//public int[] Classification(Vector[] features, int[] labels)
        //{
        //}
        public double[] Regression(Vector[] features, double[] values)
        {
            var wMeans = Variable.Vector(Vector.Zero(features[0].Count).ToArray());
            var wPrecision = Variable.WishartFromShapeAndRate(100, PositiveDefiniteMatrix.IdentityScaledBy(features[0].Count, 0.01));
            var w = Variable.VectorGaussianFromMeanAndPrecision(wMeans, wPrecision).Named("w");
            var numItems = Variable.New<int>().Named("numItems");
            var i = new Range(numItems).Named("i");
            i.AddAttribute(new Sequential());

            var noisePrecision = Variable.New<double>().Named("noisePrecision");

            var x = Variable.Array<Vector>(i).Named("x");
            var y = Variable.Array<double>(i).Named("y");

            using (Variable.ForEach(i))
            {
                y[i] = Variable.GaussianFromMeanAndPrecision(Variable.InnerProduct(w, x[i]), noisePrecision);
            }

            numItems.ObservedValue = features.Length;
            x.ObservedValue = features;
            y.ObservedValue = values;

            var engine = new InferenceEngine();
            engine.Compiler.UseSerialSchedules = true;
            engine.ShowProgress = false;
            var wPosterior = engine.Infer<VectorGaussian>(w);
            y.ClearObservedValue();
            w.ObservedValue = wPosterior.GetMean();
            var inferredValues = engine.Infer<IList<Gaussian>>(y);
            return inferredValues.Select(v => v.GetMean()).ToArray();
        }
Esempio n. 2
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        private static void AngleScaleTest()
        {
            Variable<double> angle = Variable.GaussianFromMeanAndVariance(Math.PI / 6, 0.4 * 0.4);
            Variable<double> scaleX = Variable.GaussianFromMeanAndVariance(1.5, 2 * 2);
            Variable<double> scaleY = Variable.GaussianFromMeanAndVariance(1.5, 2 * 2);
            Variable<PositiveDefiniteMatrix> prec = Variable<PositiveDefiniteMatrix>.Factor(ShapeFactors.MatrixFromAngleScale, scaleX, scaleY, angle);

            Vector trueMean = Vector.Zero(2);

            Range pointRange = new Range(5000);
            VariableArray<Vector> points = Variable.Array<Vector>(pointRange);
            points[pointRange] = Variable.VectorGaussianFromMeanAndPrecision(trueMean, prec).ForEach(pointRange);

            PositiveDefiniteMatrix truePrec = ShapeFactors.MatrixFromAngleScale(2.0, 3.0, Math.PI / 5);
            Vector[] observedPoints = Util.ArrayInit(pointRange.SizeAsInt, i => VectorGaussian.Sample(trueMean, truePrec));

            points.ObservedValue = observedPoints;
            prec.AddAttribute(new PointEstimate());
            prec.AddAttribute(new MarginalPrototype(new Wishart(2)));

            InferenceEngine engine = new InferenceEngine();
            engine.Compiler.RequiredQuality = engine.Compiler.RecommendedQuality = QualityBand.Unknown;
            Console.WriteLine(engine.Infer(angle));
            Console.WriteLine(engine.Infer(scaleX));
            Console.WriteLine(engine.Infer(scaleY));
        }
Esempio n. 3
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        public SentimentIndex()
            : base("Sentiment Index")
        {
            numberOfTrainingItems = Variable.New<int>();
            var rangeOfTrainingItems = new Range(numberOfTrainingItems);
            trainingInputs = Variable.Array<Vector>(rangeOfTrainingItems);
            trainingOutputs = Variable.Array<bool>(rangeOfTrainingItems);

            weights = Variable.Random(new VectorGaussian(Vector.Zero(numberOfFeatures), PositiveDefiniteMatrix.Identity(numberOfFeatures)));

            using (Variable.ForEach(rangeOfTrainingItems))
            {
                trainingOutputs[rangeOfTrainingItems] = Variable.IsPositive(Variable.GaussianFromMeanAndVariance(Variable.InnerProduct(weights, trainingInputs[rangeOfTrainingItems]), noise));
            }

            trainingEngine = new InferenceEngine();
            trainingEngine.ShowProgress = false;

            numberOfTestingItems = Variable.New<int>();
            var rangeOfTestingItems = new Range(numberOfTestingItems);
            testingInputs = Variable.Array<Vector>(rangeOfTestingItems);
            testingOutputs = Variable.Array<bool>(rangeOfTestingItems);

            weightsPosteriorDistribution = Variable.New<VectorGaussian>();
            var testWeights = Variable<Vector>.Random(weightsPosteriorDistribution);

            using (Variable.ForEach(rangeOfTestingItems))
            {
                testingOutputs[rangeOfTestingItems] = Variable.IsPositive(Variable.GaussianFromMeanAndVariance(Variable.InnerProduct(testWeights, testingInputs[rangeOfTestingItems]), noise));
            }

            testingEngine = new InferenceEngine();
            testingEngine.ShowProgress = false;
        }
Esempio n. 4
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        static void Main(string[] args)
        {
            // data
            ArrayList trainingData = readData(@"C:\Users\Jeremy\Documents\Visual Studio 2010\Projects\InferSandbox\train.txt");

            // Create target y
            VariableArray<double> y = Variable.Observed((double[])(trainingData[trainingData.Count-1])).Named("y");
            Variable<Vector> w = Variable.Random(new VectorGaussian(Vector.Zero(trainingData.Count),
                PositiveDefiniteMatrix.Identity(trainingData.Count))).Named("w");
            trainingData.RemoveAt(trainingData.Count - 1);
            BayesPointMachine(trainingData, w, y);

            InferenceEngine engine = new InferenceEngine();
            if (!(engine.Algorithm is GibbsSampling))
            {
                VectorGaussian wPosterior = engine.Infer<VectorGaussian>(w);
                Console.WriteLine("Dist over w=\n" + wPosterior);

                ArrayList testData = readData(@"C:\Users\Jeremy\Documents\Visual Studio 2010\Projects\InferSandbox\test.txt");

                VariableArray<double> ytest = Variable.Array<double>(new Range(((double[])(testData[0])).Length)).Named("ytest");
                BayesPointMachine(testData, Variable.Random(wPosterior).Named("w"), ytest);
                Console.WriteLine("output=\n" + engine.Infer(ytest));
            }
            else Console.WriteLine("This model has a non-conjugate factor, and therefore cannot use Gibbs sampling");
        }
        public SimpleBayesPointMachine(int numOfFeatures, int[] featureSelection, double noise)
            : base(numOfFeatures, featureSelection, noise)
        {
            this.isTrained = false;

            Engine = new InferenceEngine();
        }
Esempio n. 6
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 internal Context(InferenceEngine mind, WildcardTriplet rootWildcard)
 {
     _mind = mind;
     _rootLevel = getState(null, rootWildcard);
     _levelsToExpand.Enqueue(_rootLevel);
     _levelsToGenerate.Enqueue(_rootLevel);
 }
		public void Run()
		{
			// data
			double[] incomes = { 63, 16, 28, 55, 22, 20 };
			double[] ages = { 38, 23, 40, 27, 18, 40 };
			bool[] willBuy = { true, false, true, true, false, false };

			// Create target y
			VariableArray<bool> y = Variable.Observed(willBuy).Named("y");
			Variable<Vector> w = Variable.Random(new VectorGaussian(Vector.Zero(3),
				PositiveDefiniteMatrix.Identity(3))).Named("w");
			BayesPointMachine(incomes, ages, w, y);

			InferenceEngine engine = new InferenceEngine();
			if (!(engine.Algorithm is GibbsSampling))
			{
				VectorGaussian wPosterior = engine.Infer<VectorGaussian>(w);
				Console.WriteLine("Dist over w=\n"+wPosterior);

				double[] incomesTest = { 58, 18, 22 };
				double[] agesTest = { 36, 24, 37 };
				VariableArray<bool> ytest = Variable.Array<bool>(new Range(agesTest.Length)).Named("ytest");
				BayesPointMachine(incomesTest, agesTest, Variable.Random(wPosterior).Named("w"), ytest);
				Console.WriteLine("output=\n" + engine.Infer(ytest));
			}
			else Console.WriteLine("This model has a non-conjugate factor, and therefore cannot use Gibbs sampling");

		}
        public InferenceResult<Cluster[]> Infer(Vector[] observedData, int clusters)
        {
            var dimensions = observedData.First().Count;
            var evidence = Variable.Bernoulli(0.5).Named("evidence");
            var evidenceBlock = Variable.If(evidence);
            var clustersRange = new Range(clusters).Named("clustersRange");
            var meansPrior = Variable.Array<Vector>(clustersRange).Named("meansPrior");
            meansPrior[clustersRange] = Variable
                .VectorGaussianFromMeanAndPrecision(
                    Vector.Zero(dimensions),
                    PositiveDefiniteMatrix.IdentityScaledBy(dimensions, 0.01))
                .ForEach(clustersRange);

            var precisionsPrior = Variable.Array<PositiveDefiniteMatrix>(clustersRange).Named("precisionsPrior");
            precisionsPrior[clustersRange] = Variable.WishartFromShapeAndRate(100, PositiveDefiniteMatrix.IdentityScaledBy(dimensions, 0.01))
                .ForEach(clustersRange);

            var initialWeights = Enumerable.Range(0, clusters).Select(_ => 1.0).ToArray();
            var mixtureWeightsPrior = Variable.Dirichlet(clustersRange, initialWeights).Named("mixtureWeightsPrior");

            var dataRange = new Range(observedData.Length).Named("dataRange");
            var data = Variable.Array<Vector>(dataRange).Named("data");

            var latentIndex = Variable.Array<int>(dataRange).Named("latentIndex");

            using (Variable.ForEach(dataRange))
            {
                latentIndex[dataRange] = Variable.Discrete(mixtureWeightsPrior);
                using (Variable.Switch(latentIndex[dataRange]))
                {
                    data[dataRange] = Variable.VectorGaussianFromMeanAndPrecision(meansPrior[latentIndex[dataRange]], precisionsPrior[latentIndex[dataRange]]);
                }
            }

            var zinit = new Discrete[dataRange.SizeAsInt];
            for (int i = 0; i < zinit.Length; i++)
                zinit[i] = Discrete.PointMass(Rand.Int(clustersRange.SizeAsInt), clustersRange.SizeAsInt);
            latentIndex.InitialiseTo(Distribution<int>.Array(zinit));

            evidenceBlock.CloseBlock();

            data.ObservedValue = observedData;

            var ie = new InferenceEngine(new VariationalMessagePassing());
            ie.ShowProgress = false;

            var mixtureWeightsPosterior = ie.Infer(mixtureWeightsPrior);
            var meansPosterior = ie.Infer<VectorGaussian[]>(meansPrior);
            var precisionsPosterior = ie.Infer<Wishart[]>(precisionsPrior);
            var bEvidence = ie.Infer<Bernoulli>(evidence);

            var result = new List<Cluster>();
            for (var i = 0; i < clusters; i++)
            {
                result.Add(new Cluster(meansPosterior[i].GetMean(), precisionsPosterior[i].GetMean().Inverse()));
            }

            return new InferenceResult<Cluster[]>(bEvidence, result.ToArray());
        }
Esempio n. 9
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		// Sample data from a DINA/NIDA model and then use Infer.NET to recover the parameters.
		public void Run()
		{
			InferenceEngine engine = new InferenceEngine();
			if (!(engine.Algorithm is ExpectationPropagation))
			{
				Console.WriteLine("This example only runs with Expectation Propagation");
				return;
			}

			bool useDina = true;
			Beta slipPrior = new Beta(1, 10);
			Beta guessPrior = new Beta(1, 10);
			Rand.Restart(0);
			int nStudents = 100;
			int nQuestions = 20;
			int nSkills = 3;
			int[][] skillsRequired = new int[nQuestions][];
			for (int q = 0; q < nQuestions; q++) {
				// each question requires a random set of skills
				int[] skills = Rand.Perm(nSkills);
				int n = Rand.Int(nSkills)+1;
				skillsRequired[q] = Util.ArrayInit(n, i => skills[i]);
				Console.WriteLine("skillsRequired[{0}] = {1}", q, Util.CollectionToString(skillsRequired[q]));
			}
			double[] pSkill, slip, guess;
			bool[][] hasSkill;
			VariableArray<double> slipVar, guessVar, pSkillVar;
			VariableArray<VariableArray<bool>,bool[][]> hasSkillVar;
			if (useDina) {
				bool[][] responses = DinaSample(nStudents, nSkills, skillsRequired, slipPrior, guessPrior, out pSkill, out slip, out guess, out hasSkill);
				DinaModel(responses, nSkills, skillsRequired, slipPrior, guessPrior, out pSkillVar, out slipVar, out guessVar, out hasSkillVar);
			} else {
				bool[][] responses = NidaSample(nStudents, nSkills, skillsRequired, slipPrior, guessPrior, out pSkill, out slip, out guess, out hasSkill);
				NidaModel(responses, nSkills, skillsRequired, slipPrior, guessPrior, out pSkillVar, out slipVar, out guessVar, out hasSkillVar);
			}

			engine.NumberOfIterations = 10;
			Bernoulli[][] hasSkillPost = engine.Infer<Bernoulli[][]>(hasSkillVar);
			int numErrors = 0;
			for (int i = 0; i < nStudents; i++) {
				for (int s = 0; s < nSkills; s++) {
					if (hasSkill[i][s] != (hasSkillPost[i][s].LogOdds > 0)) numErrors++;
				}
			}
			Console.WriteLine("{0:0}% of skills recovered correctly", 100.0 - 100.0*numErrors/(nStudents*nSkills));
			Beta[] pSkillPost = engine.Infer<Beta[]>(pSkillVar);
			Beta[] slipPost = engine.Infer<Beta[]>(slipVar);
			Beta[] guessPost = engine.Infer<Beta[]>(guessVar);
			for (int s = 0; s < nSkills; s++) {
				Console.WriteLine("pSkill[{0}] = {1} (sampled from {2})", s, pSkillPost[s], pSkill[s].ToString("g4"));				
			}
			for (int i = 0; i < Math.Min(3,slipPost.Length); i++)	{
				Console.WriteLine("slip[{0}] = {1} (sampled from {2})", i, slipPost[i], slip[i].ToString("g4"));			 
			}
			for (int i = 0; i < Math.Min(3,guessPost.Length); i++) {
				Console.WriteLine("guess[{0}] = {1} (sampled from {2})", i, guessPost[i], guess[i].ToString("g4"));
			}
		}
Esempio n. 10
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        internal void UniqueVisitorCountingInt()
        {
            //  http://en.wikipedia.org/wiki/Unique_visitors
            // Variables describing the population
            double              noise       = 0.2;
            int                 maxVisitors = 10;
            Range               visitor     = new Range(maxVisitors + 1).Named("visitor");
            Variable <int>      numVisitors = Variable.DiscreteUniform(visitor).Named("numVisitors");
            VariableArray <int> browserOf   = Variable.Array <int>(visitor).Named("browserOf");

            // http://en.wikipedia.org/wiki/Usage_share_of_web_browsers
            double[] browserProbs = { 0.66, 0.25, 0.09 };
            int      numBrowsers  = browserProbs.Length;
            Range    browser      = new Range(numBrowsers);

            browserOf[visitor] = Variable.Discrete(browser, browserProbs).ForEach(visitor);
            Vector[] browserNoiseProbs = new Vector[numBrowsers];
            for (int i = 0; i < browserNoiseProbs.Length; i++)
            {
                double[] noiseProbs = new double[numBrowsers];
                for (int j = 0; j < noiseProbs.Length; j++)
                {
                    noiseProbs[j] = (i == j) ? 1 - noise : noise / (numBrowsers - 1);
                }
                browserNoiseProbs[i] = Vector.FromArray(noiseProbs);
            }
            var                 browserNoise    = Variable.Observed(browserNoiseProbs, browser).Named("browserNoise");
            Variable <int>      numObserved     = Variable.New <int>().Named("numObserved");
            Range               observedVisitor = new Range(numObserved).Named("observedVisitor");
            VariableArray <int> observedAgent   = Variable.Array <int>(observedVisitor).Named("observedAgent");

            using (Variable.ForEach(observedVisitor))
            {
                Variable <int> visitorIndex = Variable.DiscreteUniform(numVisitors).Named("visitorIndex");
                using (Variable.Switch(visitorIndex))
                {
                    observedAgent[observedVisitor] = Variable.Discrete(browserNoise[browserOf[visitorIndex]]);
                }
            }

            InferenceEngine engine         = new InferenceEngine();
            int             numObservedInt = 6;

            int[] data = new int[numObservedInt];
            for (int i = 0; i < data.Length; i++)
            {
                data[i] = 0;
            }
            numObserved.ObservedValue   = data.Length;
            observedAgent.ObservedValue = data;
            Console.WriteLine("browserOf = {0}", engine.Infer(browserOf));
            Console.WriteLine("numVisitors = {0}", engine.Infer(numVisitors));
            //Console.WriteLine("      exact = {0}", BirdCountingExact(maxBirds, numObservedInt, numObservedMale, noise));
        }
        public void MP_VectorGaussian()
        {
            // Need VMP as EP doesn't support random mean
            InferenceEngine       engine = new InferenceEngine(new VariationalMessagePassing());
            ModelDefinitionMethod meth   = new ModelDefinitionMethod(VectorGaussianModel);
            var ca = engine.Compiler.CompileWithoutParams(
                declaringType, meth.Method, new AttributeRegistry <object, ICompilerAttribute>(true));

            ca.Execute(1);
            VectorGaussian cMarg = ca.Marginal <VectorGaussian>("c");
        }
Esempio n. 12
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		public virtual void CreateModel()
		{
			AverageTimePrior = Variable.New<Gaussian>();
			TrafficNoisePrior = Variable.New<Gamma>();
			AverageTime = Variable.Random<double, Gaussian>(AverageTimePrior);
			TrafficNoise = Variable.Random<double, Gamma>(TrafficNoisePrior);
			if (InferenceEngine == null)
			{
				InferenceEngine = new InferenceEngine();
			}
		}
Esempio n. 13
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        internal void MixtureLinearRegressionTest()
        {
            int nFeature   = 2; // need to change w and probs in MakeData method, if this is changed
            int nComponent = 2; // need to change  w and probs in MakeData method, if this is changed

            double[]     wtVector  = new double[nComponent];
            Gaussian[][] wPriorObs = new Gaussian[nComponent][];
            for (int c = 0; c < nComponent; c++)
            {
                wtVector[c] = 2;
            }

            Range          feature   = new Range(nFeature);
            Range          component = new Range(nComponent);
            Variable <int> nItem     = Variable.New <int>().Named("nItems");
            Range          item      = new Range(nItem).Named("item");
            var            w         = Variable.Array(Variable.Array <double>(feature), component).Named("w");
            var            wPrior    = Variable.Array(Variable.Array <Gaussian>(feature), component).Named("wPrior");

            using (Variable.ForEach(component))
            {
                w[component][feature] = Variable.Random <double, Gaussian>(wPrior[component][feature]);
            }

            var x = Variable.Array(Variable.Array <double>(feature), item).Named("x");
            VariableArray <double> y       = Variable.Array <double>(item).Named("y");
            VariableArray <int>    yClass  = Variable.Array <int>(item).Named("yClass");
            Variable <Vector>      weights = Variable.Dirichlet(component, wtVector).Named("weights");


            using (Variable.ForEach(item))
            {
                yClass[item] = Variable.Discrete(weights);
                VariableArray <double> product = Variable.Array <double>(feature).Named("product");
                using (Variable.Switch(yClass[item]))
                {
                    product[feature] = w[yClass[item]][feature] * x[item][feature];
                }
                y[item] = Variable.GaussianFromMeanAndVariance(Variable.Sum(product), 1);
            }

            double[][] xData;
            double[]   yData;
            MakeData(out xData, out yData, out wPriorObs);

            wPrior.ObservedValue = wPriorObs;
            x.ObservedValue      = xData;
            y.ObservedValue      = yData;
            nItem.ObservedValue  = yData.Length;
            InferenceEngine ie = new InferenceEngine();

            ie.Infer(w);
            //    DistributionArray<GaussianArray> wInfer = ie.Infer<DistributionArray<GaussianArray>>(w);
        }
        public override void Run()
        {
            var prices = GetPrices("SPY").Select(t => t.Item2).ToArray();
            var inputs = prices.Take(prices.Length - 1).ToArray();
            var inputVectors = inputs.Select(i => Vector.FromArray(new[] { i })).ToArray();
            var outputs = prices.Skip(1).ToArray();

            // Set up the GP prior, which will be filled in later
            var prior = Variable.New<SparseGP>().Named("prior");

            // The sparse GP variable - a distribution over functions
            var f = Variable<IFunction>.Random(prior).Named("f");

            // The locations to evaluate the function
            var x = Variable.Observed(inputVectors).Named("x");
            var j = x.Range.Named("j");
            var y = Variable.Observed(outputs, j).Named("y");
            y[j] = Variable.GaussianFromMeanAndVariance(Variable.FunctionEvaluate(f, x[j]), 0.1);
            var kf = new SquaredExponential(-0.5);

            // The basis
            var rand = new Random(Environment.TickCount);
            var basis = Enumerable.Range(1, 10).Select(i => Vector.FromArray(new double[1] {i*10})).ToArray();
            //var basis = new Vector[] {
            //    Vector.FromArray(new double[1] {80}),
            //    Vector.FromArray(new double[1] {90}),
            //    Vector.FromArray(new double[1] {100})
            //};

            var gp = new GaussianProcess(new ConstantFunction(0), kf);

            prior.ObservedValue = new SparseGP(new SparseGPFixed(gp, basis));
            var engine = new InferenceEngine(new ExpectationPropagation());
            var sgp = engine.Infer<SparseGP>(f);

            var means = sgp.Mean(inputVectors).ToArray();
            var stdDevs = inputVectors.Select(iv => Math.Sqrt(sgp.Variance(iv))).ToArray();

            this.Series = new[]
            {
                new LabelledSeries<Tuple<double,double>>(
                    "input",
                    Enumerable.Range(0,inputs.Length)
                    .Select(i=> Tuple.Create((double)i, inputs[i]))),
                new LabelledSeries<Tuple<double,double>>(
                    "infered mean",
                    Enumerable.Range(0,inputs.Length)
                    .Select(i=> Tuple.Create((double)i, means[i]))),
                new LabelledSeries<Tuple<double,double>>(
                    "infered stddev",
                    Enumerable.Range(0,inputs.Length)
                    .Select(i=> Tuple.Create((double)i, stdDevs[i]))),
            };
        }
Esempio n. 15
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        public ModelData InferModelData(double[] trainingData)
        {
            ModelData posteriors = new ModelData();

            NumPoints.ObservedValue   = trainingData.Length;
            Prices.ObservedValue      = trainingData;
            posteriors.PriceLevelDist = InferenceEngine.Infer <Gaussian[]>(PriceLevels);
            posteriors.NoiseDist      = InferenceEngine.Infer <Gamma[]>(Noises);
            posteriors.Mixing         = InferenceEngine.Infer <Dirichlet>(MixingCoefficients);
            return(posteriors);
        }
    public ModelDataMixed InferModelData(double[] trainingData) //Training model
    {
        ModelDataMixed posteriors;

        NumTrips.ObservedValue      = trainingData.Length;
        TravelTimes.ObservedValue   = trainingData;
        posteriors.AverageTimeDist  = InferenceEngine.Infer <Gaussian[]>(AverageTime);
        posteriors.TrafficNoiseDist = InferenceEngine.Infer <Gamma[]>(TrafficNoise);
        posteriors.MixingDist       = InferenceEngine.Infer <Dirichlet>(MixingCoefficients);
        return(posteriors);
    }
Esempio n. 17
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        public void BugsPumpModified()
        {
            //model
            //{
            //    for (i in 1 : N) {
            //        theta[i] ~ dgamma(alpha, beta)
            //        lambda[i] <- theta[i] * t[i]
            //        x[i] ~ dpois(lambda[i])
            //    }
            //    beta ~ dgamma(0.1, 1.0)
            //}

            // Data:
            //   list(t = c(94.3, 15.7, 62.9, 126, 5.24, 31.4, 1.05, 1.05, 2.1, 10.5),
            //        x = c(5, 1, 5, 14, 3, 19, 1, 1, 4, 22), N = 10, alpha=0.7)

            Rand.Restart(12347);
            int N = 10;

            double[] tData = new double[] { 94.3, 15.7, 62.9, 126, 5.24, 31.4, 1.05, 1.05, 2.1, 10.5 };
            int[]    xData = new int[] { 5, 1, 5, 14, 3, 19, 1, 1, 4, 22 };
            // Exponential distribution is a special case of Gamma distribution (Shape =1)
            //var alpha = Variable.GammaFromShapeAndRate(1.0, 1.0);
            var   beta   = Variable.GammaFromShapeAndRate(0.1, 1.0);
            var   alpha  = 0.7;
            Range i      = new Range(N);
            var   theta  = Variable.Array <double>(i);
            var   lambda = Variable.Array <double>(i);
            var   t      = Variable.Array <double>(i);
            var   x      = Variable.Array <int>(i);

            theta[i]        = Variable.GammaFromShapeAndRate(alpha, beta).ForEach(i);
            lambda[i]       = theta[i] * t[i];
            x[i]            = Variable.Poisson(lambda[i]);
            x.ObservedValue = xData;

            t.ObservedValue = tData;
            var engine = new InferenceEngine(new GibbsSampling());

            engine.NumberOfIterations = 100000;
            engine.ShowProgress       = false;
            engine.ShowTimings        = true;

            // Expected values (from WinBugs)
            double[] expectedThetaPostMean         = { 0.06003, 0.1017, 0.08966, 0.1158, 0.5998, 0.6082, 0.8838, 0.8935, 1.565, 1.987 };
            double[] expectedThetaPostSD           = { 0.02509, 0.07878, 0.03766, 0.03019, 0.3166, 0.1401, 0.7187, 0.7132, 0.7583, 0.4219 };
            IDistribution <double[]> thetaExpected =
                Distribution <double> .Array(Util.ArrayInit(N, k => Gamma.FromMeanAndVariance(expectedThetaPostMean[k], expectedThetaPostSD[k] * expectedThetaPostSD[k])));

            var thetaPost = engine.Infer(theta);

            Console.WriteLine(StringUtil.JoinColumns("theta = ", thetaPost, " should be ", thetaExpected));
            Assert.True(thetaExpected.MaxDiff(thetaPost) < 1.3);
        }
 public virtual void CreateModel()
 {
     AverageTimePrior  = Variable.New <Gaussian>();
     TrafficNoisePrior = Variable.New <Gamma>();
     AverageTime       = Variable.Random <double, Gaussian>(AverageTimePrior);
     TrafficNoise      = Variable.Random <double, Gamma>(TrafficNoisePrior);
     if (InferenceEngine == null)
     {
         InferenceEngine = new InferenceEngine();
     }
 }
Esempio n. 19
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 private void GetInferenceEngine(string method)
 {
     _engine = method.ToLower() switch
     {
         "tt" => new TruthTable(new UniqueSymbols(_kb)),
         "fc" => new ForwardChaining(),
         "bc" => new BackwardChaining(),
         _ => throw new NotSupportedException(
                   $"The method named \"{method}\" is not supported by this program!")
     };
 }
Esempio n. 20
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        internal void ProbabilisticIndexMap()
        {
            //TODO: change the path for cross platform using
            double[,] dataIn  = MatlabReader.ReadMatrix(new double[10, 6400 * 3], @"c:\temp\pim\chand.txt", ' ');
            Vector[,] pixData = new Vector[10, 6400];
            for (int i = 0; i < pixData.GetLength(0); i++)
            {
                int ct = 0;
                for (int j = 0; j < pixData.GetLength(1); j++)
                {
                    pixData[i, j] = Vector.FromArray(dataIn[i, ct++], dataIn[i, ct++], dataIn[i, ct++]);
                }
            }
            Range images = new Range(pixData.GetLength(0));
            Range pixels = new Range(pixData.GetLength(1));
            VariableArray2D <Vector> pixelData = Variable.Constant(pixData, images, pixels);

            // For each image we have a palette of L multivariate Gaussians
            Range L = new Range(2);
            VariableArray2D <Vector> means = Variable.Array <Vector>(images, L).Named("means");

            means[images, L] = Variable.VectorGaussianFromMeanAndPrecision(
                Vector.FromArray(0.5, 0.5, 0.5),
                PositiveDefiniteMatrix.Identity(3)).ForEach(images, L);
            VariableArray2D <PositiveDefiniteMatrix> precs = Variable.Array <PositiveDefiniteMatrix>(images, L).Named("precs");

            precs[images, L] = Variable.WishartFromShapeAndScale(1.0, PositiveDefiniteMatrix.Identity(3)).ForEach(images, L);

            // Across all pixels we have a
            VariableArray <Vector> pi = Variable.Array <Vector>(pixels);

            pi[pixels] = Variable.Dirichlet(L, new double[] { 1.1, 1.0 }).ForEach(pixels);
            // For each pixel of each image we have a discrete indicator
            VariableArray2D <int> ind = Variable.Array <int>(images, pixels).Named("ind");

            ind[images, pixels] = Variable.Discrete(pi[pixels]).ForEach(images);

            using (Variable.ForEach(pixels))
            {
                using (Variable.ForEach(images))
                {
                    using (Variable.Switch(ind[images, pixels]))
                    {
                        pixelData[images, pixels] = Variable.VectorGaussianFromMeanAndPrecision(means[images, ind[images, pixels]],
                                                                                                precs[images, ind[images, pixels]]);
                    }
                }
            }
            InferenceEngine ie = new InferenceEngine(new VariationalMessagePassing());

            ie.ShowProgress       = true;
            ie.NumberOfIterations = 5;
            Console.WriteLine("Dist over L: " + ie.Infer(pi));
        }
Esempio n. 21
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        public void IsGreaterThan()
        {
            var x2 = Variable.GaussianFromMeanAndVariance(0, 1);
            var y2 = Variable.GaussianFromMeanAndVariance(0, 1);

            Variable.ConstrainPositive(x2 - y2);
            var ie2    = new InferenceEngine(new ExpectationPropagation());
            var epPost = ie2.Infer <Gaussian>(x2);

            Console.WriteLine("EP: " + epPost);
        }
        public void MP_Product()
        {
            InferenceEngine       engine = new InferenceEngine();
            ModelDefinitionMethod meth   = new ModelDefinitionMethod(ProductModel);
            var ca = engine.Compiler.CompileWithoutParams(
                declaringType, meth.Method, new AttributeRegistry <object, ICompilerAttribute>(true));

            ca.Execute(1);
            Gaussian cMarg = ca.Marginal <Gaussian>("c");
            Gaussian dMarg = ca.Marginal <Gaussian>("d");
        }
        public void MP_Plus()
        {
            InferenceEngine engine = new InferenceEngine();

            engine.Compiler.DeclarationProvider = Microsoft.ML.Probabilistic.Compiler.RoslynDeclarationProvider.Instance;
            var ca = engine.Compiler.Compile(PlusModel);

            ca.Execute(1);
            Gaussian cMarg = ca.Marginal <Gaussian>("c");
            Gaussian dMarg = ca.Marginal <Gaussian>("d");
        }
        public void MP_GetItemSparseGP()
        {
            InferenceEngine       engine = new InferenceEngine();
            ModelDefinitionMethod meth   = new ModelDefinitionMethod(GetItemSparseGPModel);
            var ca = engine.Compiler.CompileWithoutParams(
                declaringType, meth.Method, new AttributeRegistry <object, ICompilerAttribute>(true));

            ca.Execute(1);
            SparseGP cMarg = ca.Marginal <SparseGP>("c");
            DistributionArray <SparseGP> dMarg = ca.Marginal <DistributionArray <SparseGP> >("d");
        }
Esempio n. 25
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 public void ConstrainPositive_Throws()
 {
     Assert.Throws <CompilationFailedException>(() =>
     {
         var x  = Variable.GaussianFromMeanAndVariance(0, 1);
         var x2 = Variable.Copy(x);
         Variable.ConstrainPositive(x2);
         var ie       = new InferenceEngine(new VariationalMessagePassing());
         var vmp2Post = ie.Infer <Gaussian>(x);
         Console.WriteLine("VMP2: " + vmp2Post);
     });
 }
Esempio n. 26
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        public void MixtureOfManyGaussiansTest()
        {
            InferenceEngine engine = new InferenceEngine();

            engine.Algorithm = new VariationalMessagePassing();
            engine.Compiler.DeclarationProvider = Microsoft.ML.Probabilistic.Compiler.RoslynDeclarationProvider.Instance;
            double[] data = { 0, 1, 2, 5, 6, 7, 10, 11, 12 };
            var      ca   = engine.Compiler.Compile(MixtureOfManyGaussiansModel, data, 3);

            ca.Execute(20);
            Console.WriteLine("mean=" + ca.Marginal("mean"));
        }
Esempio n. 27
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        public void MixtureOfTwoGaussiansWithLoopsTest()
        {
            InferenceEngine engine = new InferenceEngine(new VariationalMessagePassing());

            engine.Compiler.DeclarationProvider = Microsoft.ML.Probabilistic.Compiler.RoslynDeclarationProvider.Instance;
            double[] data = { 0.1, 9.3 };
            var      ca   = engine.Compiler.Compile(MixtureOfTwoGaussiansWithLoopsModel, data);

            ca.Execute(20);
            Console.WriteLine("mean1=" + ca.Marginal("mean1"));
            Console.WriteLine("mean2=" + ca.Marginal("mean2"));
        }
Esempio n. 28
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            public BPM_FixedNoise(int d, int n)
            {
                Range j = new Range(n).Named("j");

                y     = Variable.Array <bool>(j).Named("y");
                x     = Variable.Array <Vector>(j).Named("x");
                noise = Variable.New <double>().Named("noise");
                w     = Variable.Random(new VectorGaussian(Vector.Zero(d),
                                                           PositiveDefiniteMatrix.Identity(d))).Named("w");
                engine = new InferenceEngine();
                y[j]   = Variable.GaussianFromMeanAndVariance(Variable.InnerProduct(w, x[j]).Named("innerProduct"), noise) > 0;
            }
        public void MP_Logical()
        {
            InferenceEngine       engine = new InferenceEngine();
            ModelDefinitionMethod meth   = new ModelDefinitionMethod(LogicalModel);
            var ca = engine.Compiler.CompileWithoutParams(
                declaringType, meth.Method, new AttributeRegistry <object, ICompilerAttribute>(true));

            ca.Execute(1);
            Bernoulli cMarg = ca.Marginal <Bernoulli>("c");
            Bernoulli dMarg = ca.Marginal <Bernoulli>("d");
            Bernoulli eMarg = ca.Marginal <Bernoulli>("e");
        }
        public RobustSudokuModel()
        {
            Range valuesRange = new Range(CellDomain.Count).Named("valuesRange");
            Range cellsRange  = new Range(CellIndices.Count).Named("cellsRange");


            CellsPrior            = Variable.Array <Dirichlet>(cellsRange).Named("CellsPrior");
            ProbCells             = Variable.Array <Vector>(cellsRange).Named("ProbCells");
            ProbCells[cellsRange] = Variable <Vector> .Random(CellsPrior[cellsRange]);

            ProbCells.SetValueRange(valuesRange);


            // Initialisation des distribution a priori de façon uniforme (les valeurs sont équiprobables pour chaque cellule)

            Dirichlet[] dirUnifArray =
                Enumerable.Repeat(Dirichlet.Uniform(CellDomain.Count), CellIndices.Count).ToArray();
            CellsPrior.ObservedValue = dirUnifArray;

            Cells             = Variable.Array <int>(cellsRange);
            Cells[cellsRange] = Variable.Discrete(ProbCells[cellsRange]);



            //Ajout des contraintes de Sudoku (all diff pour tous les voisinages)
            foreach (var cellIndex in GrilleSudoku.IndicesCellules)
            {
                foreach (var neighbourCellIndex in GrilleSudoku.VoisinagesParCellule[cellIndex])
                {
                    if (neighbourCellIndex > cellIndex)
                    {
                        Variable.ConstrainFalse(Cells[cellIndex] == Cells[neighbourCellIndex]);
                    }
                }
            }


            //Todo: tester d'autres algo et paramétrages associés

            IAlgorithm algo = new ExpectationPropagation();

            //IAlgorithm algo = new GibbsSampling();
            //IAlgorithm algo = new VariationalMessagePassing();
            //IAlgorithm algo = new MaxProductBeliefPropagation();
            //les algos ont ete teste cependant la difference lors du lancement du projet n'est pas facilement visible
            algo.DefaultNumberOfIterations = 50;
            //algo.DefaultNumberOfIterations = 200;


            InferenceEngine = new InferenceEngine(algo);

            //InferenceEngine.OptimiseForVariables = new IVariable[] { Cells };
        }
Esempio n. 31
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        public static (Vector[] dataX, double[] dataY) GenerateRandomData(int numData, double proportionCorrupt)
        {
            int randomSeed = 9876;

            Random rng = new Random(randomSeed);

            Rand.Restart(randomSeed);

            InferenceEngine engine = Utilities.GetInferenceEngine();

            // The points to evaluate
            Vector[] randomInputs = Utilities.VectorRange(0, 1, numData, null);

            var gaussianProcessGenerator = new GaussianProcessRegressor(randomInputs);

            // The basis
            Vector[] basis = Utilities.VectorRange(0, 1, 6, rng);

            // The kernel
            var kf = new SummationKernel(new SquaredExponential(-1)) + new WhiteNoise();

            // Fill in the sparse GP prior
            GaussianProcess gp = new GaussianProcess(new ConstantFunction(0), kf);

            gaussianProcessGenerator.Prior.ObservedValue = new SparseGP(new SparseGPFixed(gp, basis));

            // Infer the posterior Sparse GP, and sample a random function from it
            SparseGP sgp        = engine.Infer <SparseGP>(gaussianProcessGenerator.F);
            var      randomFunc = sgp.Sample();

            double[] randomOutputs = new double[randomInputs.Length];
            int      numCorrupted  = (int)Math.Ceiling(numData * proportionCorrupt);
            var      subset        = Enumerable.Range(0, randomInputs.Length + 1).OrderBy(x => rng.Next()).Take(numCorrupted);

            // get random data
            for (int i = 0; i < randomInputs.Length; i++)
            {
                double post = randomFunc.Evaluate(randomInputs[i]);
                // corrupt data point if it we haven't exceed the proportion we wish to corrupt
                if (subset.Contains(i))
                {
                    double sign     = rng.NextDouble() > 0.5 ? 1 : -1;
                    double distance = rng.NextDouble() * 1;
                    post = (sign * distance) + post;
                }

                randomOutputs[i] = post;
            }

            Console.WriteLine("Model complete: Generated {0} points with {1} corrupted", numData, numCorrupted);

            return(randomInputs, randomOutputs);
        }
Esempio n. 32
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		public Inference(int numRanks)
		{
			nRanks = numRanks;
			probNextIfNotClick = Variable.New<double>();
			probNextIfClickNotRel = Variable.New<double>();
			probNextIfClickRel = Variable.New<double>();
			nUsers = Variable.New<int>();
			Range u = new Range(nUsers);

			appeal = new Variable<double>[nRanks];
			relevance = new Variable<double>[nRanks];
			examine = new VariableArray<bool>[nRanks];
			click = new VariableArray<bool>[nRanks];
			isRel = new VariableArray<bool>[nRanks];

			// user independent variables
			for (int d = 0; d < nRanks; d++)
			{
				appeal[d] = Variable.Beta(1, 1);
				relevance[d] = Variable.Beta(1, 1);
			}

			// Main model code
			for (int d = 0; d < nRanks; d++)
			{
				examine[d] = Variable.Array<bool>(u);
				click[d] = Variable.Array<bool>(u);
				isRel[d] = Variable.Array<bool>(u);
				if (d == 0)
					examine[d][u] = Variable.Bernoulli(1).ForEach(u);
				else
					using (Variable.ForEach(u))
					{
						var nextIfClick = Variable.New<bool>();
						using (Variable.If(isRel[d-1][u]))
							nextIfClick.SetTo(Variable.Bernoulli(probNextIfClickRel));
						using (Variable.IfNot(isRel[d-1][u]))
							nextIfClick.SetTo(Variable.Bernoulli(probNextIfClickNotRel));
						var nextIfNotClick = Variable.Bernoulli(probNextIfNotClick);
						var next = 
							(((!click[d - 1][u]) & nextIfNotClick) | (click[d - 1][u]  & nextIfClick));
						examine[d][u] = examine[d - 1][u] & next;
								  
					}

				using (Variable.ForEach(u))
				{
					click[d][u] = examine[d][u] & Variable.Bernoulli(appeal[d]);
					isRel[d][u] = click[d][u] & Variable.Bernoulli(relevance[d]);
				}
			}
			ie = new InferenceEngine();
		}
        public void Run()
        {
            // Define a range for the number of mixture components
            Range k = new Range(2).Named("k");

            // Mixture component means
            VariableArray <Vector> means = Variable.Array <Vector>(k).Named("means");

            means[k] = Variable.VectorGaussianFromMeanAndPrecision(
                Vector.FromArray(0.0, 0.0),
                PositiveDefiniteMatrix.IdentityScaledBy(2, 0.01)).ForEach(k);

            // Mixture component precisions
            VariableArray <PositiveDefiniteMatrix> precs = Variable.Array <PositiveDefiniteMatrix>(k).Named("precs");

            precs[k] = Variable.WishartFromShapeAndScale(100.0, PositiveDefiniteMatrix.IdentityScaledBy(2, 0.01)).ForEach(k);

            // Mixture weights
            Variable <Vector> weights = Variable.Dirichlet(k, new double[] { 1, 1 }).Named("weights");

            // Create a variable array which will hold the data
            Range n = new Range(300).Named("n");
            VariableArray <Vector> data = Variable.Array <Vector>(n).Named("x");

            // Create latent indicator variable for each data point
            VariableArray <int> z = Variable.Array <int>(n).Named("z");

            // The mixture of Gaussians model
            using (Variable.ForEach(n))
            {
                z[n] = Variable.Discrete(weights);
                using (Variable.Switch(z[n]))
                {
                    data[n] = Variable.VectorGaussianFromMeanAndPrecision(means[z[n]], precs[z[n]]);
                }
            }

            // Attach some generated data
            data.ObservedValue = GenerateData(n.SizeAsInt);

            // Initialise messages randomly to break symmetry
            VariableArray <Discrete> zInit = Variable.Array <Discrete>(n).Named("zInit");

            zInit.ObservedValue = Util.ArrayInit(n.SizeAsInt, i => Discrete.PointMass(Rand.Int(k.SizeAsInt), k.SizeAsInt));
            z[n].InitialiseTo(zInit[n]);

            // The inference
            InferenceEngine engine = new InferenceEngine();

            Console.WriteLine("Dist over pi=" + engine.Infer(weights));
            Console.WriteLine("Dist over means=\n" + engine.Infer(means));
            Console.WriteLine("Dist over precs=\n" + engine.Infer(precs));
        }
Esempio n. 34
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		public void CreateModel()
		{
			CommonEngine = new InferenceEngine();

			cyclist1 = new CyclistPrediction() { InferenceEngine = CommonEngine };
			cyclist1.CreateModel();
			cyclist2 = new CyclistPrediction() { InferenceEngine = CommonEngine };
			cyclist2.CreateModel();

			TimeDifference = cyclist1.TomorrowsTime - cyclist2.TomorrowsTime;
			Cyclist1IsFaster = cyclist1.TomorrowsTime < cyclist2.TomorrowsTime;
		}
Esempio n. 35
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        public void InferConcatenationFromArgumentsTest()
        {
            var str1           = Variable.Random(StringDistribution.OneOf("No man ", "Man "));
            var str2           = Variable.Random(StringDistribution.OneOf("is an island", "is not an island"));
            var s              = str1 + str2;
            var engine         = new InferenceEngine();
            var posteriorOverS = engine.Infer <StringDistribution>(s);

            StringInferenceTestUtilities.TestProbability(
                posteriorOverS, 0.25, "No man is an island", "Man is an island", "Man is not an island", "No man is not an island");
            StringInferenceTestUtilities.TestProbability(posteriorOverS, 0.0, "No man ", "Man ", "is an island", "is not an island");
        }
Esempio n. 36
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        public void InferEqualityFromArgumentsTest()
        {
            Variable <string> str1  = Variable.Random(StringDistribution.OneOf("a", "b"));
            Variable <string> str2  = Variable.Random(StringDistribution.OneOf("a", "b", "c"));
            Variable <bool>   equal = str1 == str2;

            var engine = new InferenceEngine();

            var equalPosterior = engine.Infer <Bernoulli>(equal);

            Assert.Equal(1.0 / 3.0, equalPosterior.GetProbTrue(), 1e-6);
        }
Esempio n. 37
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		public void Run()
		{
			// Define a range for the number of mixture components
			Range k = new Range(2).Named("k");

			// Mixture component means
			VariableArray<Vector> means = Variable.Array<Vector>(k).Named("means");			
			means[k] = Variable.VectorGaussianFromMeanAndPrecision(
				Vector.FromArray(0.0,0.0),
				PositiveDefiniteMatrix.IdentityScaledBy(2,0.01)).ForEach(k);
	
			// Mixture component precisions
			VariableArray<PositiveDefiniteMatrix> precs = Variable.Array<PositiveDefiniteMatrix>(k).Named("precs");
			precs[k] = Variable.WishartFromShapeAndScale(100.0, PositiveDefiniteMatrix.IdentityScaledBy(2,0.01)).ForEach(k);
			
			// Mixture weights 
			Variable<Vector> weights = Variable.Dirichlet(k, new double[] { 1, 1 }).Named("weights");	

			// Create a variable array which will hold the data
			Range n = new Range(300).Named("n");
			VariableArray<Vector> data = Variable.Array<Vector>(n).Named("x");
			// Create latent indicator variable for each data point
			VariableArray<int> z = Variable.Array<int>(n).Named("z");

			// The mixture of Gaussians model
			using (Variable.ForEach(n)) {
				z[n] = Variable.Discrete(weights);
				using (Variable.Switch(z[n])) {
					data[n] = Variable.VectorGaussianFromMeanAndPrecision(means[z[n]], precs[z[n]]);
				}
			}

			// Attach some generated data
			data.ObservedValue = GenerateData(n.SizeAsInt);

			// Initialise messages randomly so as to break symmetry
			Discrete[] zinit = new Discrete[n.SizeAsInt];		
			for (int i = 0; i < zinit.Length; i++) 
			  zinit[i] = Discrete.PointMass(Rand.Int(k.SizeAsInt), k.SizeAsInt);
			z.InitialiseTo(Distribution<int>.Array(zinit)); 

			// The inference
			InferenceEngine ie = new InferenceEngine();
			if (!(ie.Algorithm is ExpectationPropagation))
			{
				Console.WriteLine("Dist over pi=" + ie.Infer(weights));
				Console.WriteLine("Dist over means=\n" + ie.Infer(means));
				Console.WriteLine("Dist over precs=\n" + ie.Infer(precs));
			}
			else
				Console.WriteLine("This example is not supported by Expectation Propagation");
		}
Esempio n. 38
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        internal void LogisticIrtTest()
        {
            Variable <int>         numStudents = Variable.New <int>().Named("numStudents");
            Range                  student     = new Range(numStudents);
            VariableArray <double> ability     = Variable.Array <double>(student).Named("ability");

            ability[student] = Variable.GaussianFromMeanAndPrecision(0, 1e-6).ForEach(student);
            Variable <int>         numQuestions = Variable.New <int>().Named("numQuestions");
            Range                  question     = new Range(numQuestions);
            VariableArray <double> difficulty   = Variable.Array <double>(question).Named("difficulty");

            difficulty[question] = Variable.GaussianFromMeanAndPrecision(0, 1e-6).ForEach(question);
            VariableArray <double> discrimination = Variable.Array <double>(question).Named("discrimination");

            discrimination[question] = Variable.Exp(Variable.GaussianFromMeanAndPrecision(0, 1).ForEach(question));
            VariableArray2D <bool> response = Variable.Array <bool>(student, question).Named("response");

            response[student, question] = Variable.BernoulliFromLogOdds(((ability[student] - difficulty[question]).Named("minus") * discrimination[question]).Named("product"));
            bool[,] data;
            double[] discriminationTrue = new double[0];
            bool     useDummyData       = false;

            if (useDummyData)
            {
                data = new bool[4, 2];
                for (int i = 0; i < data.GetLength(0); i++)
                {
                    for (int j = 0; j < data.GetLength(1); j++)
                    {
                        data[i, j] = (i > j);
                    }
                }
            }
            else
            {
                // simulated data
                // also try IRT2PL_10_250.mat
                //TODO: change path for cross platform using
                Dictionary <string, object> dict = MatlabReader.Read(@"..\..\..\Tests\Data\IRT2PL_10_1000.mat");
                Matrix m = (Matrix)dict["Y"];
                data = ConvertToBool(m.ToArray());
                m    = (Matrix)dict["discrimination"];
                discriminationTrue = Util.ArrayInit(data.GetLength(1), i => m[i]);
            }
            numStudents.ObservedValue  = data.GetLength(0);
            numQuestions.ObservedValue = data.GetLength(1);
            response.ObservedValue     = data;
            InferenceEngine engine = new InferenceEngine();

            engine.Algorithm = new VariationalMessagePassing();
            Console.WriteLine(StringUtil.JoinColumns(engine.Infer(discrimination), " should be ", StringUtil.ToString(discriminationTrue)));
        }
Esempio n. 39
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        internal void ProbabilisticIndexMapNoGate()
        {
            //TODO: change path for cross platform using
            double[,] pixData = MatlabReader.ReadMatrix(new double[10, 6400], @"c:\temp\pim\chand2.txt", ' ');
            Range images = new Range(pixData.GetLength(0));
            Range pixels = new Range(pixData.GetLength(1));
            VariableArray2D <double> pixelData = Variable.Constant(pixData, images, pixels);
            //pixelData.QuoteInMSL = false;

            // For each image we have a palette of L multivariate Gaussians
            VariableArray <double> means = Variable.Array <double>(images).Named("means");

            means[images] = Variable.GaussianFromMeanAndPrecision(0.5, 1).ForEach(images);
            VariableArray <double> precs = Variable.Array <double>(images).Named("precs");

            precs[images] = Variable.GammaFromShapeAndScale(1.0, 1.0).ForEach(images);

            // Across all pixels we have a
            VariableArray <Vector> pi = Variable.Array <Vector>(pixels).Named("pi");

            Dirichlet[] dinit = new Dirichlet[pixels.SizeAsInt];
            for (int i = 0; i < dinit.Length; i++)
            {
                double d = Rand.Double();
                dinit[i] = new Dirichlet(1.0 + d / 10, 1.0 - d / 10);
            }
            pi[pixels] = Variable.Dirichlet(new double[] { 1.0 }).ForEach(pixels);
            // For each pixel of each image we have a discrete indicator
            VariableArray2D <int> ind = Variable.Array <int>(images, pixels).Named("ind");

            ind[images, pixels] = Variable.Discrete(pi[pixels]).ForEach(images);

            using (Variable.ForEach(pixels))
            {
                using (Variable.ForEach(images))
                {
                    pixelData[images, pixels] = Variable.GaussianFromMeanAndPrecision(means[images], //10);
                                                                                      precs[images]);
                    Variable.ConstrainEqualRandom(ind[images, pixels], Discrete.Uniform(1));
                }
            }
            InferenceEngine ie = new InferenceEngine(new VariationalMessagePassing());

            ie.ModelName          = "PIM_NoGate";
            ie.NumberOfIterations = 8;
            ie.ShowTimings        = true;
            DistributionArray <Dirichlet> piDist = ie.Infer <DistributionArray <Dirichlet> >(pi);

            //Console.WriteLine("Dist over pi: " + ie.Infer(pi));
            //TODO: change path for cross platform using
            WriteMatrix(piDist.ToArray(), @"C:\temp\pim\results.txt");
        }
Esempio n. 40
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        static void Main(string[] args)
        {
            // number of data points
            int n = 100;

            // array that will host our data
            double[] data = new double[n];

            // reading the data from a comma separated .csv file
            String line = String.Empty;

            System.IO.StreamReader file = new System.IO.StreamReader("data.csv");

            int iter = 0;

            while ((line = file.ReadLine()) != null)
            {
                String[] parts_of_line = line.Split(',');
                for (int i = 0; i < parts_of_line.Length; i++)
                {
                    parts_of_line[i] = parts_of_line[i].Trim();
                    data[iter]       = double.Parse(parts_of_line[i], System.Globalization.CultureInfo.InvariantCulture);
                }
                iter++;
            }

            // use "Ranges" and "VariableArrays" for efficiency see: https://dotnet.github.io/infer/userguide/Arrays%20and%20ranges.html
            Range numDataPoints = new Range(n);

            // create a Gaussian distribution over a random variable called "mean"
            var mean = Variable.GaussianFromMeanAndPrecision(0, 1).Named("Mean");

            // create a variable array of doubles, which will host the observed data points
            VariableArray <double> x = Variable.Array <double>(numDataPoints).Named("x");

            // create a stream of Gaussian distributions using "ForEach" see: https://dotnet.github.io/infer/userguide/ForEach%20blocks.html
            x[numDataPoints] = Variable.GaussianFromMeanAndPrecision(mean, 1).ForEach(numDataPoints);

            // observe the data
            x.ObservedValue = data;

            // define the inference engine settings see: https://dotnet.github.io/infer/userguide/inference%20engine%20settings.html
            var engine = new InferenceEngine();

            engine.Algorithm = new VariationalMessagePassing();
            //engine.ShowFactorGraph = true;

            // infer the mean posterior Gaussian distribution
            Gaussian postMean = engine.Infer <Gaussian>(mean);

            Console.WriteLine("Posterior Gaussian (Gaussian mean): {0}", postMean);
        }
Esempio n. 41
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		public void Run()
		{
			for (double thresh = 0; thresh <= 1; thresh += 0.1)
			{
				Variable<double> x = Variable.GaussianFromMeanAndVariance(0, 1).Named("x");
				Variable.ConstrainTrue(x > thresh);
				InferenceEngine engine = new InferenceEngine();
				if (engine.Algorithm is ExpectationPropagation)
					Console.WriteLine("Dist over x given thresh of " + thresh + "=" + engine.Infer(x));
				else
					Console.WriteLine("This example only runs with Expectation Propagation");
			}
		}
Esempio n. 42
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        public void InferArgumentsFromConcatenationTest1()
        {
            var str1 = Variable.StringUpper(minLength: 0);
            var str2 = Variable.StringLower(minLength: 0);
            var s    = str1 + str2;

            s.ObservedValue = "ABc";
            var engine            = new InferenceEngine();
            var posteriorOverStr1 = engine.Infer <StringDistribution>(str1);

            Assert.True(posteriorOverStr1.IsPointMass);
            Assert.Equal("AB", posteriorOverStr1.Point);
        }
Esempio n. 43
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        public virtual void CreateModel()
        {
            using (Variable.ForEach(m))
            {
                theta[m] = Variable.Random <double, Beta>(thetaPriors[m]);
                phi[m]   = Variable.Random <Vector, Dirichlet>(phiPriors[m]);
            }

            if (InferenceEngine == null)
            {
                InferenceEngine = new InferenceEngine();
            }
        }
Esempio n. 44
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        public void InferArgumentsFromEqualityTest()
        {
            Variable <string> str1  = Variable.Random(StringDistribution.OneOf("a", "b"));
            Variable <string> str2  = Variable.Random(StringDistribution.OneOf("a", "b", "c"));
            Variable <bool>   equal = str1 == str2;

            equal.ObservedValue = true;

            var engine        = new InferenceEngine();
            var str2Posterior = engine.Infer <StringDistribution>(str2);

            StringInferenceTestUtilities.TestProbability(str2Posterior, 0.5, "a", "b");
        }
Esempio n. 45
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 public static VectorGaussian Conditional(Tuple<VectorGaussian, Wishart> priors, int observedIndex, double observedValue)
 {
     Variable<Vector> mean = Variable.Random(priors.Item1);
     Variable<PositiveDefiniteMatrix> prec = Variable.Random(priors.Item2);
     Variable<Vector> v = Variable.VectorGaussianFromMeanAndPrecision(mean, prec);
     // Initialise v to a proper distribution (to avoid improper messages)
     v.InitialiseTo(new VectorGaussian(priors.Item1.GetMean(), priors.Item2.GetMean()));
     Variable<double> observedV = Variable.GetItem(v, observedIndex);
     observedV.ObservedValue = observedValue;
     var engine = new InferenceEngine(new VariationalMessagePassing());
     var vPosterior = engine.Infer<VectorGaussian>(v);
     return vPosterior;
 }
Esempio n. 46
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        public void InferModel(string[] varsToInfer)
        {
            var engine = new InferenceEngine();

            engine.Compiler.RecommendedQuality =
                Microsoft.ML.Probabilistic.Factors.Attributes.QualityBand.Experimental;
            foreach (var variable in varsToInfer)
            {
                var varToInfer = parameters.GetNamed(variable);
                var infered    = engine.Infer(varToInfer);
                Console.WriteLine(variable + ": " + infered);
            }
        }
Esempio n. 47
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 //Addition by Guy Templeton, get log evidence from learned mixing coeff.
 public double GetLogEvidence()
 {
     Variable<bool> evidence = Variable.Bernoulli(0.5).Named("evidence");
     Range classes = new Range(numOfClasses);
     IfBlock block = Variable.If(evidence);
     VectorGaussian[] wObserved = trainModel.wPrior.ObservedValue;
     VectorGaussian[] empty = Util.ArrayInit(numOfClasses, c => (c == 0) ?
         VectorGaussian.PointMass(Vector.Zero(numOfFeatures)) :
         VectorGaussian.FromMeanAndPrecision(Vector.Zero(numOfFeatures), PositiveDefiniteMatrix.Identity(numOfFeatures)));
     block.CloseBlock();
     InferenceEngine engine = new InferenceEngine();
     return engine.Infer<Bernoulli>(evidence).LogOdds;
 }
Esempio n. 48
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        public override void Run()
        {
            var rangeMin = -10;
            var interval = 0.1;
            var observationSize = 100;
            var aActual = 0.2;
            var bActual = 2.3;
            var rand = new System.Random();
            var actuals = Enumerable.Range(rangeMin, observationSize)
                    .Select(i => i * interval)
                    .Select(i => Tuple.Create((double) i, bActual * i + aActual))
                    .ToArray();
            var samples = actuals.Select(tuple => Tuple.Create(tuple.Item1, tuple.Item2 + ((rand.NextDouble() - 0.5) * 10))).ToArray();

            var series = new List<LabelledSeries<Tuple<double, double>>>();
            series.Add(new LabelledSeries<Tuple<double, double>>(string.Format("Actual a+bx a={0} b={1}", aActual, bActual), actuals));

            var aPrior = Variable.GaussianFromMeanAndPrecision(0, 0.01).Named("aPrior");
            var bPrior = Variable.GaussianFromMeanAndPrecision(0, 0.01).Named("bPrior");
            var noisePrior = Variable.GammaFromShapeAndScale(1, 5).Named("noisePrior");
            var obsRange = new Range(samples.Length);
            var xArray = Variable.Array<double>(obsRange);
            var exprArray = Variable.Array<double>(obsRange);
            using (Variable.ForEach(obsRange))
            {
                exprArray[obsRange] = Variable.GaussianFromMeanAndPrecision(aPrior + xArray[obsRange] * bPrior, noisePrior);
            }

            xArray.ObservedValue = samples.Select(t => (double)t.Item1).ToArray();
            exprArray.ObservedValue = samples.Select(t => t.Item2).ToArray();

            var engine = new InferenceEngine();
            var aPosterior = engine.Infer<Gaussian>(aPrior);
            var bPosterior = engine.Infer<Gaussian>(bPrior);
            var noisePosterior = engine.Infer<Gamma>(noisePrior);

            var aInferred = aPosterior.GetMean();
            var bInferred = bPosterior.GetMean();
            var inferred = Enumerable.Range(rangeMin, observationSize)
                            .Select(i => i * interval)
                            .Select(i => Tuple.Create((double)i, bInferred * i + aInferred))
                            .ToArray();

            series.Add(new LabelledSeries<Tuple<double, double>>(string.Format("Inferred a+bx a={0} b={1}", Math.Round(aInferred, 4), Math.Round(bInferred, 4)), inferred));

            series.Add(new LabelledSeries<Tuple<double, double>>(string.Format("Data", aActual, bActual), samples) { IsScatter = true });

            this.Series = series.ToArray();
        }
Esempio n. 49
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		public void Run()
		{
			Rand.Restart(12347);

			// The model
			int N = RatsHeightData.GetLength(0);
			int T = RatsHeightData.GetLength(1);
			Range r = new Range(N).Named("N");
			Range w = new Range(T).Named("T");

			Variable<double> alphaC = Variable.GaussianFromMeanAndPrecision(0.0, 1e-4).Named("alphaC");
			Variable<double> alphaTau = Variable.GammaFromShapeAndRate(1e-3, 1e-3).Named("alphaTau");
			VariableArray<double> alpha = Variable.Array<double>(r).Named("alpha");
			alpha[r] = Variable.GaussianFromMeanAndPrecision(alphaC, alphaTau).ForEach(r);

			Variable<double> betaC = Variable.GaussianFromMeanAndPrecision(0.0, 1e-4).Named("betaC");
			Variable<double> betaTau = Variable.GammaFromShapeAndRate(1e-3, 1e-3).Named("betaTau");
			VariableArray<double> beta = Variable.Array<double>(r).Named("beta");
			beta[r] = Variable.GaussianFromMeanAndPrecision(betaC, betaTau).ForEach(r);

			Variable<double> tauC = Variable.GammaFromShapeAndRate(1e-3, 1e-3).Named("tauC");
			VariableArray<double> x = Variable.Observed<double>(RatsXData, w).Named("x");
			Variable<double> xbar = Variable.Sum(x)/T;
			VariableArray2D<double> y = Variable.Observed<double>(RatsHeightData, r, w).Named("y");
			y[r, w] = Variable.GaussianFromMeanAndPrecision(alpha[r] + (beta[r] * (x[w]-xbar)), tauC);
			Variable<double> alpha0 = (alphaC - betaC * xbar).Named("alpha0");

			// Initialise with the mean of the prior (needed for Gibbs to converge quickly)
			alphaC.InitialiseTo(Gaussian.PointMass(0.0));
			tauC.InitialiseTo(Gamma.PointMass(1.0));
			alphaTau.InitialiseTo(Gamma.PointMass(1.0));
			betaTau.InitialiseTo(Gamma.PointMass(1.0));

			// Inference engine
			InferenceEngine ie = new InferenceEngine();
			if (!(ie.Algorithm is ExpectationPropagation))
			{
				Gaussian betaCMarg = ie.Infer<Gaussian>(betaC);
				Gaussian alpha0Marg = ie.Infer<Gaussian>(alpha0);
				Gamma tauCMarg = ie.Infer<Gamma>(tauC);

				// Inference
				Console.WriteLine("alpha0 = {0}[sd={1}]", alpha0Marg, Math.Sqrt(alpha0Marg.GetVariance()).ToString("g4"));
				Console.WriteLine("betaC = {0}[sd={1}]", betaCMarg, Math.Sqrt(betaCMarg.GetVariance()).ToString("g4"));
				Console.WriteLine("tauC = {0}", tauCMarg);
			}
			else
				Console.WriteLine("This example does not run with Expectation Propagation");
		}
Esempio n. 50
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		public void Run()
		{
			Variable<bool> firstCoin = Variable.Bernoulli(0.5).Named("firstCoin");
			Variable<bool> secondCoin = Variable.Bernoulli(0.5).Named("secondCoin");
			Variable<bool> bothHeads  = (firstCoin & secondCoin).Named("bothHeads");
			InferenceEngine ie = new InferenceEngine();
			if (!(ie.Algorithm is VariationalMessagePassing))
			{
				Console.WriteLine("Probability both coins are heads: "+ie.Infer(bothHeads));
				bothHeads.ObservedValue=false;
				Console.WriteLine("Probability distribution over firstCoin: " + ie.Infer(firstCoin));
			}
			else
				Console.WriteLine("This example does not run with Variational Message Passing");
		}
Esempio n. 51
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		/// <summary>
		/// Model constructor
		/// </summary>
		public BayesianPCAModel()
		{
			// The various dimensions will be set externally...
			vN = Variable.New<int>().Named("NumObs");
			vD = Variable.New<int>().Named("NumFeats");
			vM = Variable.New<int>().Named("MaxComponents");
			rN = new Range(vN).Named("N");
			rD = new Range(vD).Named("D");
			rM = new Range(vM).Named("M");
			// ... as will the data
			vData = Variable.Array<double>(rN, rD).Named("data");
			// ... and the priors
			priorAlpha = Variable.New<Gamma>().Named("PriorAlpha");
			priorMu = Variable.New<Gaussian>().Named("PriorMu");
			priorPi = Variable.New<Gamma>().Named("PriorPi");
			// Mixing matrix. Each row is drawn from a Gaussian with zero mean and
			// a precision which will be learnt. This is a form of Automatic
			// Relevance Determination (ARD). The larger the precisions become, the
			// less important that row in the mixing matrix is in explaining the data
			vAlpha = Variable.Array<double>(rM).Named("Alpha");
			vW = Variable.Array<double>(rM, rD).Named("W");
			vAlpha[rM] = Variable.Random<double, Gamma>(priorAlpha).ForEach(rM);
			vW[rM, rD] = Variable.GaussianFromMeanAndPrecision(0, vAlpha[rM]).ForEach(rD);
			// Latent variables are drawn from a standard Gaussian
			vZ = Variable.Array<double>(rN, rM).Named("Z");
			vZ[rN, rM] = Variable.GaussianFromMeanAndPrecision(0.0, 1.0).ForEach(rN, rM);
			// Multiply the latent variables with the mixing matrix...
			vT = Variable.MatrixMultiply(vZ, vW).Named("T");
			// ... add in a bias ...
			vMu = Variable.Array<double>(rD).Named("mu");
			vMu[rD] = Variable.Random<double, Gaussian>(priorMu).ForEach(rD);
			vU = Variable.Array<double>(rN, rD).Named("U");
			vU[rN, rD] = vT[rN, rD] + vMu[rD];
			// ... and add in some observation noise ...
			vPi = Variable.Array<double>(rD).Named("pi");
			vPi[rD] = Variable.Random<double, Gamma>(priorPi).ForEach(rD);
			// ... to give the likelihood of observing the data
			vData[rN, rD] = Variable.GaussianFromMeanAndPrecision(vU[rN, rD], vPi[rD]);
			// Inference engine
			engine = new InferenceEngine();
			return;
		}
Esempio n. 52
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        public static Tuple<VectorGaussian, Wishart> LearnGaussian(Vector[] obs)
        {
            int numData = obs.Length;
            int dim = obs[0].Count;
            Variable<Vector> mean = Variable.VectorGaussianFromMeanAndPrecision(
                Vector.Zero(dim),
                PositiveDefiniteMatrix.IdentityScaledBy(dim, 10.0)).Named("mean");
            Variable<PositiveDefiniteMatrix> prec = Variable.WishartFromShapeAndScale(
                100.0, PositiveDefiniteMatrix.IdentityScaledBy(dim, 0.01));
            Range n = new Range(obs.Length).Named("n");
            VariableArray<Vector> data = Variable.Array<Vector>(n).Named("x");
            data[n] = Variable.VectorGaussianFromMeanAndPrecision(mean, prec).ForEach(n);
            data.ObservedValue = obs;

            var engine = new InferenceEngine(new VariationalMessagePassing());
            var meanPosterior = engine.Infer<VectorGaussian>(mean);
            var precPosterior = engine.Infer<Wishart>(prec);

            return new Tuple<VectorGaussian, Wishart>(meanPosterior, precPosterior);
        }
Esempio n. 53
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		public static void RunCyclingTime1()
		{
			//[1] The model
			Variable<double> averageTime = Variable.GaussianFromMeanAndPrecision(15, 0.01);
			Variable<double> trafficNoise = Variable.GammaFromShapeAndScale(2.0, 0.5);

			Variable<double> travelTimeMonday = Variable.GaussianFromMeanAndPrecision(averageTime, trafficNoise);
			Variable<double> travelTimeTuesday = Variable.GaussianFromMeanAndPrecision(averageTime, trafficNoise);
			Variable<double> travelTimeWednesday = Variable.GaussianFromMeanAndPrecision(averageTime, trafficNoise);

			//[2] Train the model
			travelTimeMonday.ObservedValue = 13;
			travelTimeTuesday.ObservedValue = 17;
			travelTimeWednesday.ObservedValue = 16;

			InferenceEngine engine = new InferenceEngine();

			Gaussian averageTimePosterior = engine.Infer<Gaussian>(averageTime);
			Gamma trafficNoisePosterior = engine.Infer<Gamma>(trafficNoise);

			Console.WriteLine("averageTimePosterior: " + averageTimePosterior);
			Console.WriteLine("trafficNoisePosterior: " + trafficNoisePosterior);

			//[3] Add a prediction variable and retrain the model
			Variable<double> tomorrowsTime = Variable.GaussianFromMeanAndPrecision(
				averageTime,
				trafficNoise);

			Gaussian tomorrowsTimeDist = engine.Infer<Gaussian>(tomorrowsTime);
			double tomorrowsMean = tomorrowsTimeDist.GetMean();
			double tomorrowsStdDev = Math.Sqrt(tomorrowsTimeDist.GetVariance());

			// Write out the results.
			Console.WriteLine("Tomorrows predicted time: {0:f2} plus or minus {1:f2}", tomorrowsMean, tomorrowsStdDev);

			// You can also ask other questions of the model 
			double probTripTakesLongerThan18Minutes = engine.Infer<Bernoulli>(tomorrowsTime < 18.0).GetProbTrue();
			Console.WriteLine(
				"Probability that the trip takes less than 18 min: {0:f2}",
				probTripTakesLongerThan18Minutes);
		}
Esempio n. 54
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		public void Run()
		{
			// Sample data from standard Gaussian
			double[] data = new double[100];
			for (int i = 0; i < data.Length; i++) data[i] = Rand.Normal(0, 1);

			// Create mean and precision random variables
			Variable<double> mean = Variable.GaussianFromMeanAndVariance(0, 100).Named("mean");
			Variable<double> precision = Variable.GammaFromShapeAndScale(1, 1).Named("precision");

			for (int i = 0; i < data.Length; i++)
			{
				Variable<double> x= Variable.GaussianFromMeanAndPrecision(mean, precision).Named("x"+i);
				x.ObservedValue=data[i];
			}

			InferenceEngine engine = new InferenceEngine();
			// Retrieve the posterior distributions
			Console.WriteLine("mean=" + engine.Infer(mean));
			Console.WriteLine("prec=" + engine.Infer(precision));
		}
Esempio n. 55
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        public override void Run()
        {
            var inputs = new[]
            {
                JoinArrays(GetColorAttributeArray(Color.Blue), GetShapeAttributeArray(Shapes.Rectangle), new double[] { 10 }),
                JoinArrays(GetColorAttributeArray(Color.Red), GetShapeAttributeArray(Shapes.Rectangle), new double[] { 10 }),
                JoinArrays(GetColorAttributeArray(Color.Blue), GetShapeAttributeArray(Shapes.Star), new double[] { 10 }),
                JoinArrays(GetColorAttributeArray(Color.Blue), GetShapeAttributeArray(Shapes.Ring), new double[] { 15 }),
                JoinArrays(GetColorAttributeArray(Color.Green), GetShapeAttributeArray(Shapes.Circle), new double[] { 10 }),
                JoinArrays(GetColorAttributeArray(Color.Yellow), GetShapeAttributeArray(Shapes.Circle), new double[] { 10 }),
                JoinArrays(GetColorAttributeArray(Color.Yellow), GetShapeAttributeArray(Shapes.Circle), new double[] { 10 }),
                JoinArrays(GetColorAttributeArray(Color.Blue), GetShapeAttributeArray(Shapes.Rectangle), new double[] { 15 }),

                JoinArrays(GetColorAttributeArray(Color.Yellow), GetShapeAttributeArray(Shapes.Star), new double[] { 10 }),
                JoinArrays(GetColorAttributeArray(Color.Red), GetShapeAttributeArray(Shapes.Arrow), new double[] { 10 }),
                JoinArrays(GetColorAttributeArray(Color.Green), GetShapeAttributeArray(Shapes.Trapezium), new double[] { 15 }),
                JoinArrays(GetColorAttributeArray(Color.Green), GetShapeAttributeArray(Shapes.Diamond), new double[] { 15 }),
                JoinArrays(GetColorAttributeArray(Color.Yellow), GetShapeAttributeArray(Shapes.Triangle), new double[] { 15 }),
                JoinArrays(GetColorAttributeArray(Color.Red), GetShapeAttributeArray(Shapes.Ring), new double[] { 15 }),
                JoinArrays(GetColorAttributeArray(Color.Yellow), GetShapeAttributeArray(Shapes.Circle), new double[] { 15 }),
                JoinArrays(GetColorAttributeArray(Color.Red), GetShapeAttributeArray(Shapes.Ellipse), new double[] { 15 }),
            };

            var outputs = new bool[] { true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false };

            var j = new Range(inputs.Length);
            var noise = Variable.GammaFromMeanAndVariance(1, 1);
            var X = Variable.Observed(inputs.Select(i => Vector.FromArray(i)).ToArray(), j).Named("X");
            var Y = Variable.Observed(outputs, j).Named("Y");
            var weights = Variable.VectorGaussianFromMeanAndPrecision(Vector.Zero(inputs.First().Length), PositiveDefiniteMatrix.Identity(inputs.First().Length))
                .Named("weights");
            Y[j] = Variable.GaussianFromMeanAndPrecision(Variable.InnerProduct(X[j], weights), noise) > 0;
            var engine = new InferenceEngine();
            var posteriorWeightsDist = engine.Infer<VectorGaussian>(weights);
            var posteriorNoiseDist = engine.Infer<Gamma>(noise);
            weights = Variable.Random(posteriorWeightsDist);
            var testCase = JoinArrays(GetColorAttributeArray(Color.Red), GetShapeAttributeArray(Shapes.Trapezium), new double[] { 15 });
            var testClassification = engine.Infer<Bernoulli>(Variable.InnerProduct(Vector.FromArray(testCase), weights) > 0);
        }
Esempio n. 56
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		public void Run()
		{
			// Data from clinical trial
			VariableArray<bool> controlGroup = 
				Variable.Observed(new bool[] { false, false, true, false, false }).Named("controlGroup");
			VariableArray<bool> treatedGroup = 
				Variable.Observed(new bool[] { true, false, true, true, true }).Named("treatedGroup");
			Range i = controlGroup.Range.Named("i"); Range j = treatedGroup.Range.Named("j");

			// Prior on being effective treatment
			Variable<bool> isEffective = Variable.Bernoulli(0.5).Named("isEffective");
			Variable<double> probIfTreated, probIfControl;
			using (Variable.If(isEffective))
			{
				// Model if treatment is effective
				probIfControl = Variable.Beta(1, 1).Named("probIfControl");
				controlGroup[i] = Variable.Bernoulli(probIfControl).ForEach(i);
				probIfTreated = Variable.Beta(1, 1).Named("probIfTreated");
				treatedGroup[j] = Variable.Bernoulli(probIfTreated).ForEach(j);
			}
			using (Variable.IfNot(isEffective))
			{
				// Model if treatment is not effective
				Variable<double> probAll = Variable.Beta(1, 1).Named("probAll");
				controlGroup[i] = Variable.Bernoulli(probAll).ForEach(i);
				treatedGroup[j] = Variable.Bernoulli(probAll).ForEach(j);
			}
			InferenceEngine ie = new InferenceEngine();
			if (!(ie.Algorithm is GibbsSampling))
			{
				Console.WriteLine("Probability treatment has an effect = " + ie.Infer(isEffective));
				Console.WriteLine("Probability of good outcome if given treatment = " 
										+ (float)ie.Infer<Beta>(probIfTreated).GetMean());
				Console.WriteLine("Probability of good outcome if control = " 
										+ (float)ie.Infer<Beta>(probIfControl).GetMean());
			}
			else
				Console.WriteLine("This model is not supported by Gibbs sampling.");
		}
Esempio n. 57
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		public virtual void CreateModel()
		{
			NumComponents = 2;
			Range ComponentRange = new Range(NumComponents);
			InferenceEngine = new InferenceEngine(new VariationalMessagePassing());
			InferenceEngine.ShowProgress = false;

			AverageTimePriors = Variable.Array<Gaussian>(ComponentRange);
			TrafficNoisePriors = Variable.Array<Gamma>(ComponentRange);
			AverageTime = Variable.Array<double>(ComponentRange);
			TrafficNoise = Variable.Array<double>(ComponentRange);

			using (Variable.ForEach(ComponentRange))
			{
				AverageTime[ComponentRange] = Variable.Random<double, Gaussian>(AverageTimePriors[ComponentRange]);
				TrafficNoise[ComponentRange] = Variable.Random<double, Gamma>(TrafficNoisePriors[ComponentRange]);
			}

			//Mixing coefficients
			MixingPrior = Variable.New<Dirichlet>();
			MixingCoefficients = Variable<Vector>.Random(MixingPrior);
			MixingCoefficients.SetValueRange(ComponentRange);
		}
        public BayesPointMachine(int nFeatures, double noise)
        {
            // Training model
            nTrain = Variable.New<int>().Named("nTrain");
            Range trainItem = new Range(nTrain).Named("trainItem");
            trainingLabels = Variable.Array<bool>(trainItem).Named("trainingLabels");
            trainingItems = Variable.Array<Vector>(trainItem).Named("trainingItems");
            weights = Variable.Random(new VectorGaussian(Vector.Zero(nFeatures),
                PositiveDefiniteMatrix.Identity(nFeatures))).Named("weights");
            trainingLabels[trainItem] = Variable.IsPositive(Variable.GaussianFromMeanAndVariance(Variable.InnerProduct(weights, trainingItems[trainItem]), noise));

            // Testing model
            nTest = Variable.New<int>().Named("nTest");
            Range testItem = new Range(nTest).Named("testItem");
            testItems = Variable.Array<Vector>(testItem).Named("testItems");
            testLabels = Variable.Array<bool>(testItem).Named("testLabels");
            if (singleModel)
            {
                testLabels[testItem] = Variable.IsPositive(Variable.GaussianFromMeanAndVariance(Variable.InnerProduct(weights, testItems[testItem]), noise));

                testEngine = new InferenceEngine(new ExpectationPropagation());
                testEngine.NumberOfIterations = 2;
            }
            else
            {
                weightPosterior = Variable.New<VectorGaussian>().Named("weightPosterior");
                Variable<Vector> testWeights = Variable<Vector>.Random(weightPosterior);
                testLabels[testItem] = Variable.IsPositive(Variable.GaussianFromMeanAndVariance(Variable.InnerProduct(testWeights, testItems[testItem]), noise));

                trainEngine = new InferenceEngine(new ExpectationPropagation());
                trainEngine.ShowProgress = false;
                trainEngine.NumberOfIterations = 5;
                testEngine = new InferenceEngine(new ExpectationPropagation());
                testEngine.ShowProgress = false;
                testEngine.NumberOfIterations = 1;
            }
        }
Esempio n. 59
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        public override void Run()
        {
            var sourceMean = 11.4;
            var sourcePrecision = 0.01;
            var source = Gaussian.FromMeanAndPrecision(sourceMean, sourcePrecision);
            var series = new List<LabelledSeries<Tuple<double, double>>>();
            series.Add(new LabelledSeries<Tuple<double, double>>(string.Format("Actual mean {0} precision {1}", source.GetMean(), source.Precision), Enumerable.Range(-30, 80).Select(x => Tuple.Create((double)x, Math.Exp(source.GetLogProb(x))))));

            // Prior distributions
            var meanPriorDistr = Gaussian.FromMeanAndPrecision(0, 0.01);
            var precisionPriorDistr = Gamma.FromMeanAndVariance(2, 5);

            var meanPrior = Variable.Random(meanPriorDistr).Named("mean");
            var precPrior = Variable.Random(precisionPriorDistr).Named("precision");
            var tv = Variable.New<int>();
            var tr = new Range(tv).Named("tr");
            var engine = new InferenceEngine();
            var xv = Variable.GaussianFromMeanAndPrecision(meanPrior, precPrior).Named("xv");
            var xs = Variable.Array<double>(tr).Named("xs");
            xs[tr] = xv.ForEach(tr);

            var maxSampleSize = 250;
            var sampleData = Enumerable.Range(0, maxSampleSize + 1).Select(_ => source.Sample()).ToArray();

            for (var i = 50; i <= maxSampleSize; i += 50)
            {
                tv.ObservedValue = i;
                xs.ObservedValue = sampleData.Take(i).ToArray();
                var meanPost = engine.Infer<Gaussian>(meanPrior);
                var precPost = engine.Infer<Gamma>(precPrior);
                var estimateDist = Gaussian.FromMeanAndPrecision(meanPost.GetMean(), precPost.GetMean());
                series.Add(new LabelledSeries<Tuple<double, double>>(string.Format("Implied mean {0} precision {1} with {2} samples", Math.Round(estimateDist.GetMean(), 4), Math.Round(estimateDist.Precision, 4), i), Enumerable.Range(-30, 80).Select(x => Tuple.Create((double)x, Math.Exp(estimateDist.GetLogProb(x))))));
            }

            this.Series = series.ToArray();
        }
Esempio n. 60
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		public void Run()
		{
			// This example requires EP
			InferenceEngine engine = new InferenceEngine();
			if (!(engine.Algorithm is ExpectationPropagation)) {
				Console.WriteLine("This example only runs with Expectation Propagation");
				return;
			}

			// Define counts
			int numUsers = 50;
			int numItems = 10;
			int numTraits = 2;
			Variable<int> numObservations = Variable.Observed(100).Named("numObservations");
			int numLevels = 2;

			// Define ranges
			Range user = new Range(numUsers).Named("user");
			Range item = new Range(numItems).Named("item");
			Range trait = new Range(numTraits).Named("trait");
			Range observation = new Range(numObservations).Named("observation");
			Range level = new Range(numLevels).Named("level");

			// Define latent variables
			var userTraits = Variable.Array(Variable.Array<double>(trait), user).Named("userTraits");
			var itemTraits = Variable.Array(Variable.Array<double>(trait), item).Named("itemTraits");
			var userBias = Variable.Array<double>(user).Named("userBias");
			var itemBias = Variable.Array<double>(item).Named("itemBias");
			var userThresholds = Variable.Array(Variable.Array<double>(level), user).Named("userThresholds");

			// Define priors
			var userTraitsPrior = Variable.Array(Variable.Array<Gaussian>(trait), user).Named("userTraitsPrior");
			var itemTraitsPrior = Variable.Array(Variable.Array<Gaussian>(trait), item).Named("itemTraitsPrior");
			var userBiasPrior = Variable.Array<Gaussian>(user).Named("userBiasPrior");
			var itemBiasPrior = Variable.Array<Gaussian>(item).Named("itemBiasPrior");
			var userThresholdsPrior = Variable.Array(Variable.Array<Gaussian>(level), user).Named("userThresholdsPrior");

			// Define latent variables statistically
			userTraits[user][trait] = Variable<double>.Random(userTraitsPrior[user][trait]);
			itemTraits[item][trait] = Variable<double>.Random(itemTraitsPrior[item][trait]);
			userBias[user] = Variable<double>.Random(userBiasPrior[user]);
			itemBias[item] = Variable<double>.Random(itemBiasPrior[item]);
			userThresholds[user][level] = Variable<double>.Random(userThresholdsPrior[user][level]);

			// Initialise priors
			Gaussian traitPrior = Gaussian.FromMeanAndVariance(0.0, 1.0);
			Gaussian biasPrior = Gaussian.FromMeanAndVariance(0.0, 1.0);

			userTraitsPrior.ObservedValue = Util.ArrayInit(numUsers, u => Util.ArrayInit(numTraits, t => traitPrior));
			itemTraitsPrior.ObservedValue = Util.ArrayInit(numItems, i => Util.ArrayInit(numTraits, t => traitPrior));
			userBiasPrior.ObservedValue = Util.ArrayInit(numUsers, u => biasPrior);
			itemBiasPrior.ObservedValue = Util.ArrayInit(numItems, i => biasPrior);
			userThresholdsPrior.ObservedValue = Util.ArrayInit(numUsers, u =>
					Util.ArrayInit(numLevels, l => Gaussian.FromMeanAndVariance(l - numLevels / 2.0 + 0.5, 1.0)));

			// Break symmetry and remove ambiguity in the traits
			for (int i = 0; i < numTraits; i++) {
				// Assume that numTraits < numItems
				for (int j = 0; j < numTraits; j++) {
					itemTraitsPrior.ObservedValue[i][j] = Gaussian.PointMass(0);
				}
				itemTraitsPrior.ObservedValue[i][i] = Gaussian.PointMass(1);
			}

			// Declare training data variables
			var userData = Variable.Array<int>(observation).Named("userData");
			var itemData = Variable.Array<int>(observation).Named("itemData");
			var ratingData = Variable.Array(Variable.Array<bool>(level), observation).Named("ratingData");

			// Set model noises explicitly
			Variable<double> affinityNoiseVariance = Variable.Observed(0.1).Named("affinityNoiseVariance");
			Variable<double> thresholdsNoiseVariance = Variable.Observed(0.1).Named("thresholdsNoiseVariance");

			// Model
			using (Variable.ForEach(observation)) {
				VariableArray<double> products = Variable.Array<double>(trait).Named("products");
				products[trait] = userTraits[userData[observation]][trait] * itemTraits[itemData[observation]][trait];

				Variable<double> bias = (userBias[userData[observation]] + itemBias[itemData[observation]]).Named("bias");
				Variable<double> affinity = (bias + Variable.Sum(products).Named("productSum")).Named("affinity");
				Variable<double> noisyAffinity = Variable.GaussianFromMeanAndVariance(affinity, affinityNoiseVariance).Named("noisyAffinity");

				VariableArray<double> noisyThresholds = Variable.Array<double>(level).Named("noisyThresholds");
				noisyThresholds[level] = Variable.GaussianFromMeanAndVariance(userThresholds[userData[observation]][level], thresholdsNoiseVariance);
				ratingData[observation][level] = noisyAffinity > noisyThresholds[level];
			}

			// Observe training data
			GenerateData(numUsers, numItems, numTraits, numObservations.ObservedValue, numLevels,
									 userData, itemData, ratingData,
									 userTraitsPrior.ObservedValue, itemTraitsPrior.ObservedValue,
									 userBiasPrior.ObservedValue, itemBiasPrior.ObservedValue, userThresholdsPrior.ObservedValue,
									 affinityNoiseVariance.ObservedValue, thresholdsNoiseVariance.ObservedValue);

			// Allow EP to process the product factor as if running VMP
			// as in Stern, Herbrich, Graepel paper.
			engine.Compiler.GivePriorityTo(typeof(GaussianProductOp_SHG09));
			engine.Compiler.ShowWarnings = true;

			// Run inference
			var userTraitsPosterior = engine.Infer<Gaussian[][]>(userTraits);
			var itemTraitsPosterior = engine.Infer<Gaussian[][]>(itemTraits);
			var userBiasPosterior = engine.Infer<Gaussian[]>(userBias);
			var itemBiasPosterior = engine.Infer<Gaussian[]>(itemBias);
			var userThresholdsPosterior = engine.Infer<Gaussian[][]>(userThresholds);

			// Feed in the inferred posteriors as the new priors
			userTraitsPrior.ObservedValue = userTraitsPosterior;
			itemTraitsPrior.ObservedValue = itemTraitsPosterior;
			userBiasPrior.ObservedValue = userBiasPosterior;
			itemBiasPrior.ObservedValue = itemBiasPosterior;
			userThresholdsPrior.ObservedValue = userThresholdsPosterior;

			// Make a prediction
			numObservations.ObservedValue = 1;
			userData.ObservedValue = new int[] { 5 };
			itemData.ObservedValue = new int[] { 6 };
			ratingData.ClearObservedValue();

			Bernoulli[] predictedRating = engine.Infer<Bernoulli[][]>(ratingData)[0];
			Console.WriteLine("Predicted rating:");
			foreach (var rating in predictedRating) Console.WriteLine(rating);
		}