/// <summary>
/// Initializes the CBCC model with a number of communities.
/// </summary>
/// <param name="taskCount">The number of tasks.</param>
/// <param name="labelCount">The number of labels.</param>
/// <param name="communityCount">The number of communities.</param>
public virtual void CreateModel(int taskCount, int labelCount, int communityCount)
{
Evidence = Variable <bool> .Random(this.EvidencePrior);
var evidenceBlock = Variable.If(Evidence);
CommunityCount = communityCount;
CommunityProbPriorObserved = Dirichlet.Symmetric(communityCount, CommunityPseudoCount);
DefineVariablesAndRanges(taskCount, labelCount);
DefineGenerativeProcess();
DefineInferenceEngine();
evidenceBlock.CloseBlock();
if (ScoreMeanParameters == null)
{
var scoreMatrixPrior = GetScoreMatrixPrior();
CommunityScoreMatrixPriorObserved = Util.ArrayInit(CommunityCount, comm => Util.ArrayInit(labelCount, lab => new VectorGaussian(scoreMatrixPrior[lab])));
}
else
{
CommunityScoreMatrixPriorObserved = Util.ArrayInit(
CommunityCount,
comm => Util.ArrayInit(
labelCount, lab => VectorGaussian.FromMeanAndPrecision(
Vector.FromArray(
Util.ArrayInit(labelCount, lab1 => lab == lab1 ? ScoreMeanParameters[comm].Item1 : ScoreMeanParameters[comm].Item2)),
PositiveDefiniteMatrix.IdentityScaledBy(LabelCount, ScorePrecisionParameters[comm]))));
}
}
/// <summary>
/// Generates a data set from a particular true model.
/// </summary>
public Vector[] GenerateData(int nData)
{
Vector trueM1 = Vector.FromArray(2.0, 3.0);
Vector trueM2 = Vector.FromArray(7.0, 5.0);
PositiveDefiniteMatrix trueP1 = new PositiveDefiniteMatrix(
new double[, ] {
{ 3.0, 0.2 }, { 0.2, 2.0 }
});
PositiveDefiniteMatrix trueP2 = new PositiveDefiniteMatrix(
new double[, ] {
{ 2.0, 0.4 }, { 0.4, 4.0 }
});
VectorGaussian trueVG1 = VectorGaussian.FromMeanAndPrecision(trueM1, trueP1);
VectorGaussian trueVG2 = VectorGaussian.FromMeanAndPrecision(trueM2, trueP2);
double truePi = 0.6;
Bernoulli trueB = new Bernoulli(truePi);
// Restart the infer.NET random number generator
Rand.Restart(12347);
Vector[] data = new Vector[nData];
for (int j = 0; j < nData; j++)
{
bool bSamp = trueB.Sample();
data[j] = bSamp ? trueVG1.Sample() : trueVG2.Sample();
}
return(data);
}
public static Vector VectorGaussianScaled(double scaling, PositiveDefiniteMatrix precision)
{
var scaledPrec = new PositiveDefiniteMatrix(precision);
scaledPrec.Scale(scaling);
return(VectorGaussian.FromMeanAndPrecision(Vector.Zero(precision.Cols), scaledPrec).Sample());
}
private void VectorGaussianModel()
{
Vector mm = Vector.FromArray(0.1, 0.2);
Vector m = Factor.Random(VectorGaussian.FromMeanAndPrecision(mm, PositiveDefiniteMatrix.Identity(2)));
PositiveDefiniteMatrix p = Factor.Random(Wishart.FromShapeAndRate(2, 1.0, 1.0));
Vector c = Factor.VectorGaussian(m, p);
InferNet.Infer(c, nameof(c));
}
private void MatrixVectorProductModel()
{
Matrix a = new Matrix(new double[, ] {
{ 1, 2, 3 }, { 4, 5, 6 }
});
Vector m = Vector.FromArray(0.1, 1.2, 2.3);
Vector b = Factor.Random(VectorGaussian.FromMeanAndPrecision(m, PositiveDefiniteMatrix.Identity(3)));
Vector c = Factor.Product(a, b);
InferNet.Infer(c, nameof(c));
}
//Addition by Guy Templeton, get log evidence from learned mixing coeff.
public double GetLogEvidence()
{
Variable <bool> evidence = Variable.Bernoulli(0.5).Named("evidence");
Range classes = new Range(numOfClasses);
IfBlock block = Variable.If(evidence);
VectorGaussian[] wObserved = trainModel.wPrior.ObservedValue;
VectorGaussian[] empty = Util.ArrayInit(numOfClasses, c => (c == 0) ?
VectorGaussian.PointMass(Vector.Zero(numOfFeatures)) :
VectorGaussian.FromMeanAndPrecision(Vector.Zero(numOfFeatures), PositiveDefiniteMatrix.Identity(numOfFeatures)));
block.CloseBlock();
InferenceEngine engine = new InferenceEngine();
return(engine.Infer <Bernoulli>(evidence).LogOdds);
}
private VectorGaussian[] InitializePrior(int numClasses, int numFeatures)
{
return(Util.ArrayInit(numClasses, c => (c == 0) ?
VectorGaussian.PointMass(Vector.Zero(numFeatures)) :
VectorGaussian.FromMeanAndPrecision(Vector.Zero(numFeatures), PositiveDefiniteMatrix.Identity(numFeatures))));
}
