27c82ef103
The 803-line NationalFuelPredictionService had six private compute*Signal methods, a private linearRegression helper, and a private disabledSignal shape factory all crammed together. Each signal is now an independently testable class. - App\Services\Prediction\Signals\Signal — interface - App\Services\Prediction\Signals\SignalContext — input value object (FuelType + optional lat/lng + hasCoordinates() helper) - App\Services\Prediction\Signals\AbstractSignal — shared disabledSignal() and linearRegression() helpers - TrendSignal, DayOfWeekSignal, BrandBehaviourSignal, StickinessSignal, RegionalMomentumSignal, OilSignal — one class each, extending AbstractSignal NationalFuelPredictionService receives the 6 signal classes via constructor injection and orchestrates them. The lat/lng null-guard for regional momentum now lives inside RegionalMomentumSignal::compute() so the coordinator no longer branches on coordinate presence. Aggregation, weekly summary, and reasoning helpers stay in the service for now — they are coupled to the public predict() output shape and are candidates for a follow-up extraction once a stable API is locked in. Service: 803 → 414 lines. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
62 lines
1.6 KiB
PHP
62 lines
1.6 KiB
PHP
<?php
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namespace App\Services\Prediction\Signals;
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abstract class AbstractSignal implements Signal
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{
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/** @return array{score: 0.0, confidence: 0.0, direction: 'stable', detail: string, data_points: 0, enabled: false} */
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protected function disabledSignal(string $detail): array
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{
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return [
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'score' => 0.0,
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'confidence' => 0.0,
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'direction' => 'stable',
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'detail' => $detail,
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'data_points' => 0,
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'enabled' => false,
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];
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}
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/**
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* Least-squares linear regression. x = array index, y = value.
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*
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* @param float[] $values
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* @return array{slope: float, r_squared: float}
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*/
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protected function linearRegression(array $values): array
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{
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$n = count($values);
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if ($n < 2) {
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return ['slope' => 0.0, 'r_squared' => 0.0];
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}
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$xMean = ($n - 1) / 2.0;
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$yMean = array_sum($values) / $n;
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$numerator = 0.0;
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$denominator = 0.0;
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foreach ($values as $i => $y) {
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$x = $i - $xMean;
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$numerator += $x * ($y - $yMean);
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$denominator += $x * $x;
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}
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$slope = $denominator > 0.0 ? $numerator / $denominator : 0.0;
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$ssRes = 0.0;
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$ssTot = 0.0;
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foreach ($values as $i => $y) {
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$predicted = $yMean + $slope * ($i - $xMean);
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$ssRes += ($y - $predicted) ** 2;
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$ssTot += ($y - $yMean) ** 2;
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}
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$rSquared = $ssTot > 0.0 ? max(0.0, 1.0 - ($ssRes / $ssTot)) : 0.0;
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return ['slope' => $slope, 'r_squared' => $rSquared];
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}
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}
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