// initialize the matrices
public void Initialize() {
// compute the histogram matrix
ComputeHistogram();
// create the extended Y
YExtended = new Matrix(numTotalInstances, numLabels);
// set all the cells to zero initially
for (int i = 0; i < numTrainInstances; i++)
for (int l = 0; l < numLabels; l++) YExtended.set(i, l, 0.0);
// set to 1 only the column corresponding to the label
for (int i = 0; i < numTotalInstances; i++) YExtended.set(i, (int) Y.get(i), 1.0);
// randomly initialize the latent matrices
S = new Matrix(numTotalInstances, D);
S.RandomlyInitializeCells(0, 1);
P = new Matrix(D, numPatterns);
P.RandomlyInitializeCells(0, 1);
biasP = new double[numPatterns];
for (int l = 0; l < numPatterns; l++) biasP[l] = H.GetColumnMean(l);
W = new Matrix(D, numLabels);
W.RandomlyInitializeCells(0, 1);
biasW = new double[numLabels];
for (int l = 0; l < numLabels; l++) biasW[l] = YExtended.GetColumnMean(l);
// record the observed histogram values
HObserved = new ArrayList<Tripple>();
for (int i = 0; i < H.getDimRows(); i++)
for (int j = 0; j < H.getDimColumns(); j++)
if (H.get(i, j) != GlobalValues.MISSING_VALUE) HObserved.add(new Tripple(i, j));
Collections.shuffle(HObserved);
// record the observed label values
YObserved = new ArrayList<Tripple>();
for (int i = 0; i < numTrainInstances; i++)
for (int l = 0; l < YExtended.getDimColumns(); l++)
if (YExtended.get(i, l) != GlobalValues.MISSING_VALUE) YObserved.add(new Tripple(i, l));
Collections.shuffle(YObserved);
}
// compute the histogram matrix
public void ComputeHistogram() {
BagOfPatterns bop = new BagOfPatterns();
bop.representationType = RepresentationType.Polynomial;
bop.slidingWindowSize = slidingWindowSize;
bop.representationType = RepresentationType.Polynomial;
bop.innerDimension = innerDimension;
bop.alphabetSize = alphabetSize;
bop.polyDegree = degree;
H = bop.CreateWordFrequenciesMatrix(X);
numPatterns = H.getDimColumns();
for (int i = 0; i < H.getDimRows(); i++)
for (int j = 0; j < H.getDimColumns(); j++)
if (H.get(i, j) == 0) {
// H.set(i, j, GlobalValues.MISSING_VALUE);
}
Logging.println("Histogram Sparsity: " + H.GetSparsityRatio(), LogLevel.DEBUGGING_LOG);
}