* * Learning rate should be a float value between 0.0(exclusive) and 1.0 (inclusive)
* Maximum number of iterations can be an integer value greater than 0
* If normalizeInputs is set to true, then every input given to the algorithm will be standardized * by use of standard deviation and mean calculation * * @param float $learningRate * @param int $maxIterations * @param bool $normalizeInputs * @param int $trainingType * * @throws \Exception */ public function __construct( float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true, int $trainingType = self::BATCH_TRAINING ) { if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) { throw new \Exception('Adaline can only be trained with batch and online/stochastic gradient descent algorithm'); } $this->trainingType = $trainingType; parent::__construct($learningRate, $maxIterations, $normalizeInputs); } /** * Adapts the weights with respect to given samples and targets * by use of gradient descent learning rule * * @param array $samples * @param array $targets */ protected function runTraining(array $samples, array $targets) { // The cost function is the sum of squares $callback = function ($weights, $sample, $target) { $this->weights = $weights; $output = $this->output($sample); $gradient = $output - $target; $error = $gradient ** 2; return [$error, $gradient]; }; $isBatch = $this->trainingType == self::BATCH_TRAINING; return parent::runGradientDescent($samples, $targets, $callback, $isBatch); } }