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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\Helper\Predictable; |
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use Phpml\Helper\Trainable; |
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use Phpml\Classification\Classifier; |
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class Perceptron implements Classifier |
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{ |
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use Predictable; |
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/** |
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* The function whose result will be used to calculate the network error |
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* for each instance |
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* |
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* @var string |
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*/ |
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protected static $errorFunction = 'outputClass'; |
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/** |
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* @var array |
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*/ |
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protected $samples = []; |
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/** |
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* @var array |
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*/ |
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protected $targets = []; |
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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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* Initalize a perceptron classifier with given learning rate and maximum |
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* number of iterations used while training the perceptron <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 |
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* @param int $learningRate |
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* @param int $maxIterations |
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*/ |
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public function __construct(float $learningRate = 0.001, int $maxIterations = 1000) |
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{ |
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if ($learningRate <= 0.0 || $learningRate > 1.0) { |
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throw new \Exception("Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)"); |
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} |
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if ($maxIterations <= 0) { |
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throw new \Exception("Maximum number of iterations should be an integer greater than 0"); |
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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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/** |
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* @param array $samples |
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* @param array $targets |
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*/ |
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public function train(array $samples, array $targets) |
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{ |
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$this->labels = array_keys(array_count_values($targets)); |
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if (count($this->labels) > 2) { |
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throw new \Exception("Perceptron is for only binary (two-class) classification"); |
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} |
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// Set all target values to either -1 or 1 |
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$this->labels = [1 => $this->labels[0], -1 => $this->labels[1]]; |
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foreach ($targets as $target) { |
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$this->targets[] = $target == $this->labels[1] ? 1 : -1; |
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} |
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// Set samples and feature count vars |
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$this->samples = array_merge($this->samples, $samples); |
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$this->featureCount = count($this->samples[0]); |
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// Init weights with random values |
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$this->weights = array_fill(0, $this->featureCount + 1, 0); |
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foreach ($this->weights as &$weight) { |
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$weight = rand() / (float) getrandmax(); |
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} |
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// Do training |
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$this->runTraining(); |
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} |
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/** |
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* Adapts the weights with respect to given samples and targets |
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* by use of perceptron learning rule |
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*/ |
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protected function runTraining() |
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{ |
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$currIter = 0; |
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while ($this->maxIterations > $currIter++) { |
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foreach ($this->samples as $index => $sample) { |
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$target = $this->targets[$index]; |
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$prediction = $this->{static::$errorFunction}($sample); |
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$update = $target - $prediction; |
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// Update bias |
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$this->weights[0] += $update * $this->learningRate; // Bias |
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// Update other weights |
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for ($i=1; $i <= $this->featureCount; $i++) { |
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$this->weights[$i] += $update * $sample[$i - 1] * $this->learningRate; |
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} |
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} |
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} |
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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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* @param array $sample |
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* @return int |
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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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* @param array $sample |
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* @return int |
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*/ |
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protected function outputClass(array $sample) |
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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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* @param array $sample |
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* @return mixed |
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*/ |
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protected function predictSample(array $sample) |
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{ |
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$predictedClass = $this->outputClass($sample); |
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return $this->labels[ $predictedClass ]; |
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} |
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} |
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php-ai /
php-ml
