@Test
public void testConstructFromProbability() {
float[] probability = {0.0f, 0.125f, 0.375f, 0.0f, 0.5f, 0.0f};
CategoricalPrediction prediction = new CategoricalPrediction(probability);
assertEquals(FeatureType.CATEGORICAL, prediction.getFeatureType());
assertEquals(4, prediction.getMostProbableCategoryID());
assertNull(prediction.getCategoryCounts());
assertArrayEquals(probability, prediction.getCategoryProbabilities());
}
@Test
public void testConstruct() {
int[] counts = {0, 1, 3, 0, 4, 0};
CategoricalPrediction prediction = new CategoricalPrediction(counts);
assertEquals(FeatureType.CATEGORICAL, prediction.getFeatureType());
assertEquals(4, prediction.getMostProbableCategoryID());
assertArrayEquals(counts, prediction.getCategoryCounts());
assertArrayEquals(
new float[] {0.0f, 0.125f, 0.375f, 0.0f, 0.5f, 0.0f},
prediction.getCategoryProbabilities());
}
@Test
public void testUpdate() {
int[] counts = {0, 1, 3, 0, 4, 0};
CategoricalPrediction prediction = new CategoricalPrediction(counts);
Example example = new Example(CategoricalFeature.forValue(2));
prediction.update(example);
prediction.update(example);
assertEquals(2, prediction.getMostProbableCategoryID());
counts[2] += 2;
assertArrayEquals(counts, prediction.getCategoryCounts());
assertArrayEquals(
new float[] {0.0f, 0.1f, 0.5f, 0.0f, 0.4f, 0.0f}, prediction.getCategoryProbabilities());
}
