/// <summary>
/// Constructs a multi-component Bayes Point Machine using shared variables for chunking data
/// </summary>
/// <param name="nClass">Number of components (classes)</param>
/// <param name="nFeatures">Number of features</param>
/// <param name="noisePrec">Noise precision</param>
/// <param name="trainChunkSize">Chunk size for training</param>
/// <param name="testChunkSize">Chunk size for testing</param>
public BPM_Shared(int nClass, int nFeatures, double noisePrec, int trainChunkSize, int testChunkSize)
{
this.nClass = nClass;
this.nFeatures = nFeatures;
this.trainChunkSize = trainChunkSize;
this.testChunkSize = testChunkSize;
NoisePrec = noisePrec;
feature = new Range(nFeatures).Named("feature");
// The set of weight vectors (one for each component) are shared between all data chunks
w = new SharedVariable<Vector>[nClass];
VectorGaussian wPrior0 = VectorGaussian.PointMass(Vector.Zero(nFeatures));
VectorGaussian wPrior = VectorGaussian.FromMeanAndPrecision(Vector.Zero(nFeatures), PositiveDefiniteMatrix.Identity(nFeatures));
for (int c = 0; c < nClass; c++)
{
w[c] = (c == 0)
? SharedVariable<Vector>.Random(VectorGaussian.PointMass(Vector.Zero(nFeatures))).Named("w_" + c)
: SharedVariable<Vector>.Random(wPrior).Named("w_" + c);
}
trainModel = SpecifyTrainModel("_train", trainChunkSize);
testModel = SpecifyTestModel("_test", testChunkSize);
}
/// <summary>
/// Specify the training model
/// </summary>
/// <param name="s">The name of the training model</param>
/// <param name="nChunks">The number of chunks</param>
/// <returns>A <see cref="BPMVarsModelForTrain"/> instance</returns>
private BPMVarsModelForTrain SpecifyTrainModel(string s, int nChunks)
{
// An array of feature vectors - their observed values will be
// set by the calling program
VariableArray<Vector>[] xValues = new VariableArray<Vector>[nClass];
// The number of items for each component
Variable<int>[] nItem = new Variable<int>[nClass];
// Ranges over the items for each component
Range[] item = new Range[nClass];
// The model identifier for the shared variables
Model model = new Model(nChunks).Named("model" + s);
// The weight vector within a submodel
Variable<Vector>[] wModel = new Variable<Vector>[nClass];
for (int c = 0; c < nClass; c++)
{
// Get a copy of the shared weight vector variable for the submodel
wModel[c] = w[c].GetCopyFor(model).Named("wModel_" + c + s);
}
// Loop over the components
for (int c = 0; c < nClass; c++)
{
// The number of items for each component - set by the calling program
nItem[c] = Variable.New<int>().Named("nItem_" + c + s);
// The item range for each component
item[c] = new Range(nItem[c]).Named("item_" + c + s);
// The items for each component - set by the calling program
xValues[c] = Variable.Array<Vector>(item[c]);
using (Variable.ForEach(item[c]))
{
// The score for this item across all components
Variable<double>[] score = BPMUtils.ComputeClassScores(wModel, xValues[c][item[c]], NoisePrec);
// The constraint imposed by the observed component
BPMUtils.ConstrainArgMax(c, score);
}
}
// Store the variables
BPMVarsModelForTrain bpmVar = new BPMVarsModelForTrain();
bpmVar.ie = new InferenceEngine();
bpmVar.xValues = xValues;
bpmVar.nItems = nItem;
bpmVar.model = model;
return bpmVar;
}
