public void TestKDTreeSearchObject()
{
TreeReferencableObject[,] testVectors = new TreeReferencableObject[50, 3];
Random rand = new Random(3567);
for (int n = 0; n < 50; n++)
{
testVectors[n, 0] = new TreeReferencableObject(rand.NextDouble());
testVectors[n, 1] = new TreeReferencableObject(rand.NextDouble());
testVectors[n, 2] = new TreeReferencableObject(rand.NextDouble());
}
KDTree tree = new KDTree(testVectors);
TreeReferencableObject[] zero = new TreeReferencableObject[3];
zero[0] = new TreeReferencableObject(0.0);
zero[1] = new TreeReferencableObject(0.0);
zero[2] = new TreeReferencableObject(0.0);
List <KDTreePoint> distances = new List <KDTreePoint>();
tree.Search(zero, 5, ref distances);
Assert.AreEqual(5, distances.Count);
zero[0] = new TreeReferencableObject(0.6);
zero[1] = new TreeReferencableObject(0.6);
zero[2] = new TreeReferencableObject(0.6);
tree.Search(zero, 5, ref distances);
Assert.AreEqual(5, distances.Count);
zero[0] = testVectors[0, 0];
zero[1] = testVectors[0, 1];
zero[2] = testVectors[0, 2];
tree.Search(zero, 5, ref distances);
// check that the target, which is also part of the dataset is the first
Assert.AreEqual(0, ((KDTreePoint)distances[0]).index);
Assert.AreEqual(0.0, ((KDTreePoint)distances[0]).distSquared);
}
public void TestKDTreeUniqueSearchObject()
{
TreeReferencableObject[,] testVectors = new TreeReferencableObject[50, 3];
Random rand = new Random(3567);
for (int n = 0; n < 25; n++)
{
testVectors[n, 0] = new TreeReferencableObject(rand.NextDouble());
testVectors[n, 1] = new TreeReferencableObject(rand.NextDouble());
testVectors[n, 2] = new TreeReferencableObject(rand.NextDouble());
}
//second part of array is duplicate of first
for (int n = 25; n < 50; n++)
{
testVectors[n, 0] = testVectors[n - 25, 0];
testVectors[n, 1] = testVectors[n - 25, 1];
testVectors[n, 2] = testVectors[n - 25, 2];
}
KDTree tree = new KDTree(testVectors);
double[] zero = new double[3];
zero[0] = 0.0;
zero[1] = 0.0;
zero[2] = 0.0;
List <KDTreePoint> distances = new List <KDTreePoint>();
tree.UniqueSearch(zero, 5, ref distances);
Assert.AreEqual(5, distances.Count);
zero[0] = testVectors[0, 0].GetValue();
zero[1] = testVectors[0, 0].GetValue();
zero[2] = testVectors[0, 0].GetValue();
tree.UniqueSearch(zero, 5, ref distances);
Assert.AreEqual(5, distances.Count);
Assert.IsFalse((int)((KDTreePoint)distances[0]).index == 0);
Assert.IsFalse(((KDTreePoint)distances[0]).distSquared == 0.0);
Assert.IsFalse(((KDTreePoint)distances[0]).index == ((KDTreePoint)distances[1]).index);
Assert.IsFalse(((KDTreePoint)distances[1]).index == ((KDTreePoint)distances[2]).index);
Assert.IsFalse(((KDTreePoint)distances[2]).index == ((KDTreePoint)distances[3]).index);
Assert.IsFalse(((KDTreePoint)distances[3]).index == ((KDTreePoint)distances[4]).index);
zero[0] = 0.6;
zero[1] = 0.6;
zero[2] = 0.6;
tree.UniqueSearch(zero, 5, ref distances);
Assert.AreEqual(5, distances.Count);
}
public void TestKDTreeSearchByDistanceObject()
{
TreeReferencableObject[,] testVectors = new TreeReferencableObject[50, 3];
Random rand = new Random(3567);
bool finished = false;
int n = 0;
while (!finished)
{
double x = rand.NextDouble();
double y = rand.NextDouble();
double z = rand.NextDouble();
//create vector, reject if not within 0.25 of 0,0,0
if (x * x + y * y + z * z < 0.25)
{
testVectors[n, 0] = new TreeReferencableObject(x);
testVectors[n, 1] = new TreeReferencableObject(y);
testVectors[n, 2] = new TreeReferencableObject(z);
n++;
if (n == 25)
{
finished = true;
}
}
}
finished = false;
while (!finished)
{
double x = rand.NextDouble();
double y = rand.NextDouble();
double z = rand.NextDouble();
//create vector, reject if not within 0.25 of 0,0,0
if (x * x + y * y + z * z > 0.25)
{
testVectors[n, 0] = new TreeReferencableObject(x);
testVectors[n, 1] = new TreeReferencableObject(y);
testVectors[n, 2] = new TreeReferencableObject(z);
n++;
if (n == 50)
{
finished = true;
}
}
}
//now search by distance should return the first 25 values.
KDTree tree = new KDTree(testVectors);
double[] zero = new double[3];
zero[0] = 0.0;
zero[1] = 0.0;
zero[2] = 0.0;
int count;
List <KDTreePoint> distances = new List <KDTreePoint>();
tree.SearchByDistance(zero, 0.5, out count, ref distances, 30);
Assert.AreEqual(25, count);
Assert.IsTrue(((KDTreePoint)distances[count - 1]).distSquared < 0.25);
zero[0] = testVectors[0, 0].GetValue();
zero[1] = testVectors[0, 1].GetValue();
zero[2] = testVectors[0, 2].GetValue();
tree.SearchByDistance(zero, 0.5, out count, ref distances, 30);
// check that the target, which is also part of the dataset is the first
Assert.AreEqual(0, ((KDTreePoint)distances[0]).index);
Assert.AreEqual(0.0, ((KDTreePoint)distances[0]).distSquared);
}
