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turftracker/frontend/node_modules/density-clustering/test/KMEANS.test.js
2026-04-09 13:19:47 -05:00

71 lines
1.8 KiB
JavaScript

require('should');
var KMEANS = require('../lib/index.js').KMEANS;
describe('KMEANS', function() {
describe('run', function() {
it('should return correct clusters', function() {
var kmeans = new KMEANS();
var k = 3;
var dataset = [
[1,1],[0,1],[1,0],
[10,10],[10,13],[13,13],
[54,54],[55,55],[89,89],[57,55]
];
var clusters = kmeans.run(dataset, k);
clusters.should.have.lengthOf(k);
clusters.forEach(function(cluster) {
(cluster instanceof Array).should.be.true;
cluster.length.should.be.greaterThan(0);
});
});
it('should return correct clusters for high dimensional data', function() {
var kmeans = new KMEANS();
var k = 4;
var dataset = generateData(k, 10, 10);
var clusters = kmeans.run(dataset, k);
clusters.should.have.lengthOf(k);
clusters.forEach(function(cluster) {
(cluster instanceof Array).should.be.true;
cluster.length.should.be.greaterThan(0);
});
});
});
describe('randomCentroid', function() {
it('should return extremes', function() {
var dataset = [
[-10, -20], [0,0], [30, 20]
];
var kmeans = new KMEANS(dataset);
var centroid = kmeans.randomCentroid();
centroid[0].should.be.within(-10, 30);
centroid[1].should.be.within(-20, 20);
});
});
});
function generateData(clusters, points, dimensions) {
var dataset = [];
for (var i = 0; i < clusters; i++) {
for (var p = 0; p < points; p++) {
var point = new Array(dimensions);
for (var d = 0; d < dimensions; d++) {
point[d] = Math.random() + (i * 100);
}
dataset.push(point);
}
}
return dataset;
}