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<?php |
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/** |
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* |
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* (c) Ruben Dorado <[email protected]> |
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* |
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* For the full copyright and license information, please view the LICENSE |
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* file that was distributed with this source code. |
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*/ |
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namespace SiteAnalyzer; |
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use Exception; |
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/** |
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* class ML |
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* |
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* @package SiteAnalyzer |
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* @author Ruben Dorado <[email protected]> |
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* @copyright 2018 Ruben Dorado |
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* @license http://www.opensource.org/licenses/MIT The MIT License |
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*/ |
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class ML |
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{ |
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/* |
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* @param |
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*/ |
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public static function kmeans($data, $nclusters) |
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{ |
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$resp = []; |
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$finished = false; |
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$niter = 0; |
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$maxiter = 100; |
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$npoints = count($data); |
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if ($npoints <= 0) throw new \Exception("Not enough data. "); |
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$ndimensions = count($data[0]); |
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$centroids = self::select_disjoint($data, $nclusters); |
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while (!$finished && $niter < $maxiter) { |
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// Assign each one of the points to one centroid |
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$niter++; |
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$nresp = []; |
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for ($j = 0; $j < $npoints; $j++) { |
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$best = -1; |
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$bdist = INF; |
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for ($i = 0; $i < $nclusters; $i++) { |
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$ndist = self::eclideanDistance($data[$j], $centroids[$i]); |
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if($bdist > $ndist) { |
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$bdist = $ndist; |
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$best = $i; |
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} |
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} |
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$nresp[] = $best; |
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} |
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// Check change |
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$finished = true; |
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if (count($resp) > 0) { |
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for ($j=0; $j < $npoints; $j++) { |
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if ($resp[$j]!==$nresp[$j]) { |
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$finished = false; |
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break; |
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} |
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} |
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} else { |
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$finished = false; |
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} |
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$resp = $nresp; |
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// Recalculate the centroids |
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$centroids = self::initCentroids($nclusters, $ndimensions, function(){return 0;}); |
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$counts = array_fill(0, $nclusters, 0); |
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for ($j = 0; $j < $npoints; $j++) { |
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$centroids[$resp[$j]] = Matrix::sumArray($centroids[$resp[$j]], $data[$j]); |
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$counts[$resp[$j]]++; |
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} |
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$centroids = self::normalizeCentroids($centroids, $counts); |
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} |
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return ["clusters"=>$resp, "centroids"=>$centroids]; |
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} |
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/* |
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* @param |
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*/ |
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private static function select_disjoint($data, $n) |
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{ |
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$resp = []; |
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foreach ($data as $row) { |
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if ( !self::contains_point($resp, $row) ) { |
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$resp[] = $row; |
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} |
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if (count($resp) == $n) { |
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return $resp; |
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} |
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} |
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throw new \Exception("Not enough unique points."); |
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} |
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/* |
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* @param |
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*/ |
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private static function contains_point($matrix, $array) |
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{ |
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foreach ($matrix as $row){ |
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if (self::isEqual($row, $array)) return true; |
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} |
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return false; |
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} |
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/* |
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* @param |
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*/ |
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private static function isEqual($array1, $array2) |
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{ |
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$len = count($array1); |
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if($len != count($array2) ) return false; |
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for ($i=0; $i<$len; $i++) { |
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if ($array1[$i] != $array2[$i]) return false; |
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} |
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return true; |
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} |
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/* |
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* @param |
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*/ |
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private static function normalizeCentroids($centroids, $counts) |
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{ |
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$resp = []; |
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$n = count($centroids); |
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$d = count($centroids[0]); |
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for ($i=0;$i<$n;$i++) { |
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$tmp = []; |
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for ($j=0;$j<$d;$j++){ |
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$tmp[] = $centroids[$i][$j]/$counts[$i]; |
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} |
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$resp[] = $tmp; |
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} |
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return $resp; |
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} |
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/* |
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* @param |
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*/ |
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public static function initCentroids($nclusters, $ndimensions, $fvalue) |
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{ |
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$resp = []; |
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for ($i = 0; $i < $nclusters; $i++) { |
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$centroid = []; |
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for ($d = 0; $d < $ndimensions; $d++) { |
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$centroid[] = $fvalue(); |
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} |
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$resp[] = $centroid; |
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} |
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return $resp; |
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} |
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/* |
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* @param |
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*/ |
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public static function eclideanDistance($p1, $p2) { |
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$len = count($p1); |
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$acum = 0; |
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for($i=0; $i<$len; $i++) { |
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$acum += ($p1[$i] - $p2[$i])**2; |
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} |
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return sqrt($acum); |
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} |
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} |
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rdorado /
site-analyzer
