/** Randomly generate several searches and check the result */
@Test
public void randomTests() {
KdTreeSearchN alg = createAlg();
KdTree tree = StandardKdTreeSearch1Tests.createTreeA();
alg.setTree(tree);
List<double[]> data = new ArrayList<double[]>();
flattenTree(tree.root, data);
for (int i = 0; i < 100; i++) {
int searchN = rand.nextInt(data.size() + 5) + 1;
double[] target = data.get(rand.nextInt(data.size()));
double maxDistance = rand.nextDouble() * 10;
List<double[]> expected = findNeighbors(data, target, maxDistance, searchN);
found.reset();
alg.setMaxDistance(maxDistance);
alg.findNeighbor(target, searchN, found);
assertEquals(expected.size(), found.size);
for (int j = 0; j < expected.size(); j++) {
checkContains(expected.get(j));
}
}
}
/** The tree is empty and it should always fail */
@Test
public void findClosest_empty() {
KdTreeSearchN alg = createAlg();
alg.setTree(new KdTree(2));
found.reset();
alg.findNeighbor(new double[] {11, 8}, 2, found);
assertEquals(0, found.size());
}
/** See if it can handle a null leaf */
@Test
public void findClosest_nullLeaf() {
KdTreeSearchN alg = createAlg();
KdTree tree = StandardKdTreeSearch1Tests.createTreeWithNull();
alg.setTree(tree);
alg.setMaxDistance(Double.MAX_VALUE);
// the first decision will be incorrect and it will need to back track
found.reset();
alg.findNeighbor(new double[] {2, 3}, 1, found);
assertTrue(found.get(0).node == tree.root);
}
/** See if max distance is being respected */
@Test
public void findClosest_maxDistance() {
KdTree tree = new KdTree(2);
tree.root = new KdTree.Node(new double[] {1, 2}, null);
KdTreeSearchN alg = createAlg();
alg.setTree(tree);
alg.setMaxDistance(2);
found.reset();
alg.findNeighbor(new double[] {11, 8}, 1, found);
assertEquals(0, found.size);
found.reset();
alg.findNeighbor(new double[] {1, 1.5}, 1, found);
assertEquals(1, found.size);
assertTrue(found.data[0].node == tree.root);
}
/**
* Try several searches and see if they all produce good results. Just fine the nearest-neighbor
*/
@Test
public void findClosest_basic_1() {
KdTreeSearchN alg = createAlg();
KdTree tree = StandardKdTreeSearch1Tests.createTreeA();
alg.setTree(tree);
alg.setMaxDistance(Double.MAX_VALUE);
// the first decision will be incorrect and it will need to back track
found.reset();
alg.findNeighbor(new double[] {11, 8}, 1, found);
assertEquals(1, found.size);
assertTrue(found.data[0].node == tree.root.right.right);
// the root will be the best
found.reset();
alg.findNeighbor(new double[] {1.001, 1.99999}, 1, found);
assertTrue(found.data[0].node == tree.root);
// a point on the left branch will be a perfect fit
found.reset();
alg.findNeighbor(new double[] {2, 0.8}, 1, found);
assertTrue(found.data[0].node == tree.root.left.right);
// a point way outside the tree's bounds
found.reset();
alg.findNeighbor(new double[] {-10000, 0.5}, 1, found);
assertTrue(found.data[0].node == tree.root.left.left);
}
/** Make sure the distance it returns is correct */
@Test
public void checkDistance() {
KdTreeSearchN alg = createAlg();
KdTree tree = StandardKdTreeSearch1Tests.createTreeA();
alg.setTree(tree);
alg.setMaxDistance(Double.MAX_VALUE);
double[] pt = new double[] {11.5, 8.2};
found.reset();
alg.findNeighbor(pt, 1, found);
assertEquals(1, found.size);
double d0 = found.get(0).node.point[0] - pt[0];
double d1 = found.get(0).node.point[1] - pt[1];
assertEquals(d0 * d0 + d1 * d1, found.get(0).distance, 1e-8);
}
/** See of it can handle duplicate values correctly */
@Test
public void checkDuplicates() {
KdTreeSearchN alg = createAlg();
KdTree tree = createTreeDuplicates();
alg.setTree(tree);
alg.setMaxDistance(Double.MAX_VALUE);
double[] pt = new double[] {1, 2};
found.reset();
alg.findNeighbor(pt, 3, found);
assertEquals(3, found.size);
// make sure each instance is unique
for (int i = 0; i < 3; i++) {
double[] a = found.get(i).node.point;
for (int j = i + 1; j < 3; j++) {
assertTrue(found.get(j).node.point != a);
}
}
}
