implement data Normalizer with L1 and L2 norm

src/Phpml/Exception/NormalizerException.php

@@ -0,0 +1,16 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Exception;
+
+class NormalizerException extends \Exception
+{
+    /**
+     * @return NormalizerException
+     */
+    public static function unknownNorm()
+    {
+        return new self('Unknown norm supplied.');
+    }
+}

src/Phpml/Preprocessing/Normalizer.php

@@ -0,0 +1,83 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Preprocessing;
+
+use Phpml\Exception\NormalizerException;
+
+class Normalizer implements Preprocessor
+{
+    const NORM_L1 = 1;
+    const NORM_L2 = 2;
+
+    /**
+     * @var int
+     */
+    private $norm;
+
+    /**
+     * @param int $norm
+     *
+     * @throws NormalizerException
+     */
+    public function __construct(int $norm = self::NORM_L2)
+    {
+        if (!in_array($norm, [self::NORM_L1, self::NORM_L2])) {
+            throw NormalizerException::unknownNorm();
+        }
+
+        $this->norm = $norm;
+    }
+
+    /**
+     * @param array $samples
+     */
+    public function preprocess(array &$samples)
+    {
+        $method = sprintf('normalizeL%s', $this->norm);
+        foreach ($samples as &$sample) {
+            $this->$method($sample);
+        }
+    }
+
+    /**
+     * @param array $sample
+     */
+    private function normalizeL1(array &$sample)
+    {
+        $norm1 = 0;
+        foreach ($sample as $feature) {
+            $norm1 += abs($feature);
+        }
+
+        if (0 == $norm1) {
+            $count = count($sample);
+            $sample = array_fill(0, $count, 1.0 / $count);
+        } else {
+            foreach ($sample as &$feature) {
+                $feature = $feature / $norm1;
+            }
+        }
+    }
+
+    /**
+     * @param array $sample
+     */
+    private function normalizeL2(array &$sample)
+    {
+        $norm2 = 0;
+        foreach ($sample as $feature) {
+            $norm2 += $feature * $feature;
+        }
+        $norm2 = sqrt($norm2);
+
+        if (0 == $norm2) {
+            $sample = array_fill(0, count($sample), 1);
+        } else {
+            foreach ($sample as &$feature) {
+                $feature = $feature / $norm2;
+            }
+        }
+    }
+}

tests/Phpml/Preprocessing/NormalizerTest.php

@@ -0,0 +1,58 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Preprocessing;
+
+use Phpml\Preprocessing\Normalizer;
+
+class NormalizerTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @expectedException \Phpml\Exception\NormalizerException
+     */
+    public function testThrowExceptionOnInvalidNorm()
+    {
+        new Normalizer(99);
+    }
+
+    public function testNormalizeSamplesWithL2Norm()
+    {
+        $samples = [
+            [1, -1, 2],
+            [2, 0, 0],
+            [0, 1, -1],
+        ];
+
+        $normalized = [
+            [0.4, -0.4, 0.81],
+            [1.0, 0.0, 0.0],
+            [0.0, 0.7, -0.7],
+        ];
+
+        $normalizer = new Normalizer();
+        $normalizer->preprocess($samples);
+
+        $this->assertEquals($normalized, $samples, '', $delta = 0.01);
+    }
+
+    public function testNormalizeSamplesWithL1Norm()
+    {
+        $samples = [
+            [1, -1, 2],
+            [2, 0, 0],
+            [0, 1, -1],
+        ];
+
+        $normalized = [
+            [0.25, -0.25, 0.5],
+            [1.0, 0.0, 0.0],
+            [0.0, 0.5, -0.5],
+        ];
+
+        $normalizer = new Normalizer(Normalizer::NORM_L1);
+        $normalizer->preprocess($samples);
+
+        $this->assertEquals($normalized, $samples, '', $delta = 0.01);
+    }
+}