/// <summary>Computations that depend on the observed value of y and biasPrior and xValueCount and xValues and wPrior and xIndices</summary>
public void Changed_y_biasPrior_xValueCount_xValues_wPrior_xIndices()
{
if (this.Changed_y_biasPrior_xValueCount_xValues_wPrior_xIndices_iterationsDone == 1)
{
return;
}
this.vdouble13_use_B = IsPositiveOp.XAverageConditional(this.Y, this.vdouble13_F);
this.vdouble11_B = GaussianFromMeanAndVarianceOp.MeanAverageConditional(this.vdouble13_use_B, 1);
this.bias_use_B = DoublePlusOp.BAverageConditional(this.vdouble11_B, this.score_F);
this.bias_marginal_F = VariableOp.MarginalAverageConditional <Gaussian>(this.bias_use_B, this.BiasPrior, this.bias_marginal_F);
this.score_B = DoublePlusOp.AAverageConditional(this.vdouble11_B, this.BiasPrior);
this.product_B = FastSumOp.ArrayAverageConditional <DistributionStructArray <Gaussian, double> >(this.score_B, this.score_F, this.product_F, this.product_B);
for (int userFeature = 0; userFeature < this.XValueCount; userFeature++)
{
this.wSparse_use_B[userFeature] = GaussianProductOp.BAverageConditional(this.product_B[userFeature], this.XValues[userFeature]);
}
this.wSparse_marginal_F = DerivedVariableOp.MarginalAverageConditional <DistributionStructArray <Gaussian, double> >(this.wSparse_use_B, this.wSparse_F,
this.wSparse_marginal_F);
this.Changed_y_biasPrior_xValueCount_xValues_wPrior_xIndices_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 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;
}