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

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