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<?php |
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declare(strict_types=1); |
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namespace Phpml\Classification\Linear; |
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use Phpml\Exception\InvalidArgumentException; |
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class Adaline extends Perceptron |
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{ |
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/** |
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* Batch training is the default Adaline training algorithm |
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*/ |
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public const BATCH_TRAINING = 1; |
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/** |
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* Online training: Stochastic gradient descent learning |
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*/ |
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public const ONLINE_TRAINING = 2; |
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/** |
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* Training type may be either 'Batch' or 'Online' learning |
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* |
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* @var string|int |
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*/ |
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protected $trainingType; |
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/** |
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* Initalize an Adaline (ADAptive LInear NEuron) classifier with given learning rate and maximum |
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* number of iterations used while training the classifier <br> |
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* |
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* Learning rate should be a float value between 0.0(exclusive) and 1.0 (inclusive) <br> |
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* Maximum number of iterations can be an integer value greater than 0 <br> |
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* If normalizeInputs is set to true, then every input given to the algorithm will be standardized |
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* by use of standard deviation and mean calculation |
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* |
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* @throws InvalidArgumentException |
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*/ |
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public function __construct( |
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float $learningRate = 0.001, |
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int $maxIterations = 1000, |
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bool $normalizeInputs = true, |
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int $trainingType = self::BATCH_TRAINING |
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) { |
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if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING], true)) { |
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throw new InvalidArgumentException('Adaline can only be trained with batch and online/stochastic gradient descent algorithm'); |
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} |
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$this->trainingType = $trainingType; |
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parent::__construct($learningRate, $maxIterations, $normalizeInputs); |
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} |
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/** |
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* Adapts the weights with respect to given samples and targets |
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* by use of gradient descent learning rule |
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*/ |
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protected function runTraining(array $samples, array $targets): void |
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{ |
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// The cost function is the sum of squares |
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$callback = function ($weights, $sample, $target): array { |
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$this->weights = $weights; |
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$output = $this->output($sample); |
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$gradient = $output - $target; |
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$error = $gradient ** 2; |
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return [$error, $gradient]; |
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}; |
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$isBatch = $this->trainingType == self::BATCH_TRAINING; |
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parent::runGradientDescent($samples, $targets, $callback, $isBatch); |
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} |
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} |
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