/// <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 numberOfIterationsDecreased and WeightPriors and FeatureIndexes and InstanceCount and InstanceFeatureCounts and FeatureValues and Labels and WeightConstraints</summary>
/// <param name="numberOfIterations">The number of times to iterate each loop</param>
private void Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7(int numberOfIterations)
{
if (this.Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7_iterationsDone == numberOfIterations)
{
return;
}
for (int iteration = this.Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7_iterationsDone; iteration < numberOfIterations; iteration++)
{
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_B_toDef = ReplicateOp_Divide.ToDef <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.Weights_uses_B_toDef);
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_F_marginal = ReplicateOp_Divide.Marginal <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B_toDef, this.weightPriors, this.Weights_uses_F_marginal);
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_F[1] = ReplicateOp_Divide.UsesAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B[1], this.Weights_uses_F_marginal, 1, this.Weights_uses_F[1]);
// Message to 'Weights_FeatureIndexes' from JaggedSubarray factor
this.Weights_uses_F_1__marginal = JaggedSubarrayOp <double> .Marginal <DistributionStructArray <Gaussian, double>, Gaussian, object, DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1], this.IndexedWeights_B, this.featureIndexes, this.Weights_uses_F_1__marginal);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
// Message to 'Weights_FeatureIndexes' from JaggedSubarray factor
this.Weights_FeatureIndexes_F[InstanceRange] = JaggedSubarrayOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B[InstanceRange], this.Weights_uses_F[1], this.Weights_uses_F_1__marginal, this.featureIndexes, InstanceRange, this.Weights_FeatureIndexes_F[InstanceRange]);
for (int InstanceFeatureRanges = 0; InstanceFeatureRanges < this.instanceFeatureCounts[InstanceRange]; InstanceFeatureRanges++)
{
// Message to 'FeatureScores' from Product factor
this.FeatureScores_F[InstanceRange][InstanceFeatureRanges] = GaussianProductOp.ProductAverageConditional(this.featureValues[InstanceRange][InstanceFeatureRanges], this.Weights_FeatureIndexes_F[InstanceRange][InstanceFeatureRanges]);
}
// 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 'NoisyScore_use' from IsPositive factor
this.NoisyScore_use_B[InstanceRange] = IsPositiveOp_Proper.XAverageConditional(Bernoulli.PointMass(this.labels[InstanceRange]), this.NoisyScore_F[InstanceRange]);
// Message to 'Score' from GaussianFromMeanAndVariance factor
this.Score_B[InstanceRange] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(this.NoisyScore_use_B[InstanceRange], 1.0);
// Message to 'FeatureScores' from Sum factor
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++)
{
// Message to 'IndexedWeights' from Product factor
this.IndexedWeights_B[InstanceRange][InstanceFeatureRanges] = GaussianProductOp.BAverageConditional(this.FeatureScores_B[InstanceRange][InstanceFeatureRanges], this.featureValues[InstanceRange][InstanceFeatureRanges]);
}
this.Weights_uses_F_1__marginal = JaggedSubarrayOp <double> .MarginalIncrement <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.Weights_uses_F_1__marginal, this.Weights_FeatureIndexes_F[InstanceRange], this.IndexedWeights_B[InstanceRange], this.featureIndexes, InstanceRange);
}
// Message to 'Weights_uses' from JaggedSubarray factor
this.Weights_uses_B[1] = JaggedSubarrayOp <double> .ArrayAverageConditional <Gaussian, DistributionStructArray <Gaussian, double>, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B, this.featureIndexes, this.Weights_uses_B[1]);
this.OnProgressChanged(new ProgressChangedEventArgs(iteration));
}
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_B_toDef = ReplicateOp_Divide.ToDef <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.Weights_uses_B_toDef);
// Message to 'Weights_marginal' from Variable factor
this.Weights_marginal_F = VariableOp.MarginalAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B_toDef, this.weightPriors, this.Weights_marginal_F);
this.Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7_iterationsDone = numberOfIterations;
}
/// <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 xValueCount and xValues and wPrior and xIndices</summary>
public void Changed_xValueCount_xValues_wPrior_xIndices()
{
if (this.Changed_xValueCount_xValues_wPrior_xIndices_iterationsDone == 1)
{
return;
}
for (int userFeature = 0; userFeature < this.XValueCount; userFeature++)
{
this.product_F[userFeature] = GaussianProductOp.ProductAverageConditional(this.XValues[userFeature], this.wSparse_F[userFeature]);
}
this.score_F = FastSumOp.SumAverageConditional(this.product_F);
this.Changed_xValueCount_xValues_wPrior_xIndices_iterationsDone = 1;
this.Changed_biasPrior_xValueCount_xValues_wPrior_xIndices_iterationsDone = 0;
this.Changed_y_biasPrior_xValueCount_xValues_wPrior_xIndices_iterationsDone = 0;
}
/// <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 FeatureRange = 0; FeatureRange < this.featureCount; FeatureRange++)
{
this.Weights_depth1_B[FeatureRange] = ArrayHelper.SetTo <Gaussian>(this.Weights_depth1_B[FeatureRange], this.Weights_depth1_rep_B_toDef[FeatureRange]);
}
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_B[_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>
/// Computes the inner product of the feature weights and values.
/// </summary>
/// <param name="weights">The feature weights.</param>
/// <param name="nonZeroValues">The sparse feature values.</param>
/// <param name="nonZeroIndices">The sparse feature indices.</param>
/// <returns>The contribution of the features.</returns>
private static Gaussian ComputeFeatureContribution(IList <Gaussian> weights, IList <double> nonZeroValues, IList <int> nonZeroIndices)
{
Debug.Assert(nonZeroValues.Count == nonZeroIndices.Count, "The number of values must be equal to the number of indices.");
int count = nonZeroValues.Count;
var nonZeroWeights = new Gaussian[count];
SubarrayOp <double> .ItemsAverageConditional(weights, nonZeroIndices, nonZeroWeights);
var products = new List <Gaussian>(count);
for (int i = 0; i < count; ++i)
{
products.Add(GaussianProductOp.ProductAverageConditional(nonZeroWeights[i], nonZeroValues[i]));
}
return(FastSumOp.SumAverageConditional(products));
}
/// <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 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 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;
}
