/// <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_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 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;
}
