php-ml/src/Phpml/Classification/Linear/Perceptron.php

<?php

declare(strict_types=1);

namespace Phpml\Classification\Linear;

use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
use Phpml\Classification\Classifier;
use Phpml\Preprocessing\Normalizer;

class Perceptron implements Classifier
{
    use Predictable;

    /**
     * The function whose result will be used to calculate the network error
     * for each instance
     *
     * @var string
     */
    protected static $errorFunction = 'outputClass';

   /**
     * @var array
     */
    protected $samples = [];

    /**
     * @var array
     */
    protected $targets = [];

    /**
     * @var array
     */
    protected $labels = [];

    /**
     * @var int
     */
    protected $featureCount = 0;

    /**
     * @var array
     */
    protected $weights;

    /**
     * @var float
     */
    protected $learningRate;

    /**
     * @var int
     */
    protected $maxIterations;

    /**
     * @var Normalizer
     */
    protected $normalizer;

    /**
     * Initalize a perceptron classifier with given learning rate and maximum
     * number of iterations used while training the perceptron <br>
     *
     * Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive) <br>
     * Maximum number of iterations can be an integer value greater than 0
     * @param int $learningRate
     * @param int $maxIterations
     */
    public function __construct(float $learningRate = 0.001, int $maxIterations = 1000,
        bool $normalizeInputs = true)
    {
        if ($learningRate <= 0.0 || $learningRate > 1.0) {
            throw new \Exception("Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)");
        }

        if ($maxIterations <= 0) {
            throw new \Exception("Maximum number of iterations should be an integer greater than 0");
        }

        if ($normalizeInputs) {
            $this->normalizer = new Normalizer(Normalizer::NORM_STD);
        }

        $this->learningRate = $learningRate;
        $this->maxIterations = $maxIterations;
    }

   /**
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets)
    {
        $this->labels = array_keys(array_count_values($targets));
        if (count($this->labels) > 2) {
            throw new \Exception("Perceptron is for only binary (two-class) classification");
        }

        if ($this->normalizer) {
            $this->normalizer->transform($samples);
        }

        // Set all target values to either -1 or 1
        $this->labels = [1 => $this->labels[0], -1 => $this->labels[1]];
        foreach ($targets as $target) {
            $this->targets[] = $target == $this->labels[1] ? 1 : -1;
        }

        // Set samples and feature count vars
        $this->samples = array_merge($this->samples, $samples);
        $this->featureCount = count($this->samples[0]);

        // Init weights with random values
        $this->weights = array_fill(0, $this->featureCount + 1, 0);
        foreach ($this->weights as &$weight) {
            $weight = rand() / (float) getrandmax();
        }
        // Do training
        $this->runTraining();
    }

    /**
     * Adapts the weights with respect to given samples and targets
     * by use of perceptron learning rule
     */
    protected function runTraining()
    {
        $currIter = 0;
        while ($this->maxIterations > $currIter++) {
            foreach ($this->samples as $index => $sample) {
                $target = $this->targets[$index];
                $prediction = $this->{static::$errorFunction}($sample);
                $update = $target - $prediction;
                // Update bias
                $this->weights[0] += $update * $this->learningRate; // Bias
                // Update other weights
                for ($i=1; $i <= $this->featureCount; $i++) {
                    $this->weights[$i] += $update * $sample[$i - 1] * $this->learningRate;
                }
            }
        }
    }

    /**
     * Calculates net output of the network as a float value for the given input
     *
     * @param array $sample
     * @return int
     */
    protected function output(array $sample)
    {
        $sum = 0;
        foreach ($this->weights as $index => $w) {
            if ($index == 0) {
                $sum += $w;
            } else {
                $sum += $w * $sample[$index - 1];
            }
        }

        return $sum;
    }

    /**
     * Returns the class value (either -1 or 1) for the given input
     *
     * @param array $sample
     * @return int
     */
    protected function outputClass(array $sample)
    {
        return $this->output($sample) > 0 ? 1 : -1;
    }

    /**
     * @param array $sample
     * @return mixed
     */
    protected function predictSample(array $sample)
    {
        if ($this->normalizer) {
            $samples = [$sample];
            $this->normalizer->transform($samples);
            $sample = $samples[0];
        }

        $predictedClass = $this->outputClass($sample);

        return $this->labels[ $predictedClass ];
    }
}