/// <summary>
/// Constructs an instance from number of components and number of features
/// </summary>
/// <param name="nClass">Number of classes</param>
/// <param name="nFeatures">Number of features</param>
/// <param name="noisePrec">Noise precision</param>
public BPM(int nClass, int nFeatures, double noisePrec)
{
this.nClass = nClass;
this.nFeatures = nFeatures;
NoisePrec = noisePrec;
trainModel = SpecifyTrainModel("_train");
testModel = SpecifyTestModel("_test");
}
/// <summary>
/// Specifies the training model
/// </summary>
/// <param name="s">The name of the training model</param>
/// <returns>A <see cref="BPMVarsForTrain"/> instance</returns>
private BPMVarsForTrain SpecifyTrainModel(string s)
{
// 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 weight vector for each component
Variable<Vector>[] w = new Variable<Vector>[nClass];
// The prior weight distributions for each component
Variable<VectorGaussian>[] wInit = new Variable<VectorGaussian>[nClass];
for (int c = 0; c < nClass; c++)
{
// The prior distributions will be set by the calling program
wInit[c] = Variable.New<VectorGaussian>();
// The weight vectors are drawn from the prior distribution
w[c] = Variable<Vector>.Random(wInit[c]);
}
// 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]);
// Loop over the items
using (Variable.ForEach(item[c]))
{
// The score for this item across all components
Variable<double>[] score = BPMUtils.ComputeClassScores(w, xValues[c][item[c]], NoisePrec);
// The constraint imposed by the observed component
BPMUtils.ConstrainArgMax(c, score);
}
}
// Store the variables
BPMVarsForTrain bpmVar = new BPMVarsForTrain();
bpmVar.ie = new InferenceEngine();
bpmVar.xValues = xValues;
bpmVar.nItems = nItem;
bpmVar.w = w;
bpmVar.wInit = wInit;
return bpmVar;
}
