@Test
public void testDampedEqualKeys() {
long[] keys = {2, 5, 6};
double[] val1 = {1, 2, 1};
double[] val2 = {1, 2, 5};
SparseVector v1 = MutableSparseVector.wrap(keys, val1).freeze();
SparseVector v2 = MutableSparseVector.wrap(keys, val2).freeze();
assertEquals(0.375, dampedSimilarity.similarity(v1, v1), EPSILON);
assertEquals(0.42705098, dampedSimilarity.similarity(v1, v2), EPSILON);
}
@Test
public void testEqualKeys() {
long[] keys = {2, 5, 6};
double[] val1 = {1, 2, 1};
double[] val2 = {1, 2, 5};
SparseVector v1 = MutableSparseVector.wrap(keys, val1).freeze();
SparseVector v2 = MutableSparseVector.wrap(keys, val2).freeze();
assertEquals(1, similarity.similarity(v1, v1), EPSILON);
assertEquals(0.745355993, similarity.similarity(v1, v2), EPSILON);
}
@Test
public void testOverlap() {
long[] k1 = {1, 2, 5, 6};
double[] val1 = {3, 1, 2, 1};
long[] k2 = {2, 3, 5, 6, 7};
double[] val2 = {1, 7, 2, 5, 0};
SparseVector v1 = MutableSparseVector.wrap(k1, val1).freeze();
SparseVector v2 = MutableSparseVector.wrap(k2, val2).freeze();
assertEquals(1, similarity.similarity(v1, v1), EPSILON);
assertEquals(1, similarity.similarity(v2, v2), EPSILON);
assertEquals(0.29049645, similarity.similarity(v1, v2), EPSILON);
}
@Test
public void testDisjoint() {
long[] k1 = {2, 5, 6};
double[] val1 = {1, 3, 2};
long[] k2 = {3, 4, 7};
double[] val2 = {1, 3, 2};
SparseVector v1, v2;
v1 = MutableSparseVector.wrap(k1, val1).freeze();
v2 = MutableSparseVector.wrap(k2, val2).freeze();
assertEquals(0, similarity.similarity(v1, v2), EPSILON);
assertEquals(0, dampedSimilarity.similarity(v1, v2), EPSILON);
}
@Test
public void testItemMeanBaseline() {
ItemScorer pred = new ItemMeanRatingItemScorer.Builder(dao, 0.0).get();
long[] items = {5, 7, 10};
double[] values = {3, 6, 4};
SparseVector map = MutableSparseVector.wrap(items, values).freeze();
// unseen item, should be global mean
assertThat(pred.score(10, 2), closeTo(RATINGS_DAT_MEAN, 0.001));
// seen item - should be item average
assertThat(pred.score(10, 5), closeTo(3.0, 0.001));
}
private SparseVector emptyVector() {
long[] keys = {};
double[] values = {};
return MutableSparseVector.wrap(keys, values);
}