/// <summary>Update the buffer <c>Buffer</c>.</summary>
/// <param name="Buffer">Buffer <c>Buffer</c>.</param>
/// <param name="list">Incoming message from <c>list</c>.</param>
/// <param name="IndexOfMaximumDouble">Constant value for <c>indexOfMaximumDouble</c>.</param>
/// <returns>New value of buffer <c>Buffer</c>.</returns>
/// <remarks>
/// <para />
/// </remarks>
/// <typeparam name="GaussianList">The type of an incoming message from <c>list</c>.</typeparam>
public static IndexOfMaximumBuffer Buffer <GaussianList>(
IndexOfMaximumBuffer Buffer, GaussianList list, int IndexOfMaximumDouble) // redundant parameters required for correct dependency graph
where GaussianList : IList <Gaussian>
{
var max_marginal = Buffer.to_list[IndexOfMaximumDouble] * list[IndexOfMaximumDouble];
Gaussian product = Gaussian.Uniform();
//var order = Rand.Perm(list.Count);
for (int i = 0; i < list.Count; i++)
{
//int c = order[i];
int c = i;
if (c != IndexOfMaximumDouble)
{
var msg_to_sum = max_marginal / Buffer.MessagesToMax[c];
var msg_to_positiveop = DoublePlusOp.AAverageConditional(Sum: msg_to_sum, b: list[c]);
var msgFromPositiveOp = IsPositiveOp.XAverageConditional(true, msg_to_positiveop);
Buffer.MessagesToMax[c] = DoublePlusOp.SumAverageConditional(list[c], msgFromPositiveOp);
Buffer.to_list[c] = DoublePlusOp.AAverageConditional(Sum: msg_to_sum, b: msgFromPositiveOp);
max_marginal = msg_to_sum * Buffer.MessagesToMax[c];
product.SetToProduct(product, Buffer.MessagesToMax[c]);
}
}
//Buffer.to_list[IndexOfMaximumDouble] = max_marginal / list[IndexOfMaximumDouble];
Buffer.to_list[IndexOfMaximumDouble] = product;
return(Buffer);
}
/// <summary>Evidence message for EP.</summary>
/// <param name="Buffer">Buffer <c>Buffer</c>.</param>
/// <param name="list">Incoming message from <c>list</c>.</param>
/// <param name="IndexOfMaximumDouble">Constant value for <c>indexOfMaximumDouble</c>.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions.</returns>
/// <remarks>
/// <para>The formula for the result is <c>log(sum_(list) p(list) factor(indexOfMaximumDouble,list))</c>.</para>
/// </remarks>
/// <typeparam name="GaussianList">The type of an incoming message from <c>list</c>.</typeparam>
public static double LogAverageFactor <GaussianList>(IndexOfMaximumBuffer Buffer, GaussianList list, int IndexOfMaximumDouble)
where GaussianList : IList <Gaussian>
{
double evidence = 0;
var max_marginal = list[IndexOfMaximumDouble] * Buffer.to_list[IndexOfMaximumDouble];
for (int c = 0; c < list.Count; c++)
{
if (c != IndexOfMaximumDouble)
{
var msg_to_sum = max_marginal / Buffer.MessagesToMax[c];
var msg_to_positiveop = DoublePlusOp.AAverageConditional(Sum: msg_to_sum, b: list[c]);
evidence += IsPositiveOp.LogEvidenceRatio(true, msg_to_positiveop);
// sum operator does not contribute because no projection is involved
// the x[index]-x[c] variable does not contribute because it only connects to two factors
evidence -= msg_to_sum.GetLogAverageOf(Buffer.MessagesToMax[c]);
if (max_marginal.IsPointMass)
{
evidence += Buffer.MessagesToMax[c].GetLogAverageOf(max_marginal);
}
else
{
evidence -= Buffer.MessagesToMax[c].GetLogNormalizer();
}
}
}
//evidence += ReplicateOp.LogEvidenceRatio<Gaussian>(MessagesToMax, list[IndexOfMaximumDouble], MessagesToMax.Select(o => max_marginal / o).ToArray());
if (!max_marginal.IsPointMass)
{
evidence += max_marginal.GetLogNormalizer() - list[IndexOfMaximumDouble].GetLogNormalizer();
}
//evidence -= Buffer.MessagesToMax.Sum(o => o.GetLogNormalizer());
//evidence -= Buffer.MessagesToMax.Sum(o => (max_marginal / o).GetLogAverageOf(o));
return(evidence);
}
/// <summary>
/// Computes the average parameter of an array of user/item parameters
/// excluding the feature contribution for the users or items.
/// </summary>
/// <param name="entityParameters">The array of user/item parameters to get the average of.</param>
/// <param name="weights">The feature weights of each user/item.</param>
/// <param name="features">The feature values of each user/item.</param>
/// <returns>The average parameter distribution excluding feature contributions.</returns>
private static Gaussian GetAverageParameterExcludingFeatureContribution(
IEnumerable <Gaussian> entityParameters,
IList <Gaussian> weights,
SparseFeatureMatrix features)
{
var adjustedEntityParameters = entityParameters.Select((gaussian, i) =>
DoublePlusOp.AAverageConditional(gaussian, ComputeFeatureContribution(weights, features.NonZeroFeatureValues[i], features.NonZeroFeatureIndices[i])));
return(GetAverageParameter(adjustedEntityParameters));
}
/// <summary>Computations that depend on the observed value of x1</summary>
private void Changed_x1()
{
if (this.Changed_x1_isDone)
{
return;
}
Gaussian x2_F = default(Gaussian);
this.x2_marginal_F = Gaussian.Uniform();
Gaussian x2_use_B = Gaussian.Uniform();
// Message to 'x2' from GaussianFromMeanAndVariance factor
x2_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(0.0, 100.0);
// Message to 'x2_marginal' from Variable factor
this.x2_marginal_F = VariableOp.MarginalAverageConditional <Gaussian>(x2_use_B, x2_F, this.x2_marginal_F);
Gaussian vdouble26_F = default(Gaussian);
this.vdouble26_marginal_F = Gaussian.Uniform();
Gaussian vdouble26_use_B = Gaussian.Uniform();
Gaussian[] vdouble26_uses_F;
Gaussian[] vdouble26_uses_B;
// Create array for 'vdouble26_uses' Forwards messages.
vdouble26_uses_F = new Gaussian[2];
// Create array for 'vdouble26_uses' Backwards messages.
vdouble26_uses_B = new Gaussian[2];
vdouble26_uses_B[1] = Gaussian.Uniform();
vdouble26_uses_B[0] = Gaussian.Uniform();
vdouble26_uses_F[1] = Gaussian.Uniform();
vdouble26_uses_F[0] = Gaussian.Uniform();
// Message to 'vdouble26' from Difference factor
vdouble26_F = DoublePlusOp.AAverageConditional(x2_F, this.X1);
// Message to 'vdouble26' from Copy factor
vdouble26_F = Distribution.SetPoint <Gaussian, double>(vdouble26_F, Factor.Copy <double>(0.0));
// FactorManager.Any(vdouble26_F, vdouble26_F) is now updated in all contexts
// Message to 'vdouble26_uses' from Replicate factor
vdouble26_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(vdouble26_uses_B, vdouble26_F, 0, vdouble26_uses_F[0]);
// Message to 'vdouble26_uses' from Replicate factor
vdouble26_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(vdouble26_uses_B, vdouble26_F, 1, vdouble26_uses_F[1]);
// Message to 'vdouble26_uses' from Replicate factor
vdouble26_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(vdouble26_uses_B, vdouble26_F, 0, vdouble26_uses_F[0]);
// FactorManager.Any(vdouble26_uses_F[0], vdouble26_uses_F[0]) is now updated in all contexts
// Message to 'vdouble26_marginal' from DerivedVariable factor
this.vdouble26_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble26_use_B, vdouble26_uses_F[0], this.vdouble26_marginal_F);
// Message to 'vdouble26_uses' from Replicate factor
vdouble26_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(vdouble26_uses_B, vdouble26_F, 1, vdouble26_uses_F[1]);
// FactorManager.Any(vdouble26_uses_F[1], vdouble26_uses_F[1]) is now updated in all contexts
// Message to 'vdouble26_marginal' from DerivedVariable factor
this.vdouble26_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble26_use_B, vdouble26_uses_F[1], this.vdouble26_marginal_F);
this.x1_marginal_F = Gaussian.Uniform();
// Message to 'x1_marginal' from DerivedVariable factor
this.x1_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian, double>(this.X1, this.x1_marginal_F);
// FactorManager.Any(vdouble26_marginal_F, vdouble26_marginal_F) is now updated in all contexts
this.Changed_x1_isDone = true;
}
/// <summary>Computations that depend on the observed value of biasPrior and xValueCount and xValues and wPrior and xIndices</summary>
public void Changed_biasPrior_xValueCount_xValues_wPrior_xIndices()
{
if (this.Changed_biasPrior_xValueCount_xValues_wPrior_xIndices_iterationsDone == 1)
{
return;
}
this.vdouble11_F = DoublePlusOp.SumAverageConditional(this.score_F, this.BiasPrior);
this.vdouble13_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(this.vdouble11_F, 1);
this.Changed_biasPrior_xValueCount_xValues_wPrior_xIndices_iterationsDone = 1;
this.Changed_y_biasPrior_xValueCount_xValues_wPrior_xIndices_iterationsDone = 0;
}
/// <summary>
/// Adds the feature contribution for a given user/item to its traits and bias.
/// </summary>
/// <param name="parameters">The user/item parameters.</param>
/// <param name="featureWeights">The feature weights.</param>
/// <param name="featureValues">The feature values.</param>
/// <param name="adjustedTraits">The resulting traits.</param>
/// <param name="adjustedBias">The resulting bias.</param>
public static void AddFeatureContribution(
EntityParameterDistribution parameters,
FeatureParameterDistribution featureWeights,
SparseFeatureVector featureValues,
out GaussianArray adjustedTraits,
out Gaussian adjustedBias)
{
adjustedTraits = new GaussianArray(
parameters.Traits.Count,
i => DoublePlusOp.SumAverageConditional(
parameters.Traits[i],
ComputeFeatureContribution(featureWeights.TraitWeights[i], featureValues.NonZeroFeatureValues, featureValues.NonZeroFeatureIndices)));
adjustedBias = DoublePlusOp.SumAverageConditional(
parameters.Bias,
ComputeFeatureContribution(featureWeights.BiasWeights, featureValues.NonZeroFeatureValues, featureValues.NonZeroFeatureIndices));
}
/// <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;
}
// redundant parameters required for correct dependency graph
public static IndexOfMaximumBuffer Buffer <GaussianList>(IndexOfMaximumBuffer Buffer, GaussianList list, int IndexOfMaximumDouble)
where GaussianList : IList <Gaussian>
{
var max_marginal = Buffer.to_list[IndexOfMaximumDouble] * list[IndexOfMaximumDouble];
//var order = Rand.Perm(list.Count);
for (int i = 0; i < list.Count; i++)
{
//int c = order[i];
int c = i;
if (c != IndexOfMaximumDouble)
{
var msg_to_sum = max_marginal / Buffer.MessagesToMax[c];
var msg_to_positiveop = DoublePlusOp.AAverageConditional(Sum: msg_to_sum, b: list[c]);
var msgFromPositiveOp = IsPositiveOp.XAverageConditional(true, msg_to_positiveop);
Buffer.MessagesToMax[c] = DoublePlusOp.SumAverageConditional(list[c], msgFromPositiveOp);
Buffer.to_list[c] = DoublePlusOp.AAverageConditional(Sum: msg_to_sum, b: msgFromPositiveOp);
max_marginal = msg_to_sum * Buffer.MessagesToMax[c];
}
}
Buffer.to_list[IndexOfMaximumDouble] = max_marginal / list[IndexOfMaximumDouble];
return(Buffer);
}
/// <summary>Computations that depend on the observed value of numberOfIterations and vint__0 and vint__1</summary>
/// <param name="numberOfIterations">The number of times to iterate each loop</param>
private void Changed_numberOfIterations_vint__0_vint__1(int numberOfIterations)
{
if (this.Changed_numberOfIterations_vint__0_vint__1_isDone)
{
return;
}
DistributionStructArray <Gaussian, double> vdouble__0_F;
Gaussian vdouble__0_F_reduced;
// Create array for 'vdouble__0' Forwards messages.
vdouble__0_F = new DistributionStructArray <Gaussian, double>(5);
// Message to 'vdouble__0' from GaussianFromMeanAndVariance factor
vdouble__0_F_reduced = GaussianFromMeanAndVarianceOp.SampleAverageConditional(6.0, 9.0);
for (int index1 = 0; index1 < 5; index1++)
{
vdouble__0_F[index1] = vdouble__0_F_reduced;
vdouble__0_F[index1] = vdouble__0_F_reduced;
}
// Create array for 'vdouble__0_marginal' Forwards messages.
this.vdouble__0_marginal_F = new DistributionStructArray <Gaussian, double>(5);
DistributionStructArray <Gaussian, double> vdouble__0_use_B;
// Create array for 'vdouble__0_use' Backwards messages.
vdouble__0_use_B = new DistributionStructArray <Gaussian, double>(5);
for (int index1 = 0; index1 < 5; index1++)
{
vdouble__0_use_B[index1] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double>[] vdouble__0_uses_F;
// Create array for 'vdouble__0_uses' Forwards messages.
vdouble__0_uses_F = new DistributionStructArray <Gaussian, double> [2];
// Create array for 'vdouble__0_uses' Forwards messages.
vdouble__0_uses_F[1] = new DistributionStructArray <Gaussian, double>(5);
for (int index1 = 0; index1 < 5; index1++)
{
vdouble__0_uses_F[1][index1] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double> vdouble__0_uses_F_1__marginal;
// Message to 'vdouble__0_itemvint__1_index0_' from GetItems factor
vdouble__0_uses_F_1__marginal = GetItemsOp <double> .MarginalInit <DistributionStructArray <Gaussian, double> >(vdouble__0_uses_F[1]);
DistributionStructArray <Gaussian, double> vdouble__0_itemvint__1_index0__F;
// Create array for 'vdouble__0_itemvint__1_index0_' Forwards messages.
vdouble__0_itemvint__1_index0__F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble__0_itemvint__1_index0__F[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble11_F'
DistributionStructArray <Gaussian, double> vdouble11_F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble11_F[index0] = Gaussian.Uniform();
}
// Create array for 'vdouble__0_uses' Forwards messages.
vdouble__0_uses_F[0] = new DistributionStructArray <Gaussian, double>(5);
for (int index1 = 0; index1 < 5; index1++)
{
vdouble__0_uses_F[0][index1] = Gaussian.Uniform();
}
DistributionStructArray <Gaussian, double> vdouble__0_uses_F_0__marginal;
// Message to 'vdouble__0_itemvint__0_index0_' from GetItems factor
vdouble__0_uses_F_0__marginal = GetItemsOp <double> .MarginalInit <DistributionStructArray <Gaussian, double> >(vdouble__0_uses_F[0]);
DistributionStructArray <Gaussian, double> vdouble__0_itemvint__0_index0__F;
// Create array for 'vdouble__0_itemvint__0_index0_' Forwards messages.
vdouble__0_itemvint__0_index0__F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble__0_itemvint__0_index0__F[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble8_F'
DistributionStructArray <Gaussian, double> vdouble8_F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble8_F[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble12_F'
DistributionStructArray <Gaussian, double> vdouble12_F = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble12_F[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble12_B'
DistributionStructArray <Gaussian, double> vdouble12_B = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble12_B[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble8_use_B'
DistributionStructArray <Gaussian, double> vdouble8_use_B = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble8_use_B[index0] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble11_use_B'
DistributionStructArray <Gaussian, double> vdouble11_use_B = new DistributionStructArray <Gaussian, double>(6);
for (int index0 = 0; index0 < 6; index0++)
{
vdouble11_use_B[index0] = Gaussian.Uniform();
}
for (int iteration = this.numberOfIterationsDone; iteration < numberOfIterations; iteration++)
{
// Message to 'vdouble__0_uses' from Replicate factor
vdouble__0_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <DistributionStructArray <Gaussian, double> >(this.vdouble__0_uses_B, vdouble__0_F, 1, vdouble__0_uses_F[1]);
// Message to 'vdouble__0_itemvint__1_index0_' from GetItems factor
vdouble__0_uses_F_1__marginal = GetItemsOp <double> .Marginal <DistributionStructArray <Gaussian, double>, Gaussian>(vdouble__0_uses_F[1], this.vdouble__0_uses_B[1], vdouble__0_uses_F_1__marginal);
// Message to 'vdouble__0_uses' from Replicate factor
vdouble__0_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <DistributionStructArray <Gaussian, double> >(this.vdouble__0_uses_B, vdouble__0_F, 0, vdouble__0_uses_F[0]);
// Message to 'vdouble__0_itemvint__0_index0_' from GetItems factor
vdouble__0_uses_F_0__marginal = GetItemsOp <double> .Marginal <DistributionStructArray <Gaussian, double>, Gaussian>(vdouble__0_uses_F[0], this.vdouble__0_uses_B[0], vdouble__0_uses_F_0__marginal);
for (int index0 = 0; index0 < 6; index0++)
{
// Message to 'vdouble__0_itemvint__1_index0_' from GetItems factor
vdouble__0_itemvint__1_index0__F[index0] = GetItemsOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian>(this.vdouble__0_itemvint__1_index0__B[index0], vdouble__0_uses_F[1], vdouble__0_uses_F_1__marginal, this.Vint__1, index0, vdouble__0_itemvint__1_index0__F[index0]);
// Message to 'vdouble11' from GaussianFromMeanAndVariance factor
vdouble11_F[index0] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(vdouble__0_itemvint__1_index0__F[index0], 1.0);
// Message to 'vdouble__0_itemvint__0_index0_' from GetItems factor
vdouble__0_itemvint__0_index0__F[index0] = GetItemsOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian>(this.vdouble__0_itemvint__0_index0__B[index0], vdouble__0_uses_F[0], vdouble__0_uses_F_0__marginal, this.Vint__0, index0, vdouble__0_itemvint__0_index0__F[index0]);
// Message to 'vdouble8' from GaussianFromMeanAndVariance factor
vdouble8_F[index0] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(vdouble__0_itemvint__0_index0__F[index0], 1.0);
// Message to 'vdouble12' from Difference factor
vdouble12_F[index0] = DoublePlusOp.AAverageConditional(vdouble8_F[index0], vdouble11_F[index0]);
// Message to 'vdouble12' from IsPositive factor
vdouble12_B[index0] = IsPositiveOp_Proper.XAverageConditional(Bernoulli.PointMass(true), vdouble12_F[index0]);
// Message to 'vdouble8_use' from Difference factor
vdouble8_use_B[index0] = DoublePlusOp.SumAverageConditional(vdouble12_B[index0], vdouble11_F[index0]);
// Message to 'vdouble__0_itemvint__0_index0_' from GaussianFromMeanAndVariance factor
this.vdouble__0_itemvint__0_index0__B[index0] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(vdouble8_use_B[index0], 1.0);
// Message to 'vdouble11_use' from Difference factor
vdouble11_use_B[index0] = DoublePlusOp.BAverageConditional(vdouble8_F[index0], vdouble12_B[index0]);
// Message to 'vdouble__0_itemvint__1_index0_' from GaussianFromMeanAndVariance factor
this.vdouble__0_itemvint__1_index0__B[index0] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(vdouble11_use_B[index0], 1.0);
}
// Message to 'vdouble__0_uses' from GetItems factor
this.vdouble__0_uses_B[0] = GetItemsOp <double> .ArrayAverageConditional <Gaussian, DistributionStructArray <Gaussian, double> >(this.vdouble__0_itemvint__0_index0__B, this.Vint__0, this.vdouble__0_uses_B[0]);
// Message to 'vdouble__0_uses' from GetItems factor
this.vdouble__0_uses_B[1] = GetItemsOp <double> .ArrayAverageConditional <Gaussian, DistributionStructArray <Gaussian, double> >(this.vdouble__0_itemvint__1_index0__B, this.Vint__1, this.vdouble__0_uses_B[1]);
this.OnProgressChanged(new ProgressChangedEventArgs(iteration));
}
// Message to 'vdouble__0_use' from Replicate factor
vdouble__0_use_B = ReplicateOp_NoDivide.DefAverageConditional <DistributionStructArray <Gaussian, double> >(this.vdouble__0_uses_B, vdouble__0_use_B);
for (int index1 = 0; index1 < 5; index1++)
{
this.vdouble__0_marginal_F[index1] = Gaussian.Uniform();
// Message to 'vdouble__0_marginal' from Variable factor
this.vdouble__0_marginal_F[index1] = VariableOp.MarginalAverageConditional <Gaussian>(vdouble__0_use_B[index1], vdouble__0_F_reduced, this.vdouble__0_marginal_F[index1]);
}
this.Changed_numberOfIterations_vint__0_vint__1_isDone = true;
}
/// <summary>
/// Returns the probability that A>B
/// </summary>
/// <param name="A"></param>
/// <param name="B"></param>
/// <returns></returns>
private static Bernoulli ProbGreater(Gaussian A, Gaussian B)
{
Gaussian diff = DoublePlusOp.AAverageConditional(Sum: A, b: B);
return(IsPositiveOp.IsPositiveAverageConditional(diff));
}
/// <summary>Computations that do not depend on observed values</summary>
private void Constant()
{
if (this.Constant_isDone)
{
return;
}
Gaussian t1_F = default(Gaussian);
this.t1_marginal_F = Gaussian.Uniform();
Gaussian t1_use_B = Gaussian.Uniform();
// Message to 't1' from GaussianFromMeanAndVariance factor
t1_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(1.0, 1.0);
Gaussian[] t1_uses_F;
Gaussian[] t1_uses_B;
// Create array for 't1_uses' Forwards messages.
t1_uses_F = new Gaussian[2];
// Create array for 't1_uses' Backwards messages.
t1_uses_B = new Gaussian[2];
t1_uses_B[1] = Gaussian.Uniform();
t1_uses_B[0] = Gaussian.Uniform();
t1_uses_F[1] = Gaussian.Uniform();
t1_uses_F[0] = Gaussian.Uniform();
// Message to 't1_marginal' from Variable factor
this.t1_marginal_F = VariableOp.MarginalAverageConditional <Gaussian>(t1_use_B, t1_F, this.t1_marginal_F);
// Message to 't1_uses' from Replicate factor
t1_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t1_uses_B, t1_F, 0, t1_uses_F[0]);
// Message to 't1_uses' from Replicate factor
t1_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t1_uses_B, t1_F, 1, t1_uses_F[1]);
Gaussian t2_F = default(Gaussian);
this.t2_marginal_F = Gaussian.Uniform();
Gaussian t2_use_B = Gaussian.Uniform();
// Message to 't2' from GaussianFromMeanAndVariance factor
t2_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(10.0, 1.0);
Gaussian[] t2_uses_F;
Gaussian[] t2_uses_B;
// Create array for 't2_uses' Forwards messages.
t2_uses_F = new Gaussian[2];
// Create array for 't2_uses' Backwards messages.
t2_uses_B = new Gaussian[2];
t2_uses_B[1] = Gaussian.Uniform();
t2_uses_B[0] = Gaussian.Uniform();
t2_uses_F[1] = Gaussian.Uniform();
t2_uses_F[0] = Gaussian.Uniform();
// Message to 't2_marginal' from Variable factor
this.t2_marginal_F = VariableOp.MarginalAverageConditional <Gaussian>(t2_use_B, t2_F, this.t2_marginal_F);
// Message to 't2_uses' from Replicate factor
t2_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t2_uses_B, t2_F, 0, t2_uses_F[0]);
// Message to 't2_uses' from Replicate factor
t2_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t2_uses_B, t2_F, 1, t2_uses_F[1]);
Gaussian vdouble6_F = default(Gaussian);
this.vdouble6_marginal_F = Gaussian.Uniform();
Gaussian vdouble6_use_B = Gaussian.Uniform();
// Message to 'vdouble6' from Plus factor
vdouble6_F = DoublePlusOp.SumAverageConditional(t1_uses_F[0], t2_uses_F[0]);
// Message to 'vdouble6_marginal' from DerivedVariable factor
this.vdouble6_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble6_use_B, vdouble6_F, this.vdouble6_marginal_F);
Gaussian t4_F = default(Gaussian);
this.t4_marginal_F = Gaussian.Uniform();
Gaussian t4_use_B = Gaussian.Uniform();
// Message to 't4' from GaussianFromMeanAndVariance factor
t4_F = GaussianFromMeanAndVarianceOp.SampleAverageConditional(2.0, 1.0);
Gaussian[] t4_uses_F;
Gaussian[] t4_uses_B;
// Create array for 't4_uses' Forwards messages.
t4_uses_F = new Gaussian[2];
// Create array for 't4_uses' Backwards messages.
t4_uses_B = new Gaussian[2];
t4_uses_B[1] = Gaussian.Uniform();
t4_uses_B[0] = Gaussian.Uniform();
t4_uses_F[1] = Gaussian.Uniform();
t4_uses_F[0] = Gaussian.Uniform();
// Message to 't4_marginal' from Variable factor
this.t4_marginal_F = VariableOp.MarginalAverageConditional <Gaussian>(t4_use_B, t4_F, this.t4_marginal_F);
// Message to 't4_uses' from Replicate factor
t4_uses_F[0] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t4_uses_B, t4_F, 0, t4_uses_F[0]);
// Message to 't4_uses' from Replicate factor
t4_uses_F[1] = ReplicateOp_NoDivide.UsesAverageConditional <Gaussian>(t4_uses_B, t4_F, 1, t4_uses_F[1]);
Gaussian vdouble10_F = default(Gaussian);
this.vdouble10_marginal_F = Gaussian.Uniform();
Gaussian vdouble10_use_B = Gaussian.Uniform();
// Message to 'vdouble10' from Plus factor
vdouble10_F = DoublePlusOp.SumAverageConditional(t1_uses_F[1], t2_uses_F[1]);
// Message to 'vdouble10_marginal' from DerivedVariable factor
this.vdouble10_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(vdouble10_use_B, vdouble10_F, this.vdouble10_marginal_F);
Gaussian t5_F = default(Gaussian);
this.t5_marginal_F = Gaussian.Uniform();
Gaussian t5_use_B = Gaussian.Uniform();
// Message to 't5' from Product factor
t5_F = GaussianProductOp.ProductAverageConditional(t5_use_B, vdouble10_F, t4_uses_F[0]);
// Message to 't5_marginal' from DerivedVariable factor
this.t5_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(t5_use_B, t5_F, this.t5_marginal_F);
Gaussian t7_F = default(Gaussian);
this.t7_marginal_F = Gaussian.Uniform();
Gaussian t7_use_B = Gaussian.Uniform();
// Message to 't7' from Product factor
t7_F = GaussianProductOp.ProductAverageConditional(t7_use_B, vdouble6_F, t4_uses_F[1]);
// Message to 't7_marginal' from DerivedVariable factor
this.t7_marginal_F = DerivedVariableOp.MarginalAverageConditional <Gaussian>(t7_use_B, t7_F, this.t7_marginal_F);
this.Constant_isDone = true;
}
