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74 lines
2.6 KiB
PHP
74 lines
2.6 KiB
PHP
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<?php
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namespace Moserware\Skills\TrueSkill\Factors;
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/// <summary>
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/// Factor representing a team difference that has exceeded the draw margin.
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/// </summary>
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/// <remarks>See the accompanying math paper for more details.</remarks>
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class GaussianGreaterThanFactor extends GaussianFactor
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{
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private $_epsilon;
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public function __construct($epsilon, Variable $variable)
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{
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parent::_construct("{0} > {1:0.000}");
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$this->_epsilon = $epsilon;
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$this->createVariableToMessageBinding($variable);
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}
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public function getLogNormalization()
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{
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$vars = $this->getVariables();
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$marginal = $vars[0]->getValue();
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$messages = $this->getMessages();
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$message = $messages[0]->getValue();
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$messageFromVariable = GaussianDistribution::divide($marginal, $message);
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return -GaussianDistribution::logProductNormalization($messageFromVariable, $message)
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+
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log(
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GaussianDistribution::cumulativeTo(($messageFromVariable->getMean() - $this->_epsilon)/
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$messageFromVariable->getStandardDeviation()));
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}
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protected function updateMessageVariable(Message $message, Variable $variable)
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{
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$oldMarginal = clone $variable->getValue();
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$oldMessage = clone $message->getValue();
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$messageFromVar = GaussianDistribution::divide($oldMarginal, $oldMessage);
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$c = $messageFromVar->getPrecision();
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$d = $messageFromVar->getPrecisionMean();
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$sqrtC = sqrt($c);
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$dOnSqrtC = $d/$sqrtC;
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$epsilsonTimesSqrtC = $this->_epsilon*$sqrtC;
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$d = $messageFromVar->getPrecisionMean();
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$denom = 1.0 - TruncatedGaussianCorrectionFunctions::vExceedsMargin($dOnSqrtC, $epsilsonTimesSqrtC);
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$newPrecision = $c/$denom;
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$newPrecisionMean = ($d +
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$sqrtC*
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TruncatedGaussianCorrectionFunctions::vExceedsMargin($dOnSqrtC, $epsilsonTimesSqrtC))/
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$denom;
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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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/// 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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?>
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