/// <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; }