private static void GetGaussianFromQuantiles(double x0, double p0, double x1, double p1, out double mean, out double deviation)
{
// solve for the Gaussian mean and stddev that yield:
// x0 = mean + stddev * NormalCdfInv(p0)
double z0 = MMath.NormalCdfInv(p0);
double z1 = MMath.NormalCdfInv(p1);
Matrix Z = new Matrix(new double[, ] {
{ 1, z0 }, { 1, z1 }
});
DenseVector X = DenseVector.FromArray(x0, x1);
DenseVector A = DenseVector.Zero(2);
A.SetToLeastSquares(X, Z);
mean = A[0];
deviation = A[1];
}
/// <summary>Computations that depend on the observed value of vVector__343</summary>
private void Changed_vVector__343()
{
if (this.Changed_vVector__343_iterationsDone == 1)
{
return;
}
this.vVector__343_marginal = new PointMass <Vector[]>(this.VVector__343);
// The constant 'vVectorGaussian343'
VectorGaussian vVectorGaussian343 = VectorGaussian.FromNatural(DenseVector.FromArray(new double[3] {
1547829870.0, 525077980.0, 200270.0
}), new PositiveDefiniteMatrix(new double[3, 3] {
{ 4254590363351.0, 1127383488860.0, 433199230.0 }, { 1127383488860.0, 482723521821.0, 146764360.0 }, { 433199230.0, 146764360.0, 56221.0 }
}));
this.vVector1029_marginal_F = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian343);
// Buffer for ReplicateOp_Divide.Marginal<VectorGaussian>
VectorGaussian vVector1029_rep_B_toDef = default(VectorGaussian);
// Message to 'vVector1029_rep' from Replicate factor
vVector1029_rep_B_toDef = ReplicateOp_Divide.ToDefInit <VectorGaussian>(vVectorGaussian343);
// Message to 'vVector1029_marginal' from Variable factor
this.vVector1029_marginal_F = VariableOp.MarginalAverageConditional <VectorGaussian>(vVector1029_rep_B_toDef, vVectorGaussian343, this.vVector1029_marginal_F);
DistributionStructArray <Gaussian, double> vdouble__1029_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__1029' Forwards messages.
vdouble__1029_F = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vdouble__1029_F[index343] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double> vdouble__1030_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__1030' Forwards messages.
vdouble__1030_F = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vdouble__1030_F[index343] = Gaussian.Uniform();
}
DistributionRefArray <VectorGaussian, Vector> vVector1029_rep_F = default(DistributionRefArray <VectorGaussian, Vector>);
DistributionRefArray <VectorGaussian, Vector> vVector1029_rep_B = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector1029_rep' Forwards messages.
vVector1029_rep_F = new DistributionRefArray <VectorGaussian, Vector>(1);
// Create array for 'vVector1029_rep' Backwards messages.
vVector1029_rep_B = new DistributionRefArray <VectorGaussian, Vector>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vVector1029_rep_B[index343] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian343);
vVector1029_rep_F[index343] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian343);
}
// Buffer for ReplicateOp_Divide.UsesAverageConditional<VectorGaussian>
VectorGaussian vVector1029_rep_F_marginal = default(VectorGaussian);
// Message to 'vVector1029_rep' from Replicate factor
vVector1029_rep_F_marginal = ReplicateOp_Divide.MarginalInit <VectorGaussian>(vVectorGaussian343);
// Message to 'vVector1029_rep' from Replicate factor
vVector1029_rep_F_marginal = ReplicateOp_Divide.Marginal <VectorGaussian>(vVector1029_rep_B_toDef, vVectorGaussian343, vVector1029_rep_F_marginal);
// Buffer for InnerProductOp.InnerProductAverageConditional
// Create array for replicates of 'vVector1029_rep_F_index343__AMean'
Vector[] vVector1029_rep_F_index343__AMean = new Vector[1];
for (int index343 = 0; index343 < 1; index343++)
{
// Message to 'vdouble__1030' from InnerProduct factor
vVector1029_rep_F_index343__AMean[index343] = InnerProductOp.AMeanInit(vVector1029_rep_F[index343]);
}
// Buffer for InnerProductOp.AMean
// Create array for replicates of 'vVector1029_rep_F_index343__AVariance'
PositiveDefiniteMatrix[] vVector1029_rep_F_index343__AVariance = new PositiveDefiniteMatrix[1];
for (int index343 = 0; index343 < 1; index343++)
{
// Message to 'vdouble__1030' from InnerProduct factor
vVector1029_rep_F_index343__AVariance[index343] = InnerProductOp.AVarianceInit(vVector1029_rep_F[index343]);
// Message to 'vVector1029_rep' from Replicate factor
vVector1029_rep_F[index343] = ReplicateOp_Divide.UsesAverageConditional <VectorGaussian>(vVector1029_rep_B[index343], vVector1029_rep_F_marginal, index343, vVector1029_rep_F[index343]);
}
// Create array for 'vdouble__1030_marginal' Forwards messages.
this.vdouble__1030_marginal_F = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
this.vdouble__1030_marginal_F[index343] = Gaussian.Uniform();
}
// Message from use of 'vdouble__1030'
DistributionStructArray <Gaussian, double> vdouble__1030_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__1030_use' Backwards messages.
vdouble__1030_use_B = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vdouble__1030_use_B[index343] = Gaussian.Uniform();
// Message to 'vdouble__1030' from InnerProduct factor
vVector1029_rep_F_index343__AVariance[index343] = InnerProductOp.AVariance(vVector1029_rep_F[index343], vVector1029_rep_F_index343__AVariance[index343]);
// Message to 'vdouble__1030' from InnerProduct factor
vVector1029_rep_F_index343__AMean[index343] = InnerProductOp.AMean(vVector1029_rep_F[index343], vVector1029_rep_F_index343__AVariance[index343], vVector1029_rep_F_index343__AMean[index343]);
// Message to 'vdouble__1030' from InnerProduct factor
vdouble__1030_F[index343] = InnerProductOp.InnerProductAverageConditional(vVector1029_rep_F_index343__AMean[index343], vVector1029_rep_F_index343__AVariance[index343], this.VVector__343[index343]);
// Message to 'vdouble__1030_marginal' from DerivedVariable factor
this.vdouble__1030_marginal_F[index343] = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble__1030_use_B[index343], vdouble__1030_F[index343], this.vdouble__1030_marginal_F[index343]);
}
// Create array for 'vdouble__1029_marginal' Forwards messages.
this.vdouble__1029_marginal_F = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
this.vdouble__1029_marginal_F[index343] = Gaussian.Uniform();
}
// Message from use of 'vdouble__1029'
DistributionStructArray <Gaussian, double> vdouble__1029_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__1029_use' Backwards messages.
vdouble__1029_use_B = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vdouble__1029_use_B[index343] = Gaussian.Uniform();
// Message to 'vdouble__1029' from GaussianFromMeanAndVariance factor
vdouble__1029_F[index343] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(vdouble__1030_F[index343], 0.1);
// Message to 'vdouble__1029_marginal' from Variable factor
this.vdouble__1029_marginal_F[index343] = VariableOp.MarginalAverageConditional <Gaussian>(vdouble__1029_use_B[index343], vdouble__1029_F[index343], this.vdouble__1029_marginal_F[index343]);
}
this.Changed_vVector__343_iterationsDone = 1;
}
/// <summary>Computations that depend on the observed value of vVector__134 and vdouble__402</summary>
private void Changed_vVector__134_vdouble__402()
{
if (this.Changed_vVector__134_vdouble__402_iterationsDone == 1)
{
return;
}
this.vVector__134_marginal = new PointMass <Vector[]>(this.VVector__134);
this.vdouble__402_marginal = new DistributionStructArray <Gaussian, double>(5622, delegate(int index134) {
return(Gaussian.Uniform());
});
this.vdouble__402_marginal = Distribution.SetPoint <DistributionStructArray <Gaussian, double>, double[]>(this.vdouble__402_marginal, this.Vdouble__402);
// The constant 'vVectorGaussian134'
VectorGaussian vVectorGaussian134 = VectorGaussian.FromNatural(DenseVector.FromArray(new double[3] {
0.0, 0.0, 0.0
}), new PositiveDefiniteMatrix(new double[3, 3] {
{ 1.0, 0.0, 0.0 }, { 0.0, 1.0, 0.0 }, { 0.0, 0.0, 1.0 }
}));
this.vVector403_marginal_F = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian134);
// Message from use of 'vdouble__403'
DistributionStructArray <Gaussian, double> vdouble__403_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__403_use' Backwards messages.
vdouble__403_use_B = new DistributionStructArray <Gaussian, double>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
vdouble__403_use_B[index134] = Gaussian.Uniform();
// Message to 'vdouble__403_use' from GaussianFromMeanAndVariance factor
vdouble__403_use_B[index134] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(this.Vdouble__402[index134], 0.1);
}
DistributionRefArray <VectorGaussian, Vector> vVector403_rep_B = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector403_rep' Backwards messages.
vVector403_rep_B = new DistributionRefArray <VectorGaussian, Vector>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
vVector403_rep_B[index134] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian134);
// Message to 'vVector403_rep' from InnerProduct factor
vVector403_rep_B[index134] = InnerProductOp.AAverageConditional(vdouble__403_use_B[index134], this.VVector__134[index134], vVector403_rep_B[index134]);
}
// Buffer for ReplicateOp_Divide.Marginal<VectorGaussian>
VectorGaussian vVector403_rep_B_toDef = default(VectorGaussian);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_B_toDef = ReplicateOp_Divide.ToDefInit <VectorGaussian>(vVectorGaussian134);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_B_toDef = ReplicateOp_Divide.ToDef <VectorGaussian>(vVector403_rep_B, vVector403_rep_B_toDef);
// Message to 'vVector403_marginal' from Variable factor
this.vVector403_marginal_F = VariableOp.MarginalAverageConditional <VectorGaussian>(vVector403_rep_B_toDef, vVectorGaussian134, this.vVector403_marginal_F);
DistributionStructArray <Gaussian, double> vdouble__403_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__403' Forwards messages.
vdouble__403_F = new DistributionStructArray <Gaussian, double>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
vdouble__403_F[index134] = Gaussian.Uniform();
}
DistributionRefArray <VectorGaussian, Vector> vVector403_rep_F = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector403_rep' Forwards messages.
vVector403_rep_F = new DistributionRefArray <VectorGaussian, Vector>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
vVector403_rep_F[index134] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian134);
}
// Buffer for ReplicateOp_Divide.UsesAverageConditional<VectorGaussian>
VectorGaussian vVector403_rep_F_marginal = default(VectorGaussian);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_F_marginal = ReplicateOp_Divide.MarginalInit <VectorGaussian>(vVectorGaussian134);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_F_marginal = ReplicateOp_Divide.Marginal <VectorGaussian>(vVector403_rep_B_toDef, vVectorGaussian134, vVector403_rep_F_marginal);
// Buffer for InnerProductOp.InnerProductAverageConditional
// Create array for replicates of 'vVector403_rep_F_index134__AMean'
Vector[] vVector403_rep_F_index134__AMean = new Vector[5622];
for (int index134 = 0; index134 < 5622; index134++)
{
// Message to 'vdouble__403' from InnerProduct factor
vVector403_rep_F_index134__AMean[index134] = InnerProductOp.AMeanInit(vVector403_rep_F[index134]);
}
// Buffer for InnerProductOp.AMean
// Create array for replicates of 'vVector403_rep_F_index134__AVariance'
PositiveDefiniteMatrix[] vVector403_rep_F_index134__AVariance = new PositiveDefiniteMatrix[5622];
for (int index134 = 0; index134 < 5622; index134++)
{
// Message to 'vdouble__403' from InnerProduct factor
vVector403_rep_F_index134__AVariance[index134] = InnerProductOp.AVarianceInit(vVector403_rep_F[index134]);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_F[index134] = ReplicateOp_Divide.UsesAverageConditional <VectorGaussian>(vVector403_rep_B[index134], vVector403_rep_F_marginal, index134, vVector403_rep_F[index134]);
}
// Create array for 'vdouble__403_marginal' Forwards messages.
this.vdouble__403_marginal_F = new DistributionStructArray <Gaussian, double>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
this.vdouble__403_marginal_F[index134] = Gaussian.Uniform();
// Message to 'vdouble__403' from InnerProduct factor
vVector403_rep_F_index134__AVariance[index134] = InnerProductOp.AVariance(vVector403_rep_F[index134], vVector403_rep_F_index134__AVariance[index134]);
// Message to 'vdouble__403' from InnerProduct factor
vVector403_rep_F_index134__AMean[index134] = InnerProductOp.AMean(vVector403_rep_F[index134], vVector403_rep_F_index134__AVariance[index134], vVector403_rep_F_index134__AMean[index134]);
// Message to 'vdouble__403' from InnerProduct factor
vdouble__403_F[index134] = InnerProductOp.InnerProductAverageConditional(vVector403_rep_F_index134__AMean[index134], vVector403_rep_F_index134__AVariance[index134], this.VVector__134[index134]);
// Message to 'vdouble__403_marginal' from DerivedVariable factor
this.vdouble__403_marginal_F[index134] = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble__403_use_B[index134], vdouble__403_F[index134], this.vdouble__403_marginal_F[index134]);
}
this.Changed_vVector__134_vdouble__402_iterationsDone = 1;
}
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 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);
}
