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Differences Between: [Versions 310 and 400] [Versions 311 and 400] [Versions 39 and 400]

   1  <?php
   2  
   3  declare(strict_types=1);
   4  
   5  namespace Phpml\Preprocessing;
   6  
   7  use Phpml\Exception\NormalizerException;
   8  use Phpml\Math\Statistic\Mean;
   9  use Phpml\Math\Statistic\StandardDeviation;
  10  
  11  class Normalizer implements Preprocessor
  12  {
  13      public const NORM_L1 = 1;
  14  
  15      public const NORM_L2 = 2;
  16  
  17      public const NORM_STD = 3;
  18  
  19      /**
  20       * @var int
  21       */
  22      private $norm;
  23  
  24      /**
  25       * @var bool
  26       */
  27      private $fitted = false;
  28  
  29      /**
  30       * @var array
  31       */
  32      private $std = [];
  33  
  34      /**
  35       * @var array
  36       */
  37      private $mean = [];
  38  
  39      /**
  40       * @throws NormalizerException
  41       */
  42      public function __construct(int $norm = self::NORM_L2)
  43      {
  44          if (!in_array($norm, [self::NORM_L1, self::NORM_L2, self::NORM_STD], true)) {
  45              throw new NormalizerException('Unknown norm supplied.');
  46          }
  47  
  48          $this->norm = $norm;
  49      }
  50  
  51      public function fit(array $samples, ?array $targets = null): void
  52      {
  53          if ($this->fitted) {
  54              return;
  55          }
  56  
  57          if ($this->norm === self::NORM_STD) {
  58              $features = range(0, count($samples[0]) - 1);
  59              foreach ($features as $i) {
  60                  $values = array_column($samples, $i);
  61                  $this->std[$i] = StandardDeviation::population($values);
  62                  $this->mean[$i] = Mean::arithmetic($values);
  63              }
  64          }
  65  
  66          $this->fitted = true;
  67      }
  68  
  69      public function transform(array &$samples, ?array &$targets = null): void
  70      {
  71          $methods = [
  72              self::NORM_L1 => 'normalizeL1',
  73              self::NORM_L2 => 'normalizeL2',
  74              self::NORM_STD => 'normalizeSTD',
  75          ];
  76          $method = $methods[$this->norm];
  77  
  78          $this->fit($samples);
  79  
  80          foreach ($samples as &$sample) {
  81              $this->{$method}($sample);
  82          }
  83      }
  84  
  85      private function normalizeL1(array &$sample): void
  86      {
  87          $norm1 = 0;
  88          foreach ($sample as $feature) {
  89              $norm1 += abs($feature);
  90          }
  91  
  92          if ($norm1 == 0) {
  93              $count = count($sample);
  94              $sample = array_fill(0, $count, 1.0 / $count);
  95          } else {
  96              array_walk($sample, function (&$feature) use ($norm1): void {
  97                  $feature /= $norm1;
  98              });
  99          }
 100      }
 101  
 102      private function normalizeL2(array &$sample): void
 103      {
 104          $norm2 = 0;
 105          foreach ($sample as $feature) {
 106              $norm2 += $feature * $feature;
 107          }
 108  
 109          $norm2 **= .5;
 110  
 111          if ($norm2 == 0) {
 112              $sample = array_fill(0, count($sample), 1);
 113          } else {
 114              array_walk($sample, function (&$feature) use ($norm2): void {
 115                  $feature /= $norm2;
 116              });
 117          }
 118      }
 119  
 120      private function normalizeSTD(array &$sample): void
 121      {
 122          foreach (array_keys($sample) as $i) {
 123              if ($this->std[$i] != 0) {
 124                  $sample[$i] = ($sample[$i] - $this->mean[$i]) / $this->std[$i];
 125              } else {
 126                  // Same value for all samples.
 127                  $sample[$i] = 0;
 128              }
 129          }
 130      }
 131  }