/*
* LensKit, an open source recommender systems toolkit.
* Copyright 2010-2014 LensKit Contributors. See CONTRIBUTORS.md.
* Work on LensKit has been funded by the National Science Foundation under
* grants IIS 05-34939, 08-08692, 08-12148, and 10-17697.
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of the
* License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
* details.
*
* You should have received a copy of the GNU General Public License along with
* this program; if not, write to the Free Software Foundation, Inc., 51
* Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*/
package org.grouplens.lenskit.vectors.similarity;
import org.grouplens.lenskit.vectors.MutableSparseVector;
import org.grouplens.lenskit.vectors.SparseVector;
import org.junit.Before;
import org.junit.Test;
import static org.junit.Assert.assertEquals;
public class CosineSimilarityTest {
private static final double EPSILON = 1.0e-6;
private CosineVectorSimilarity similarity, dampedSimilarity;
@Before
public void setUp() throws Exception {
similarity = new CosineVectorSimilarity();
dampedSimilarity = new CosineVectorSimilarity(10);
}
private SparseVector emptyVector() {
long[] keys = {};
double[] values = {};
return MutableSparseVector.wrap(keys, values);
}
@Test
public void testEmpty() {
assertEquals(0, similarity.similarity(emptyVector(), emptyVector()), EPSILON);
}
@Test
public void testEmptyDamped() {
assertEquals(0, dampedSimilarity.similarity(emptyVector(), emptyVector()), 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 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 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 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);
}
}