src/utils/StatisticUtils.ts

   1 import { isEmptyArray, isNullOrUndefined } from './Utils';
   2
   3 export const median = (dataSet: number[]): number => {
   4   if (isEmptyArray(dataSet)) {
   5     return 0;
   6   }
   7   if (Array.isArray(dataSet) === true && dataSet.length === 1) {
   8     return dataSet[0];
   9   }
  10   const sortedDataSet = dataSet.slice().sort((a, b) => a - b);
  11   return (
  12     (sortedDataSet[(sortedDataSet.length - 1) >> 1] + sortedDataSet[sortedDataSet.length >> 1]) / 2
  13   );
  14 };
  15
  16 // TODO: use order statistics tree https://en.wikipedia.org/wiki/Order_statistic_tree
  17 export const nthPercentile = (dataSet: number[], percentile: number): number => {
  18   if (percentile < 0 && percentile > 100) {
  19     throw new RangeError('Percentile is not between 0 and 100');
  20   }
  21   if (isEmptyArray(dataSet)) {
  22     return 0;
  23   }
  24   const sortedDataSet = dataSet.slice().sort((a, b) => a - b);
  25   if (percentile === 0 || sortedDataSet.length === 1) {
  26     return sortedDataSet[0];
  27   }
  28   if (percentile === 100) {
  29     return sortedDataSet[sortedDataSet.length - 1];
  30   }
  31   const percentileIndexBase = (percentile / 100) * (sortedDataSet.length - 1);
  32   const percentileIndexInteger = Math.floor(percentileIndexBase);
  33   if (!isNullOrUndefined(sortedDataSet[percentileIndexInteger + 1])) {
  34     return (
  35       sortedDataSet[percentileIndexInteger] +
  36       (percentileIndexBase - percentileIndexInteger) *
  37         (sortedDataSet[percentileIndexInteger + 1] - sortedDataSet[percentileIndexInteger])
  38     );
  39   }
  40   return sortedDataSet[percentileIndexInteger];
  41 };
  42
  43 export const stdDeviation = (dataSet: number[]): number => {
  44   let totalDataSet = 0;
  45   for (const data of dataSet) {
  46     totalDataSet += data;
  47   }
  48   const dataSetMean = totalDataSet / dataSet.length;
  49   let totalGeometricDeviation = 0;
  50   for (const data of dataSet) {
  51     const deviation = data - dataSetMean;
  52     totalGeometricDeviation += deviation * deviation;
  53   }
  54   return Math.sqrt(totalGeometricDeviation / dataSet.length);
  55 };