als-statistics/tests/integration.test.js

A powerful and lightweight JavaScript library for descriptive statistics, regression, clustering, outlier detection, and noise analysis using a flexible table/column architecture.

const { describe, it } = require('node:test');
const assert = require('node:assert');
const Statistics = require('../index');
const Table = require('../lib/table/index');
const RatioColumn = require('../lib/ratio-column/index');

describe('Integration Tests', () => {
   it('should preserve filtering when transposed', () => {
      const table = new Table();
      table.addColumn('A', [10, 20, 30, 40, 50]);
      table.addColumn('B', [5, 15, 25, 35, 45]);
      table.filterRows([1, 2, 3]); // Фильтруем индексы 1, 2, 3

      const transposed = table.transpose();
      assert.strictEqual(Object.keys(transposed.columns).length, 2); // Осталось 2 строки (0 и 4)
   });

   it('should correctly handle different column lengths', () => {
      const table = new Table();
      table.addColumn('Short', [10, 20, 30]);
      table.addColumn('Long', [5, 15, 25, 35, 45]);

      assert.strictEqual(table.n, 5); // Максимальная длина
      table.filterRows([1, 2, 3]);
      assert.strictEqual(table.columns['Short'].n, 1); // Остался только индекс 0
      assert.strictEqual(table.columns['Long'].n, 2); // Остались индексы 0 и 4
   });

   it('should compute new column correctly with filtering', () => {
      const table = new Table();
      table.addColumn('A', [1, 2, 3, 4, 5]);
      table.addColumn('B', [10, 20, 30, 40, 50]);
      table.filterRows([1, 2, 3]);

      const sumCol = table.compute(({ A = 0, B = 0 }) => A + B, 'Sum');
      assert.deepStrictEqual(sumCol.values, [11, 55]); // Остались индексы 0 и 4
   });

   it('should apply descriptive metrics correctly after filtering', () => {
      const table = new Table();
      table.addColumn('Numbers', [10, 20, 30, 40, 50]);
      table.filterRows([2, 3, 4]);

      const descTable = table.descriptive('mean');
      assert.strictEqual(descTable.values[0], 15); // Среднее из [10, 20]
   });

   it('should correctly compare two filtered columns', () => {
      const table = new Table();
      table.addColumn('A', [10, 20, 30, 40, 50]);
      table.addColumn('B', [5, 15, 25, 35, 45]);
      table.filterRows([2, 3, 4]);

      const comparison = table.compare('A', 'B');
      assert.strictEqual(comparison.correlationSample.toFixed(2), '1.00'); // Линейная зависимость
   });

   it('should correctly perform two-sample t-test after filtering', () => {
      const table = new Table();
      table.addColumn('Group1', [10, 20, 30, 40, 50]);
      table.addColumn('Group2', [12, 22, 32, 42, 52]);
      table.filterRows([0, 1, 2]); // Оставляем последние два

      const comparison = table.compare('Group1', 'Group2');
      const { t } = comparison.twoSampleTTest();
      assert.ok(t > -1 && t < 1); // Небольшое отличие
   });

   it('should apply filtering across all metrics', () => {
      const table = new Table();
      table.addColumn('Numbers', [10, 20, 30, 40, 50]);
      table.filterRows([0, 1, 2]); // Оставляем 40 и 50

      assert.strictEqual(table.columns['Numbers'].mean, 45); // (40+50)/2
      assert.strictEqual(table.columns['Numbers'].n, 2);
   });

   it('should clone table with filtered values', () => {
      const table = new Table();
      table.addColumn('A', [10, 20, 30, 40, 50]);
      table.filterRows([1, 2, 3]);
      const cloned = table.clone(true);
      assert.strictEqual(cloned.columns['A'].n, 2); // Остались 10 и 50
      assert.deepStrictEqual(cloned.columns['A'].values, [10, 50]);
   });
});