public void predictAsLink(
Graph graph, Iterable<Edge> unknownedges, boolean removenotexist, String existfeature) {
// Add co-reference edge schema and feature, but do nothing else
// Add schema, if not already defined
if (!graph.hasSchema(this.edgeschemaid)) {
graph.addSchema(this.edgeschemaid, new Schema(SchemaType.UNDIRECTED));
}
// Add existence feature, if not already defined
Schema schema = graph.getSchema(this.edgeschemaid);
if (!schema.hasFeature(existfeature)) {
schema.addFeature(existfeature, LinkPredictor.EXISTENCEFEATURE);
graph.updateSchema(this.edgeschemaid, schema);
}
}
public void predictAsLink(Graph graph, PotentialLinkGenerator generator) {
// Add co-reference edge schema, but do nothing else
if (!graph.hasSchema(this.edgeschemaid)) {
graph.addSchema(this.edgeschemaid, new Schema(SchemaType.UNDIRECTED));
}
}
@Override
public void addNoise(Graph g) {
if (initialize) {
this.initialize();
}
Schema schema = g.getSchema(schemaid);
// Load feature ids, if needed
if (fids == null) {
fids =
FeatureUtils.parseFeatureList(
this, schema, IteratorUtils.iterator2stringlist(schema.getFeatureIDs()));
}
for (String fid : fids) {
Feature f = schema.getFeature(fid);
// Flip attributes
if (f instanceof ExplicitCateg) {
// Handle explicit categorical features
UnmodifiableList<String> cats = ((ExplicitCateg) f).getAllCategories();
int catsize = cats.size();
// Iterate over graph all graph items with the given schema
Iterator<GraphItem> gitr = g.getGraphItems(schemaid);
while (gitr.hasNext()) {
GraphItem gi = gitr.next();
String oldvalue = gi.getFeatureValue(fid).getStringValue();
if (sparsevalue != null && !oldvalue.equals(sparsevalue)) {
// Only flip the non-sparse values
// Done so for sparse sets, the common value doesn't become
// too common.
continue;
}
// Only flip given some probability
if (rand.nextDouble() > probflip) {
continue;
}
// Randomly select a category
int index = rand.nextInt(catsize);
if (changevalue && catsize > 1) {
while (oldvalue.equals(cats.get(index))) {
index = rand.nextInt(catsize);
}
}
gi.setFeatureValue(fid, new CategValue(cats.get(index)));
}
} else if (f instanceof ExplicitNum) {
// Iterate over graph all graph items with the given schema
Iterator<GraphItem> gitr = g.getGraphItems(schemaid);
while (gitr.hasNext()) {
GraphItem gi = gitr.next();
double oldvalue = ((NumValue) gi.getFeatureValue(fid)).getNumber();
if (oldvalue != 0.0 && oldvalue != 1.0) {
throw new UnsupportedTypeException(
"Numeric values must be either 0 or 1: " + gi + "." + fid + "=" + oldvalue);
}
if (sparsevalue != null && oldvalue != Double.parseDouble(sparsevalue)) {
// Only flip the non-sparse values
// Done so for sparse sets, the common value doesn't become
// too common.
continue;
}
// Only flip given some probability
if (rand.nextDouble() > probflip) {
continue;
}
double value = 0;
if (changevalue && oldvalue == value) {
value = (oldvalue + 1) % 2;
} else {
// Randomly select a category
value = rand.nextInt(2);
}
gi.setFeatureValue(fid, new NumValue(value));
}
} else {
throw new UnsupportedTypeException("Unsupported Type: " + f.getClass().getCanonicalName());
}
}
}