def getPartialUpdate(prior, fullPosterior, updatePercentage):
priorGaussian = GaussianDistribution(prior.getMean(),
prior.getStandardDeviation())
posteriorGaussian = GaussianDistribution(
fullPosterior.getMean(), fullPosterior.getStandardDeviation())
# From a clarification email from Ralf Herbrich:
# "the idea is to compute a linear interpolation between the prior and posterior skills of each player
# ... in the canonical space of parameters"
precisionDifference = posteriorGaussian.getPrecision(
) - priorGaussian.getPrecision()
partialPrecisionDifference = updatePercentage * precisionDifference
precisionMeanDifference = posteriorGaussian.getPrecisionMean(
) - priorGaussian.getPrecisionMean()
partialPrecisionMeanDifference = updatePercentage * precisionMeanDifference
partialPosteriorGaussion = GaussianDistribution.fromPrecisionMean(
priorGaussian.getPrecisionMean() + partialPrecisionMeanDifference,
priorGaussian.getPrecision() + partialPrecisionDifference)
return Rating(partialPosteriorGaussion.getMean(),
partialPosteriorGaussion.getStandardDeviation(),
prior._conservativeStandardDeviationMultiplier)
def __init__(self, mean, variance, variable):
name = "Prior value going to %s" % variable
super(GaussianPriorFactor, self).__init__(name)
msg = "message from %s to %s" % (self, variable)
self._newMessage = GaussianDistribution(mean, sqrt(variance))
newMessage = Message(GaussianDistribution.fromPrecisionMean(0, 0), msg)
self.createVariableToMessageBindingWithMessage(variable, newMessage)
