/** 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); } }