@Override
public DDAG clone() {
DDAG clone = new DDAG(baseClassifier.clone(), isConcurrentTraining());
if (oneVone != null) {
clone.oneVone = new Classifier[oneVone.length][];
for (int i = 0; i < oneVone.length; i++) {
clone.oneVone[i] = new Classifier[oneVone[i].length];
for (int j = 0; j < oneVone[i].length; j++) clone.oneVone[i][j] = oneVone[i][j].clone();
}
}
if (predicting != null) clone.predicting = predicting.clone();
return clone;
}
@Test
public void testClone() {
System.out.println("clone");
ClassificationDataSet t1 = FixedProblems.getSimpleKClassLinear(1000, 7);
ClassificationDataSet t2 = FixedProblems.getSimpleKClassLinear(1000, 9);
DDAG instance = new DDAG(new DCDs());
instance = instance.clone();
instance.trainC(t1);
DDAG result = instance.clone();
for (int i = 0; i < t1.getSampleSize(); i++)
assertEquals(t1.getDataPointCategory(i), result.classify(t1.getDataPoint(i)).mostLikely());
result.trainC(t2);
for (int i = 0; i < t1.getSampleSize(); i++)
assertEquals(t1.getDataPointCategory(i), instance.classify(t1.getDataPoint(i)).mostLikely());
for (int i = 0; i < t2.getSampleSize(); i++)
assertEquals(t2.getDataPointCategory(i), result.classify(t2.getDataPoint(i)).mostLikely());
}
