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02:19
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src/ML.php (1 issue)

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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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        $centroids = [];
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        for ($i=0;$i<$nclusters;$i++){
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            $centroids[] = $data[$i];
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        }
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        print( SiteAnalyzer::transform($centroids, "html") );
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        //$centroids = self::initCentroids($nclusters, $ndimensions, function(){return rand(0,100)/100;});   
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        while (!$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[] = $best;
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            }
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            print(SiteAnalyzer::transform([$nresp], "html"));
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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, $counts);
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        }
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        return [$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 normalizeCentroids($centroids, $counts)
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    {
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        var_dump($counts);
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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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