public double[][] compute(SingleDTGraph data) {
SparseVector[] featureVectors = computeFeatureVectors(data);
double[][] kernel =
KernelUtils.initMatrix(data.getInstances().size(), data.getInstances().size());
long tic = System.currentTimeMillis();
kernel = KernelUtils.computeKernelMatrix(featureVectors, kernel);
compTime += System.currentTimeMillis() - tic;
return kernel;
}
public SparseVector[] computeFeatureVectors(SingleDTGraph data) {
SparseVector[] featureVectors = new SparseVector[data.getInstances().size()];
for (int i = 0; i < featureVectors.length; i++) {
featureVectors[i] = new SparseVector();
}
init(data.getGraph(), data.getInstances());
WeisfeilerLehmanIterator<DTGraph<StringLabel, StringLabel>> wl =
new WeisfeilerLehmanDTGraphIterator(reverse, noDuplicateSubtrees);
List<DTGraph<StringLabel, StringLabel>> gList =
new ArrayList<DTGraph<StringLabel, StringLabel>>();
gList.add(rdfGraph);
long tic = System.currentTimeMillis();
wl.wlInitialize(gList);
double weight = 1.0;
computeFVs(rdfGraph, instanceVertices, weight, featureVectors, wl.getLabelDict().size() - 1, 0);
for (int i = 0; i < iterations; i++) {
wl.wlIterate(gList);
computeFVs(
rdfGraph, instanceVertices, weight, featureVectors, wl.getLabelDict().size() - 1, i + 1);
}
compTime = System.currentTimeMillis() - tic;
if (this.normalize) {
featureVectors = KernelUtils.normalize(featureVectors);
}
return featureVectors;
}
