/// <summary>
/// Initializes a new instance of the <see cref="BPMSparseShared"/> 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 BPMSparseShared(int numClasses, double noisePrecision, int numFeatures, int numChunksTraining, int numChunksTesting)
{
// Ranges over classes and features.
this.c = new Range(numClasses).Named("c");
this.f = new Range(numFeatures).Named("f");
// Setup weights and weights' prior.
this.weightsPrior = InitializePrior(numClasses, numFeatures);
this.weights = SharedVariable<double>.Random(Variable.Array<double>(this.f), 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;
}
public Model(ISharedVariableArray<VariableArray<double>, double[][]> w, Range c, int numChunks)
{
// Items.
numItems = Variable.New<int>().Named("numItems");
i = new Range(numItems).Named("i");
i.AddAttribute(new Sequential());
// Features per item.
numFeaturesPerItem = Variable.Array<int>(i).Named("numFeaturesPerItem");
fItem = new Range(numFeaturesPerItem[i]).Named("fItem");
// The model identifier for the shared variables.
model = new MicrosoftResearch.Infer.Models.Model(numChunks).Named("model");
// The weight vector for each submodel.
wModel = w.GetCopyFor(model).Named("wModel");
noisePrecision = Variable.New<double>().Named("noisePrecision");
// Jagged array of feature values - each item is an array of data values
// whose indices are given by the corresponding indices[i].
values = Variable.Array(Variable.Array<double>(fItem), i).Named("values");
// Jagged array of indices for the items.
indices = Variable.Array(Variable.Array<int>(fItem), i).Named("indices");
// Labels.
y = Variable.Array<int>(i).Named("y");
// For all items...
using (Variable.ForEach(i))
{
// ...compute the score of this item across all classes...
score = BPMUtils.ComputeClassScores(wModel, values[i], indices[i], fItem, noisePrecision);
y[i] = Variable.DiscreteUniform(c);
// ... and constrain the output.
BPMUtils.ConstrainMaximum(y[i], score);
}
// Inference engine settings (EP).
engine.Compiler.UseSerialSchedules = true;
engine.ShowProgress = false;
}
