public static Gamma RateAverageConditional(TruncatedGamma sample, double shape, double lowerBound, double upperBound)
{
if (sample.IsPointMass)
{
return(RateAverageConditional(sample.Point, shape, lowerBound, upperBound));
}
if (shape != 1)
{
throw new NotImplementedException();
}
if (!double.IsPositiveInfinity(upperBound))
{
throw new NotImplementedException();
}
// when shape=1 and upperBound=infinity, the factor reduces to: f(x,b) = b exp(-b(x-L))
if (!sample.Gamma.IsUniform())
{
throw new NotImplementedException();
}
if (!double.IsPositiveInfinity(sample.UpperBound))
{
throw new NotImplementedException();
}
if (sample.LowerBound <= lowerBound)
{
return(Gamma.Uniform());
}
// when the message from x is uniform with a lower bound of L2 > L, then f(b) = int_L2^inf b exp(-b(x-L)) dx = exp(-b(L2-L))
return(Gamma.FromShapeAndRate(shape, sample.LowerBound - lowerBound));
}
// GammaPower = TruncatedGamma ^ y /////////////////////////////////////////////////////////
public static GammaPower PowAverageConditional([SkipIfUniform] TruncatedGamma x, double y, GammaPower result)
{
if (result.Power == -1)
{
y = -y;
}
else if (result.Power != 1)
{
throw new ArgumentException($"result.Power ({result.Power}) is not 1 or -1", nameof(result));
}
double mean = x.GetMeanPower(y);
if (x.LowerBound > 0)
{
double meanInverse = x.GetMeanPower(-y);
Gamma result1 = GammaFromMeanAndMeanInverse(mean, meanInverse);
return(GammaPower.FromShapeAndRate(result1.Shape, result1.Rate, result.Power));
}
else
{
double variance = x.GetMeanPower(2 * y) - mean * mean;
Gamma result1 = Gamma.FromMeanAndVariance(mean, variance);
return(GammaPower.FromShapeAndRate(result1.Shape, result1.Rate, result.Power));
}
}
public static Gamma CopyAverageConditional(TruncatedGamma value)
{
if (!value.IsPointMass)
{
throw new ArgumentException("value is not a point mass");
}
return(value.ToGamma());
}
public static RpropBufferData BufferTGa([NoInit] TruncatedGamma use, TruncatedGamma def, TruncatedGamma to_marginal, RpropBufferData bufferTGa)
{
var currDist = use * def;
if (currDist.IsPointMass)
{
if (double.IsInfinity(currDist.Point) || double.IsNaN(currDist.Point))
{
throw new ArgumentOutOfRangeException();
}
if (VariablePointOp_RpropGamma.UseMean)
{
bufferTGa.nextPoint = Math.Log(currDist.Point);
}
else
{
bufferTGa.nextPoint = currDist.Point;
}
return(bufferTGa);
}
// cannot use buffer.nextPoint as currPoint since Marginal could be initialized by user
double currPoint;
if (to_marginal.IsPointMass)
{
currPoint = to_marginal.Point;
}
else
{
currPoint = currDist.GetMean();
}
double currDeriv, currDeriv2;
if (VariablePointOp_RpropGamma.UseMean)
{
bufferTGa.lowerBound = Math.Log(currDist.LowerBound);
bufferTGa.upperBound = Math.Log(currDist.UpperBound);
currDeriv = currDist.Gamma.Shape - currDist.Gamma.Rate * currPoint;
//Trace.WriteLine($"use deriv = {(use.Gamma.Shape-1) - use.Gamma.Rate*currPoint} def deriv = {def.Gamma.Shape - def.Gamma.Rate*currPoint} total deriv = {currDeriv}");
if (currPoint <= 0)
{
throw new ArgumentException($"currPoint ({currPoint}) <= 0");
}
bufferTGa.SetNextPoint(Math.Log(currPoint), currDeriv);
}
else
{
bufferTGa.lowerBound = currDist.LowerBound;
bufferTGa.upperBound = currDist.UpperBound;
currDist.Gamma.GetDerivatives(currPoint, out currDeriv, out currDeriv2);
bufferTGa.SetNextPoint(currPoint, currDeriv);
}
return(bufferTGa);
}
public static TruncatedGamma AAverageConditional([SkipIfUniform] TruncatedGamma min, double b)
{
if (min.IsUniform())
{
return(TruncatedGamma.Uniform());
}
if (!min.IsPointMass)
{
throw new ArgumentException("min is not a point mass");
}
return(TruncatedGamma.PointMass(min.Point));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="VariablePointOp_RpropTruncatedGamma"]/message_doc[@name="MarginalAverageConditional(TruncatedGamma, TruncatedGamma, RpropBufferData, TruncatedGamma)"]/*'/>
public static TruncatedGamma MarginalAverageConditional([IgnoreDependency] TruncatedGamma use, [IgnoreDependency] TruncatedGamma def, [RequiredArgument] RpropBufferData bufferTGa, TruncatedGamma result)
{
if (VariablePointOp_RpropGamma.UseMean)
{
result.Point = Math.Exp(bufferTGa.nextPoint);
}
else
{
result.Point = bufferTGa.nextPoint;
}
return(result);
}
// Gamma = TruncatedGamma ^ y /////////////////////////////////////////////////////////
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="PowerOp"]/message_doc[@name="PowAverageConditional(TruncatedGamma, double)"]/*'/>
public static Gamma PowAverageConditional([SkipIfUniform] TruncatedGamma x, double y)
{
double mean = x.GetMeanPower(y);
if (x.LowerBound > 0)
{
double meanInverse = x.GetMeanPower(-y);
return(GammaFromMeanAndMeanInverse(mean, meanInverse));
}
else
{
double variance = x.GetMeanPower(2 * y) - mean * mean;
return(Gamma.FromMeanAndVariance(mean, variance));
}
}
public static TruncatedGamma XAverageConditional([SkipIfUniform] Gamma pow, TruncatedGamma x, double y)
{
// message computed below should be uniform when pow is uniform, but may not due to roundoff error.
if (pow.IsUniform())
{
return(TruncatedGamma.Uniform());
}
// Factor is (x^y)^(pow.Shape/pow.Power - 1) * exp(-pow.Rate*(x^y)^1/pow.Power)
// =propto x^(pow.Shape/(pow.Power/y) - y) * exp(-pow.Rate*x^y/pow.Power)
// newShape/(pow.Power/y) - 1 = pow.Shape/(pow.Power/y) - y
// newShape = pow.Shape + (1-y)*(pow.Power/y)
double power = 1 / y;
var toPow = PowAverageConditional(x, y);
var powMarginal = pow * toPow;
// xMarginal2 is the exact distribution of pow^(1/y) where pow has distribution powMarginal
GammaPower xMarginal2 = GammaPower.FromShapeAndRate(powMarginal.Shape, powMarginal.Rate, power);
var xMarginal = new TruncatedGamma(GammaFromGammaPower(xMarginal2), x.LowerBound, x.UpperBound);
var result = xMarginal;
result.SetToRatio(xMarginal, x, GammaProductOp_Laplace.ForceProper);
return(result);
}
public static TruncatedGamma BAverageConditional([SkipIfUniform] TruncatedGamma min, double a)
{
return(AAverageConditional(min, a));
}
public static TruncatedGamma MinAverageConditional(double a, TruncatedGamma b)
{
return(MinAverageConditional(b, a));
}
public static TruncatedGamma MinAverageConditional(TruncatedGamma a, double b)
{
return(new TruncatedGamma(a.Gamma, a.LowerBound, Math.Min(b, a.UpperBound)));
}
public void Initialize(bool skipStringDistributions = false)
{
// DO NOT make this a constructor, because it makes the test not notice complete lack of serialization as an empty object is set up exactly as the thing
// you are trying to deserialize.
this.pareto = new Pareto(1.2, 3.5);
this.poisson = new Poisson(2.3);
this.wishart = new Wishart(20, new PositiveDefiniteMatrix(new double[, ] {
{ 22, 21 }, { 21, 23 }
}));
this.vectorGaussian = new VectorGaussian(Vector.FromArray(13, 14), new PositiveDefiniteMatrix(new double[, ] {
{ 16, 15 }, { 15, 17 }
}));
this.unnormalizedDiscrete = UnnormalizedDiscrete.FromLogProbs(DenseVector.FromArray(5.1, 5.2, 5.3));
this.pointMass = PointMass <double> .Create(1.1);
this.gaussian = new Gaussian(11.0, 12.0);
this.nonconjugateGaussian = new NonconjugateGaussian(1.2, 2.3, 3.4, 4.5);
this.gamma = new Gamma(9.0, 10.0);
this.gammaPower = new GammaPower(5.6, 2.8, 3.4);
this.discrete = new Discrete(6.0, 7.0, 8.0);
this.conjugateDirichlet = new ConjugateDirichlet(1.2, 2.3, 3.4, 4.5);
this.dirichlet = new Dirichlet(3.0, 4.0, 5.0);
this.beta = new Beta(2.0, 1.0);
this.binomial = new Binomial(5, 0.8);
this.bernoulli = new Bernoulli(0.6);
this.sparseBernoulliList = SparseBernoulliList.Constant(4, new Bernoulli(0.1));
this.sparseBernoulliList[1] = new Bernoulli(0.9);
this.sparseBernoulliList[3] = new Bernoulli(0.7);
this.sparseBetaList = SparseBetaList.Constant(5, new Beta(2.0, 2.0));
this.sparseBetaList[0] = new Beta(3.0, 4.0);
this.sparseBetaList[1] = new Beta(5.0, 6.0);
this.sparseGaussianList = SparseGaussianList.Constant(6, Gaussian.FromMeanAndPrecision(0.1, 0.2));
this.sparseGaussianList[4] = Gaussian.FromMeanAndPrecision(0.3, 0.4);
this.sparseGaussianList[5] = Gaussian.FromMeanAndPrecision(0.5, 0.6);
this.sparseGammaList = SparseGammaList.Constant(1, Gamma.FromShapeAndRate(1.0, 2.0));
this.truncatedGamma = new TruncatedGamma(1.2, 2.3, 3.4, 4.5);
this.truncatedGaussian = new TruncatedGaussian(1.2, 3.4, 5.6, 7.8);
this.wrappedGaussian = new WrappedGaussian(1.2, 2.3, 3.4);
ga = Distribution <double> .Array(new[] { this.gaussian, this.gaussian });
vga = Distribution <Vector> .Array(new[] { this.vectorGaussian, this.vectorGaussian });
ga2D = Distribution <double> .Array(new[, ] {
{ this.gaussian, this.gaussian }, { this.gaussian, this.gaussian }
});
vga2D = Distribution <Vector> .Array(new[, ] {
{ this.vectorGaussian, this.vectorGaussian }, { this.vectorGaussian, this.vectorGaussian }
});
gaJ = Distribution <double> .Array(new[] { new[] { this.gaussian, this.gaussian }, new[] { this.gaussian, this.gaussian } });
vgaJ = Distribution <Vector> .Array(new[] { new[] { this.vectorGaussian, this.vectorGaussian }, new[] { this.vectorGaussian, this.vectorGaussian } });
var gp = new GaussianProcess(new ConstantFunction(0), new SquaredExponential(0));
var basis = Util.ArrayInit(2, i => Vector.FromArray(1.0 * i));
this.sparseGp = new SparseGP(new SparseGPFixed(gp, basis));
this.quantileEstimator = new QuantileEstimator(0.01);
this.quantileEstimator.Add(5);
this.outerQuantiles = OuterQuantiles.FromDistribution(3, this.quantileEstimator);
this.innerQuantiles = InnerQuantiles.FromDistribution(3, this.outerQuantiles);
if (!skipStringDistributions)
{
// String distributions can not be serialized by some formatters (namely BinaryFormatter)
// That is fine because this combination is never used in practice
this.stringDistribution1 = StringDistribution.String("aa")
.Append(StringDistribution.OneOf("b", "ccc")).Append("dddd");
this.stringDistribution2 = new StringDistribution();
this.stringDistribution2.SetToProduct(StringDistribution.OneOf("a", "b"),
StringDistribution.OneOf("b", "c"));
}
}
public static Gamma RateAverageLogarithm(TruncatedGamma sample, double shape, double lowerBound, double upperBound)
{
return(RateAverageConditional(sample, shape, lowerBound, upperBound));
}
public static double TruncatedGammaFromShapeAndRate(double shape, double rate, double lowerBound, double upperBound)
{
return(TruncatedGamma.Sample(Gamma.FromShapeAndRate(shape, rate), lowerBound, upperBound));
}
public void Initialize()
{
// DO NOT make this a constructor, because it makes the test not notice complete lack of serialization as an empty object is set up exactly as the thing
// you are trying to deserialize.
this.pareto = new Pareto(1.2, 3.5);
this.poisson = new Poisson(2.3);
this.wishart = new Wishart(20, new PositiveDefiniteMatrix(new double[, ] {
{ 22, 21 }, { 21, 23 }
}));
this.vectorGaussian = new VectorGaussian(Vector.FromArray(13, 14), new PositiveDefiniteMatrix(new double[, ] {
{ 16, 15 }, { 15, 17 }
}));
this.unnormalizedDiscrete = UnnormalizedDiscrete.FromLogProbs(DenseVector.FromArray(5.1, 5.2, 5.3));
this.pointMass = PointMass <double> .Create(1.1);
this.gaussian = new Gaussian(11.0, 12.0);
this.nonconjugateGaussian = new NonconjugateGaussian(1.2, 2.3, 3.4, 4.5);
this.gamma = new Gamma(9.0, 10.0);
this.gammaPower = new GammaPower(5.6, 2.8, 3.4);
this.discrete = new Discrete(6.0, 7.0, 8.0);
this.conjugateDirichlet = new ConjugateDirichlet(1.2, 2.3, 3.4, 4.5);
this.dirichlet = new Dirichlet(3.0, 4.0, 5.0);
this.beta = new Beta(2.0, 1.0);
this.binomial = new Binomial(5, 0.8);
this.bernoulli = new Bernoulli(0.6);
this.sparseBernoulliList = SparseBernoulliList.Constant(4, new Bernoulli(0.1));
this.sparseBernoulliList[1] = new Bernoulli(0.9);
this.sparseBernoulliList[3] = new Bernoulli(0.7);
this.sparseBetaList = SparseBetaList.Constant(5, new Beta(2.0, 2.0));
this.sparseBetaList[0] = new Beta(3.0, 4.0);
this.sparseBetaList[1] = new Beta(5.0, 6.0);
this.sparseGaussianList = SparseGaussianList.Constant(6, Gaussian.FromMeanAndPrecision(0.1, 0.2));
this.sparseGaussianList[4] = Gaussian.FromMeanAndPrecision(0.3, 0.4);
this.sparseGaussianList[5] = Gaussian.FromMeanAndPrecision(0.5, 0.6);
this.sparseGammaList = SparseGammaList.Constant(1, Gamma.FromShapeAndRate(1.0, 2.0));
this.truncatedGamma = new TruncatedGamma(1.2, 2.3, 3.4, 4.5);
this.truncatedGaussian = new TruncatedGaussian(1.2, 3.4, 5.6, 7.8);
this.wrappedGaussian = new WrappedGaussian(1.2, 2.3, 3.4);
ga = Distribution <double> .Array(new[] { this.gaussian, this.gaussian });
vga = Distribution <Vector> .Array(new[] { this.vectorGaussian, this.vectorGaussian });
ga2D = Distribution <double> .Array(new[, ] {
{ this.gaussian, this.gaussian }, { this.gaussian, this.gaussian }
});
vga2D = Distribution <Vector> .Array(new[, ] {
{ this.vectorGaussian, this.vectorGaussian }, { this.vectorGaussian, this.vectorGaussian }
});
gaJ = Distribution <double> .Array(new[] { new[] { this.gaussian, this.gaussian }, new[] { this.gaussian, this.gaussian } });
vgaJ = Distribution <Vector> .Array(new[] { new[] { this.vectorGaussian, this.vectorGaussian }, new[] { this.vectorGaussian, this.vectorGaussian } });
var gp = new GaussianProcess(new ConstantFunction(0), new SquaredExponential(0));
var basis = Util.ArrayInit(2, i => Vector.FromArray(1.0 * i));
this.sparseGp = new SparseGP(new SparseGPFixed(gp, basis));
this.quantileEstimator = new QuantileEstimator(0.01);
this.quantileEstimator.Add(5);
}