public function testClusterMinMax()
{
$cluster = new Cluster();
$cluster[] = new Point(1, 2);
$this->assertEquals($cluster->getXMin(), 1);
$this->assertEquals($cluster->getXMax(), 1);
$this->assertEquals($cluster->getYMin(), 2);
$this->assertEquals($cluster->getYMax(), 2);
$cluster[] = new Point(2, 2);
$this->assertEquals($cluster->getXMin(), 1);
$this->assertEquals($cluster->getXMax(), 2);
$this->assertEquals($cluster->getYMin(), 2);
$this->assertEquals($cluster->getYMax(), 2);
$cluster[] = new Point(1.5, 2);
$this->assertEquals($cluster->getXMin(), 1);
$this->assertEquals($cluster->getXMax(), 2);
$this->assertEquals($cluster->getYMin(), 2);
$this->assertEquals($cluster->getYMax(), 2);
$cluster[] = new Point(0, 2);
$this->assertEquals($cluster->getXMin(), 0);
$this->assertEquals($cluster->getXMax(), 2);
$this->assertEquals($cluster->getYMin(), 2);
$this->assertEquals($cluster->getYMax(), 2);
$cluster[] = new Point(0, -2);
$this->assertEquals($cluster->getXMin(), 0);
$this->assertEquals($cluster->getXMax(), 2);
$this->assertEquals($cluster->getYMin(), -2);
$this->assertEquals($cluster->getYMax(), 2);
$cluster[] = new Point(0, 10);
$this->assertEquals($cluster->getXMin(), 0);
$this->assertEquals($cluster->getXMax(), 2);
$this->assertEquals($cluster->getYMin(), -2);
$this->assertEquals($cluster->getYMax(), 10);
}
private function init($data)
{
// Fill search class with all the points
$this->search->setData($data);
// Initialize auxiliary variables
$this->visited = array();
$this->cluster = array();
$this->noise = array();
// Check if any parameter is missing to calculate heuristic values
if ($this->eps === null || $this->minPoints === null) {
$data_clustered = $data;
if (!$data_clustered instanceof Cluster) {
// Convert array to cluster
$data_clustered = new Cluster($data);
}
// Calculate heuristic values and set them as DBSCAN parameters
$x_diff = $data_clustered->getXMax() - $data_clustered->getXMin();
$y_diff = $data_clustered->getYMax() - $data_clustered->getYMin();
$this->eps = max($x_diff, $y_diff) / $this::HEURISTIC_DIVISION;
if ($this->eps <= 0) {
// If all the points are in the same place, eps will be 0,
// and because the distance comparison is a "less strict"
// all points would be noise. This way they will all belong
// to the same cluster
$this->eps = 1;
}
$this->minPoints = 1;
}
}