@Override
public void addNoise(Decorable d) {
if (initialize) {
this.initialize();
}
Schema schema = d.getSchema();
// 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();
String oldvalue = d.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);
}
}
d.setFeatureValue(fid, new CategValue(cats.get(index)));
} else if (f instanceof ExplicitNum) {
double oldvalue = ((NumValue) d.getFeatureValue(fid)).getNumber();
if (oldvalue != 0.0 && oldvalue != 1.0) {
throw new UnsupportedTypeException(
"Numeric values must be either 0 or 1: " + d + "." + 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);
}
d.setFeatureValue(fid, new NumValue(value));
} else {
throw new UnsupportedTypeException("Unsupported Type: " + f.getClass().getCanonicalName());
}
}
}
