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Differences Between: [Versions 311 and 400] [Versions 311 and 401] [Versions 311 and 402] [Versions 311 and 403]

   1  <?php
   2  
   3  declare(strict_types=1);
   4  
   5  namespace Phpml\Classification\Linear;
   6  
   7  use Phpml\Exception\InvalidArgumentException;
   8  
   9  class Adaline extends Perceptron
  10  {
  11      /**
  12       * Batch training is the default Adaline training algorithm
  13       */
  14      public const BATCH_TRAINING = 1;
  15  
  16      /**
  17       * Online training: Stochastic gradient descent learning
  18       */
  19      public const ONLINE_TRAINING = 2;
  20  
  21      /**
  22       * Training type may be either 'Batch' or 'Online' learning
  23       *
  24       * @var string|int
  25       */
  26      protected $trainingType;
  27  
  28      /**
  29       * Initalize an Adaline (ADAptive LInear NEuron) classifier with given learning rate and maximum
  30       * number of iterations used while training the classifier <br>
  31       *
  32       * Learning rate should be a float value between 0.0(exclusive) and 1.0 (inclusive) <br>
  33       * Maximum number of iterations can be an integer value greater than 0 <br>
  34       * If normalizeInputs is set to true, then every input given to the algorithm will be standardized
  35       * by use of standard deviation and mean calculation
  36       *
  37       * @throws InvalidArgumentException
  38       */
  39      public function __construct(
  40          float $learningRate = 0.001,
  41          int $maxIterations = 1000,
  42          bool $normalizeInputs = true,
  43          int $trainingType = self::BATCH_TRAINING
  44      ) {
  45          if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING], true)) {
  46              throw new InvalidArgumentException('Adaline can only be trained with batch and online/stochastic gradient descent algorithm');
  47          }
  48  
  49          $this->trainingType = $trainingType;
  50  
  51          parent::__construct($learningRate, $maxIterations, $normalizeInputs);
  52      }
  53  
  54      /**
  55       * Adapts the weights with respect to given samples and targets
  56       * by use of gradient descent learning rule
  57       */
  58      protected function runTraining(array $samples, array $targets): void
  59      {
  60          // The cost function is the sum of squares
  61          $callback = function ($weights, $sample, $target) {
  62              $this->weights = $weights;
  63  
  64              $output = $this->output($sample);
  65              $gradient = $output - $target;
  66              $error = $gradient ** 2;
  67  
  68              return [$error, $gradient];
  69          };
  70  
  71          $isBatch = $this->trainingType == self::BATCH_TRAINING;
  72  
  73          parent::runGradientDescent($samples, $targets, $callback, $isBatch);
  74      }
  75  }