/// <summary>Computations that depend on the observed value of FeatureIndexes and FeatureValues and InstanceCount and InstanceFeatureCounts and WeightConstraints and WeightPriors</summary>
private void Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_WeightConstraints_WeightPri6()
{
if (this.Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_WeightConstraints_WeightPri6_isDone)
{
return;
}
for (int InstanceRange = 0; InstanceRange < this.InstanceCount; InstanceRange++)
{
for (int InstanceFeatureRanges = 0; InstanceFeatureRanges < this.InstanceFeatureCounts[InstanceRange]; InstanceFeatureRanges++)
{
this.FeatureScores_F[InstanceRange][InstanceFeatureRanges] = GaussianProductOpBase.ProductAverageConditional(this.FeatureValues[InstanceRange][InstanceFeatureRanges], this.Weights_FeatureIndexes_F[InstanceRange][InstanceFeatureRanges]);
}
this.Score_F[InstanceRange] = FastSumOp.SumAverageConditional(this.FeatureScores_F[InstanceRange]);
this.NoisyScore_F[InstanceRange] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(this.Score_F[InstanceRange], 1.0);
this.Labels_F[InstanceRange] = IsPositiveOp.IsPositiveAverageConditional(this.NoisyScore_F[InstanceRange]);
this.Labels_marginal_F[InstanceRange] = DerivedVariableOp.MarginalAverageConditional <Bernoulli>(this.Labels_use_B_reduced, this.Labels_F[InstanceRange], this.Labels_marginal_F[InstanceRange]);
}
this.Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_WeightConstraints_WeightPri6_isDone = true;
}
/// <summary>Computations that depend on the observed value of InstanceCount and FeatureCount and FeatureValues and numberOfIterationsDecreased and WeightPriors and WeightConstraints</summary>
private void Changed_InstanceCount_FeatureCount_FeatureValues_numberOfIterationsDecreased_WeightPriors_WeightCons10()
{
if (this.Changed_InstanceCount_FeatureCount_FeatureValues_numberOfIterationsDecreased_WeightPriors_WeightCons10_iterationsDone == 1)
{
return;
}
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
for (int FeatureRange = 0; FeatureRange < this.featureCount; FeatureRange++)
{
// Message to 'FeatureScores' from Product factor
this.FeatureScores_F[InstanceRange][FeatureRange] = GaussianProductOp.ProductAverageConditional(this.featureValues[InstanceRange][FeatureRange], this.Weights_depth1_rep_F[FeatureRange][InstanceRange]);
}
// Message to 'Score' from Sum factor
this.Score_F[InstanceRange] = FastSumOp.SumAverageConditional(this.FeatureScores_F[InstanceRange]);
// Message to 'NoisyScore' from GaussianFromMeanAndVariance factor
this.NoisyScore_F[InstanceRange] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(this.Score_F[InstanceRange], 1.0);
// Message to 'Labels' from IsPositive factor
this.Labels_F[InstanceRange] = IsPositiveOp.IsPositiveAverageConditional(this.NoisyScore_F[InstanceRange]);
// Message to 'Labels_marginal' from DerivedVariable factor
this.Labels_marginal_F[InstanceRange] = DerivedVariableOp.MarginalAverageConditional <Bernoulli>(this.Labels_use_B[InstanceRange], this.Labels_F[InstanceRange], this.Labels_marginal_F[InstanceRange]);
}
this.Changed_InstanceCount_FeatureCount_FeatureValues_numberOfIterationsDecreased_WeightPriors_WeightCons10_iterationsDone = 1;
}