/// <summary>Computations that depend on the observed value of InstanceCount</summary>
private void Changed_InstanceCount()
{
if (this.Changed_InstanceCount_isDone)
{
return;
}
this.Labels_F = new DistributionStructArray <Bernoulli, bool>(this.InstanceCount);
this.NoisyScore_F = new Gaussian[this.InstanceCount];
this.FeatureScores_F = new DistributionStructArray <Gaussian, double> [this.InstanceCount];
this.Weights_FeatureIndexes_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.InstanceCount);
this.Score_F = new Gaussian[this.InstanceCount];
this.IndexedWeights_B = new DistributionStructArray <Gaussian, double> [this.InstanceCount];
this.Labels_marginal_F = new DistributionStructArray <Bernoulli, bool>(this.InstanceCount);
this.Labels_use_B_reduced = default(Bernoulli);
if (this.InstanceCount > 0)
{
this.Labels_use_B_reduced = Bernoulli.Uniform();
}
for (int InstanceRange = 0; InstanceRange < this.InstanceCount; InstanceRange++)
{
this.Labels_F[InstanceRange] = Bernoulli.Uniform();
this.Score_F[InstanceRange] = Gaussian.Uniform();
this.NoisyScore_F[InstanceRange] = Gaussian.Uniform();
this.Labels_marginal_F[InstanceRange] = Bernoulli.Uniform();
}
this.Changed_InstanceCount_isDone = true;
}
/// <summary>Computations that depend on the observed value of InstanceCount</summary>
private void Changed_InstanceCount()
{
if (this.Changed_InstanceCount_isDone)
{
return;
}
this.FeatureScores_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
this.Score_F = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Score_F[InstanceRange] = Gaussian.Uniform();
}
this.NoisyScore_F = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.NoisyScore_F[InstanceRange] = Gaussian.Uniform();
}
this.NoisyScore_use_B = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.NoisyScore_use_B[InstanceRange] = Gaussian.Uniform();
}
this.Score_B = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Score_B[InstanceRange] = Gaussian.Uniform();
}
this.FeatureScores_B = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
this.ModelSelector_selector_cases_0_rep8_B = new DistributionStructArray <Bernoulli, bool>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.ModelSelector_selector_cases_0_rep8_B[InstanceRange] = Bernoulli.Uniform();
}
this.Changed_InstanceCount_isDone = true;
}
/// <summary>Computations that depend on the observed value of InstanceCount</summary>
private void Changed_InstanceCount()
{
if (this.Changed_InstanceCount_isDone)
{
return;
}
this.Weights_FeatureIndexes_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
this.FeatureScores_F = new DistributionStructArray <Gaussian, double> [this.instanceCount];
this.Score_F = new Gaussian[this.instanceCount];
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Score_F[InstanceRange] = Gaussian.Uniform();
}
this.NoisyScore_F = new Gaussian[this.instanceCount];
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.NoisyScore_F[InstanceRange] = Gaussian.Uniform();
}
this.NoisyScore_use_B = new Gaussian[this.instanceCount];
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.NoisyScore_use_B[InstanceRange] = Gaussian.Uniform();
}
this.Score_B = new Gaussian[this.instanceCount];
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Score_B[InstanceRange] = Gaussian.Uniform();
}
this.FeatureScores_B = new DistributionStructArray <Gaussian, double> [this.instanceCount];
this.IndexedWeights_B = new DistributionStructArray <Gaussian, double> [this.instanceCount];
this.Weights_FeatureIndexes_B = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
this.Changed_InstanceCount_isDone = true;
}
/// <summary>Computations that depend on the observed value of FeatureCount</summary>
private void Changed_FeatureCount()
{
if (this.Changed_FeatureCount_isDone)
{
return;
}
this.Weights_depth1_rep_F_marginal = new DistributionStructArray <Gaussian, double>(this.featureCount);
this.Weights_depth1_rep_B_toDef = new DistributionStructArray <Gaussian, double>(this.featureCount);
this.Weights_depth1_rep_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.featureCount);
this.Weights_depth1_rep_B = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.featureCount);
this.Changed_FeatureCount_isDone = true;
}
public virtual void SolveSudoku(GrilleSudoku s)
{
Dirichlet[] dirArray = Enumerable.Repeat(Dirichlet.Uniform(CellDomain.Count), CellIndices.Count).ToArray();
//On affecte les valeurs fournies par le masque à résoudre en affectant les distributions de probabilités initiales
foreach (var cellIndex in GrilleSudoku.IndicesCellules)
{
if (s.Cellules[cellIndex] > 0)
{
Vector v = Vector.Zero(CellDomain.Count);
v[s.Cellules[cellIndex] - 1] = 1.0;
//Todo: Alternative: le fait de mettre une proba non nulle permet d'éviter l'erreur "zero probability" du Sudoku Easy-n°2, mais le Easy#3 n'est plus résolu
//Vector v = Vector.Constant(CellDomain.Count, EpsilonProba);
//v[s.Cellules[cellIndex] - 1] = FixedValueProba;
dirArray[cellIndex] = Dirichlet.PointMass(v);
}
}
CellsPrior.ObservedValue = dirArray;
// Todo: tester en inférant sur d'autres variables aléatoire,
// et/ou en ayant une approche itérative: On conserve uniquement les cellules dont les valeurs ont les meilleures probabilités et on réinjecte ces valeurs dans CellsPrior comme c'est également fait dans le projet neural nets.
DistributionRefArray <Discrete, int> cellsPosterior = (DistributionRefArray <Discrete, int>)InferenceEngine.Infer(Cells);
var cellValues = cellsPosterior.Point.Select(i => i + 1).ToList();
//Autre possibilité de variable d'inférence (bis)
//Dirichlet[] cellsProbsPosterior = InferenceEngine.Infer<Dirichlet[]>(ProbCells);
foreach (var cellIndex in GrilleSudoku.IndicesCellules)
{
if (s.Cellules[cellIndex] == 0)
{
s.Cellules[cellIndex] = cellValues[cellIndex];
//Autre possibilité de variable d'inférence (bis)
//var mode = cellsProbsPosterior[cellIndex].GetMode();
//var value = mode.IndexOf(1.0) + 1;
//s.Cellules[cellIndex] = value;
}
}
}
public static DistributionRefArray <TDist, T> ItemsAverageConditionalInit <TDist>(
[IgnoreDependency] DistributionRefArray <TDist, T> array, IList <int> indices)
where TDist : class,
SettableTo <TDist>,
SettableToProduct <TDist>,
SettableToRatio <TDist>,
SettableToPower <TDist>,
SettableToWeightedSum <TDist>,
CanGetLogAverageOf <TDist>,
CanGetLogAverageOfPower <TDist>,
CanGetAverageLog <TDist>,
IDistribution <T>,
Sampleable <T>
{
return(new DistributionRefArray <TDist, T>(indices.Count, i => (TDist)array[indices[i]].Clone()));
}
/// <summary>
/// Initializes a new instance of the <see cref="BPMShared"/> class.
/// </summary>
/// <param name="numClasses">The number of classes.</param>
/// <param name="noisePrecision">The precision of the noise.</param>
/// <param name="numFeatures">The number of features.</param>
/// <param name="numChunksTraining">The number of training set chunks.</param>
/// <param name="numChunksTesting">The number of test set chunks.</param>
public BPMShared(int numClasses, double noisePrecision, int numFeatures, int numChunksTraining, int numChunksTesting)
{
// Range over classes.
this.c = new Range(numClasses).Named("c");
// Setup shared weights and weights' prior.
this.weightsPrior = InitializePrior(numClasses, numFeatures);
this.weights = SharedVariable<Vector>.Random(this.c, this.weightsPrior).Named("w");
// Configure models.
this.trainModel = new Model(this.weights, this.c, numChunksTraining);
this.testModel = new Model(this.weights, this.c, numChunksTesting);
// Observe the noise precision.
this.trainModel.noisePrecision.ObservedValue = noisePrecision;
this.testModel.noisePrecision.ObservedValue = noisePrecision;
}
/// <summary>Computations that depend on the observed value of InstanceCount</summary>
private void Changed_InstanceCount()
{
if (this.Changed_InstanceCount_iterationsDone == 1)
{
return;
}
// Create array for 'Labels' Forwards messages.
this.Labels_F = new DistributionStructArray <Bernoulli, bool>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Labels_F[InstanceRange] = Bernoulli.Uniform();
}
// Create array for replicates of 'IndexedWeights_B'
this.IndexedWeights_B = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
// Create array for 'Weights_FeatureIndexes' Forwards messages.
this.Weights_FeatureIndexes_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
// Create array for replicates of 'FeatureScores_F'
this.FeatureScores_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
// Create array for replicates of 'Score_F'
this.Score_F = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Score_F[InstanceRange] = Gaussian.Uniform();
}
// Create array for replicates of 'NoisyScore_F'
this.NoisyScore_F = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.NoisyScore_F[InstanceRange] = Gaussian.Uniform();
}
// Create array for 'Labels_marginal' Forwards messages.
this.Labels_marginal_F = new DistributionStructArray <Bernoulli, bool>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Labels_marginal_F[InstanceRange] = Bernoulli.Uniform();
}
// Create array for 'Labels_use' Backwards messages.
this.Labels_use_B = new DistributionStructArray <Bernoulli, bool>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Labels_use_B[InstanceRange] = Bernoulli.Uniform();
}
this.Changed_InstanceCount_iterationsDone = 1;
this.Changed_InstanceCount_InstanceFeatureCounts_iterationsDone = 0;
this.Changed_InstanceCount_InstanceFeatureCounts_FeatureValues_FeatureIndexes_WeightPriors_WeightConstrai6_iterationsDone = 0;
}
/// <summary>Computations that depend on the observed value of FeatureCount</summary>
private void Changed_FeatureCount()
{
if (this.Changed_FeatureCount_iterationsDone == 1)
{
return;
}
// Create array for replicates of 'Weights_depth1_rep_B_toDef'
this.Weights_depth1_rep_B_toDef = new DistributionStructArray <Gaussian, double>(this.featureCount);
// Create array for replicates of 'Weights_depth1_rep_F_marginal'
this.Weights_depth1_rep_F_marginal = new DistributionStructArray <Gaussian, double>(this.featureCount);
// Create array for replicates of 'Weights_depth1_rep_F'
this.Weights_depth1_rep_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.featureCount);
// Create array for replicates of 'Weights_depth1_rep_B'
this.Weights_depth1_rep_B = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.featureCount);
this.Changed_FeatureCount_iterationsDone = 1;
this.Changed_FeatureCount_WeightPriors_iterationsDone = 0;
this.Changed_FeatureCount_WeightPriors_Init_numberOfIterationsDecreased_InstanceCount_WeightConstraints_iterationsDone = 0;
this.Changed_FeatureCount_InstanceCount_iterationsDone = 0;
}
/// <summary>Computations that depend on the observed value of InstanceCount</summary>
private void Changed_InstanceCount()
{
if (this.Changed_InstanceCount_iterationsDone == 1)
{
return;
}
// Create array for 'Weights_FeatureIndexes' Forwards messages.
this.Weights_FeatureIndexes_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
// Create array for replicates of 'FeatureScores_F'
this.FeatureScores_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
// Create array for replicates of 'Score_F'
this.Score_F = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Score_F[InstanceRange] = Gaussian.Uniform();
}
// Create array for replicates of 'NoisyScore_F'
this.NoisyScore_F = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.NoisyScore_F[InstanceRange] = Gaussian.Uniform();
}
// Create array for replicates of 'NoisyScore_use_B'
this.NoisyScore_use_B = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.NoisyScore_use_B[InstanceRange] = Gaussian.Uniform();
}
// Create array for replicates of 'Score_B'
this.Score_B = new DistributionStructArray <Gaussian, double>(this.instanceCount);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
this.Score_B[InstanceRange] = Gaussian.Uniform();
}
// Create array for replicates of 'FeatureScores_B'
this.FeatureScores_B = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
// Create array for replicates of 'IndexedWeights_B'
this.IndexedWeights_B = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.instanceCount);
this.Changed_InstanceCount_iterationsDone = 1;
this.Changed_InstanceCount_InstanceFeatureCounts_iterationsDone = 0;
this.Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7_iterationsDone = 0;
}
/// <summary>Computations that depend on the observed value of FeatureCount</summary>
private void Changed_FeatureCount()
{
if (this.Changed_FeatureCount_isDone)
{
return;
}
this.Weights_depth1_rep_F_marginal = new DistributionStructArray <Gaussian, double>(this.featureCount);
this.Weights_depth1_rep_B_toDef = new DistributionStructArray <Gaussian, double>(this.featureCount);
this.Weights_depth1_rep_F = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.featureCount);
this.Weights_depth1_rep_B = new DistributionRefArray <DistributionStructArray <Gaussian, double>, double[]>(this.featureCount);
this.ModelSelector_selector_cases_0_rep3_uses_B = new Bernoulli[this.featureCount][];
this.ModelSelector_selector_cases_0_rep3_B = new DistributionStructArray <Bernoulli, bool>(this.featureCount);
for (int FeatureRange = 0; FeatureRange < this.featureCount; FeatureRange++)
{
this.ModelSelector_selector_cases_0_rep3_uses_B[FeatureRange] = new Bernoulli[2];
this.ModelSelector_selector_cases_0_rep3_uses_B[FeatureRange][0] = Bernoulli.Uniform();
this.ModelSelector_selector_cases_0_rep3_uses_B[FeatureRange][1] = Bernoulli.Uniform();
this.ModelSelector_selector_cases_0_rep3_B[FeatureRange] = Bernoulli.Uniform();
}
this.Changed_FeatureCount_isDone = true;
}
/// <summary>Computations that depend on the observed value of vVector__343</summary>
private void Changed_vVector__343()
{
if (this.Changed_vVector__343_iterationsDone == 1)
{
return;
}
this.vVector__343_marginal = new PointMass <Vector[]>(this.VVector__343);
// The constant 'vVectorGaussian343'
VectorGaussian vVectorGaussian343 = VectorGaussian.FromNatural(DenseVector.FromArray(new double[3] {
1547829870.0, 525077980.0, 200270.0
}), new PositiveDefiniteMatrix(new double[3, 3] {
{ 4254590363351.0, 1127383488860.0, 433199230.0 }, { 1127383488860.0, 482723521821.0, 146764360.0 }, { 433199230.0, 146764360.0, 56221.0 }
}));
this.vVector1029_marginal_F = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian343);
// Buffer for ReplicateOp_Divide.Marginal<VectorGaussian>
VectorGaussian vVector1029_rep_B_toDef = default(VectorGaussian);
// Message to 'vVector1029_rep' from Replicate factor
vVector1029_rep_B_toDef = ReplicateOp_Divide.ToDefInit <VectorGaussian>(vVectorGaussian343);
// Message to 'vVector1029_marginal' from Variable factor
this.vVector1029_marginal_F = VariableOp.MarginalAverageConditional <VectorGaussian>(vVector1029_rep_B_toDef, vVectorGaussian343, this.vVector1029_marginal_F);
DistributionStructArray <Gaussian, double> vdouble__1029_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__1029' Forwards messages.
vdouble__1029_F = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vdouble__1029_F[index343] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double> vdouble__1030_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__1030' Forwards messages.
vdouble__1030_F = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vdouble__1030_F[index343] = Gaussian.Uniform();
}
DistributionRefArray <VectorGaussian, Vector> vVector1029_rep_F = default(DistributionRefArray <VectorGaussian, Vector>);
DistributionRefArray <VectorGaussian, Vector> vVector1029_rep_B = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector1029_rep' Forwards messages.
vVector1029_rep_F = new DistributionRefArray <VectorGaussian, Vector>(1);
// Create array for 'vVector1029_rep' Backwards messages.
vVector1029_rep_B = new DistributionRefArray <VectorGaussian, Vector>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vVector1029_rep_B[index343] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian343);
vVector1029_rep_F[index343] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian343);
}
// Buffer for ReplicateOp_Divide.UsesAverageConditional<VectorGaussian>
VectorGaussian vVector1029_rep_F_marginal = default(VectorGaussian);
// Message to 'vVector1029_rep' from Replicate factor
vVector1029_rep_F_marginal = ReplicateOp_Divide.MarginalInit <VectorGaussian>(vVectorGaussian343);
// Message to 'vVector1029_rep' from Replicate factor
vVector1029_rep_F_marginal = ReplicateOp_Divide.Marginal <VectorGaussian>(vVector1029_rep_B_toDef, vVectorGaussian343, vVector1029_rep_F_marginal);
// Buffer for InnerProductOp.InnerProductAverageConditional
// Create array for replicates of 'vVector1029_rep_F_index343__AMean'
Vector[] vVector1029_rep_F_index343__AMean = new Vector[1];
for (int index343 = 0; index343 < 1; index343++)
{
// Message to 'vdouble__1030' from InnerProduct factor
vVector1029_rep_F_index343__AMean[index343] = InnerProductOp.AMeanInit(vVector1029_rep_F[index343]);
}
// Buffer for InnerProductOp.AMean
// Create array for replicates of 'vVector1029_rep_F_index343__AVariance'
PositiveDefiniteMatrix[] vVector1029_rep_F_index343__AVariance = new PositiveDefiniteMatrix[1];
for (int index343 = 0; index343 < 1; index343++)
{
// Message to 'vdouble__1030' from InnerProduct factor
vVector1029_rep_F_index343__AVariance[index343] = InnerProductOp.AVarianceInit(vVector1029_rep_F[index343]);
// Message to 'vVector1029_rep' from Replicate factor
vVector1029_rep_F[index343] = ReplicateOp_Divide.UsesAverageConditional <VectorGaussian>(vVector1029_rep_B[index343], vVector1029_rep_F_marginal, index343, vVector1029_rep_F[index343]);
}
// Create array for 'vdouble__1030_marginal' Forwards messages.
this.vdouble__1030_marginal_F = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
this.vdouble__1030_marginal_F[index343] = Gaussian.Uniform();
}
// Message from use of 'vdouble__1030'
DistributionStructArray <Gaussian, double> vdouble__1030_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__1030_use' Backwards messages.
vdouble__1030_use_B = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vdouble__1030_use_B[index343] = Gaussian.Uniform();
// Message to 'vdouble__1030' from InnerProduct factor
vVector1029_rep_F_index343__AVariance[index343] = InnerProductOp.AVariance(vVector1029_rep_F[index343], vVector1029_rep_F_index343__AVariance[index343]);
// Message to 'vdouble__1030' from InnerProduct factor
vVector1029_rep_F_index343__AMean[index343] = InnerProductOp.AMean(vVector1029_rep_F[index343], vVector1029_rep_F_index343__AVariance[index343], vVector1029_rep_F_index343__AMean[index343]);
// Message to 'vdouble__1030' from InnerProduct factor
vdouble__1030_F[index343] = InnerProductOp.InnerProductAverageConditional(vVector1029_rep_F_index343__AMean[index343], vVector1029_rep_F_index343__AVariance[index343], this.VVector__343[index343]);
// Message to 'vdouble__1030_marginal' from DerivedVariable factor
this.vdouble__1030_marginal_F[index343] = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble__1030_use_B[index343], vdouble__1030_F[index343], this.vdouble__1030_marginal_F[index343]);
}
// Create array for 'vdouble__1029_marginal' Forwards messages.
this.vdouble__1029_marginal_F = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
this.vdouble__1029_marginal_F[index343] = Gaussian.Uniform();
}
// Message from use of 'vdouble__1029'
DistributionStructArray <Gaussian, double> vdouble__1029_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__1029_use' Backwards messages.
vdouble__1029_use_B = new DistributionStructArray <Gaussian, double>(1);
for (int index343 = 0; index343 < 1; index343++)
{
vdouble__1029_use_B[index343] = Gaussian.Uniform();
// Message to 'vdouble__1029' from GaussianFromMeanAndVariance factor
vdouble__1029_F[index343] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(vdouble__1030_F[index343], 0.1);
// Message to 'vdouble__1029_marginal' from Variable factor
this.vdouble__1029_marginal_F[index343] = VariableOp.MarginalAverageConditional <Gaussian>(vdouble__1029_use_B[index343], vdouble__1029_F[index343], this.vdouble__1029_marginal_F[index343]);
}
this.Changed_vVector__343_iterationsDone = 1;
}
/// <summary>Computations that depend on the observed value of vVector__134 and vdouble__402</summary>
private void Changed_vVector__134_vdouble__402()
{
if (this.Changed_vVector__134_vdouble__402_iterationsDone == 1)
{
return;
}
this.vVector__134_marginal = new PointMass <Vector[]>(this.VVector__134);
this.vdouble__402_marginal = new DistributionStructArray <Gaussian, double>(5622, delegate(int index134) {
return(Gaussian.Uniform());
});
this.vdouble__402_marginal = Distribution.SetPoint <DistributionStructArray <Gaussian, double>, double[]>(this.vdouble__402_marginal, this.Vdouble__402);
// The constant 'vVectorGaussian134'
VectorGaussian vVectorGaussian134 = VectorGaussian.FromNatural(DenseVector.FromArray(new double[3] {
0.0, 0.0, 0.0
}), new PositiveDefiniteMatrix(new double[3, 3] {
{ 1.0, 0.0, 0.0 }, { 0.0, 1.0, 0.0 }, { 0.0, 0.0, 1.0 }
}));
this.vVector403_marginal_F = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian134);
// Message from use of 'vdouble__403'
DistributionStructArray <Gaussian, double> vdouble__403_use_B = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__403_use' Backwards messages.
vdouble__403_use_B = new DistributionStructArray <Gaussian, double>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
vdouble__403_use_B[index134] = Gaussian.Uniform();
// Message to 'vdouble__403_use' from GaussianFromMeanAndVariance factor
vdouble__403_use_B[index134] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(this.Vdouble__402[index134], 0.1);
}
DistributionRefArray <VectorGaussian, Vector> vVector403_rep_B = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector403_rep' Backwards messages.
vVector403_rep_B = new DistributionRefArray <VectorGaussian, Vector>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
vVector403_rep_B[index134] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian134);
// Message to 'vVector403_rep' from InnerProduct factor
vVector403_rep_B[index134] = InnerProductOp.AAverageConditional(vdouble__403_use_B[index134], this.VVector__134[index134], vVector403_rep_B[index134]);
}
// Buffer for ReplicateOp_Divide.Marginal<VectorGaussian>
VectorGaussian vVector403_rep_B_toDef = default(VectorGaussian);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_B_toDef = ReplicateOp_Divide.ToDefInit <VectorGaussian>(vVectorGaussian134);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_B_toDef = ReplicateOp_Divide.ToDef <VectorGaussian>(vVector403_rep_B, vVector403_rep_B_toDef);
// Message to 'vVector403_marginal' from Variable factor
this.vVector403_marginal_F = VariableOp.MarginalAverageConditional <VectorGaussian>(vVector403_rep_B_toDef, vVectorGaussian134, this.vVector403_marginal_F);
DistributionStructArray <Gaussian, double> vdouble__403_F = default(DistributionStructArray <Gaussian, double>);
// Create array for 'vdouble__403' Forwards messages.
vdouble__403_F = new DistributionStructArray <Gaussian, double>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
vdouble__403_F[index134] = Gaussian.Uniform();
}
DistributionRefArray <VectorGaussian, Vector> vVector403_rep_F = default(DistributionRefArray <VectorGaussian, Vector>);
// Create array for 'vVector403_rep' Forwards messages.
vVector403_rep_F = new DistributionRefArray <VectorGaussian, Vector>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
vVector403_rep_F[index134] = ArrayHelper.MakeUniform <VectorGaussian>(vVectorGaussian134);
}
// Buffer for ReplicateOp_Divide.UsesAverageConditional<VectorGaussian>
VectorGaussian vVector403_rep_F_marginal = default(VectorGaussian);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_F_marginal = ReplicateOp_Divide.MarginalInit <VectorGaussian>(vVectorGaussian134);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_F_marginal = ReplicateOp_Divide.Marginal <VectorGaussian>(vVector403_rep_B_toDef, vVectorGaussian134, vVector403_rep_F_marginal);
// Buffer for InnerProductOp.InnerProductAverageConditional
// Create array for replicates of 'vVector403_rep_F_index134__AMean'
Vector[] vVector403_rep_F_index134__AMean = new Vector[5622];
for (int index134 = 0; index134 < 5622; index134++)
{
// Message to 'vdouble__403' from InnerProduct factor
vVector403_rep_F_index134__AMean[index134] = InnerProductOp.AMeanInit(vVector403_rep_F[index134]);
}
// Buffer for InnerProductOp.AMean
// Create array for replicates of 'vVector403_rep_F_index134__AVariance'
PositiveDefiniteMatrix[] vVector403_rep_F_index134__AVariance = new PositiveDefiniteMatrix[5622];
for (int index134 = 0; index134 < 5622; index134++)
{
// Message to 'vdouble__403' from InnerProduct factor
vVector403_rep_F_index134__AVariance[index134] = InnerProductOp.AVarianceInit(vVector403_rep_F[index134]);
// Message to 'vVector403_rep' from Replicate factor
vVector403_rep_F[index134] = ReplicateOp_Divide.UsesAverageConditional <VectorGaussian>(vVector403_rep_B[index134], vVector403_rep_F_marginal, index134, vVector403_rep_F[index134]);
}
// Create array for 'vdouble__403_marginal' Forwards messages.
this.vdouble__403_marginal_F = new DistributionStructArray <Gaussian, double>(5622);
for (int index134 = 0; index134 < 5622; index134++)
{
this.vdouble__403_marginal_F[index134] = Gaussian.Uniform();
// Message to 'vdouble__403' from InnerProduct factor
vVector403_rep_F_index134__AVariance[index134] = InnerProductOp.AVariance(vVector403_rep_F[index134], vVector403_rep_F_index134__AVariance[index134]);
// Message to 'vdouble__403' from InnerProduct factor
vVector403_rep_F_index134__AMean[index134] = InnerProductOp.AMean(vVector403_rep_F[index134], vVector403_rep_F_index134__AVariance[index134], vVector403_rep_F_index134__AMean[index134]);
// Message to 'vdouble__403' from InnerProduct factor
vdouble__403_F[index134] = InnerProductOp.InnerProductAverageConditional(vVector403_rep_F_index134__AMean[index134], vVector403_rep_F_index134__AVariance[index134], this.VVector__134[index134]);
// Message to 'vdouble__403_marginal' from DerivedVariable factor
this.vdouble__403_marginal_F[index134] = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble__403_use_B[index134], vdouble__403_F[index134], this.vdouble__403_marginal_F[index134]);
}
this.Changed_vVector__134_vdouble__402_iterationsDone = 1;
}