php-ml/src/Phpml/Helper/Optimizer/GD.php

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<?php
declare(strict_types=1);
namespace Phpml\Helper\Optimizer;
/**
* Batch version of Gradient Descent to optimize the weights
* of a classifier given samples, targets and the objective function to minimize
*/
class GD extends StochasticGD
{
/**
* Number of samples given
*
* @var int
*/
protected $sampleCount;
/**
* @param array $samples
* @param array $targets
* @param \Closure $gradientCb
*
* @return array
*/
public function runOptimization(array $samples, array $targets, \Closure $gradientCb)
{
$this->samples = $samples;
$this->targets = $targets;
$this->gradientCb = $gradientCb;
$this->sampleCount = count($this->samples);
// Batch learning is executed:
$currIter = 0;
$this->costValues = [];
while ($this->maxIterations > $currIter++) {
$theta = $this->theta;
// Calculate update terms for each sample
list($errors, $updates, $totalPenalty) = $this->gradient($theta);
$this->updateWeightsWithUpdates($updates, $totalPenalty);
$this->costValues[] = array_sum($errors)/$this->sampleCount;
if ($this->earlyStop($theta)) {
break;
}
}
return $this->theta;
}
/**
* Calculates gradient, cost function and penalty term for each sample
* then returns them as an array of values
*
* @param array $theta
*
* @return array
*/
protected function gradient(array $theta)
{
$costs = [];
$gradient= [];
$totalPenalty = 0;
foreach ($this->samples as $index => $sample) {
$target = $this->targets[$index];
$result = ($this->gradientCb)($theta, $sample, $target);
list($cost, $grad, $penalty) = array_pad($result, 3, 0);
$costs[] = $cost;
$gradient[]= $grad;
$totalPenalty += $penalty;
}
$totalPenalty /= $this->sampleCount;
return [$costs, $gradient, $totalPenalty];
}
/**
* @param array $updates
* @param float $penalty
*/
protected function updateWeightsWithUpdates(array $updates, float $penalty)
{
// Updates all weights at once
for ($i=0; $i <= $this->dimensions; $i++) {
if ($i == 0) {
$this->theta[0] -= $this->learningRate * array_sum($updates);
} else {
$col = array_column($this->samples, $i - 1);
$error = 0;
foreach ($col as $index => $val) {
$error += $val * $updates[$index];
}
$this->theta[$i] -= $this->learningRate *
($error + $penalty * $this->theta[$i]);
}
}
}
}