/*
* To use BKTree:
* 1. Create a class dervied from BKTreeNode
* 2. Add a member variable of your data to be sorted / retrieved
* 3. Override the calculateDistance method to calculate the distance metric
* between two nodes for the data to be sorted / retrieved.
* 4. Instantiate a BKTree with the type name of the class created in (1).
*/
static void Main(string[] args)
{
/*
* NOTE: More comprehensive examples of BK-Tree methods in unit tests
*/
// Exercise static distance metric methods -- just because
Console.WriteLine(
DistanceMetric.calculateHammingDistance(
new byte[] { 0xEF, 0x35, 0x20 },
new byte[] { 0xAD, 0x13, 0x87 }));
Console.WriteLine(
DistanceMetric.calculateLeeDistance(
new int[] { 196, 105, 48 },
new int[] { 201, 12, 51 }));
Console.WriteLine(
DistanceMetric.calculateLevenshteinDistance(
"kitten",
"sitting"));
// Create BKTree with derived node class from top of file
BKTree <ExampleNodeRecord> tree = new BKTree <ExampleNodeRecord>();
// Add some nodes
tree.add(new ExampleNodeRecord(1, new int[] { 100, 200, 300 }));
tree.add(new ExampleNodeRecord(2, new int[] { 110, 210, 310 }));
tree.add(new ExampleNodeRecord(3, new int[] { 120, 220, 320 }));
tree.add(new ExampleNodeRecord(4, new int[] { 130, 230, 330 }));
tree.add(new ExampleNodeRecord(5, new int[] { 140, 240, 340 }));
// Get best node from our tree with best distance
Dictionary <ExampleNodeRecord, Int32> results =
tree.findBestNodeWithDistance(
new ExampleNodeRecord(new int[] { 103, 215, 303 }));
// Get best nodes below threshold
results = tree.query(
new ExampleNodeRecord(new int[] { 103, 215, 303 }),
10); // arbitrary threshold
// Dictionaries don't print well; so invent your own handy print routine
}
public void BKTree_should_FindBestNodeWithDistance()
{
BKTree <TestNode> tree = new BKTree <TestNode>();
TestNode search = new TestNode(new int[] { 365, 422, 399 });
TestNode best = new TestNode(4, new int[] { 400, 400, 400 });
tree.add(new TestNode(1, new int[] { 100, 100, 100 }));
tree.add(new TestNode(2, new int[] { 200, 200, 200 }));
tree.add(new TestNode(3, new int[] { 300, 300, 300 }));
tree.add(best);
tree.add(new TestNode(5, new int[] { 500, 500, 500 }));
Dictionary <TestNode, Int32> result = tree.findBestNodeWithDistance(search);
Assert.Equal(1, result.Count);
Assert.Equal(58, DistanceMetric.calculateLeeDistance(search.Data, best.Data));
Assert.Equal(58, result.Values.ElementAt(0));
Assert.Equal(4, result.Keys.ElementAt(0).Id);
Assert.Equal(best.Data, result.Keys.ElementAt(0).Data);
}
/*
* To use BKTree:
* 1. Create a class dervied from BKTreeNode
* 2. Add a member variable of your data to be sorted / retrieved
* 3. Override the calculateDistance method to calculate the distance metric
* between two nodes for the data to be sorted / retrieved.
* 4. Instantiate a BKTree with the type name of the class created in (1).
*/
static void Main(string[] args)
{
/*
* NOTE: More comprehensive examples of BK-Tree methods in unit tests
*/
// Exercise static distance metric methods -- just because
Console.WriteLine(
DistanceMetric.calculateHammingDistance(
new byte[] { 0xEF, 0x35, 0x20 },
new byte[] { 0xAD, 0x13, 0x87 }));
Console.WriteLine(
DistanceMetric.calculateLeeDistance(
new int[] { 196, 105, 48 },
new int[] { 201, 12, 51 }));
Console.WriteLine(
DistanceMetric.calculateLevenshteinDistance(
"kitten",
"sitting"));
// Create BKTree with derived node class from top of file
BKTree<ExampleNodeRecord> tree = new BKTree<ExampleNodeRecord>();
// Add some nodes
tree.add( new ExampleNodeRecord( 1, new int[] {100,200,300}) );
tree.add( new ExampleNodeRecord( 2, new int[] {110,210,310}) );
tree.add( new ExampleNodeRecord( 3, new int[] {120,220,320}) );
tree.add( new ExampleNodeRecord( 4, new int[] {130,230,330}) );
tree.add( new ExampleNodeRecord( 5, new int[] {140,240,340}) );
// Get best node from our tree with best distance
Dictionary<ExampleNodeRecord, Int32> results =
tree.findBestNodeWithDistance(
new ExampleNodeRecord( new int[] { 103, 215, 303 }) );
// Get best nodes below threshold
results = tree.query(
new ExampleNodeRecord(new int[] { 103, 215, 303 }),
10 ); // arbitrary threshold
// Dictionaries don't print well; so invent your own handy print routine
}
public void BKTree_should_FindBestNodeWithDistance()
{
BKTree<TestNode> tree = new BKTree<TestNode>();
TestNode search = new TestNode(new int[] { 365, 422, 399 });
TestNode best = new TestNode(4, new int[] { 400, 400, 400 });
tree.add(new TestNode(1, new int[] { 100, 100, 100 }));
tree.add(new TestNode(2, new int[] { 200, 200, 200 }));
tree.add(new TestNode(3, new int[] { 300, 300, 300 }));
tree.add(best);
tree.add(new TestNode(5, new int[] { 500, 500, 500 }));
Dictionary<TestNode,Int32> result = tree.findBestNodeWithDistance(search);
Assert.Equal(1, result.Count);
Assert.Equal(58, DistanceMetric.calculateLeeDistance(search.Data, best.Data));
Assert.Equal(58, result.Values.ElementAt(0));
Assert.Equal(4, result.Keys.ElementAt(0).Id);
Assert.Equal(best.Data, result.Keys.ElementAt(0).Data);
}