/** Test of learn method, of class LatentDirichletAllocationVectorGibbsSampler. */
@Test
public void testDoubleReuse() throws Exception {
final VectorFactory<?> factory = VectorFactory.getSparseDefault();
final ArrayList<Vector> data = new ArrayList<Vector>();
data.add(factory.copyValues(0, 0, 4, 2, 5, 6, 0, 3, 0));
data.add(factory.copyValues(0, 0, 0, 8, 0, 3, 0, 0, 0));
data.add(factory.copyValues(4, 0, 6, 0, 0, 0, 3, 5, 0));
data.add(factory.copyValues(1, 0, 0, 3, 2, 0, 3, 8, 0));
data.add(factory.copyValues(3, 0, 5, 3, 0, 5, 6, 0, 0));
data.add(factory.copyValues(0, 0, 0, 1, 3, 3, 3, 2, 0));
int termCount = 9;
int topicCount = 3;
Random randDoubleReuse = new DoubleReuseRandom(1000);
LatentDirichletAllocationVectorGibbsSampler instance =
new LatentDirichletAllocationVectorGibbsSampler(
topicCount, 2.0, 0.5, 50, 20, 10, randDoubleReuse);
assertNull(instance.learn(null));
assertNull(instance.learn(new ArrayList<Vector>()));
LatentDirichletAllocationVectorGibbsSampler.Result result = instance.learn(data);
assertEquals(topicCount, result.getTopicCount());
assertEquals(topicCount, result.topicTermProbabilities.length);
assertEquals(data.size(), result.getDocumentCount());
assertEquals(data.size(), result.documentTopicProbabilities.length);
assertEquals(termCount, result.getTermCount());
for (int i = 0; i < topicCount; i++) {
assertEquals(termCount, result.topicTermProbabilities[i].length);
for (int j = 0; j < termCount; j++) {
assertIsProbability(result.topicTermProbabilities[i][j]);
}
}
for (int i = 0; i < data.size(); i++) {
assertEquals(topicCount, result.documentTopicProbabilities[i].length);
for (int j = 0; j < topicCount; j++) {
assertIsProbability(result.documentTopicProbabilities[i][j]);
}
}
}
/**
* A test that first shows a bug in the code as it was, and then makes sure the issue doesn't come
* back.
*/
@Test
public void testVectorWithDoubles() {
double[] d1 = {0, 0.4, 1.1, 1.5, 2.0};
double[] d2 = {1.2, 1.5, 2.3, 0, 1.2};
double[] d3 = {0, 2.5, 3.3, 1.0, 1.2};
final ArrayList<Vector> data = new ArrayList<Vector>();
data.add(VectorFactory.getDenseDefault().copyArray(d1));
data.add(VectorFactory.getDenseDefault().copyArray(d2));
data.add(VectorFactory.getDenseDefault().copyArray(d3));
LatentDirichletAllocationVectorGibbsSampler lda =
new LatentDirichletAllocationVectorGibbsSampler();
// This used to throw an exception, but doesn't anymore
// Refer to issue MachineLearning/FoundryLearning#9 for details
lda.learn(data);
}
