Exemple #1
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 public GaussianPriorFactor(double mean, double variance, Variable <GaussianDistribution> variable)
     : base(string.Format("Prior value going to {0}", variable))
 {
     mNewMessage = new GaussianDistribution(mean, Math.Sqrt(variance));
     CreateVariableToMessageBinding(variable,
                                    new Message <GaussianDistribution>(GaussianDistribution.FromPrecisionMean(0, 0), "message from {0} to {1}", this, variable));
 }
Exemple #2
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        protected override double UpdateMessage(Message <GaussianDistribution> message, Variable <GaussianDistribution> variable)
        {
            var oldMarginal    = variable.Value.Clone();
            var oldMessage     = message.Value.Clone();
            var messageFromVar = oldMarginal / oldMessage;

            var c = messageFromVar.Precision;
            var d = messageFromVar.PrecisionMean;

            var sqrtC    = Math.Sqrt(c);
            var dOnSqrtC = d / sqrtC;

            var epsilonTimesSqrtC = mEpsilon * sqrtC;

            d = messageFromVar.PrecisionMean;

            var denom = 1.0 - TruncatedGaussianCorrectionFunctions.WExceedsMargin(dOnSqrtC, epsilonTimesSqrtC);

            var newPrecision     = c / denom;
            var newPrecisionMean = (d + sqrtC * TruncatedGaussianCorrectionFunctions.VExceedsMargin(dOnSqrtC, epsilonTimesSqrtC)) / denom;

            var newMarginal = GaussianDistribution.FromPrecisionMean(newPrecisionMean, newPrecision);
            var newMessage  = oldMessage * newMarginal / oldMarginal;

            message.Value  = newMessage;
            variable.Value = newMarginal;

            return(newMarginal - oldMarginal);
        }
Exemple #3
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        protected override double UpdateMessage(Message <GaussianDistribution> message, Variable <GaussianDistribution> variable)
        {
            var oldMarginal = variable.Value.Clone();
            var oldMessage  = message;
            var newMarginal = GaussianDistribution.FromPrecisionMean(
                oldMarginal.PrecisionMean + mNewMessage.PrecisionMean - oldMessage.Value.PrecisionMean,
                oldMarginal.Precision + mNewMessage.Precision - oldMessage.Value.Precision);

            variable.Value = newMarginal;
            message.Value  = mNewMessage;
            return(oldMarginal - newMarginal);
        }
Exemple #4
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 public TrueSkillFactorGraph(GameInfo gameInfo, IEnumerable <IDictionary <TPlayer, Rating> > teams, int[] teamRanks)
 {
     mPriorLayer     = new PlayerPriorValuesToSkillsLayer <TPlayer>(this, teams);
     GameInfo        = gameInfo;
     VariableFactory = new VariableFactory <GaussianDistribution>(() => GaussianDistribution.FromPrecisionMean(0, 0));
     mLayers         = new List <FactorGraphLayerBase <GaussianDistribution> >
     {
         mPriorLayer,
         new PlayerSkillsToPerformancesLayer <TPlayer>(this),
         new PlayerPerformancesToTeamPerformancesLayer <TPlayer>(this),
         new IteratedTeamDifferencesInnerLayer <TPlayer>(
             this,
             new TeamPerformancesToTeamPerformanceDifferencesLayer <TPlayer>(this),
             new TeamDifferencesComparisonLayer <TPlayer>(this, teamRanks))
     };
 }
Exemple #5
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        /// <summary>
        /// Get a partial update of a rating.
        /// </summary>
        /// <param name="prior">The rating before adjustment.</param>
        /// <param name="fullPosterior">The rating after adjustment.</param>
        /// <param name="updatePercentage">The percentage of the update to apply.</param>
        /// <returns>The resulting rating after a partial update.</returns>
        public static Rating GetPartialUpdate(Rating prior, Rating fullPosterior,
                                              double updatePercentage)
        {
            var priorGaussian     = new GaussianDistribution(prior.Mean, prior.StandardDeviation);
            var posteriorGaussian = new GaussianDistribution(fullPosterior.Mean, fullPosterior.StandardDeviation);

            var precisionDifference        = posteriorGaussian.Precision - priorGaussian.Precision;
            var partialPrecisionDifference = updatePercentage * precisionDifference;

            var    precisionMeanDifference        = posteriorGaussian.PrecisionMean - priorGaussian.PrecisionMean;
            double partialPrecisionMeanDifference = updatePercentage * precisionMeanDifference;

            GaussianDistribution partialPosteriorGaussian = GaussianDistribution.FromPrecisionMean(
                priorGaussian.PrecisionMean + partialPrecisionMeanDifference,
                priorGaussian.Precision + partialPrecisionDifference);

            return(new Rating(partialPosteriorGaussian.Mean, partialPosteriorGaussian.StandardDeviation,
                              prior.mConservativeStandardDeviationMultiplier));
        }
        private double UpdateHelper(double[] weights, double[] weightsSquared,
                                    IList <Message <GaussianDistribution> > messages,
                                    IList <Variable <GaussianDistribution> > variables)
        {
            var message0  = messages[0].Value.Clone();
            var marginal0 = variables[0].Value.Clone();

            var inverseOfNewPrecisionSum        = 0.0;
            var anotherInverseOfNewPrecisionSum = 0.0;
            var weightedMeanSum        = 0.0;
            var anotherWeightedMeanSum = 0.0;

            for (var i = 0; i < weightsSquared.Length; ++i)
            {
                inverseOfNewPrecisionSum += weightsSquared[i] /
                                            (variables[i + 1].Value.Precision - messages[i + 1].Value.Precision);

                var diff = (variables[i + 1].Value / messages[i + 1].Value);
                anotherInverseOfNewPrecisionSum += weightsSquared[i] / diff.Precision;

                weightedMeanSum += weights[i] *
                                   (variables[i + 1].Value.PrecisionMean - messages[i + 1].Value.PrecisionMean) /
                                   (variables[i + 1].Value.Precision - messages[i + 1].Value.Precision);

                anotherWeightedMeanSum += weights[i] * diff.PrecisionMean / diff.Precision;
            }

            var newPrecision        = 1.0 / inverseOfNewPrecisionSum;
            var anotherNewPrecision = 1.0 / anotherInverseOfNewPrecisionSum;

            var newPrecisionMean        = newPrecision * weightedMeanSum;
            var anotherNewPrecisionMean = anotherNewPrecision * anotherWeightedMeanSum;

            var newMessage = GaussianDistribution.FromPrecisionMean(newPrecisionMean, newPrecision);
            var oldMarginalWithoutMessage = marginal0 / message0;
            var newMarginal = oldMarginalWithoutMessage * newMessage;

            messages[0].Value  = newMessage;
            variables[0].Value = newMarginal;

            return(newMarginal - marginal0);
        }
        private double UpdateHelper(Message <GaussianDistribution> message1, Message <GaussianDistribution> message2,
                                    Variable <GaussianDistribution> variable1, Variable <GaussianDistribution> variable2)
        {
            var message1Value = message1.Value.Clone();
            var message2Value = message2.Value.Clone();

            var marginal1 = variable1.Value.Clone();
            var marginal2 = variable2.Value.Clone();

            var a = mPrecision / (mPrecision + marginal2.Precision - message2Value.Precision);

            var newMessage = GaussianDistribution.FromPrecisionMean(
                a * (marginal2.PrecisionMean - message2Value.PrecisionMean),
                a * (marginal2.Precision - message2Value.Precision));

            var oldMarginalWithoutMessage = marginal1 / message1Value;
            var newMarginal = oldMarginalWithoutMessage * newMessage;

            message1.Value  = newMessage;
            variable1.Value = newMarginal;

            return(newMarginal - marginal1);
        }