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efrain |
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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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