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<?php |
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declare(strict_types=1); |
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namespace Phpml\Clustering; |
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use Phpml\Clustering\KMeans\Cluster; |
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use Phpml\Clustering\KMeans\Point; |
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use Phpml\Clustering\KMeans\Space; |
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use Phpml\Exception\InvalidArgumentException; |
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use Phpml\Math\Distance\Euclidean; |
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class FuzzyCMeans implements Clusterer |
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{ |
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/** |
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* @var int |
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*/ |
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private $clustersNumber; |
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/** |
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* @var array|Cluster[] |
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*/ |
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private $clusters = null; |
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/** |
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* @var Space |
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*/ |
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private $space; |
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/** |
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* @var array|float[][] |
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*/ |
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private $membership = []; |
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/** |
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* @var float |
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*/ |
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private $fuzziness; |
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/** |
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* @var float |
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*/ |
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private $epsilon; |
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/** |
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* @var int |
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*/ |
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private $maxIterations; |
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/** |
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* @var int |
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*/ |
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private $sampleCount; |
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/** |
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* @var array |
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*/ |
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private $samples = []; |
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/** |
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* @throws InvalidArgumentException |
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*/ |
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public function __construct(int $clustersNumber, float $fuzziness = 2.0, float $epsilon = 1e-2, int $maxIterations = 100) |
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{ |
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if ($clustersNumber <= 0) { |
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throw InvalidArgumentException::invalidClustersNumber(); |
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} |
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$this->clustersNumber = $clustersNumber; |
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$this->fuzziness = $fuzziness; |
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$this->epsilon = $epsilon; |
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$this->maxIterations = $maxIterations; |
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} |
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public function getMembershipMatrix(): array |
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{ |
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return $this->membership; |
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} |
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/** |
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* @param Point[]|int[][] $samples |
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*/ |
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public function cluster(array $samples): array |
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{ |
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// Initialize variables, clusters and membership matrix |
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$this->sampleCount = count($samples); |
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$this->samples = &$samples; |
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$this->space = new Space(count($samples[0])); |
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$this->initClusters(); |
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// Our goal is minimizing the objective value while |
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// executing the clustering steps at a maximum number of iterations |
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$lastObjective = 0.0; |
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$iterations = 0; |
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do { |
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// Update the membership matrix and cluster centers, respectively |
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$this->updateMembershipMatrix(); |
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$this->updateClusters(); |
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// Calculate the new value of the objective function |
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$objectiveVal = $this->getObjective(); |
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$difference = abs($lastObjective - $objectiveVal); |
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$lastObjective = $objectiveVal; |
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} while ($difference > $this->epsilon && $iterations++ <= $this->maxIterations); |
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// Attach (hard cluster) each data point to the nearest cluster |
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for ($k = 0; $k < $this->sampleCount; ++$k) { |
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$column = array_column($this->membership, $k); |
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arsort($column); |
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reset($column); |
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$i = key($column); |
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$cluster = $this->clusters[$i]; |
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$cluster->attach(new Point($this->samples[$k])); |
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} |
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// Return grouped samples |
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$grouped = []; |
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foreach ($this->clusters as $cluster) { |
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$grouped[] = $cluster->getPoints(); |
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} |
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return $grouped; |
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} |
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protected function initClusters(): void |
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{ |
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// Membership array is a matrix of cluster number by sample counts |
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// We initilize the membership array with random values |
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$dim = $this->space->getDimension(); |
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$this->generateRandomMembership($dim, $this->sampleCount); |
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$this->updateClusters(); |
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} |
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protected function generateRandomMembership(int $rows, int $cols): void |
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{ |
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$this->membership = []; |
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for ($i = 0; $i < $rows; ++$i) { |
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$row = []; |
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$total = 0.0; |
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for ($k = 0; $k < $cols; ++$k) { |
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$val = random_int(1, 5) / 10.0; |
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$row[] = $val; |
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$total += $val; |
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} |
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$this->membership[] = array_map(function ($val) use ($total) { |
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return $val / $total; |
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}, $row); |
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} |
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} |
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protected function updateClusters(): void |
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{ |
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$dim = $this->space->getDimension(); |
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if (!$this->clusters) { |
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$this->clusters = []; |
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for ($i = 0; $i < $this->clustersNumber; ++$i) { |
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$this->clusters[] = new Cluster($this->space, array_fill(0, $dim, 0.0)); |
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} |
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} |
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for ($i = 0; $i < $this->clustersNumber; ++$i) { |
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$cluster = $this->clusters[$i]; |
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$center = $cluster->getCoordinates(); |
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for ($k = 0; $k < $dim; ++$k) { |
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$a = $this->getMembershipRowTotal($i, $k, true); |
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$b = $this->getMembershipRowTotal($i, $k, false); |
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$center[$k] = $a / $b; |
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} |
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$cluster->setCoordinates($center); |
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} |
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} |
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protected function getMembershipRowTotal(int $row, int $col, bool $multiply) |
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{ |
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$sum = 0.0; |
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for ($k = 0; $k < $this->sampleCount; ++$k) { |
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$val = pow($this->membership[$row][$k], $this->fuzziness); |
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if ($multiply) { |
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$val *= $this->samples[$k][$col]; |
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} |
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$sum += $val; |
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} |
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return $sum; |
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} |
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protected function updateMembershipMatrix(): void |
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{ |
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for ($i = 0; $i < $this->clustersNumber; ++$i) { |
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for ($k = 0; $k < $this->sampleCount; ++$k) { |
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$distCalc = $this->getDistanceCalc($i, $k); |
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$this->membership[$i][$k] = 1.0 / $distCalc; |
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} |
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} |
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} |
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protected function getDistanceCalc(int $row, int $col): float |
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{ |
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$sum = 0.0; |
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$distance = new Euclidean(); |
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$dist1 = $distance->distance( |
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$this->clusters[$row]->getCoordinates(), |
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$this->samples[$col] |
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); |
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for ($j = 0; $j < $this->clustersNumber; ++$j) { |
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$dist2 = $distance->distance( |
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$this->clusters[$j]->getCoordinates(), |
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$this->samples[$col] |
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); |
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$val = pow($dist1 / $dist2, 2.0 / ($this->fuzziness - 1)); |
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$sum += $val; |
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} |
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return $sum; |
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} |
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/** |
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* The objective is to minimize the distance between all data points |
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* and all cluster centers. This method returns the summation of all |
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* these distances |
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*/ |
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protected function getObjective() |
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{ |
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$sum = 0.0; |
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$distance = new Euclidean(); |
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for ($i = 0; $i < $this->clustersNumber; ++$i) { |
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$clust = $this->clusters[$i]->getCoordinates(); |
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for ($k = 0; $k < $this->sampleCount; ++$k) { |
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$point = $this->samples[$k]; |
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$sum += $distance->distance($clust, $point); |
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} |
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} |
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return $sum; |
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} |
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} |
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If a method or function can return multiple different values and unless you are sure that you only can receive a single value in this context, we recommend to add an additional type check:
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