def _calculateNewRating(self, gameInfo, selfRating, opponentRating, comparison): drawMargin = getDrawMarginFromDrawProbability(gameInfo.drawProbability, gameInfo.beta) c = sqrt((selfRating.standardDeviation**2.0) + (opponentRating.standardDeviation**2.0) + 2.0*(gameInfo.beta**2.0)) winningMean = selfRating.mean losingMean = opponentRating.mean if comparison == PairwiseComparison.LOSE: winningMean = opponentRating.mean losingMean = selfRating.mean meanDelta = winningMean - losingMean v = None w = None rankMultiplier = None if comparison != PairwiseComparison.DRAW: v = vExceedsMargin(meanDelta, drawMargin, c) w = wExceedsMargin(meanDelta, drawMargin, c) rankMultiplier = comparison else: v = vWithinMargin(meanDelta, drawMargin, c) w = wWithinMargin(meanDelta, drawMargin, c) rankMultiplier = 1.0 meanMultiplier = ((selfRating.standardDeviation**2.0) + (gameInfo.dynamicsFactor**2.0)) / c varianceWithDynamics = (selfRating.standardDeviation**2) + (gameInfo.dynamicsFactor**2) stdDevMultiplier = varianceWithDynamics/(c**2.0) newMean = selfRating.mean + (rankMultiplier*meanMultiplier*v) newStdDev = sqrt(varianceWithDynamics*(1 - w*stdDevMultiplier)) return Rating(newMean, newStdDev)
def _updatePlayerRatings(self, gameInfo, newPlayerRatings, selfTeam, otherTeam, selfToOtherComparison): drawMargin = getDrawMarginFromDrawProbability(gameInfo.drawProbability, gameInfo.beta) betaSquared = gameInfo.beta**2.0 tauSquared = gameInfo.dynamicsFactor**2.0 totalPlayers = selfTeam.size + otherTeam.size selfTeamMeanSum = selfTeam.meanSum otherTeamMeanSum = otherTeam.meanSum selfTeamStandardDeviationSum = selfTeam.standardDeviationSquaredSum otherTeamStandardDeviationSum = otherTeam.standardDeviationSquaredSum c = sqrt(selfTeamStandardDeviationSum + otherTeamStandardDeviationSum + totalPlayers*betaSquared) winningMean = selfTeamMeanSum losingMean = otherTeamMeanSum if selfToOtherComparison == PairwiseComparison.LOSE: winningMean = otherTeamMeanSum losingMean = selfTeamMeanSum meanDelta = winningMean - losingMean v = None w = None rankMultiplier = None if selfToOtherComparison != PairwiseComparison.DRAW: v = vExceedsMargin(meanDelta, drawMargin, c) w = wExceedsMargin(meanDelta, drawMargin, c) rankMultiplier = selfToOtherComparison else: v = vWithinMargin(meanDelta, drawMargin, c) w = wWithinMargin(meanDelta, drawMargin, c) rankMultiplier = 1 for playerTuple in selfTeam.asListOfTuples: previousPlayerRating = playerTuple[1] meanMultiplier = ((previousPlayerRating.standardDeviation**2.0) + tauSquared) / c stdDevMultiplier = ((previousPlayerRating.standardDeviation**2.0) + tauSquared) / (c**2.0) playerMeanDelta = rankMultiplier*meanMultiplier*v newMean = previousPlayerRating.mean + playerMeanDelta newStdDev = sqrt(((previousPlayerRating.standardDeviation**2.0) + tauSquared) * (1 - w*stdDevMultiplier)) newPlayerRatings.append((playerTuple[0], Rating(newMean, newStdDev)))
def _updateMessageInternal(self, message, variable): oldMarginal = deepcopy(variable.value) oldMessage = deepcopy(message.value) messageFromVar = oldMarginal/oldMessage c = messageFromVar.precision d = messageFromVar.precisionMean sqrtC = sqrt(c) dOnSqrtC = d/sqrtC epsilonTimesSqrtC = self._epsilon*sqrtC denom = 1.0 - wExceedsMargin(dOnSqrtC, epsilonTimesSqrtC) newPrecision = c/denom newPrecisionMean = (d + sqrtC*vExceedsMargin(dOnSqrtC, epsilonTimesSqrtC))/denom newMarginal = fromPrecisionMean(newPrecisionMean, newPrecision) newMessage = oldMessage*newMarginal/oldMarginal message.value = newMessage variable.value = newMarginal return newMarginal - oldMarginal
