public function testEuclideanClustering()
{
$clust = new KMeans(2, new Euclidean(), new EuclidCF(), 0.001);
$tset = new TrainingSet();
for ($i = 0; $i < 500; $i++) {
$tset->addDocument('A', EuclideanPoint::getRandomPointAround(100, 100, 45));
}
for ($i = 0; $i < 500; $i++) {
$tset->addDocument('B', EuclideanPoint::getRandomPointAround(200, 100, 45));
}
list($clusters, $centroids, $distances) = $clust->cluster($tset, new DataAsFeatures());
$im = $this->drawClusters($tset, $clusters, $centroids, false);
if ($im) {
imagepng($im, TEST_DATA_DIR . "/Clustering/KmeansTest/clusters.png");
}
// since the dataset is artificial and clearly separated, the kmeans
// algorithm should always cluster it correctly
foreach ($clusters as $clust) {
$classes = array();
foreach ($clust as $point_idx) {
$class = $tset[$point_idx]->getClass();
if (!isset($classes[$class])) {
$classes[$class] = true;
}
}
// assert that all the documents (points) in this cluster belong
// in the same class
$this->assertCount(1, $classes);
}
}
public function testClustering2()
{
$N = 50;
$tset = new TrainingSet();
for ($i = 0; $i < $N; $i++) {
$tset->addDocument('', EuclideanPoint::getRandomPointAround(100, 100, 45));
}
for ($i = 0; $i < $N; $i++) {
$tset->addDocument('', EuclideanPoint::getRandomPointAround(200, 100, 45));
}
$hc = new Hierarchical(new SingleLink(), new Euclidean());
list($dendrogram) = $hc->cluster($tset, new DataAsFeatures());
$dg = $this->drawDendrogram($tset, $dendrogram, 600);
$clusters = Hierarchical::dendrogramToClusters($dendrogram, 2);
$im = $this->drawClusters($tset, $clusters, null, false, 10);
if ($dg) {
imagepng($dg, TEST_DATA_DIR . "/Clustering/HierarchicalTest/dendrogram.png");
}
if ($im) {
imagepng($im, TEST_DATA_DIR . "/Clustering/HierarchicalTest/clusters.png");
}
}