How to use the ml.ArrayStat function in ml
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repetere / modelscript / build / modelscript.esm.js View on Github 
checkType: true,
}, options, {
delimiter: '\t',
});
return loadCSV(filepath, tsvOptions);
}
var csvUtils = /*#__PURE__*/Object.freeze({
loadCSVURI: loadCSVURI,
loadCSV: loadCSV,
loadTSV: loadTSV
});
const avg = MachineLearning.ArrayStat.mean;
const mean = avg;
const sum = MachineLearning.ArrayStat.sum;
const scale = (a, d) => a.map(x => (x - avg(a)) / d);
const max = a => a.concat([]).sort((x, y) => x < y)[0];
const min = a => a.concat([]).sort((x, y) => x > y)[0];
const sd = MachineLearning.ArrayStat.standardDeviation; //(a, av) => Math.sqrt(avg(a.map(x => (x - av) * x)));
/**
* Returns an array of the squared different of two arrays
* @memberOf util
* @param {Number[]} left
* @param {Number[]} right
* @returns {Number[]} Squared difference of left minus right array
*/
function squaredDifference(left, right) {
return left.reduce((result, val, index, arr) => {
result.push(Math.pow((right[index]-val), 2));async function loadTSV(filepath, options) {
const tsvOptions = Object.assign({
checkType: true,
}, options, {
delimiter: '\t',
});
return loadCSV(filepath, tsvOptions);
}
var csvUtils = /*#__PURE__*/Object.freeze({
loadCSVURI: loadCSVURI,
loadCSV: loadCSV,
loadTSV: loadTSV
});
const avg = MachineLearning.ArrayStat.mean;
const mean = avg;
const sum = MachineLearning.ArrayStat.sum;
const scale = (a, d) => a.map(x => (x - avg(a)) / d);
const max = a => a.concat([]).sort((x, y) => x < y)[0];
const min = a => a.concat([]).sort((x, y) => x > y)[0];
const sd = MachineLearning.ArrayStat.standardDeviation; //(a, av) => Math.sqrt(avg(a.map(x => (x - av) * x)));
/**
* Returns an array of the squared different of two arrays
* @memberOf util
* @param {Number[]} left
* @param {Number[]} right
* @returns {Number[]} Squared difference of left minus right array
*/
function squaredDifference(left, right) {
