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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 Closure; |
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use Phpml\Classification\Classifier; |
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use Phpml\Exception\InvalidArgumentException; |
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use Phpml\Helper\OneVsRest; |
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use Phpml\Helper\Optimizer\GD; |
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use Phpml\Helper\Optimizer\Optimizer; |
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use Phpml\Helper\Optimizer\StochasticGD; |
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use Phpml\Helper\Predictable; |
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use Phpml\IncrementalEstimator; |
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use Phpml\Preprocessing\Normalizer; |
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class Perceptron implements Classifier, IncrementalEstimator |
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{ |
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use Predictable; |
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use OneVsRest; |
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/** |
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* @var Optimizer|GD|StochasticGD|null |
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*/ |
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protected $optimizer; |
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/** |
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* @var array |
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*/ |
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protected $labels = []; |
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/** |
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* @var int |
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*/ |
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protected $featureCount = 0; |
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/** |
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* @var array |
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*/ |
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protected $weights = []; |
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/** |
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* @var float |
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*/ |
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protected $learningRate; |
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/** |
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* @var int |
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*/ |
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protected $maxIterations; |
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/** |
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* @var Normalizer |
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*/ |
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protected $normalizer; |
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/** |
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* @var bool |
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*/ |
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protected $enableEarlyStop = true; |
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/** |
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* Initalize a perceptron classifier with given learning rate and maximum |
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* number of iterations used while training the perceptron |
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* |
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* @param float $learningRate Value between 0.0(exclusive) and 1.0(inclusive) |
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* @param int $maxIterations Must be at least 1 |
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* |
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* @throws InvalidArgumentException |
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*/ |
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public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true) |
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{ |
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if ($learningRate <= 0.0 || $learningRate > 1.0) { |
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throw new InvalidArgumentException('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)'); |
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} |
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if ($maxIterations <= 0) { |
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throw new InvalidArgumentException('Maximum number of iterations must be an integer greater than 0'); |
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} |
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if ($normalizeInputs) { |
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$this->normalizer = new Normalizer(Normalizer::NORM_STD); |
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} |
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$this->learningRate = $learningRate; |
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$this->maxIterations = $maxIterations; |
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} |
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public function partialTrain(array $samples, array $targets, array $labels = []): void |
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{ |
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$this->trainByLabel($samples, $targets, $labels); |
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} |
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public function trainBinary(array $samples, array $targets, array $labels): void |
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{ |
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if ($this->normalizer !== null) { |
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$this->normalizer->transform($samples); |
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} |
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// Set all target values to either -1 or 1 |
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$this->labels = [ |
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1 => $labels[0], |
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-1 => $labels[1], |
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]; |
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foreach ($targets as $key => $target) { |
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$targets[$key] = (string) $target == (string) $this->labels[1] ? 1 : -1; |
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} |
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// Set samples and feature count vars |
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$this->featureCount = count($samples[0]); |
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$this->runTraining($samples, $targets); |
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} |
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/** |
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* Normally enabling early stopping for the optimization procedure may |
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* help saving processing time while in some cases it may result in |
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* premature convergence.<br> |
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* |
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* If "false" is given, the optimization procedure will always be executed |
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* for $maxIterations times |
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* |
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* @return $this |
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*/ |
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public function setEarlyStop(bool $enable = true) |
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{ |
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$this->enableEarlyStop = $enable; |
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return $this; |
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} |
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/** |
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* Returns the cost values obtained during the training. |
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*/ |
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public function getCostValues(): array |
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{ |
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return $this->costValues; |
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} |
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protected function resetBinary(): void |
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{ |
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$this->labels = []; |
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$this->optimizer = null; |
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$this->featureCount = 0; |
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$this->weights = []; |
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$this->costValues = []; |
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} |
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/** |
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* Trains the perceptron model with Stochastic Gradient Descent optimization |
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* to get the correct set of weights |
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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 = $weights; |
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$prediction = $this->outputClass($sample); |
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$gradient = $prediction - $target; |
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$error = $gradient ** 2; |
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return [$error, $gradient]; |
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}; |
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$this->runGradientDescent($samples, $targets, $callback); |
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} |
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/** |
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* Executes a Gradient Descent algorithm for |
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* the given cost function |
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*/ |
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protected function runGradientDescent(array $samples, array $targets, Closure $gradientFunc, bool $isBatch = false): void |
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{ |
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$class = $isBatch ? GD::class : StochasticGD::class; |
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if ($this->optimizer === null) { |
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$this->optimizer = (new $class($this->featureCount)) |
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->setLearningRate($this->learningRate) |
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->setMaxIterations($this->maxIterations) |
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->setChangeThreshold(1e-6) |
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->setEarlyStop($this->enableEarlyStop); |
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} |
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$this->weights = $this->optimizer->runOptimization($samples, $targets, $gradientFunc); |
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$this->costValues = $this->optimizer->getCostValues(); |
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} |
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/** |
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* Checks if the sample should be normalized and if so, returns the |
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* normalized sample |
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*/ |
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protected function checkNormalizedSample(array $sample): array |
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{ |
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if ($this->normalizer !== null) { |
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$samples = [$sample]; |
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$this->normalizer->transform($samples); |
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$sample = $samples[0]; |
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} |
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return $sample; |
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} |
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/** |
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* Calculates net output of the network as a float value for the given input |
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* |
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* @return int|float |
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*/ |
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protected function output(array $sample) |
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{ |
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$sum = 0; |
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foreach ($this->weights as $index => $w) { |
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if ($index == 0) { |
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$sum += $w; |
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} else { |
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$sum += $w * $sample[$index - 1]; |
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} |
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} |
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return $sum; |
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} |
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/** |
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* Returns the class value (either -1 or 1) for the given input |
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*/ |
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protected function outputClass(array $sample): int |
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{ |
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return $this->output($sample) > 0 ? 1 : -1; |
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} |
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/** |
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* Returns the probability of the sample of belonging to the given label. |
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* |
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* The probability is simply taken as the distance of the sample |
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* to the decision plane. |
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* |
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* @param mixed $label |
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*/ |
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protected function predictProbability(array $sample, $label): float |
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{ |
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$predicted = $this->predictSampleBinary($sample); |
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if ((string) $predicted == (string) $label) { |
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$sample = $this->checkNormalizedSample($sample); |
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return (float) abs($this->output($sample)); |
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} |
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return 0.0; |
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} |
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/** |
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* @return mixed |
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*/ |
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protected function predictSampleBinary(array $sample) |
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{ |
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$sample = $this->checkNormalizedSample($sample); |
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$predictedClass = $this->outputClass($sample); |
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return $this->labels[$predictedClass]; |
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} |
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} |
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