/// <summary>
/// Assess the fitted model on held out data.
/// </summary>
/// <param name="model">Fitted model</param>
/// <param name="data">All data</param>
/// <param name="isMissing">Which elements are missing (heldout)</param>
/// <param name="logTransform">Whether the data was log transformed</param>
/// <param name="means">The means used to normalise the data, if normalised</param>
/// <param name="vars">The variances used to normalise the data, if normalised</param>
/// <returns>Array of error measures</returns>
public static double[] AssessOnMissing(INetworkModel model, double[,] data, bool[,] isMissing, bool logTransform, double[] means = null, double[] vars = null)
{
double logProb = 0, error = 0, counter = 0;
for (int d = 0; d < data.GetLength(0); d++)
{
for (int n = 0; n < data.GetLength(1); n++)
{
if (isMissing[d, n])
{
counter += 1;
var prediction = GaussianOp.SampleAverageLogarithm(model.Marginal <Gaussian[, ]>("noiseLessY")[d, n], model.Marginal <Gamma[]>("noisePrecisionArray")[d]);
logProb += prediction.GetLogProb(data[d, n]);
double predMean = prediction.GetMean();
double dataPoint = data[d, n];
if (means != null)
{
predMean = predMean * Math.Sqrt(vars[d]) + means[d];
dataPoint = dataPoint * Math.Sqrt(vars[d]) + means[d];
}
if (logTransform)
{
predMean = Math.Exp(predMean);
dataPoint = Math.Exp(dataPoint);
}
error += Math.Abs(predMean - dataPoint);
}
}
}
logProb /= counter;
error /= counter;
return(new double[] { logProb, error });
}
/// <summary>Computations that depend on the observed value of numberOfIterationsDecreased and vGamma__1 and vGaussian__1 and vDirichlet1</summary>
/// <param name="numberOfIterations">The number of times to iterate each loop</param>
private void Changed_numberOfIterationsDecreased_vGamma__1_vGaussian__1_vDirichlet1(int numberOfIterations)
{
if (this.Changed_numberOfIterationsDecreased_vGamma__1_vGaussian__1_vDirichlet1_iterationsDone == numberOfIterations)
{
return;
}
// Create array for 'vdouble__3_marginal' Forwards messages.
this.vdouble__3_marginal_F = new DistributionStructArray <Gaussian, double>(2);
for (int index2 = 0; index2 < 2; index2++)
{
this.vdouble__3_marginal_F[index2] = Gaussian.Uniform();
}
this.vVector1_marginal_F = ArrayHelper.MakeUniform <Dirichlet>(this.VDirichlet1);
Discrete vint9_F = ArrayHelper.MakeUniform <Discrete>(Discrete.Uniform(2));
Discrete[] vint9_selector_uses_B = default(Discrete[]);
// Create array for 'vint9_selector_uses' Backwards messages.
vint9_selector_uses_B = new Discrete[3];
for (int _ind = 0; _ind < 3; _ind++)
{
vint9_selector_uses_B[_ind] = ArrayHelper.MakeUniform <Discrete>(Discrete.Uniform(2));
}
DistributionStructArray <Bernoulli, bool>[] vint9_selector_cases_uses_B = default(DistributionStructArray <Bernoulli, bool>[]);
// Create array for 'vint9_selector_cases_uses' Backwards messages.
vint9_selector_cases_uses_B = new DistributionStructArray <Bernoulli, bool> [2];
for (int _ind = 0; _ind < 2; _ind++)
{
// Create array for 'vint9_selector_cases_uses' Backwards messages.
vint9_selector_cases_uses_B[_ind] = new DistributionStructArray <Bernoulli, bool>(2);
for (int _iv = 0; _iv < 2; _iv++)
{
vint9_selector_cases_uses_B[_ind][_iv] = Bernoulli.Uniform();
}
}
DistributionStructArray <Bernoulli, bool>[] vint9_selector_cases_depth1_uses_B = default(DistributionStructArray <Bernoulli, bool>[]);
// Create array for 'vint9_selector_cases_depth1_uses' Backwards messages.
vint9_selector_cases_depth1_uses_B = new DistributionStructArray <Bernoulli, bool> [2];
for (int _iv = 0; _iv < 2; _iv++)
{
// Create array for 'vint9_selector_cases_depth1_uses' Backwards messages.
vint9_selector_cases_depth1_uses_B[_iv] = new DistributionStructArray <Bernoulli, bool>(10);
for (int _ind = 0; _ind < 10; _ind++)
{
vint9_selector_cases_depth1_uses_B[_iv][_ind] = Bernoulli.Uniform();
}
}
// Create array for replicates of 'vdouble17__index2__F'
DistributionStructArray <Gaussian, double> vdouble17__index2__F = new DistributionStructArray <Gaussian, double>(2);
for (int index2 = 0; index2 < 2; index2++)
{
vdouble17__index2__F[index2] = Gaussian.Uniform();
}
// Message from use of 'vdouble17'
Gaussian vdouble17_use_B = Gaussian.Uniform();
// Message to marginal of 'vdouble17__index2_'
// Create array for replicates of 'vdouble17__index2__marginal_F'
DistributionStructArray <Gaussian, double> vdouble17__index2__marginal_F = new DistributionStructArray <Gaussian, double>(2);
for (int index2 = 0; index2 < 2; index2++)
{
vdouble17__index2__marginal_F[index2] = Gaussian.Uniform();
}
// Create array for replicates of 'vdouble__4_index2__B'
DistributionStructArray <Gamma, double> vdouble__4_index2__B = new DistributionStructArray <Gamma, double>(2);
for (int index2 = 0; index2 < 2; index2++)
{
vdouble__4_index2__B[index2] = Gamma.Uniform();
}
// Create array for 'vdouble__4_marginal' Forwards messages.
this.vdouble__4_marginal_F = new DistributionStructArray <Gamma, double>(2);
for (int index2 = 0; index2 < 2; index2++)
{
this.vdouble__4_marginal_F[index2] = Gamma.Uniform();
}
DistributionStructArray <Bernoulli, bool> vint9_selector_cases_depth1_B = default(DistributionStructArray <Bernoulli, bool>);
// Create array for 'vint9_selector_cases_depth1' Backwards messages.
vint9_selector_cases_depth1_B = new DistributionStructArray <Bernoulli, bool>(2);
for (int _iv = 0; _iv < 2; _iv++)
{
vint9_selector_cases_depth1_B[_iv] = Bernoulli.Uniform();
}
DistributionStructArray <Bernoulli, bool> vint9_selector_cases_B = default(DistributionStructArray <Bernoulli, bool>);
// Create array for 'vint9_selector_cases' Backwards messages.
vint9_selector_cases_B = new DistributionStructArray <Bernoulli, bool>(2);
for (int _iv = 0; _iv < 2; _iv++)
{
vint9_selector_cases_B[_iv] = Bernoulli.Uniform();
}
this.vint9_marginal_F = ArrayHelper.MakeUniform <Discrete>(Discrete.Uniform(2));
// Create array for replicates of 'vdouble__3_index2__B'
DistributionStructArray <Gaussian, double> vdouble__3_index2__B = new DistributionStructArray <Gaussian, double>(2);
for (int index2 = 0; index2 < 2; index2++)
{
vdouble__3_index2__B[index2] = Gaussian.Uniform();
// Message to 'vdouble__3_marginal' from Variable factor
this.vdouble__3_marginal_F[index2] = VariableVmpOp.MarginalAverageLogarithm <Gaussian>(this.vdouble__3_use_B[index2], this.VGaussian__1[index2], this.vdouble__3_marginal_F[index2]);
// Message to 'vdouble__4_marginal' from Variable factor
this.vdouble__4_marginal_F[index2] = VariableVmpOp.MarginalAverageLogarithm <Gamma>(this.vdouble__4_use_B[index2], this.VGamma__1[index2], this.vdouble__4_marginal_F[index2]);
// Message to 'vdouble17__index2_' from Gaussian factor
vdouble17__index2__F[index2] = GaussianOp.SampleAverageLogarithm(this.vdouble__3_marginal_F[index2], this.vdouble__4_marginal_F[index2]);
// Message to 'vdouble17__index2__marginal' from Variable factor
vdouble17__index2__marginal_F[index2] = VariableVmpOp.MarginalAverageLogarithm <Gaussian>(vdouble17_use_B, vdouble17__index2__F[index2], vdouble17__index2__marginal_F[index2]);
}
// Message to 'vVector1_marginal' from Variable factor
this.vVector1_marginal_F = VariableVmpOp.MarginalAverageLogarithm <Dirichlet>(this.vVector1_use_B, this.VDirichlet1, this.vVector1_marginal_F);
// Message to 'vint9' from Discrete factor
vint9_F = DiscreteFromDirichletOp.SampleAverageLogarithm(this.vVector1_marginal_F, vint9_F);
// Message to 'vint9_marginal' from Variable factor
this.vint9_marginal_F = VariableVmpOp.MarginalAverageLogarithm <Discrete>(this.vint9_selector_B, vint9_F, this.vint9_marginal_F);
for (int iteration = this.Changed_numberOfIterationsDecreased_vGamma__1_vGaussian__1_vDirichlet1_iterationsDone; iteration < numberOfIterations; iteration++)
{
for (int index2 = 0; index2 < 2; index2++)
{
// Message to 'vdouble__4_marginal' from Variable factor
this.vdouble__4_marginal_F[index2] = VariableVmpOp.MarginalAverageLogarithm <Gamma>(this.vdouble__4_use_B[index2], this.VGamma__1[index2], this.vdouble__4_marginal_F[index2]);
// Message to 'vdouble__3_index2_' from Gaussian factor
vdouble__3_index2__B[index2] = GaussianOp.MeanAverageLogarithm(vdouble17__index2__marginal_F[index2], this.vdouble__4_marginal_F[index2]);
// Message to 'vdouble__3_use' from EnterOne factor
this.vdouble__3_use_B[index2] = GateEnterOneOp <double> .ValueAverageLogarithm <Gaussian>(vdouble__3_index2__B[index2], this.vint9_marginal_F, index2, this.vdouble__3_use_B[index2]);
// Message to 'vdouble__3_marginal' from Variable factor
this.vdouble__3_marginal_F[index2] = VariableVmpOp.MarginalAverageLogarithm <Gaussian>(this.vdouble__3_use_B[index2], this.VGaussian__1[index2], this.vdouble__3_marginal_F[index2]);
// Message to 'vdouble17__index2_' from Gaussian factor
vdouble17__index2__F[index2] = GaussianOp.SampleAverageLogarithm(this.vdouble__3_marginal_F[index2], this.vdouble__4_marginal_F[index2]);
// Message to 'vdouble17__index2__marginal' from Variable factor
vdouble17__index2__marginal_F[index2] = VariableVmpOp.MarginalAverageLogarithm <Gaussian>(vdouble17_use_B, vdouble17__index2__F[index2], vdouble17__index2__marginal_F[index2]);
// Message to 'vint9_selector_cases_depth1_uses' from Variable factor
vint9_selector_cases_depth1_uses_B[index2][8] = Bernoulli.FromLogOdds(VariableVmpOp.AverageLogFactor <Gaussian>(vdouble17__index2__marginal_F[index2]));
// Message to 'vint9_selector_cases_depth1_uses' from Gaussian factor
vint9_selector_cases_depth1_uses_B[index2][7] = Bernoulli.FromLogOdds(GaussianOp.AverageLogFactor(vdouble17__index2__marginal_F[index2], this.vdouble__3_marginal_F[index2], this.vdouble__4_marginal_F[index2]));
}
for (int _iv = 0; _iv < 2; _iv++)
{
// Message to 'vint9_selector_cases_depth1' from Replicate factor
vint9_selector_cases_depth1_B[_iv] = ReplicateOp.DefAverageLogarithm <Bernoulli>(vint9_selector_cases_depth1_uses_B[_iv], vint9_selector_cases_depth1_B[_iv]);
// Message to 'vint9_selector_cases_uses' from Copy factor
vint9_selector_cases_uses_B[0][_iv] = ArrayHelper.SetTo <Bernoulli>(vint9_selector_cases_uses_B[0][_iv], vint9_selector_cases_depth1_B[_iv]);
}
// Message to 'vint9_selector_cases' from Replicate factor
vint9_selector_cases_B = ReplicateOp.DefAverageLogarithm <DistributionStructArray <Bernoulli, bool> >(vint9_selector_cases_uses_B, vint9_selector_cases_B);
// Message to 'vint9_selector_uses' from CasesInt factor
vint9_selector_uses_B[0] = IntCasesOp.IAverageLogarithm(vint9_selector_cases_B, vint9_selector_uses_B[0]);
// Message to 'vint9_selector' from Replicate factor
this.vint9_selector_B = ReplicateOp.DefAverageLogarithm <Discrete>(vint9_selector_uses_B, this.vint9_selector_B);
// Message to 'vVector1_marginal' from Variable factor
this.vVector1_marginal_F = VariableVmpOp.MarginalAverageLogarithm <Dirichlet>(this.vVector1_use_B, this.VDirichlet1, this.vVector1_marginal_F);
// Message to 'vint9' from Discrete factor
vint9_F = DiscreteFromDirichletOp.SampleAverageLogarithm(this.vVector1_marginal_F, vint9_F);
// Message to 'vint9_marginal' from Variable factor
this.vint9_marginal_F = VariableVmpOp.MarginalAverageLogarithm <Discrete>(this.vint9_selector_B, vint9_F, this.vint9_marginal_F);
for (int index2 = 0; index2 < 2; index2++)
{
// Message to 'vdouble__4_index2_' from Gaussian factor
vdouble__4_index2__B[index2] = GaussianOp.PrecisionAverageLogarithm(vdouble17__index2__marginal_F[index2], this.vdouble__3_marginal_F[index2]);
// Message to 'vdouble__4_use' from EnterOne factor
this.vdouble__4_use_B[index2] = GateEnterOneOp <double> .ValueAverageLogarithm <Gamma>(vdouble__4_index2__B[index2], this.vint9_marginal_F, index2, this.vdouble__4_use_B[index2]);
}
// Message to 'vVector1_use' from Discrete factor
this.vVector1_use_B = DiscreteFromDirichletOp.ProbsAverageLogarithm(this.vint9_marginal_F, this.vVector1_use_B);
this.OnProgressChanged(new ProgressChangedEventArgs(iteration));
}
for (int index2 = 0; index2 < 2; index2++)
{
// Message to 'vdouble__3_index2_' from Gaussian factor
vdouble__3_index2__B[index2] = GaussianOp.MeanAverageLogarithm(vdouble17__index2__marginal_F[index2], this.vdouble__4_marginal_F[index2]);
// Message to 'vdouble__3_use' from EnterOne factor
this.vdouble__3_use_B[index2] = GateEnterOneOp <double> .ValueAverageLogarithm <Gaussian>(vdouble__3_index2__B[index2], this.vint9_marginal_F, index2, this.vdouble__3_use_B[index2]);
// Message to 'vdouble__3_marginal' from Variable factor
this.vdouble__3_marginal_F[index2] = VariableVmpOp.MarginalAverageLogarithm <Gaussian>(this.vdouble__3_use_B[index2], this.VGaussian__1[index2], this.vdouble__3_marginal_F[index2]);
// Message to 'vint9_selector_cases_depth1_uses' from Gaussian factor
vint9_selector_cases_depth1_uses_B[index2][7] = Bernoulli.FromLogOdds(GaussianOp.AverageLogFactor(vdouble17__index2__marginal_F[index2], this.vdouble__3_marginal_F[index2], this.vdouble__4_marginal_F[index2]));
}
for (int _iv = 0; _iv < 2; _iv++)
{
// Message to 'vint9_selector_cases_depth1' from Replicate factor
vint9_selector_cases_depth1_B[_iv] = ReplicateOp.DefAverageLogarithm <Bernoulli>(vint9_selector_cases_depth1_uses_B[_iv], vint9_selector_cases_depth1_B[_iv]);
// Message to 'vint9_selector_cases_uses' from Copy factor
vint9_selector_cases_uses_B[0][_iv] = ArrayHelper.SetTo <Bernoulli>(vint9_selector_cases_uses_B[0][_iv], vint9_selector_cases_depth1_B[_iv]);
}
// Message to 'vint9_selector_cases' from Replicate factor
vint9_selector_cases_B = ReplicateOp.DefAverageLogarithm <DistributionStructArray <Bernoulli, bool> >(vint9_selector_cases_uses_B, vint9_selector_cases_B);
// Message to 'vint9_selector_uses' from CasesInt factor
vint9_selector_uses_B[0] = IntCasesOp.IAverageLogarithm(vint9_selector_cases_B, vint9_selector_uses_B[0]);
// Message to 'vint9_selector' from Replicate factor
this.vint9_selector_B = ReplicateOp.DefAverageLogarithm <Discrete>(vint9_selector_uses_B, this.vint9_selector_B);
// Message to 'vint9_marginal' from Variable factor
this.vint9_marginal_F = VariableVmpOp.MarginalAverageLogarithm <Discrete>(this.vint9_selector_B, vint9_F, this.vint9_marginal_F);
for (int index2 = 0; index2 < 2; index2++)
{
// Message to 'vdouble__4_index2_' from Gaussian factor
vdouble__4_index2__B[index2] = GaussianOp.PrecisionAverageLogarithm(vdouble17__index2__marginal_F[index2], this.vdouble__3_marginal_F[index2]);
// Message to 'vdouble__4_use' from EnterOne factor
this.vdouble__4_use_B[index2] = GateEnterOneOp <double> .ValueAverageLogarithm <Gamma>(vdouble__4_index2__B[index2], this.vint9_marginal_F, index2, this.vdouble__4_use_B[index2]);
// Message to 'vdouble__4_marginal' from Variable factor
this.vdouble__4_marginal_F[index2] = VariableVmpOp.MarginalAverageLogarithm <Gamma>(this.vdouble__4_use_B[index2], this.VGamma__1[index2], this.vdouble__4_marginal_F[index2]);
}
// Message to 'vVector1_use' from Discrete factor
this.vVector1_use_B = DiscreteFromDirichletOp.ProbsAverageLogarithm(this.vint9_marginal_F, this.vVector1_use_B);
// Message to 'vVector1_marginal' from Variable factor
this.vVector1_marginal_F = VariableVmpOp.MarginalAverageLogarithm <Dirichlet>(this.vVector1_use_B, this.VDirichlet1, this.vVector1_marginal_F);
this.vdouble17_F = Gaussian.Uniform();
DistributionStructArray <Bernoulli, bool> vint9_selector_cases_F = default(DistributionStructArray <Bernoulli, bool>);
// Create array for 'vint9_selector_cases' Forwards messages.
vint9_selector_cases_F = new DistributionStructArray <Bernoulli, bool>(2);
for (int _iv = 0; _iv < 2; _iv++)
{
vint9_selector_cases_F[_iv] = Bernoulli.Uniform();
}
// Message to 'vint9_selector_cases' from CasesInt factor
vint9_selector_cases_F = IntCasesOp.CasesAverageLogarithm <DistributionStructArray <Bernoulli, bool> >(this.vint9_marginal_F, vint9_selector_cases_F);
// Message to 'vdouble17' from Exit factor
this.vdouble17_F = GateExitOp <double> .ExitAverageLogarithm <Gaussian>(vint9_selector_cases_F, vdouble17__index2__marginal_F, this.vdouble17_F);
this.Changed_numberOfIterationsDecreased_vGamma__1_vGaussian__1_vDirichlet1_iterationsDone = numberOfIterations;
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="GaussianFromMeanAndVarianceOp"]/message_doc[@name="SampleAverageLogarithm(Gaussian, double)"]/*'/>
public static Gaussian SampleAverageLogarithm([SkipIfUniform] Gaussian mean, double variance)
{
return(GaussianOp.SampleAverageLogarithm(mean, 1.0 / variance));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="SparseGaussianListOp"]/message_doc[@name="SampleAverageLogarithm(ISparseList{double}, SparseGammaList, SparseGaussianList)"]/*'/>
public static SparseGaussianList SampleAverageLogarithm(ISparseList <double> mean, [Proper] SparseGammaList precision, SparseGaussianList result)
{
result.SetToFunction(mean, precision, (m, p) => GaussianOp.SampleAverageLogarithm(m, p));
return(result);
}