112 lines
3.0 KiB
PHP
112 lines
3.0 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Helper\Optimizer;
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use Closure;
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use Phpml\Exception\InvalidOperationException;
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/**
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* Batch version of Gradient Descent to optimize the weights
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* of a classifier given samples, targets and the objective function to minimize
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*/
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class GD extends StochasticGD
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{
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/**
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* Number of samples given
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*
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* @var int|null
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*/
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protected $sampleCount;
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public function runOptimization(array $samples, array $targets, Closure $gradientCb): array
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{
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$this->samples = $samples;
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$this->targets = $targets;
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$this->gradientCb = $gradientCb;
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$this->sampleCount = count($this->samples);
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// Batch learning is executed:
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$currIter = 0;
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$this->costValues = [];
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while ($this->maxIterations > $currIter++) {
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$theta = $this->theta;
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// Calculate update terms for each sample
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[$errors, $updates, $totalPenalty] = $this->gradient($theta);
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$this->updateWeightsWithUpdates($updates, $totalPenalty);
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$this->costValues[] = array_sum($errors) / (int) $this->sampleCount;
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if ($this->earlyStop($theta)) {
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break;
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}
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}
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$this->clear();
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return $this->theta;
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}
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/**
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* Calculates gradient, cost function and penalty term for each sample
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* then returns them as an array of values
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*/
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protected function gradient(array $theta): array
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{
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$costs = [];
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$gradient = [];
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$totalPenalty = 0;
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if ($this->gradientCb === null) {
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throw new InvalidOperationException('Gradient callback is not defined');
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}
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foreach ($this->samples as $index => $sample) {
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$target = $this->targets[$index];
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$result = ($this->gradientCb)($theta, $sample, $target);
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[$cost, $grad, $penalty] = array_pad($result, 3, 0);
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$costs[] = $cost;
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$gradient[] = $grad;
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$totalPenalty += $penalty;
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}
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$totalPenalty /= $this->sampleCount;
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return [$costs, $gradient, $totalPenalty];
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}
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protected function updateWeightsWithUpdates(array $updates, float $penalty): void
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{
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// Updates all weights at once
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for ($i = 0; $i <= $this->dimensions; ++$i) {
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if ($i === 0) {
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$this->theta[0] -= $this->learningRate * array_sum($updates);
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} else {
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$col = array_column($this->samples, $i - 1);
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$error = 0;
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foreach ($col as $index => $val) {
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$error += $val * $updates[$index];
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}
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$this->theta[$i] -= $this->learningRate *
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($error + $penalty * $this->theta[$i]);
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}
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}
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}
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/**
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* Clears the optimizer internal vars after the optimization process.
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*/
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protected function clear(): void
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{
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$this->sampleCount = null;
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parent::clear();
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}
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}
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