14 x 1k
this.outputBias     = math.random(math.matrix([this.outputBias[0]])); // 14 x 1

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Cppn.js

GitHub: brin-eris/basic-bots

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    value+=  10*(Math.random()-0.5);
  }
  return value;
});

this.hiddenLayerBiasA = Mathjs.random([this.inputSize],-0.01, 0.01).map( function(value, index, matrix) {
  if(Math.random() < 0.2){
    value+= 10*(Math.random()-0.5);
  }
  return value;

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demo.js

GitHub: xiefengnian/js-ml

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  });
};
const init_parameters_w = function (layer) {
  const dist = distribution[layer]['w'];
  return Matrix.rand(dimension[layer - 1], dimension[layer], {
    random: () => math.random() * (dist[1] - dist[0]) + dist[0],
  });
};

const init_parameters = function () {

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neuralMath.js

GitHub: muntashir/3net.js

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employee.js

GitHub: Drakmord2/node-swarm-intelligence

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 * x(t+1) = x(t) + ø * (x(t) - randx)
 */
explore() {
    if (this.trial <= config.abc.max_trials) {
        let component = mathjs.pickRandom(this.position);
        let phi       = mathjs.random([1, dimensions], -1, 1)[0];

        let sub             = mathjs.subtract(this.position, component);
        let mul             = mathjs.dotMultiply(sub, phi);
        let new_position    = mathjs.add(this.position, mul);

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fssController.js

GitHub: Drakmord2/node-swarm-intelligence

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 * @returns {*}
 */
individual_movement(school, step, boundaries) {

    school.forEach((fish) => {
        let rand    = mathjs.random([1, dimensions], -1, 1)[0];
        let factor  = mathjs.dotMultiply(rand, step);

        let nextPosition = mathjs.add(fish.position, factor);

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index.js

GitHub: Kirigaya-Kazuton/IA

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// subtração 5 - 2 e 3 -1
console.log('subtração:', math.subtract([5, 3], [2, 1]));
// transposição [1, 3], [2, 4]
console.log('transposição:', math.transpose([[1, 2], [3, 4]]));
// seleção aleatória
console.log('seleção aleatória:', math.random([2, 2]));
// adição 3, 5
console.log('adição:', math.add([1, 2], [2, 3]));
// divisão 5, 4
console.log('divisão:', math.dotDivide([10, 12], [2, 3]));

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hillCipher.js

GitHub: OrghoN/hillCipher

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function createEncodingKey(n, max) {
    var det = 0;
    var encodingKey
    while (det === 0) {
        encodingKey = math.random([n, n], 1, max);
        encodingKey = math.map(encodingKey, function(val) {
            return math.round(val);
        });
        det = math.det(encodingKey);

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neuron.js

GitHub: rbren/axon

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      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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+ 7 other calls in file