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								<?php
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								namespace DNW\Skills\TrueSkill\Factors;
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								use DNW\Skills\FactorGraphs\Message;
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								use DNW\Skills\FactorGraphs\Variable;
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								use DNW\Skills\Numerics\GaussianDistribution;
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								use DNW\Skills\TrueSkill\TruncatedGaussianCorrectionFunctions;
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								/**
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								 * Factor representing a team difference that has not exceeded the draw margin.
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								 *
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								 * See the accompanying math paper for more details.
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								 */
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								class GaussianWithinFactor extends GaussianFactor
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								{
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								    private float $epsilon;
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								    public function __construct(float $epsilon, Variable $variable)
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								    {
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								        parent::__construct(sprintf('%s <= %.2f', (string)$variable, $epsilon));
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								        $this->epsilon = $epsilon;
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								        $this->createVariableToMessageBinding($variable);
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								    }
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								    public function getLogNormalization(): float
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								    {
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								        /**
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								 * @var Variable[] $variables
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								*/
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								        $variables = $this->getVariables();
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								        $marginal = $variables[0]->getValue();
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								        /**
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								 * @var Message[] $messages
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								*/
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								        $messages = $this->getMessages();
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								        $message = $messages[0]->getValue();
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								        $messageFromVariable = GaussianDistribution::divide($marginal, $message);
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								        $mean = $messageFromVariable->getMean();
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								        $std = $messageFromVariable->getStandardDeviation();
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								        $z = GaussianDistribution::cumulativeTo(($this->epsilon - $mean) / $std)
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								            -
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								            GaussianDistribution::cumulativeTo((-$this->epsilon - $mean) / $std);
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								        return -GaussianDistribution::logProductNormalization($messageFromVariable, $message) + log($z);
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								    }
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								    protected function updateMessageVariable(Message $message, Variable $variable): float
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								    {
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								        $oldMarginal = clone $variable->getValue();
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								        $oldMessage = clone $message->getValue();
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								        $messageFromVariable = GaussianDistribution::divide($oldMarginal, $oldMessage);
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								        $c = $messageFromVariable->getPrecision();
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								        $d = $messageFromVariable->getPrecisionMean();
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								        $sqrtC = sqrt($c);
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								        $dOnSqrtC = $d / $sqrtC;
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								        $epsilonTimesSqrtC = $this->epsilon * $sqrtC;
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								        $d = $messageFromVariable->getPrecisionMean();
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								        $denominator = 1.0 - TruncatedGaussianCorrectionFunctions::wWithinMargin($dOnSqrtC, $epsilonTimesSqrtC);
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								        $newPrecision = $c / $denominator;
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								        $newPrecisionMean = ($d +
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								                                $sqrtC *
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								                                TruncatedGaussianCorrectionFunctions::vWithinMargin($dOnSqrtC, $epsilonTimesSqrtC)
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								                            ) / $denominator;
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								        $newMarginal = GaussianDistribution::fromPrecisionMean($newPrecisionMean, $newPrecision);
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								        $newMessage = GaussianDistribution::divide(
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								            GaussianDistribution::multiply($oldMessage, $newMarginal),
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								            $oldMarginal
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								        );
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								        // Update the message and marginal
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								        $message->setValue($newMessage);
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								        $variable->setValue($newMarginal);
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								        // Return the difference in the new marginal
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								        return GaussianDistribution::subtract($newMarginal, $oldMarginal);
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								    }
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								}
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