/// <summary>Computations that depend on the observed value of vVector__111</summary>
private void Changed_vVector__111()
{
if (this.Changed_vVector__111_iterationsDone == 1)
{
return;
}
this.vVector__111_marginal = new PointMass <Vector[]>(this.VVector__111);
// The constant 'vVectorGaussian111'
VectorGaussian vVectorGaussian111 = 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.vVector333_marginal_F = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian111);
// Buffer for ReplicateOp_Divide.Marginal<VectorGaussian>
VectorGaussian vVector333_rep_B_toDef = default(VectorGaussian);
// Message to 'vVector333_rep' from Replicate factor
vVector333_rep_B_toDef = ReplicateOp_Divide.ToDefInit <VectorGaussian>(vVectorGaussian111);
// Message to 'vVector333_marginal' from Variable factor
this.vVector333_marginal_F = VariableOp.MarginalAverageConditional <VectorGaussian>(vVector333_rep_B_toDef, vVectorGaussian111, this.vVector333_marginal_F);
DistributionStructArray <Gaussian, double> vdouble__333_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__333' Forwards messages.
vdouble__333_F = new DistributionStructArray <Gaussian, double>(1);
for (int index111 = 0; index111 < 1; index111++)
{
vdouble__333_F[index111] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double> vdouble__334_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__334' Forwards messages.
vdouble__334_F = new DistributionStructArray <Gaussian, double>(1);
for (int index111 = 0; index111 < 1; index111++)
{
vdouble__334_F[index111] = Gaussian.Uniform();
}
DistributionRefArray <VectorGaussian, Vector> vVector333_rep_F = default(DistributionRefArray <VectorGaussian, Vector>);
DistributionRefArray <VectorGaussian, Vector> vVector333_rep_B = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector333_rep' Forwards messages.
vVector333_rep_F = new DistributionRefArray <VectorGaussian, Vector>(1);
// Create array for 'vVector333_rep' Backwards messages.
vVector333_rep_B = new DistributionRefArray <VectorGaussian, Vector>(1);
for (int index111 = 0; index111 < 1; index111++)
{
vVector333_rep_B[index111] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian111);
vVector333_rep_F[index111] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian111);
}
// Buffer for ReplicateOp_Divide.UsesAverageConditional<VectorGaussian>
VectorGaussian vVector333_rep_F_marginal = default(VectorGaussian);
// Message to 'vVector333_rep' from Replicate factor
vVector333_rep_F_marginal = ReplicateOp_Divide.MarginalInit <VectorGaussian>(vVectorGaussian111);
// Message to 'vVector333_rep' from Replicate factor
vVector333_rep_F_marginal = ReplicateOp_Divide.Marginal <VectorGaussian>(vVector333_rep_B_toDef, vVectorGaussian111, vVector333_rep_F_marginal);
// Buffer for InnerProductOp.InnerProductAverageConditional
// Create array for replicates of 'vVector333_rep_F_index111__AMean'
Vector[] vVector333_rep_F_index111__AMean = new Vector[1];
for (int index111 = 0; index111 < 1; index111++)
{
// Message to 'vdouble__334' from InnerProduct factor
vVector333_rep_F_index111__AMean[index111] = InnerProductOp.AMeanInit(vVector333_rep_F[index111]);
}
// Buffer for InnerProductOp.AMean
// Create array for replicates of 'vVector333_rep_F_index111__AVariance'
PositiveDefiniteMatrix[] vVector333_rep_F_index111__AVariance = new PositiveDefiniteMatrix[1];
for (int index111 = 0; index111 < 1; index111++)
{
// Message to 'vdouble__334' from InnerProduct factor
vVector333_rep_F_index111__AVariance[index111] = InnerProductOp.AVarianceInit(vVector333_rep_F[index111]);
// Message to 'vVector333_rep' from Replicate factor
vVector333_rep_F[index111] = ReplicateOp_Divide.UsesAverageConditional <VectorGaussian>(vVector333_rep_B[index111], vVector333_rep_F_marginal, index111, vVector333_rep_F[index111]);
}
// Create array for 'vdouble__334_marginal' Forwards messages.
this.vdouble__334_marginal_F = new DistributionStructArray <Gaussian, double>(1);
for (int index111 = 0; index111 < 1; index111++)
{
this.vdouble__334_marginal_F[index111] = Gaussian.Uniform();
}
// Message from use of 'vdouble__334'
DistributionStructArray <Gaussian, double> vdouble__334_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__334_use' Backwards messages.
vdouble__334_use_B = new DistributionStructArray <Gaussian, double>(1);
for (int index111 = 0; index111 < 1; index111++)
{
vdouble__334_use_B[index111] = Gaussian.Uniform();
// Message to 'vdouble__334' from InnerProduct factor
vVector333_rep_F_index111__AVariance[index111] = InnerProductOp.AVariance(vVector333_rep_F[index111], vVector333_rep_F_index111__AVariance[index111]);
// Message to 'vdouble__334' from InnerProduct factor
vVector333_rep_F_index111__AMean[index111] = InnerProductOp.AMean(vVector333_rep_F[index111], vVector333_rep_F_index111__AVariance[index111], vVector333_rep_F_index111__AMean[index111]);
// Message to 'vdouble__334' from InnerProduct factor
vdouble__334_F[index111] = InnerProductOp.InnerProductAverageConditional(vVector333_rep_F_index111__AMean[index111], vVector333_rep_F_index111__AVariance[index111], this.VVector__111[index111]);
// Message to 'vdouble__334_marginal' from DerivedVariable factor
this.vdouble__334_marginal_F[index111] = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble__334_use_B[index111], vdouble__334_F[index111], this.vdouble__334_marginal_F[index111]);
}
// Create array for 'vdouble__333_marginal' Forwards messages.
this.vdouble__333_marginal_F = new DistributionStructArray <Gaussian, double>(1);
for (int index111 = 0; index111 < 1; index111++)
{
this.vdouble__333_marginal_F[index111] = Gaussian.Uniform();
}
// Message from use of 'vdouble__333'
DistributionStructArray <Gaussian, double> vdouble__333_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__333_use' Backwards messages.
vdouble__333_use_B = new DistributionStructArray <Gaussian, double>(1);
for (int index111 = 0; index111 < 1; index111++)
{
vdouble__333_use_B[index111] = Gaussian.Uniform();
// Message to 'vdouble__333' from GaussianFromMeanAndVariance factor
vdouble__333_F[index111] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(vdouble__334_F[index111], 0.1);
// Message to 'vdouble__333_marginal' from Variable factor
this.vdouble__333_marginal_F[index111] = VariableOp.MarginalAverageConditional <Gaussian>(vdouble__333_use_B[index111], vdouble__333_F[index111], this.vdouble__333_marginal_F[index111]);
}
this.Changed_vVector__111_iterationsDone = 1;
}
/// <summary>Computations that depend on the observed value of vVector__66 and vdouble__198</summary>
private void Changed_vVector__66_vdouble__198()
{
if (this.Changed_vVector__66_vdouble__198_iterationsDone == 1)
{
return;
}
this.vVector__66_marginal = new PointMass <Vector[]>(this.VVector__66);
this.vdouble__198_marginal = new DistributionStructArray <Gaussian, double>(5622, delegate(int index66) {
return(Gaussian.Uniform());
});
this.vdouble__198_marginal = Distribution.SetPoint <DistributionStructArray <Gaussian, double>, double[]>(this.vdouble__198_marginal, this.Vdouble__198);
// The constant 'vVectorGaussian66'
VectorGaussian vVectorGaussian66 = 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.vVector199_marginal_F = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian66);
// Message from use of 'vdouble__199'
DistributionStructArray <Gaussian, double> vdouble__199_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__199_use' Backwards messages.
vdouble__199_use_B = new DistributionStructArray <Gaussian, double>(5622);
for (int index66 = 0; index66 < 5622; index66++)
{
vdouble__199_use_B[index66] = Gaussian.Uniform();
// Message to 'vdouble__199_use' from GaussianFromMeanAndVariance factor
vdouble__199_use_B[index66] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(this.Vdouble__198[index66], 0.1);
}
DistributionRefArray <VectorGaussian, Vector> vVector199_rep_B = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector199_rep' Backwards messages.
vVector199_rep_B = new DistributionRefArray <VectorGaussian, Vector>(5622);
for (int index66 = 0; index66 < 5622; index66++)
{
vVector199_rep_B[index66] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian66);
// Message to 'vVector199_rep' from InnerProduct factor
vVector199_rep_B[index66] = InnerProductOp.AAverageConditional(vdouble__199_use_B[index66], this.VVector__66[index66], vVector199_rep_B[index66]);
}
// Buffer for ReplicateOp_Divide.Marginal<VectorGaussian>
VectorGaussian vVector199_rep_B_toDef = default(VectorGaussian);
// Message to 'vVector199_rep' from Replicate factor
vVector199_rep_B_toDef = ReplicateOp_Divide.ToDefInit <VectorGaussian>(vVectorGaussian66);
// Message to 'vVector199_rep' from Replicate factor
vVector199_rep_B_toDef = ReplicateOp_Divide.ToDef <VectorGaussian>(vVector199_rep_B, vVector199_rep_B_toDef);
// Message to 'vVector199_marginal' from Variable factor
this.vVector199_marginal_F = VariableOp.MarginalAverageConditional <VectorGaussian>(vVector199_rep_B_toDef, vVectorGaussian66, this.vVector199_marginal_F);
DistributionStructArray <Gaussian, double> vdouble__199_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__199' Forwards messages.
vdouble__199_F = new DistributionStructArray <Gaussian, double>(5622);
for (int index66 = 0; index66 < 5622; index66++)
{
vdouble__199_F[index66] = Gaussian.Uniform();
}
DistributionRefArray <VectorGaussian, Vector> vVector199_rep_F = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector199_rep' Forwards messages.
vVector199_rep_F = new DistributionRefArray <VectorGaussian, Vector>(5622);
for (int index66 = 0; index66 < 5622; index66++)
{
vVector199_rep_F[index66] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian66);
}
// Buffer for ReplicateOp_Divide.UsesAverageConditional<VectorGaussian>
VectorGaussian vVector199_rep_F_marginal = default(VectorGaussian);
// Message to 'vVector199_rep' from Replicate factor
vVector199_rep_F_marginal = ReplicateOp_Divide.MarginalInit <VectorGaussian>(vVectorGaussian66);
// Message to 'vVector199_rep' from Replicate factor
vVector199_rep_F_marginal = ReplicateOp_Divide.Marginal <VectorGaussian>(vVector199_rep_B_toDef, vVectorGaussian66, vVector199_rep_F_marginal);
// Buffer for InnerProductOp.InnerProductAverageConditional
// Create array for replicates of 'vVector199_rep_F_index66__AMean'
Vector[] vVector199_rep_F_index66__AMean = new Vector[5622];
for (int index66 = 0; index66 < 5622; index66++)
{
// Message to 'vdouble__199' from InnerProduct factor
vVector199_rep_F_index66__AMean[index66] = InnerProductOp.AMeanInit(vVector199_rep_F[index66]);
}
// Buffer for InnerProductOp.AMean
// Create array for replicates of 'vVector199_rep_F_index66__AVariance'
PositiveDefiniteMatrix[] vVector199_rep_F_index66__AVariance = new PositiveDefiniteMatrix[5622];
for (int index66 = 0; index66 < 5622; index66++)
{
// Message to 'vdouble__199' from InnerProduct factor
vVector199_rep_F_index66__AVariance[index66] = InnerProductOp.AVarianceInit(vVector199_rep_F[index66]);
// Message to 'vVector199_rep' from Replicate factor
vVector199_rep_F[index66] = ReplicateOp_Divide.UsesAverageConditional <VectorGaussian>(vVector199_rep_B[index66], vVector199_rep_F_marginal, index66, vVector199_rep_F[index66]);
}
// Create array for 'vdouble__199_marginal' Forwards messages.
this.vdouble__199_marginal_F = new DistributionStructArray <Gaussian, double>(5622);
for (int index66 = 0; index66 < 5622; index66++)
{
this.vdouble__199_marginal_F[index66] = Gaussian.Uniform();
// Message to 'vdouble__199' from InnerProduct factor
vVector199_rep_F_index66__AVariance[index66] = InnerProductOp.AVariance(vVector199_rep_F[index66], vVector199_rep_F_index66__AVariance[index66]);
// Message to 'vdouble__199' from InnerProduct factor
vVector199_rep_F_index66__AMean[index66] = InnerProductOp.AMean(vVector199_rep_F[index66], vVector199_rep_F_index66__AVariance[index66], vVector199_rep_F_index66__AMean[index66]);
// Message to 'vdouble__199' from InnerProduct factor
vdouble__199_F[index66] = InnerProductOp.InnerProductAverageConditional(vVector199_rep_F_index66__AMean[index66], vVector199_rep_F_index66__AVariance[index66], this.VVector__66[index66]);
// Message to 'vdouble__199_marginal' from DerivedVariable factor
this.vdouble__199_marginal_F[index66] = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble__199_use_B[index66], vdouble__199_F[index66], this.vdouble__199_marginal_F[index66]);
}
this.Changed_vVector__66_vdouble__198_iterationsDone = 1;
}
/// <summary>Computations that depend on the observed value of numberOfIterations and vint__0 and vint__1</summary>
/// <param name="numberOfIterations">The number of times to iterate each loop</param>
private void Changed_numberOfIterations_vint__0_vint__1(int numberOfIterations)
{
if (this.Changed_numberOfIterations_vint__0_vint__1_isDone)
{
return;
}
DistributionStructArray <Gaussian, double> vdouble__0_F;
Gaussian vdouble__0_F_reduced;
// Create array for 'vdouble__0' Forwards messages.
vdouble__0_F = new DistributionStructArray <Gaussian, double>(5);
// Message to 'vdouble__0' from GaussianFromMeanAndVariance factor
vdouble__0_F_reduced = GaussianFromMeanAndVarianceOp.SampleAverageConditional(6.0, 9.0);
for (int index1 = 0; index1 < 5; index1++)
{
vdouble__0_F[index1] = vdouble__0_F_reduced;
vdouble__0_F[index1] = vdouble__0_F_reduced;
}
// Create array for 'vdouble__0_marginal' Forwards messages.
this.vdouble__0_marginal_F = new DistributionStructArray <Gaussian, double>(5);
DistributionStructArray <Gaussian, double> vdouble__0_use_B;
// Create array for 'vdouble__0_use' Backwards messages.
vdouble__0_use_B = new DistributionStructArray <Gaussian, double>(5);
for (int index1 = 0; index1 < 5; index1++)
{
vdouble__0_use_B[index1] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double>[] vdouble__0_uses_F;
// Create array for 'vdouble__0_uses' Forwards messages.
vdouble__0_uses_F = new DistributionStructArray <Gaussian, double> [2];
// Create array for 'vdouble__0_uses' Forwards messages.
vdouble__0_uses_F[1] = new DistributionStructArray <Gaussian, double>(5);
for (int index1 = 0; index1 < 5; index1++)
{
vdouble__0_uses_F[1][index1] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double> vdouble__0_uses_F_1__marginal;
// Message to 'vdouble__0_itemvint__1_index0_' from GetItems factor
vdouble__0_uses_F_1__marginal = GetItemsOp <double> .MarginalInit <DistributionStructArray <Gaussian, double> >(vdouble__0_uses_F[1]);
DistributionStructArray <Gaussian, double> vdouble__0_itemvint__1_index0__F;
// Create array for 'vdouble__0_itemvint__1_index0_' Forwards messages.
vdouble__0_itemvint__1_index0__F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble__0_itemvint__1_index0__F[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble11_F'
DistributionStructArray <Gaussian, double> vdouble11_F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble11_F[index0] = Gaussian.Uniform();
}
// Create array for 'vdouble__0_uses' Forwards messages.
vdouble__0_uses_F[0] = new DistributionStructArray <Gaussian, double>(5);
for (int index1 = 0; index1 < 5; index1++)
{
vdouble__0_uses_F[0][index1] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double> vdouble__0_uses_F_0__marginal;
// Message to 'vdouble__0_itemvint__0_index0_' from GetItems factor
vdouble__0_uses_F_0__marginal = GetItemsOp <double> .MarginalInit <DistributionStructArray <Gaussian, double> >(vdouble__0_uses_F[0]);
DistributionStructArray <Gaussian, double> vdouble__0_itemvint__0_index0__F;
// Create array for 'vdouble__0_itemvint__0_index0_' Forwards messages.
vdouble__0_itemvint__0_index0__F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble__0_itemvint__0_index0__F[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble8_F'
DistributionStructArray <Gaussian, double> vdouble8_F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble8_F[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble12_F'
DistributionStructArray <Gaussian, double> vdouble12_F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble12_F[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble12_B'
DistributionStructArray <Gaussian, double> vdouble12_B = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble12_B[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble8_use_B'
DistributionStructArray <Gaussian, double> vdouble8_use_B = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble8_use_B[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble11_use_B'
DistributionStructArray <Gaussian, double> vdouble11_use_B = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble11_use_B[index0] = Gaussian.Uniform();
}
for (int iteration = this.numberOfIterationsDone; iteration < numberOfIterations; iteration++)
{
// Message to 'vdouble__0_uses' from Replicate factor
vdouble__0_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <DistributionStructArray <Gaussian, double> >(this.vdouble__0_uses_B, vdouble__0_F, 1, vdouble__0_uses_F[1]);
// Message to 'vdouble__0_itemvint__1_index0_' from GetItems factor
vdouble__0_uses_F_1__marginal = GetItemsOp <double> .Marginal <DistributionStructArray <Gaussian, double>, Gaussian>(vdouble__0_uses_F[1], this.vdouble__0_uses_B[1], vdouble__0_uses_F_1__marginal);
// Message to 'vdouble__0_uses' from Replicate factor
vdouble__0_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <DistributionStructArray <Gaussian, double> >(this.vdouble__0_uses_B, vdouble__0_F, 0, vdouble__0_uses_F[0]);
// Message to 'vdouble__0_itemvint__0_index0_' from GetItems factor
vdouble__0_uses_F_0__marginal = GetItemsOp <double> .Marginal <DistributionStructArray <Gaussian, double>, Gaussian>(vdouble__0_uses_F[0], this.vdouble__0_uses_B[0], vdouble__0_uses_F_0__marginal);
for (int index0 = 0; index0 < 6; index0++)
{
// Message to 'vdouble__0_itemvint__1_index0_' from GetItems factor
vdouble__0_itemvint__1_index0__F[index0] = GetItemsOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian>(this.vdouble__0_itemvint__1_index0__B[index0], vdouble__0_uses_F[1], vdouble__0_uses_F_1__marginal, this.Vint__1, index0, vdouble__0_itemvint__1_index0__F[index0]);
// Message to 'vdouble11' from GaussianFromMeanAndVariance factor
vdouble11_F[index0] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(vdouble__0_itemvint__1_index0__F[index0], 1.0);
// Message to 'vdouble__0_itemvint__0_index0_' from GetItems factor
vdouble__0_itemvint__0_index0__F[index0] = GetItemsOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian>(this.vdouble__0_itemvint__0_index0__B[index0], vdouble__0_uses_F[0], vdouble__0_uses_F_0__marginal, this.Vint__0, index0, vdouble__0_itemvint__0_index0__F[index0]);
// Message to 'vdouble8' from GaussianFromMeanAndVariance factor
vdouble8_F[index0] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(vdouble__0_itemvint__0_index0__F[index0], 1.0);
// Message to 'vdouble12' from Difference factor
vdouble12_F[index0] = DoublePlusOp.AAverageConditional(vdouble8_F[index0], vdouble11_F[index0]);
// Message to 'vdouble12' from IsPositive factor
vdouble12_B[index0] = IsPositiveOp_Proper.XAverageConditional(Bernoulli.PointMass(true), vdouble12_F[index0]);
// Message to 'vdouble8_use' from Difference factor
vdouble8_use_B[index0] = DoublePlusOp.SumAverageConditional(vdouble12_B[index0], vdouble11_F[index0]);
// Message to 'vdouble__0_itemvint__0_index0_' from GaussianFromMeanAndVariance factor
this.vdouble__0_itemvint__0_index0__B[index0] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(vdouble8_use_B[index0], 1.0);
// Message to 'vdouble11_use' from Difference factor
vdouble11_use_B[index0] = DoublePlusOp.BAverageConditional(vdouble8_F[index0], vdouble12_B[index0]);
// Message to 'vdouble__0_itemvint__1_index0_' from GaussianFromMeanAndVariance factor
this.vdouble__0_itemvint__1_index0__B[index0] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(vdouble11_use_B[index0], 1.0);
}
// Message to 'vdouble__0_uses' from GetItems factor
this.vdouble__0_uses_B[0] = GetItemsOp <double> .ArrayAverageConditional <Gaussian, DistributionStructArray <Gaussian, double> >(this.vdouble__0_itemvint__0_index0__B, this.Vint__0, this.vdouble__0_uses_B[0]);
// Message to 'vdouble__0_uses' from GetItems factor
this.vdouble__0_uses_B[1] = GetItemsOp <double> .ArrayAverageConditional <Gaussian, DistributionStructArray <Gaussian, double> >(this.vdouble__0_itemvint__1_index0__B, this.Vint__1, this.vdouble__0_uses_B[1]);
this.OnProgressChanged(new ProgressChangedEventArgs(iteration));
}
// Message to 'vdouble__0_use' from Replicate factor
vdouble__0_use_B = ReplicateOp_NoDivide.DefAverageConditional <DistributionStructArray <Gaussian, double> >(this.vdouble__0_uses_B, vdouble__0_use_B);
for (int index1 = 0; index1 < 5; index1++)
{
this.vdouble__0_marginal_F[index1] = Gaussian.Uniform();
// Message to 'vdouble__0_marginal' from Variable factor
this.vdouble__0_marginal_F[index1] = VariableOp.MarginalAverageConditional <Gaussian>(vdouble__0_use_B[index1], vdouble__0_F_reduced, this.vdouble__0_marginal_F[index1]);
}
this.Changed_numberOfIterations_vint__0_vint__1_isDone = true;
}
/// <summary>Computations that depend on the observed value of FeatureCount and FeatureValues and InstanceCount and Labels and numberOfIterations and WeightConstraints and WeightPriors</summary>
/// <param name="numberOfIterations">The number of times to iterate each loop</param>
private void Changed_FeatureCount_FeatureValues_InstanceCount_Labels_numberOfIterations_WeightConstraints_WeightP8(int numberOfIterations)
{
if (this.Changed_FeatureCount_FeatureValues_InstanceCount_Labels_numberOfIterations_WeightConstraints_WeightP8_isDone)
{
return;
}
for (int iteration = this.numberOfIterationsDone; iteration < numberOfIterations; iteration++)
{
for (int FeatureRange = 0; FeatureRange < this.featureCount; FeatureRange++)
{
this.Weights_depth1_rep_F_marginal[FeatureRange] = ReplicateOp_Divide.Marginal <Gaussian>(this.Weights_depth1_rep_B_toDef[FeatureRange], this.Weights_uses_F[1][FeatureRange], this.Weights_depth1_rep_F_marginal[FeatureRange]);
}
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
for (int FeatureRange = 0; FeatureRange < this.featureCount; FeatureRange++)
{
this.Weights_depth1_rep_F[FeatureRange][InstanceRange] = ReplicateOp_Divide.UsesAverageConditional <Gaussian>(this.Weights_depth1_rep_B[FeatureRange][InstanceRange], this.Weights_depth1_rep_F_marginal[FeatureRange], InstanceRange, this.Weights_depth1_rep_F[FeatureRange][InstanceRange]);
this.FeatureScores_F[InstanceRange][FeatureRange] = GaussianProductOpBase.ProductAverageConditional(this.featureValues[InstanceRange][FeatureRange], this.Weights_depth1_rep_F[FeatureRange][InstanceRange]);
}
this.Score_F[InstanceRange] = FastSumOp.SumAverageConditional(this.FeatureScores_F[InstanceRange]);
this.NoisyScore_F[InstanceRange] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(this.Score_F[InstanceRange], 1.0);
this.NoisyScore_use_B[InstanceRange] = IsPositiveOp_Proper.XAverageConditional(Bernoulli.PointMass(this.labels[InstanceRange]), this.NoisyScore_F[InstanceRange]);
this.Score_B[InstanceRange] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(this.NoisyScore_use_B[InstanceRange], 1.0);
this.FeatureScores_B[InstanceRange] = FastSumOp.ArrayAverageConditional <DistributionStructArray <Gaussian, double> >(this.Score_B[InstanceRange], this.Score_F[InstanceRange], this.FeatureScores_F[InstanceRange], this.FeatureScores_B[InstanceRange]);
for (int FeatureRange = 0; FeatureRange < this.featureCount; FeatureRange++)
{
this.Weights_depth1_rep_B[FeatureRange][InstanceRange] = GaussianProductOpBase.BAverageConditional(this.FeatureScores_B[InstanceRange][FeatureRange], this.featureValues[InstanceRange][FeatureRange]);
this.Weights_depth1_rep_F_marginal[FeatureRange] = ReplicateOp_Divide.MarginalIncrement <Gaussian>(this.Weights_depth1_rep_F_marginal[FeatureRange], this.Weights_depth1_rep_F[FeatureRange][InstanceRange], this.Weights_depth1_rep_B[FeatureRange][InstanceRange]);
}
}
for (int FeatureRange = 0; FeatureRange < this.featureCount; FeatureRange++)
{
this.Weights_depth1_rep_B_toDef[FeatureRange] = ReplicateOp_Divide.ToDef <Gaussian>(this.Weights_depth1_rep_B[FeatureRange], this.Weights_depth1_rep_B_toDef[FeatureRange]);
}
this.OnProgressChanged(new ProgressChangedEventArgs(iteration));
}
for (int _iv = 0; _iv < this.featureCount; _iv++)
{
this.Weights_uses_B[1][_iv] = ArrayHelper.SetTo <Gaussian>(this.Weights_uses_B[1][_iv], this.Weights_depth1_rep_B_toDef[_iv]);
}
this.Weights_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.weightPriors, 0, this.Weights_uses_F[0]);
this.ModelSelector_selector_cases_0_uses_B[6] = Bernoulli.FromLogOdds(ReplicateOp.LogEvidenceRatio <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.weightPriors, this.Weights_uses_F));
this.ModelSelector_selector_cases_0_uses_B[7] = Bernoulli.FromLogOdds(ConstrainEqualRandomOp <double[]> .LogEvidenceRatio <DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[0], this.weightConstraints));
for (int FeatureRange = 0; FeatureRange < this.featureCount; FeatureRange++)
{
this.ModelSelector_selector_cases_0_rep3_uses_B[FeatureRange][1] = Bernoulli.FromLogOdds(ReplicateOp.LogEvidenceRatio <Gaussian>(this.Weights_depth1_rep_B[FeatureRange], this.Weights_uses_F[1][FeatureRange], this.Weights_depth1_rep_F[FeatureRange]));
this.ModelSelector_selector_cases_0_rep3_B[FeatureRange] = ReplicateOp_NoDivide.DefAverageConditional <Bernoulli>(this.ModelSelector_selector_cases_0_rep3_uses_B[FeatureRange], this.ModelSelector_selector_cases_0_rep3_B[FeatureRange]);
}
this.ModelSelector_selector_cases_0_uses_B[12] = ReplicateOp_NoDivide.DefAverageConditional <Bernoulli>(this.ModelSelector_selector_cases_0_rep3_B, this.ModelSelector_selector_cases_0_uses_B[12]);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.ModelSelector_selector_cases_0_rep8_B[InstanceRange] = Bernoulli.FromLogOdds(IsPositiveOp.LogEvidenceRatio(this.labels[InstanceRange], this.NoisyScore_F[InstanceRange]));
}
this.ModelSelector_selector_cases_0_uses_B[17] = ReplicateOp_NoDivide.DefAverageConditional <Bernoulli>(this.ModelSelector_selector_cases_0_rep8_B, this.ModelSelector_selector_cases_0_uses_B[17]);
this.ModelSelector_selector_cases_0_B = ReplicateOp_NoDivide.DefAverageConditional <Bernoulli>(this.ModelSelector_selector_cases_0_uses_B, this.ModelSelector_selector_cases_0_B);
this.ModelSelector_selector_cases_B[0] = ArrayHelper.SetTo <Bernoulli>(this.ModelSelector_selector_cases_B[0], this.ModelSelector_selector_cases_0_B);
this.ModelSelector_selector_B = CasesOp.BAverageConditional(this.ModelSelector_selector_cases_B);
this.ModelSelector_marginal_F = VariableOp.MarginalAverageConditional <Bernoulli>(this.ModelSelector_selector_B, this.vBernoulli0, this.ModelSelector_marginal_F);
this.Weights_use_B = ReplicateOp_NoDivide.DefAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.Weights_use_B);
this.Weights_marginal_F = VariableOp.MarginalAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_use_B, this.weightPriors, this.Weights_marginal_F);
this.Changed_FeatureCount_FeatureValues_InstanceCount_Labels_numberOfIterations_WeightConstraints_WeightP8_isDone = true;
}
/// <summary>Computations that do not depend on observed values</summary>
private void Constant()
{
if (this.Constant_isDone)
{
return;
}
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();
// 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[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t1_uses_B, t1_F, 0, t1_uses_F[0]);
// 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]);
Gaussian t2_F = default(Gaussian);
this.t2_marginal_F = Gaussian.Uniform();
Gaussian t2_use_B = Gaussian.Uniform();
// Message to 't2' from GaussianFromMeanAndVariance factor
t2_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(10.0, 1.0);
Gaussian[] t2_uses_F;
Gaussian[] t2_uses_B;
// Create array for 't2_uses' Forwards messages.
t2_uses_F = new Gaussian[2];
// Create array for 't2_uses' Backwards messages.
t2_uses_B = new Gaussian[2];
t2_uses_B[1] = Gaussian.Uniform();
t2_uses_B[0] = Gaussian.Uniform();
t2_uses_F[1] = Gaussian.Uniform();
t2_uses_F[0] = Gaussian.Uniform();
// 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[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t2_uses_B, t2_F, 0, t2_uses_F[0]);
// 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]);
Gaussian vdouble6_F = default(Gaussian);
this.vdouble6_marginal_F = Gaussian.Uniform();
Gaussian vdouble6_use_B = Gaussian.Uniform();
// Message to 'vdouble6' from Plus factor
vdouble6_F = DoublePlusOp.SumAverageConditional(t1_uses_F[0], t2_uses_F[0]);
// Message to 'vdouble6_marginal' from DerivedVariable factor
this.vdouble6_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble6_use_B, vdouble6_F, this.vdouble6_marginal_F);
Gaussian t4_F = default(Gaussian);
this.t4_marginal_F = Gaussian.Uniform();
Gaussian t4_use_B = Gaussian.Uniform();
// Message to 't4' from GaussianFromMeanAndVariance factor
t4_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(2.0, 1.0);
Gaussian[] t4_uses_F;
Gaussian[] t4_uses_B;
// Create array for 't4_uses' Forwards messages.
t4_uses_F = new Gaussian[2];
// Create array for 't4_uses' Backwards messages.
t4_uses_B = new Gaussian[2];
t4_uses_B[1] = Gaussian.Uniform();
t4_uses_B[0] = Gaussian.Uniform();
t4_uses_F[1] = Gaussian.Uniform();
t4_uses_F[0] = Gaussian.Uniform();
// 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]);
// 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 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);
Gaussian t7_F = default(Gaussian);
this.t7_marginal_F = Gaussian.Uniform();
Gaussian t7_use_B = Gaussian.Uniform();
// Message to 't7' from Product factor
t7_F = GaussianProductOp.ProductAverageConditional(t7_use_B, vdouble6_F, t4_uses_F[1]);
// Message to 't7_marginal' from DerivedVariable factor
this.t7_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(t7_use_B, t7_F, this.t7_marginal_F);
this.Constant_isDone = true;
}
