public void testWordFrequency() {
WordPredictor p = new WordPredictor();
p.train("a big brown bear".split(" "));
p.train("a big brown bench".split(" "));
p.train("a big yellow banana".split(" "));
{
List<Prediction> pns = p.predictWord("big");
assertEquals(1, pns.size());
assertPrediction("brown", 100d, pns.get(0));
}
p.train("a big yellow duck".split(" "));
{
List<Prediction> pns = p.predictWord("big");
assertEquals(2, pns.size());
assertPrediction("brown", 50d, pns.get(0));
assertPrediction("yellow", 50d, pns.get(1));
}
p.train("a big yellow daisy".split(" "));
{
List<Prediction> pns = p.predictWord("big");
assertEquals(2, pns.size());
assertPrediction("yellow", (3d / 5) * 100, pns.get(0));
assertPrediction("brown", (2d / 5) * 100, pns.get(1));
}
}
public void testEmptyInput() {
WordPredictor p = new WordPredictor();
{
try {
p.train(null);
fail("should throw NPE on null input");
} catch (NullPointerException e) {
// OK
}
}
p.train("a big brown bear".split(" "));
p.train("a big brown bench".split(" "));
{
// Empty string should give empty predictions even after training
assertEquals(Collections.emptyList(), p.predictWord(""));
}
{
try {
p.predictWord(null);
fail("Should throw NPE on null input");
} catch (NullPointerException e) {
// OK
}
}
}
public void testReset() {
WordPredictor p = new WordPredictor();
p.train("a big brown bath".split(" "));
p.train("a big brown bath".split(" "));
{
List<Prediction> pns = p.predictWord("a");
assertEquals(1, pns.size());
assertPrediction("big", 100d, pns.get(0));
}
p.reset();
{
assertEquals(Collections.emptyList(), p.predictWord("a"));
}
}
public void testWordPredictor() {
WordPredictor p = new WordPredictor();
{
// Should be empty before training
assertEquals(Collections.emptyList(), p.predictWord("a"));
}
p.train("a big brown bear".split(" "));
p.train("a big brown bench".split(" "));
{
List<Prediction> pns = p.predictWord("a");
assertEquals(1, pns.size());
assertPrediction("big", 100d, pns.get(0));
}
{
List<Prediction> pns = p.predictWord("big");
assertEquals(1, pns.size());
assertPrediction("brown", 100d, pns.get(0));
}
{
// Word must occur more than once to be considered interesting.
assertEquals(Collections.emptyList(), p.predictWord("brown"));
assertEquals(Collections.emptyList(), p.predictWord("bear"));
assertEquals(Collections.emptyList(), p.predictWord("foo"));
}
}
public void testMinFrequency() {
WordPredictor p = new WordPredictor();
for (int i = 0; i < 2; i++) {
p.train("a big brown bear".split(" "));
p.train("a big brown bench".split(" "));
p.train("a big brown bazooka".split(" "));
p.train("a big brown bazinga".split(" "));
p.train("a big brown balloon".split(" "));
p.train("a big brown boulder".split(" "));
p.train("a big brown blanket".split(" "));
p.train("a big brown balcony".split(" "));
p.train("a big brown binder".split(" "));
p.train("a big brown book".split(" "));
}
{
List<Prediction> pns = p.predictWord("brown");
assertEquals(10, pns.size());
assertPrediction("balcony", 10d, pns.get(0));
assertPrediction("boulder", 10d, pns.get(9));
}
// Add an eleventh distinct word after "brown", making none of the
// predictions higher than the minimum threshold for being considered
// interesting.
p.train("a big brown bath".split(" "));
p.train("a big brown bath".split(" "));
{
assertEquals(Collections.emptyList(), p.predictWord("brown"));
}
{
List<Prediction> pns = p.predictWord("a");
assertEquals(1, pns.size());
assertPrediction("big", 100d, pns.get(0));
}
}