/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="WrappedGaussianProductOp"]/message_doc[@name="AAverageConditional(WrappedGaussian, double, WrappedGaussian)"]/*'/>
public static WrappedGaussian AAverageConditional([SkipIfUniform] WrappedGaussian Product, double B, WrappedGaussian result)
{
result.Period = Product.Period / B;
result.Gaussian = GaussianProductOp.AAverageConditional(Product.Gaussian, B);
result.Normalize();
return(result);
}
private void GaussianProductOp_APointMass(double aMean, Gaussian Product, Gaussian B)
{
bool isProper = Product.IsProper();
Gaussian A = Gaussian.PointMass(aMean);
Gaussian result = GaussianProductOp.AAverageConditional(Product, A, B);
Console.WriteLine("{0}: {1}", A, result);
Gaussian result2 = isProper ? GaussianProductOp_Slow.AAverageConditional(Product, A, B) : result;
Console.WriteLine("{0}: {1}", A, result2);
Assert.True(result.MaxDiff(result2) < 1e-6);
var Amsg = InnerProductOp_PointB.BAverageConditional(Product, DenseVector.FromArray(B.GetMean()), new PositiveDefiniteMatrix(new double[, ] {
{ B.GetVariance() }
}), VectorGaussian.PointMass(aMean), VectorGaussian.Uniform(1));
//Console.WriteLine("{0}: {1}", A, Amsg);
Assert.True(result.MaxDiff(Amsg.GetMarginal(0)) < 1e-6);
double prevDiff = double.PositiveInfinity;
for (int i = 3; i < 40; i++)
{
double v = System.Math.Pow(0.1, i);
A = Gaussian.FromMeanAndVariance(aMean, v);
result2 = isProper ? GaussianProductOp.AAverageConditional(Product, A, B) : result;
double diff = result.MaxDiff(result2);
Console.WriteLine("{0}: {1} diff={2}", A, result2, diff.ToString("g4"));
//Assert.True(diff <= prevDiff || diff < 1e-6);
result2 = isProper ? GaussianProductOp_Slow.AAverageConditional(Product, A, B) : result;
diff = result.MaxDiff(result2);
Console.WriteLine("{0}: {1} diff={2}", A, result2, diff.ToString("g4"));
Assert.True(diff <= prevDiff || diff < 1e-6);
prevDiff = diff;
}
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="RatioGaussianOp"]/message_doc[@name="LogAverageFactor(double, Gaussian, double)"]/*'/>
public static double LogAverageFactor(double ratio, Gaussian a, double b)
{
Gaussian to_ratio = GaussianProductOp.AAverageConditional(a, b);
return(to_ratio.GetLogProb(ratio));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="RatioGaussianVmpOp"]/message_doc[@name="RatioAverageLogarithm(Gaussian, double)"]/*'/>
public static Gaussian RatioAverageLogarithm([SkipIfUniform] Gaussian A, double B)
{
return(GaussianProductOp.AAverageConditional(A, B));
}
/// <summary>Computations that depend on the observed value of t7</summary>
private void Changed_t7()
{
if (this.Changed_t7_isDone)
{
return;
}
this.t7_marginal = Gaussian.Uniform();
this.t7_marginal = Distribution.SetPoint <Gaussian, double>(this.t7_marginal, this.T7);
Gaussian t1_F = default(Gaussian);
this.t1_marginal_F = Gaussian.Uniform();
Gaussian t1_use_B = Gaussian.Uniform();
// Message to 't1' from GaussianFromMeanAndVariance factor
t1_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(1.0, 1.0);
Gaussian[] t1_uses_F;
Gaussian[] t1_uses_B;
// Create array for 't1_uses' Forwards messages.
t1_uses_F = new Gaussian[2];
// Create array for 't1_uses' Backwards messages.
t1_uses_B = new Gaussian[2];
t1_uses_B[1] = Gaussian.Uniform();
t1_uses_B[0] = Gaussian.Uniform();
t1_uses_F[1] = Gaussian.Uniform();
t1_uses_F[0] = Gaussian.Uniform();
Gaussian t2_F = default(Gaussian);
// Message to 't2' from GaussianFromMeanAndVariance factor
t2_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(10.0, 1.0);
Gaussian[] t2_uses_B;
// Create array for 't2_uses' Backwards messages.
t2_uses_B = new Gaussian[2];
t2_uses_B[1] = Gaussian.Uniform();
Gaussian[] t2_uses_F;
// Create array for 't2_uses' Forwards messages.
t2_uses_F = new Gaussian[2];
t2_uses_F[0] = Gaussian.Uniform();
// Message to 't2_uses' from Replicate factor
t2_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t2_uses_B, t2_F, 0, t2_uses_F[0]);
// Message to 't1_uses' from Replicate factor
t1_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t1_uses_B, t1_F, 0, t1_uses_F[0]);
Gaussian vdouble6_F = default(Gaussian);
// Message to 'vdouble6' from Plus factor
vdouble6_F = DoublePlusOp.SumAverageConditional(t1_uses_F[0], t2_uses_F[0]);
Gaussian t4_F = default(Gaussian);
// Message to 't4' from GaussianFromMeanAndVariance factor
t4_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(2.0, 1.0);
Gaussian[] t4_uses_B;
// Create array for 't4_uses' Backwards messages.
t4_uses_B = new Gaussian[2];
t4_uses_B[0] = Gaussian.Uniform();
Gaussian[] t4_uses_F;
// Create array for 't4_uses' Forwards messages.
t4_uses_F = new Gaussian[2];
t4_uses_F[1] = Gaussian.Uniform();
// Message to 't4_uses' from Replicate factor
t4_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t4_uses_B, t4_F, 1, t4_uses_F[1]);
Gaussian vdouble6_use_B = default(Gaussian);
// Message to 'vdouble6_use' from Product factor
vdouble6_use_B = GaussianProductOp.AAverageConditional(this.T7, vdouble6_F, t4_uses_F[1]);
// Message to 't1_uses' from Plus factor
t1_uses_B[0] = DoublePlusOp.AAverageConditional(vdouble6_use_B, t2_uses_F[0]);
// Message to 't1_use' from Replicate factor
t1_use_B = ReplicateOp_NoDivide.DefAverageConditional <Gaussian>(t1_uses_B, t1_use_B);
// Message to 't1_marginal' from Variable factor
this.t1_marginal_F = VariableOp.MarginalAverageConditional <Gaussian>(t1_use_B, t1_F, this.t1_marginal_F);
// Message to 't1_uses' from Replicate factor
t1_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t1_uses_B, t1_F, 1, t1_uses_F[1]);
this.t2_marginal_F = Gaussian.Uniform();
Gaussian t2_use_B = Gaussian.Uniform();
t2_uses_B[0] = Gaussian.Uniform();
t2_uses_F[1] = Gaussian.Uniform();
// Message to 't2_uses' from Plus factor
t2_uses_B[0] = DoublePlusOp.BAverageConditional(vdouble6_use_B, t1_uses_F[0]);
// Message to 't2_use' from Replicate factor
t2_use_B = ReplicateOp_NoDivide.DefAverageConditional <Gaussian>(t2_uses_B, t2_use_B);
// Message to 't2_marginal' from Variable factor
this.t2_marginal_F = VariableOp.MarginalAverageConditional <Gaussian>(t2_use_B, t2_F, this.t2_marginal_F);
// Message to 't2_uses' from Replicate factor
t2_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t2_uses_B, t2_F, 1, t2_uses_F[1]);
this.vdouble6_marginal_F = Gaussian.Uniform();
// Message to 'vdouble6_marginal' from DerivedVariable factor
this.vdouble6_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble6_use_B, vdouble6_F, this.vdouble6_marginal_F);
this.t4_marginal_F = Gaussian.Uniform();
Gaussian t4_use_B = Gaussian.Uniform();
t4_uses_B[1] = Gaussian.Uniform();
t4_uses_F[0] = Gaussian.Uniform();
// Message to 't4_uses' from Product factor
t4_uses_B[1] = GaussianProductOp.BAverageConditional(this.T7, vdouble6_F, t4_uses_F[1]);
// Message to 't4_use' from Replicate factor
t4_use_B = ReplicateOp_NoDivide.DefAverageConditional <Gaussian>(t4_uses_B, t4_use_B);
// Message to 't4_marginal' from Variable factor
this.t4_marginal_F = VariableOp.MarginalAverageConditional <Gaussian>(t4_use_B, t4_F, this.t4_marginal_F);
// Message to 't4_uses' from Replicate factor
t4_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t4_uses_B, t4_F, 0, t4_uses_F[0]);
Gaussian vdouble10_F = default(Gaussian);
this.vdouble10_marginal_F = Gaussian.Uniform();
Gaussian vdouble10_use_B = Gaussian.Uniform();
// Message to 'vdouble10' from Plus factor
vdouble10_F = DoublePlusOp.SumAverageConditional(t1_uses_F[1], t2_uses_F[1]);
// Message to 'vdouble10_marginal' from DerivedVariable factor
this.vdouble10_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble10_use_B, vdouble10_F, this.vdouble10_marginal_F);
Gaussian t5_F = default(Gaussian);
this.t5_marginal_F = Gaussian.Uniform();
Gaussian t5_use_B = Gaussian.Uniform();
// Message to 't5' from Product factor
t5_F = GaussianProductOp.ProductAverageConditional(t5_use_B, vdouble10_F, t4_uses_F[0]);
// Message to 't5_marginal' from DerivedVariable factor
this.t5_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(t5_use_B, t5_F, this.t5_marginal_F);
this.Changed_t7_isDone = true;
}
public void ProductOpTest()
{
Assert.True(GaussianProductVmpOp.ProductAverageLogarithm(
2.0,
Gaussian.FromMeanAndVariance(3.0, 5.0)).MaxDiff(Gaussian.FromMeanAndVariance(2 * 3, 4 * 5)) < 1e-8);
Assert.True(GaussianProductOp.ProductAverageConditional(
2.0,
Gaussian.FromMeanAndVariance(3.0, 5.0)).MaxDiff(Gaussian.FromMeanAndVariance(2 * 3, 4 * 5)) < 1e-8);
Assert.True(GaussianProductOp.ProductAverageConditional(new Gaussian(0, 1),
Gaussian.PointMass(2.0),
Gaussian.FromMeanAndVariance(3.0, 5.0)).MaxDiff(Gaussian.FromMeanAndVariance(2 * 3, 4 * 5)) < 1e-8);
Assert.True(GaussianProductVmpOp.ProductAverageLogarithm(
0.0,
Gaussian.FromMeanAndVariance(3.0, 5.0)).MaxDiff(Gaussian.PointMass(0.0)) < 1e-8);
Assert.True(GaussianProductOp.ProductAverageConditional(
0.0,
Gaussian.FromMeanAndVariance(3.0, 5.0)).MaxDiff(Gaussian.PointMass(0.0)) < 1e-8);
Assert.True(GaussianProductOp.ProductAverageConditional(new Gaussian(0, 1),
Gaussian.PointMass(0.0),
Gaussian.FromMeanAndVariance(3.0, 5.0)).MaxDiff(Gaussian.PointMass(0.0)) < 1e-8);
Assert.True(GaussianProductVmpOp.ProductAverageLogarithm(
Gaussian.FromMeanAndVariance(2, 4),
Gaussian.FromMeanAndVariance(3, 5)).MaxDiff(Gaussian.FromMeanAndVariance(2 * 3, 4 * 5 + 3 * 3 * 4 + 2 * 2 * 5)) < 1e-8);
Assert.True(GaussianProductOp.ProductAverageConditional(Gaussian.Uniform(),
Gaussian.FromMeanAndVariance(2, 4),
Gaussian.FromMeanAndVariance(3, 5)).MaxDiff(Gaussian.FromMeanAndVariance(2 * 3, 4 * 5 + 3 * 3 * 4 + 2 * 2 * 5)) <
1e-8);
Assert.True(GaussianProductOp.ProductAverageConditional(Gaussian.FromMeanAndVariance(0, 1e16),
Gaussian.FromMeanAndVariance(2, 4),
Gaussian.FromMeanAndVariance(3, 5)).MaxDiff(Gaussian.FromMeanAndVariance(2 * 3, 4 * 5 + 3 * 3 * 4 + 2 * 2 * 5)) <
1e-4);
Assert.True(GaussianProductOp.AAverageConditional(6.0, 2.0)
.MaxDiff(Gaussian.PointMass(6.0 / 2.0)) < 1e-8);
Assert.True(GaussianProductOp.AAverageConditional(6.0, new Gaussian(1, 3), Gaussian.PointMass(2.0))
.MaxDiff(Gaussian.PointMass(6.0 / 2.0)) < 1e-8);
Assert.True(GaussianProductOp.AAverageConditional(0.0, 0.0).IsUniform());
Assert.True(GaussianProductOp.AAverageConditional(Gaussian.Uniform(), 2.0).IsUniform());
Assert.True(GaussianProductOp.AAverageConditional(Gaussian.Uniform(), new Gaussian(1, 3), Gaussian.PointMass(2.0)).IsUniform());
Assert.True(GaussianProductOp.AAverageConditional(Gaussian.Uniform(), new Gaussian(1, 3), new Gaussian(2, 4)).IsUniform());
Gaussian aPrior = Gaussian.FromMeanAndVariance(0.0, 1000.0);
Assert.True((GaussianProductOp.AAverageConditional(
Gaussian.FromMeanAndVariance(10.0, 1.0),
aPrior,
Gaussian.FromMeanAndVariance(5.0, 1.0)) * aPrior).MaxDiff(
Gaussian.FromMeanAndVariance(2.208041421368822, 0.424566765678152)) < 1e-4);
Gaussian g = new Gaussian(0, 1);
Assert.True(GaussianProductOp.AAverageConditional(g, 0.0).IsUniform());
Assert.True(GaussianProductOp.AAverageConditional(0.0, 0.0).IsUniform());
Assert.True(GaussianProductVmpOp.AAverageLogarithm(g, 0.0).IsUniform());
Assert.True(Gaussian.PointMass(3.0).MaxDiff(GaussianProductVmpOp.AAverageLogarithm(6.0, 2.0)) < 1e-10);
Assert.True(GaussianProductVmpOp.AAverageLogarithm(0.0, 0.0).IsUniform());
try
{
Assert.True(GaussianProductVmpOp.AAverageLogarithm(6.0, g).IsUniform());
Assert.True(false, "Did not throw NotSupportedException");
}
catch (NotSupportedException)
{
}
try
{
g = GaussianProductOp.AAverageConditional(12.0, 0.0);
Assert.True(false, "Did not throw AllZeroException");
}
catch (AllZeroException)
{
}
try
{
g = GaussianProductVmpOp.AAverageLogarithm(12.0, 0.0);
Assert.True(false, "Did not throw AllZeroException");
}
catch (AllZeroException)
{
}
}