How to use the mean function from mathjs

getAzDoRun.js

GitHub: adoptium/aqa-test-tools

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    ) &&
    benchmarkMetricsCollection[key]
        .length > 0
) {
    const mean = math.round(
        math.mean(
            benchmarkMetricsCollection[
                key
            ]
        ),

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functions.js

GitHub: kengz/Risk-game

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    function f(x) { return m.sum(x); }
    return _.map(a, f);
},
// return mean of vector
mean: function(v) {
    return m.mean(v);
},
// composition of mean•sumrow, i.e. mean of column vector of chunk
msr: function(a) {
    return _.compose(fn.mean, fn.sumrow)(a);

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analyse-code-size.js

GitHub: theburningmonk/lambda-coldstart-comparison

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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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report

GitHub: Sable/wu-wei-benchmarking-toolkit

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keys.compilers[results[h].compiler['short-name']] = true
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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feature-extractor.js

GitHub: themaxsandelin/co-q

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  //console.log(x_target_norm)
  //Calculate MSE between song and target
  e = math.subtract(x_song_norm,x_target_norm);
  //console.log(e)
  se = math.dotPow(e, 2);
  mse = math.mean(se);
  return mse;
}

return {

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index.js

GitHub: jly36963/notes

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// stats
math.max(numbers);
math.min(numbers);
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);

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zuki_nn.js

GitHub: wattta/gekko-neuralnet

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}

let prediction = this.predictCandle() * this.scale;
let currentPrice = candle.close;
log.debug('Price = ' + currentPrice + ', Prediction = ' + prediction);
let meanp = math.mean(prediction, currentPrice);
let meanAlpha = (meanp - currentPrice) / currentPrice * 100;


// sell only if the price is higher than the buying price or if the price drops below the threshold

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model01.js

GitHub: k3larra/generalisation

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

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server.js

GitHub: vfp2/mindfind-backend

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

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

var result = {results: []};
randUniforms.forEach((randUniform) => {

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unionHull.js

GitHub: jalbelo007/Elastoplastic_1DSprings_App

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                Error(['Unrecognized representation label of polyhedron', toString(i)]);
        }
    }


    let V = S.concat(V);
    let x0 = math.mean(V, 1);
    [U.lcon[[math.range(1, 4, true)]]] = vert2lcon(V, math.reshape[[TOL]]);
    U.vert = qlcon2vert(x0, math.reshape[[U.lcon]], math.reshape[[TOL]]);
}

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index.js

GitHub: Kirigaya-Kazuton/IA

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// máximo (maior valor)
console.log('máximo:', math.max([2, 1, 3]));
// soma (retorna 6)
console.log('soma:', math.sum([2, 1, 3]));
// média (retorna 2)
console.log('média:', math.mean([2, 1, 3]));
// mediana (número entre o maior e o menor -> 2)
console.log('mediana:', math.median([2, 1, 3]));
// produto (multiplicação)
console.log('produto:', math.prod([2, 1, 3]));

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app.js

GitHub: yakisoap-bar/internship-stuff

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    "signature_name": "serving_default", 
    "instances": records
}).then(response => {
    // return correct results
    let predictions = response.data.predictions;
    predictions = math.mean(predictions, 0);
    let filteredSignals = [];

    // filter results based on passed frequency
    if (filter) {

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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);

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finetuneVectors.js

GitHub: Firstbober/abridged-word2vec

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		return Sigmoid(v)
	})]
}

avgTendency = mathjs.mean(avgTendency, amountOfLessLost / amountOfMoreLost)
console.log(`Current tokens: "${tokens}"; training for "${validToken}"; it ${iteration}; loss ${loss}; tendency ${amountOfLessLost / amountOfMoreLost}; avgTendency ${avgTendency}`)

iteration += 1
lastLoss = loss

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update_results.js

GitHub: bigf00t/bgpeen

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if (!result.playerCounts) {
  return result;
}

// Get the mean of means for all playsCounts
const expectedMean = mean(
  _.transform(
    result.playerCounts,
    (means, num, playerCount) => {
      means.push(Array(num).fill(playerCountMeans[playerCount]));

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strategy.js

GitHub: erulabs/zenbot

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  s.mean = math.mean(
    s.lookback[0].close,
    s.lookback[1].close,
    s.lookback[2].close
  )
  s.meanp = math.mean(s.prediction, oldmean)
  oldmean = s.prediction
}
// NORMAL onPeriod STUFF here
global.meanp = s.meanp

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+ 3 other calls in file