implement k nearest neighbors classifier

src/Phpml/Classifier/Classifier.php

@@ -7,15 +7,15 @@ namespace Phpml\Classifier;
 interface Classifier
 {
     /**
-     * @param array $features
+     * @param array $samples
      * @param array $labels
      */
-    public function train(array $features, array $labels);
+    public function train(array $samples, array $labels);
 
     /**
-     * @param mixed $feature
+     * @param array $sample
      *
      * @return mixed
      */
-    public function predict($feature);
+    public function predict(array $sample);
 }

src/Phpml/Classifier/KNearestNeighbors.php

@@ -4,6 +4,8 @@ declare (strict_types = 1);
 
 namespace Phpml\Classifier;
 
+use Phpml\Metric\Distance;
+
 class KNearestNeighbors implements Classifier
 {
     /**
@@ -14,7 +16,7 @@ class KNearestNeighbors implements Classifier
     /**
      * @var array
      */
-    private $features;
+    private $samples;
 
     /**
      * @var array
@@ -27,26 +29,57 @@ class KNearestNeighbors implements Classifier
     public function __construct(int $k = 3)
     {
         $this->k = $k;
-        $this->features = [];
+        $this->samples = [];
         $this->labels = [];
     }
 
     /**
-     * @param array $features
+     * @param array $samples
      * @param array $labels
      */
-    public function train(array $features, array $labels)
+    public function train(array $samples, array $labels)
     {
-        $this->features = $features;
+        $this->samples = $samples;
         $this->labels = $labels;
     }
 
     /**
-     * @param mixed $feature
+     * @param array $sample
      *
      * @return mixed
      */
-    public function predict($feature)
+    public function predict(array $sample)
     {
+        $distances = $this->kNeighborsDistances($sample);
+
+        $predictions = [];
+        foreach ($distances as $index => $distance) {
+            $predictions[$this->labels[$index]]++;
+        }
+
+        arsort($predictions);
+
+        return array_shift(array_keys($predictions));
+    }
+
+    /**
+     * @param array $sample
+     *
+     * @return array
+     *
+     * @throws \Phpml\Exception\InvalidArgumentException
+     */
+    private function kNeighborsDistances(array $sample): array
+    {
+        $distances = [];
+        foreach($this->samples as $index => $neighbor) {
+            $distances[$index] = Distance::euclidean($sample, $neighbor);
+            if(count($distances)==$this->k) {
+                break;
+            }
+        }
+        asort($distances);
+
+        return $distances;
     }
 }

src/Phpml/Classifier/NaiveBayes.php

@@ -7,19 +7,19 @@ namespace Phpml\Classifier;
 class NaiveBayes implements Classifier
 {
     /**
-     * @param array $features
+     * @param array $samples
      * @param array $labels
      */
-    public function train(array $features, array $labels)
+    public function train(array $samples, array $labels)
     {
     }
 
     /**
-     * @param mixed $feature
+     * @param array $sample
      *
      * @return mixed
      */
-    public function predict($feature)
+    public function predict(array $sample)
     {
     }
 }