nstatistics/test/test.js

Solve equations using numerical methods, linear álgebra and solver of linear equation system

const assert = require('assert');
const JNsolve = require('../index');
let data, stats;


Number.prototype.truncate = function(n) {
    return Math.floor(this * Math.pow(10, n)) / Math.pow(10, n);
};

describe('Stats module test', () => {
    before(() => {
    // create a instance of stats object
        data = [
            [ 3, 4, 5, 2, 1, 5, 6 ],
            [ 1, 4, 0, 4, 1, 5, 6 ],
            [ 6, 4, 5, 2, 1, 5, 1 ],
            [ 3, 4, 5, 5, 0, 5, 4 ],
            [ 4, 4, 5, 2, 1, 5, 12 ],
            [ 0, 4, 0, 9, 1, 5, 3 ],
            [ 6, 4, 3, 2, 0, 5, 6 ]
        ];
        stats = new JNsolve(data);
    });
    it('the data property is the array passed to class Stats ', () => {
        assert.equal(stats.data.length, data.length);
        assert.equal(stats.data[0][0], data[0][0]); // / should returns true
    });
    it(
        'the media of datas given is [ [ 3.2857142857142856 ],[ 4 ],[ 3.2857142857142856 ],[ 3.714285714285714 ],[ 0.7142857142857142 ],[ 5 ],[ 5.428571428571428 ] ] ',
        () => {
            const media = stats.media();
            assert.equal(media._(1, 1), 3.2857142857142856);
            assert.equal(media._(2, 1), 4);
            assert.equal(media._(3, 1), 3.2857142857142856);
            assert.equal(media._(4, 1), 3.714285714285714);
            assert.equal(media._(5, 1), 0.7142857142857142);
            assert.equal(media._(6, 1), 5);
            assert.equal(media._(7, 1), 5.428571428571428);
            // / should returns true
        });
    it('the matrix correlation is given by:', () => {
        const diagonal = stats.std().diagonal();
        assert.equal(diagonal._(1, 1), 2.288688541085317);
        assert.equal(diagonal._(2, 1), 0);
        assert.equal(diagonal._(3, 1), 2.3603873774083293);
        assert.equal(diagonal._(4, 1), 2.627691364061218);
        assert.equal(diagonal._(5, 1), 0.48795003647426666);
        assert.equal(diagonal._(6, 1), 0);
        assert.equal(diagonal._(7, 1),
            3.4572215654165053);
    // / should returns true
    });
    it(
        'the matrix correlation is given by:[ [ 0.4369314487526515 ],[ NaN ],[ 0.42365927286816174 ],[ 0.38056219755369364 ],[ 2.0493901531919194 ],[ NaN ], [ 0.2892496130428152 ] ]',
        () => {
            const covarianze = stats.covariance().diagonal();
            assert.equal(covarianze._(1, 1), 0.4369314487526515);
            assert.equal(covarianze._(3, 1), 0.42365927286816174);
            assert.equal(covarianze._(4, 1), 0.38056219755369364);
            assert.equal(covarianze._(5, 1), 2.0493901531919194);
            assert.equal(covarianze._(7, 1), 0.2892496130428152);
            // / should returns true
        });
});