/// <summary>Computations that depend on the observed value of FeatureIndexes and FeatureValues and InstanceCount and InstanceFeatureCounts and Labels and numberOfIterations and WeightConstraints and WeightPriors</summary> /// <param name="numberOfIterations">The number of times to iterate each loop</param> private void Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_Labels_numberOfIterations_W7(int numberOfIterations) { if (this.Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_Labels_numberOfIterations_W7_isDone) { return; } for (int iteration = this.numberOfIterationsDone; iteration < numberOfIterations; iteration++) { for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++) { this.Weights_FeatureIndexes_F[InstanceRange] = JaggedSubarrayWithMarginalOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B[InstanceRange], this.Weights_uses_F[1], this.Weights_marginal_F, this.featureIndexes, InstanceRange, this.Weights_FeatureIndexes_F[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.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 InstanceFeatureRanges = 0; InstanceFeatureRanges < this.instanceFeatureCounts[InstanceRange]; InstanceFeatureRanges++) { this.IndexedWeights_B[InstanceRange][InstanceFeatureRanges] = GaussianProductOpBase.BAverageConditional(this.FeatureScores_B[InstanceRange][InstanceFeatureRanges], this.featureValues[InstanceRange][InstanceFeatureRanges]); } this.Weights_marginal_F = JaggedSubarrayWithMarginalOp <double> .MarginalIncrementItems <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B[InstanceRange], this.Weights_FeatureIndexes_F[InstanceRange], this.featureIndexes, InstanceRange, this.Weights_marginal_F); } this.OnProgressChanged(new ProgressChangedEventArgs(iteration)); } this.Weights_uses_B[1] = JaggedSubarrayWithMarginalOp <double> .ArrayAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1], this.Weights_marginal_F, this.Weights_uses_B[1]); this.Weights_B = ReplicateOp_NoDivide.DefAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.Weights_B); this.Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_Labels_numberOfIterations_W7_isDone = true; }
/// <summary>Computations that depend on the observed value of FeatureIndexes and InstanceCount and InstanceFeatureCounts and numberOfIterationsDecreased and WeightConstraints and WeightPriors and must reset on changes to FeatureValues and Labels</summary> /// <param name="initialise">If true, reset messages that initialise loops</param> private void Changed_FeatureIndexes_InstanceCount_InstanceFeatureCounts_numberOfIterationsDecreased_WeightConstra6(bool initialise) { if (this.Changed_FeatureIndexes_InstanceCount_InstanceFeatureCounts_numberOfIterationsDecreased_WeightConstra6_isDone && ((!initialise) || this.Changed_FeatureIndexes_InstanceCount_InstanceFeatureCounts_numberOfIterationsDecreased_WeightConstra6_isInitialised)) { return; } this.Weights_marginal_F = JaggedSubarrayWithMarginalOp <double> .MarginalAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1], this.Weights_FeatureIndexes_B, this.featureIndexes, this.Weights_marginal_F); this.Changed_FeatureIndexes_InstanceCount_InstanceFeatureCounts_numberOfIterationsDecreased_WeightConstra6_isDone = true; this.Changed_FeatureIndexes_InstanceCount_InstanceFeatureCounts_numberOfIterationsDecreased_WeightConstra6_isInitialised = true; }
/// <summary>Computations that depend on the observed value of FeatureIndexes and FeatureValues and InstanceCount and InstanceFeatureCounts and Labels and numberOfIterations and WeightConstraints and WeightPriors</summary> /// <param name="numberOfIterations">The number of times to iterate each loop</param> private void Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_Labels_numberOfIterations_W7(int numberOfIterations) { if (this.Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_Labels_numberOfIterations_W7_isDone) { return; } for (int iteration = this.numberOfIterationsDone; iteration < numberOfIterations; iteration++) { for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++) { this.Weights_FeatureIndexes_F[InstanceRange] = JaggedSubarrayWithMarginalOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B[InstanceRange], this.Weights_uses_F[1], this.Weights_marginal_F, this.featureIndexes, InstanceRange, this.Weights_FeatureIndexes_F[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.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 InstanceFeatureRanges = 0; InstanceFeatureRanges < this.instanceFeatureCounts[InstanceRange]; InstanceFeatureRanges++) { this.IndexedWeights_B[InstanceRange][InstanceFeatureRanges] = GaussianProductOpBase.BAverageConditional(this.FeatureScores_B[InstanceRange][InstanceFeatureRanges], this.featureValues[InstanceRange][InstanceFeatureRanges]); } this.Weights_marginal_F = JaggedSubarrayWithMarginalOp <double> .MarginalIncrementItems <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B[InstanceRange], this.Weights_FeatureIndexes_F[InstanceRange], this.featureIndexes, InstanceRange, this.Weights_marginal_F); } this.OnProgressChanged(new ProgressChangedEventArgs(iteration)); } for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++) { this.Weights_FeatureIndexes_B[InstanceRange] = ArrayHelper.SetTo <DistributionStructArray <Gaussian, double> >(this.Weights_FeatureIndexes_B[InstanceRange], this.IndexedWeights_B[InstanceRange]); } this.Weights_uses_B[1] = JaggedSubarrayWithMarginalOp <double> .ArrayAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1], this.Weights_marginal_F, this.Weights_uses_B[1]); 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[3] = Bernoulli.FromLogOdds(ReplicateOp.LogEvidenceRatio <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.weightPriors, this.Weights_uses_F)); this.ModelSelector_selector_cases_0_uses_B[4] = Bernoulli.FromLogOdds(ConstrainEqualRandomOp <double[]> .LogEvidenceRatio <DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[0], this.weightConstraints)); this.ModelSelector_selector_cases_0_uses_B[8] = Bernoulli.FromLogOdds(JaggedSubarrayWithMarginalOp <double> .LogEvidenceRatio <Gaussian, DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>, DistributionStructArray <Gaussian, double> >(this.Weights_FeatureIndexes_B, this.Weights_uses_F[1], this.featureIndexes, this.Weights_FeatureIndexes_F)); for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++) { this.ModelSelector_selector_cases_0_rep9_B[InstanceRange] = Bernoulli.FromLogOdds(IsPositiveOp.LogEvidenceRatio(this.labels[InstanceRange], this.NoisyScore_F[InstanceRange])); } this.ModelSelector_selector_cases_0_uses_B[16] = ReplicateOp_NoDivide.DefAverageConditional <Bernoulli>(this.ModelSelector_selector_cases_0_rep9_B, this.ModelSelector_selector_cases_0_uses_B[16]); 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.vBernoulli1, this.ModelSelector_marginal_F); this.Weights_B = ReplicateOp_NoDivide.DefAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.Weights_B); this.Changed_FeatureIndexes_FeatureValues_InstanceCount_InstanceFeatureCounts_Labels_numberOfIterations_W7_isDone = true; }
/// <summary>Computations that depend on the observed value of FeatureIndexes and InstanceCount and InstanceFeatureCounts and WeightConstraints and WeightPriors</summary> private void Changed_FeatureIndexes_InstanceCount_InstanceFeatureCounts_WeightConstraints_WeightPriors() { if (this.Changed_FeatureIndexes_InstanceCount_InstanceFeatureCounts_WeightConstraints_WeightPriors_isDone) { return; } this.Weights_marginal_F = JaggedSubarrayWithMarginalOp <double> .MarginalAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1], this.IndexedWeights_B, this.FeatureIndexes, this.Weights_marginal_F); for (int InstanceRange = 0; InstanceRange < this.InstanceCount; InstanceRange++) { this.Weights_FeatureIndexes_F[InstanceRange] = JaggedSubarrayWithMarginalOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B[InstanceRange], this.Weights_uses_F[1], this.Weights_marginal_F, this.FeatureIndexes, InstanceRange, this.Weights_FeatureIndexes_F[InstanceRange]); } this.Changed_FeatureIndexes_InstanceCount_InstanceFeatureCounts_WeightConstraints_WeightPriors_isDone = true; }