public int CalculateDistance(ExampleMetric other)
{
return(DistanceMetric.CalculateLeeDistance(
Data,
other.Data
));
}
public void BKTreeShouldQueryBestMatchesBelowGivenThreshold()
{
BKTree <ExampleMetric> tree = new BKTree <ExampleMetric>();
ExampleMetric search = new ExampleMetric(new int[] { 399, 400, 400 });
ExampleMetric best1 = new ExampleMetric(41, new int[] { 400, 400, 400 });
ExampleMetric best2 = new ExampleMetric(42, new int[] { 403, 403, 403 });
ExampleMetric best3 = new ExampleMetric(43, new int[] { 406, 406, 406 });
tree.Add(new ExampleMetric(1, new int[] { 100, 100, 100 }));
tree.Add(new ExampleMetric(2, new int[] { 200, 200, 200 }));
tree.Add(new ExampleMetric(3, new int[] { 300, 300, 300 }));
tree.Add(best1);
tree.Add(best2);
tree.Add(new ExampleMetric(5, new int[] { 500, 500, 500 }));
// Query for match within distance of 1 (best1 is only expected result)
IDictionary <ExampleMetric, int> results = tree.Query(search, 1);
Assert.AreEqual(1, DistanceMetric.CalculateLeeDistance(search.Data, best1.Data));
Assert.AreEqual(1, results.Values.ElementAt(0));
Assert.AreEqual(41, results.Keys.ElementAt(0).Id);
Assert.AreEqual(best1.Data, results.Keys.ElementAt(0).Data);
// Query for match within distance of 10 (best1 & best2 are expected results)
tree.Add(best3); // exercise adding another node after already queried
results = tree.Query(search, 10);
Assert.AreEqual(2, results.Count);
Assert.AreEqual(1, DistanceMetric.CalculateLeeDistance(search.Data, best1.Data));
Assert.AreEqual(10, DistanceMetric.CalculateLeeDistance(search.Data, best2.Data));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best1, 1)));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best2, 10)));
// Query for matches within distance of 20 (best1, best2 & best3 are expected results)
results = tree.Query(search, 20);
Assert.AreEqual(3, results.Count);
Assert.AreEqual(1, DistanceMetric.CalculateLeeDistance(search.Data, best1.Data));
Assert.AreEqual(10, DistanceMetric.CalculateLeeDistance(search.Data, best2.Data));
Assert.AreEqual(19, DistanceMetric.CalculateLeeDistance(search.Data, best3.Data));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best1, 1)));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best2, 10)));
Assert.IsTrue(results.Contains(new KeyValuePair <ExampleMetric, int>(best3, 19)));
}
public void BKTreeShouldCalculateVarietyOfDistances()
{
Assert.AreEqual(10,
DistanceMetric.CalculateHammingDistance(
new byte[] { 0xEF, 0x35, 0x20 },
new byte[] { 0xAD, 0x13, 0x87 }));
Assert.AreEqual(101,
DistanceMetric.CalculateLeeDistance(
new int[] { 196, 105, 48 },
new int[] { 201, 12, 51 }));
Assert.AreEqual(3,
DistanceMetric.CalculateLevenshteinDistance(
"kitten",
"sitting"
));
}
public void BKTreeShouldFindBestNodeWithDistance()
{
BKTree <ExampleMetric> tree = new BKTree <ExampleMetric>();
ExampleMetric search = new ExampleMetric(new int[] { 365, 422, 399 });
ExampleMetric best = new ExampleMetric(4, new int[] { 400, 400, 400 });
tree.Add(new ExampleMetric(1, new int[] { 100, 100, 100 }));
tree.Add(new ExampleMetric(2, new int[] { 200, 200, 200 }));
tree.Add(new ExampleMetric(3, new int[] { 300, 300, 300 }));
tree.Add(best);
tree.Add(new ExampleMetric(5, new int[] { 500, 500, 500 }));
Tuple <ExampleMetric, int> result = tree.FindClosestElement(search);
Assert.AreEqual(58, DistanceMetric.CalculateLeeDistance(search.Data, best.Data));
Assert.AreEqual(58, result.Item2);
Assert.AreEqual(4, result.Item1.Id);
Assert.AreEqual(best.Data, result.Item1.Data);
}