How to use the std function from mathjs

analyse-code-size.js

GitHub: theburningmonk/lambda-coldstart-comparison

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stats.push(values);

return {
  codeSize: rs[0].codeSize,
  memorySize: rs[0].memorySize,
  stddev: math.std(values),
  mean: math.mean(values),
  median: math.median(values),
  '95%-tile': stats.percentile(95),
  '99%-tile': stats.percentile(99)

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getAzDoRun.js

GitHub: adoptium/aqa-test-tools

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        ]
    ),
    3
);
const stddev = math.round(
    math.std(
        benchmarkMetricsCollection[
            key
        ]
    ),

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report

GitHub: Sable/wu-wei-benchmarking-toolkit

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  keys.platforms[results[h].platform['short-name']] = true
  keys.environments[results[h].environment['short-name']] = true
  keys['input-sizes'][results[h].experiment['input-size']] = true

  results[h]['mean-time'] = math.mean(results[h].times)
  results[h]['std-time'] = math.std(results[h].times)
  results[h]['max-time'] = math.max(results[h].times)
  results[h]['min-time'] = math.min(results[h].times)
  a.push(results[h])
}

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index.js

GitHub: jly36963/notes

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math.quantileSeq([1, 2, 3, 4, 5], .5) // value at quantile

math.mean(numbers);
math.median(numbers);
math.mode(numbers)
math.std(numbers);
math.variance(numbers);
math.sum(numbers);

// probability

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index.js

GitHub: Kirigaya-Kazuton/IA

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	(4-3)²=1
	(1+1)/1=2
*/
console.log('variância sem tendência:', math.variance([2, 4], 'unbiased'));
// desvio padrão: é a raíz quadrada da variância
console.log('desvio padrão:', math.std([2, 4], 'uncorrected'));
// desvio com tendência: é a raíz quadrada da variância com tendência
console.log('desvio com tendência:', math.std([2, 4], 'biased'));
// desvio sem tendência: é a raíz quadrada da variância sem tendência
console.log('desvio sem tendência:', math.std([2, 4], 'unbiased'));

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methods-erp.js

GitHub: MissDejavu/bci-video-player

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function winsor(percentile, arr) {
    let rv = arr.slice();
    const len = rv.length;


    const mean = m.mean(rv);
    const std = m.std(rv);


    rv = rv.map(v => (v - mean) / std);


    const percentage = percentile / 100 / 2;

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SciLab.js

GitHub: MRossettiPQ/DashIMU-TCC

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  return array.map((obj) => Math.sqrt(obj))
}


exports.getStDeviation = (array) => {
  console.log('[SCILAB] Standard Deviation')
  return math.std(array)
}

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model01.js

GitHub: k3larra/generalisation

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get_std_from_pos_value(classnumber) {
  let std_value = 0;
  Object.entries(this.values.xai).forEach(([key, value]) => {
    if (value.hasOwnProperty("target_idx")) {
      if (parseInt(value.target_idx) === classnumber) {
        std_value = math.std(remove_neg_from_list(csv_to_list(value.raw_string)));
      }
    }
  });
  return std_value;

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server.js

GitHub: vfp2/mindfind-backend

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responses.forEach((response) => {
  randUniforms.push(response.data);
});

let average = math.mean(randUniforms);
let stddev = math.std(randUniforms);

var result = {results: []};
randUniforms.forEach((randUniform) => {
    // z-score: z = (x – μ) / σ

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