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)
class GaussianPriorFactor(GaussianFactor):
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)
def updateMessageVariable(self, message, variable):
oldMarginal = copy(variable.getValue())
oldMessage = message
newMarginal = GaussianDistribution.fromPrecisionMean(
oldMarginal.getPrecisionMean() +
self._newMessage.getPrecisionMean() -
oldMessage.getValue().getPrecisionMean(),
oldMarginal.getPrecision() + self._newMessage.getPrecision() -
oldMessage.getValue().getPrecision())
variable.setValue(newMarginal)
newMessage = self._newMessage
message.setValue(newMessage)
return GaussianDistribution.subtract(oldMarginal, newMarginal)
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)
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,
)
class GaussianPriorFactor(GaussianFactor) :
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)
def updateMessageVariable(self, message, variable) :
oldMarginal = copy(variable.getValue())
oldMessage = message
newMarginal = GaussianDistribution.fromPrecisionMean(oldMarginal.getPrecisionMean() + self._newMessage.getPrecisionMean() - oldMessage.getValue().getPrecisionMean(),
oldMarginal.getPrecision() + self._newMessage.getPrecision() - oldMessage.getValue().getPrecision())
variable.setValue(newMarginal)
newMessage = self._newMessage
message.setValue(newMessage)
return GaussianDistribution.subtract(oldMarginal, newMarginal)
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)
