/** Test of learn method, of class CategoryBalancedBaggingLearner. */
@Test
public void testLearn() {
CategoryBalancedBaggingLearner<Vector, Boolean> instance =
new CategoryBalancedBaggingLearner<Vector, Boolean>();
instance.setLearner(new Perceptron());
instance.setRandom(random);
instance.setMaxIterations(5);
instance.setPercentToSample(0.5);
assertNull(instance.getResult());
ArrayList<InputOutputPair<Vector, Boolean>> data =
new ArrayList<InputOutputPair<Vector, Boolean>>();
VectorFactory<?> vectorFactory = VectorFactory.getDefault();
for (int i = 0; i < 100; i++) {
data.add(
new DefaultInputOutputPair<Vector, Boolean>(
vectorFactory.createUniformRandom(14, 0.0, 1.0, random), true));
}
for (int i = 0; i < 2; i++) {
data.add(
new DefaultInputOutputPair<Vector, Boolean>(
vectorFactory.createUniformRandom(14, -1.0, 0.0, random), false));
}
WeightedVotingCategorizerEnsemble<Vector, Boolean, ?> result = instance.learn(data);
assertSame(result, instance.getResult());
assertEquals(5, result.getMembers().size());
for (WeightedValue<?> member : result.getMembers()) {
assertEquals(1.0, member.getWeight(), 0.0);
assertNotNull(member.getValue());
assertTrue(member.getValue() instanceof LinearBinaryCategorizer);
}
}
/** Test of constructors of class CategoryBalancedBaggingLearner. */
@Test
public void testConstructors() {
Perceptron learner = null;
double percentToSample = BaggingCategorizerLearner.DEFAULT_PERCENT_TO_SAMPLE;
int maxIterations = BaggingCategorizerLearner.DEFAULT_MAX_ITERATIONS;
CategoryBalancedBaggingLearner<Vector, Boolean> instance =
new CategoryBalancedBaggingLearner<Vector, Boolean>();
assertSame(learner, instance.getLearner());
assertEquals(percentToSample, instance.getPercentToSample(), 0.0);
assertEquals(maxIterations, instance.getMaxIterations());
assertNotNull(instance.getRandom());
learner = new Perceptron();
instance = new CategoryBalancedBaggingLearner<Vector, Boolean>(learner);
assertSame(learner, instance.getLearner());
assertEquals(percentToSample, instance.getPercentToSample(), 0.0);
assertEquals(maxIterations, instance.getMaxIterations());
assertNotNull(instance.getRandom());
percentToSample = percentToSample / 3.4;
maxIterations = maxIterations * 9;
instance =
new CategoryBalancedBaggingLearner<Vector, Boolean>(
learner, maxIterations, percentToSample, random);
assertSame(learner, instance.getLearner());
assertEquals(percentToSample, instance.getPercentToSample(), 0.0);
assertEquals(maxIterations, instance.getMaxIterations());
assertSame(random, instance.getRandom());
}
