public void Run()
{
BayesianPCAModel bpca = new BayesianPCAModel();
if (!(bpca.engine.Algorithm is VariationalMessagePassing))
{
Console.WriteLine("This example only runs with Variational Message Passing");
return;
}
// Set a stable random number seed for repeatbale runs
Rand.Restart(12347);
double[,] data = generateData(1000);
// Set the data
bpca.vData.ObservedValue = data;
// Set the dimensions
bpca.vN.ObservedValue = data.GetLength(0);
bpca.vD.ObservedValue = data.GetLength(1);
bpca.vM.ObservedValue = 6;
// Set the priors
bpca.priorMu.ObservedValue = Gaussian.FromMeanAndPrecision(0.0, 0.01);
bpca.priorPi.ObservedValue = Gamma.FromShapeAndRate(2.0, 2.0);
bpca.priorAlpha.ObservedValue = Gamma.FromShapeAndRate(2.0, 2.0);
// Initialize the W marginal to break symmetry
bpca.vW.InitialiseTo(randomGaussianArray(bpca.vM.ObservedValue, bpca.vD.ObservedValue));
// Infer the marginals
bpca.engine.NumberOfIterations = 200;
Gaussian[,] inferredW = bpca.engine.Infer <Gaussian[, ]>(bpca.vW);
Gaussian[] inferredMu = bpca.engine.Infer <Gaussian[]>(bpca.vMu);
Gamma[] inferredPi = bpca.engine.Infer <Gamma[]>(bpca.vPi);
// Print out the results
Console.WriteLine("Inferred W:");
printMatrixToConsole(inferredW);
Console.Write("Mean absolute means of rows in W: ");
printVectorToConsole(meanAbsoluteRowMeans(inferredW));
Console.Write(" True bias: ");
printVectorToConsole(trueMu);
Console.Write("Inferred bias: ");
printVectorToConsole(inferredMu);
Console.Write(" True noise:");
printVectorToConsole(truePi);
Console.Write("Inferred noise:");
printVectorToConsole(inferredPi);
Console.WriteLine();
}
public void Run()
{
BayesianPCAModel bpca = new BayesianPCAModel();
if (!(bpca.engine.Algorithm is VariationalMessagePassing))
{
Console.WriteLine("This example only runs with Variational Message Passing");
return;
}
// Set a stable random number seed for repeatbale runs
Rand.Restart(12347);
double[,] data = generateData(1000);
// Set the data
bpca.vData.ObservedValue = data;
// Set the dimensions
bpca.vN.ObservedValue = data.GetLength(0);
bpca.vD.ObservedValue = data.GetLength(1);
bpca.vM.ObservedValue = 6;
// Set the priors
bpca.priorMu.ObservedValue = Gaussian.FromMeanAndPrecision(0.0, 0.01);
bpca.priorPi.ObservedValue = Gamma.FromShapeAndRate(2.0, 2.0);
bpca.priorAlpha.ObservedValue = Gamma.FromShapeAndRate(2.0, 2.0);
// Initialize the W marginal to break symmetry
bpca.vW.InitialiseTo(randomGaussianArray(bpca.vM.ObservedValue, bpca.vD.ObservedValue));
// Infer the marginals
bpca.engine.NumberOfIterations = 200;
IDistribution<double[,]> wMarginal = bpca.engine.Infer<IDistribution<double[,]>>(bpca.vW);
IDistribution<double[]> muMarginal = bpca.engine.Infer<IDistribution<double[]>>(bpca.vMu);
IDistribution<double[]> piMarginal = bpca.engine.Infer<IDistribution<double[]>>(bpca.vPi);
// Convert distributions over arrays of doubles to arrays of distributions
Gaussian[,] inferredW = Distribution.ToArray<Gaussian[,]>(wMarginal);
Gaussian[] inferredMu = Distribution.ToArray<Gaussian[]>(muMarginal);
Gamma[] inferredPi = Distribution.ToArray<Gamma[]>(piMarginal);
// Print out the results
Console.WriteLine("Inferred W:");
printMatrixToConsole(inferredW);
Console.Write("Mean absolute means of rows in W: ");
printVectorToConsole(meanAbsoluteRowMeans(inferredW));
Console.Write(" True bias: ");
printVectorToConsole(trueMu);
Console.Write("Inferred bias: ");
printVectorToConsole(inferredMu);
Console.Write(" True noise:");
printVectorToConsole(truePi);
Console.Write("Inferred noise:");
printVectorToConsole(inferredPi);
Console.WriteLine();
}
