/// <summary>
/// Inserts a new value into the Min Heap.
/// </summary>
/// <param name="keyValue">The new key-value pair to be inserted in the tree.</param>
/// <param name="heapArrayLength">The length of the heap array. </param>
public override void Insert(KeyValuePair <TKey, TValue> keyValue, int heapArrayLength)
{
HeapArray.Add(keyValue);
int index = heapArrayLength;
BubbleUp_Recursively(index, heapArrayLength + 1);
}
/// <summary>Runs the Greedy search algorithm algorithm on a graph.</summary>
/// <param name="start">The node to start at.</param>
/// <param name="neighbors">Step function for all neigbors of a given node.</param>
/// <param name="heuristic">Computes the heuristic value of a given node in a graph.</param>
/// <param name="goal">Predicate for determining if we have reached the goal node.</param>
/// <returns>Stepper of the shortest path or null if no path exists.</returns>
public static Stepper <T> Greedy(T start, Neighbors neighbors, Heuristic heuristic, Goal goal)
{
// using a heap (aka priority queue) to store nodes based on their computed heuristic value
Heap <Greedy_Node> fringe = new HeapArray <Greedy_Node>(
// NOTE: I just reversed the order of left and right because smaller values are higher priority
(Greedy_Node left, Greedy_Node right) => { return(Compute <Math> .Compare(right.Priority, left.Priority)); });
// push starting node
Greedy_Node start_node = new Greedy_Node(null, start, default(Math));
fringe.Enqueue(start_node);
// run the algorithm
while (fringe.Count != 0)
{
Greedy_Node current = fringe.Dequeue();
if (goal(current.Value))
{
return(Greedy_BuildPath(current));
}
else
{
neighbors(current.Value,
(T neighbor) =>
{
Greedy_Node newNode = new Greedy_Node(current, neighbor, heuristic(neighbor));
fringe.Enqueue(newNode);
});
}
}
return(null); // goal node was not reached (no path exists)
}
internal HeapArray(HeapArray <T> heap)
{
_compare = heap._compare;
_heap = (T[])heap._heap.Clone();
_minimumCapacity = heap._minimumCapacity;
_count = heap._count;
}
/// <summary>
/// Removes the min element from the heap.
/// </summary>
/// <param name="keyValue">If the operation is successful, contains the minimum element in the array.</param>
/// <param name="heapArrayLength">The length of the heap array. </param>
/// <returns>True in case of success, and false otherwise</returns>
public override bool TryRemoveRoot(out KeyValuePair <TKey, TValue> keyValue, int heapArrayLength)
{
keyValue = new KeyValuePair <TKey, TValue>((TKey)typeof(TKey).GetField("MinValue").GetValue(null), default(TValue)); // T.MinValue;
/* If array is empty, returns false. */
if (heapArrayLength == 0)
{
return(false);
}
/* If array has only one element left, it is the minimum value, and clear the array after removing it.*/
if (heapArrayLength == 1)
{
keyValue = HeapArray[0];
HeapArray.Clear();;
return(true);
}
/* If array has more than 1 element, the next instructions are executed. */
keyValue = HeapArray[0]; /* In a minHeap the minimum value is always in the root, which is at index 0.*/
HeapArray[0] = HeapArray[heapArrayLength - 1]; /* Move the last element to the place of root, and then bubble down. */
HeapArray.RemoveAt(heapArrayLength - 1); /* Removing the last element, as it is now placed in the root's position, and needs to be bubbled down.*/
BubbleDown_Recursively(0, heapArrayLength - 1); /* Call this method to bubble down the (new) root.*/ /* Also notice that the array is shorter by one value now, thus the new array length is one smaller. */
return(true);
}
/*
* Time((V+E)logV), Space(V)
* can't handle negative distances; can return shortest paths from vertex 0 to all other vertice.
* 1. initialize the distance of vetex 0 to 0 and all others to INF.
* 2. add all vertice to a minimum heap based on their distances from vetex 0
* 2. loop until heap is empty, extract min from heap and update the distance of its all adjacent vertices.
*/
int[] Dijkstra(int n, IList <int[]>[] adj)
{
var dist = new int[n];
for (int i = 1; i < n; i++)
{
dist[i] = INF;
}
var heap = new HeapArray(dist);
var prev = new int[n]; // used to recover the path
for (int i = 0; i < n - 1; i++)
{
var min = heap.ExtractMin();
foreach (var e in adj[min])
{
int src = e[0], dst = e[1], w = e[2];
if (dist[dst] > dist[src] + w)
{
heap.Update(dst, dist[src] + w);
dist[dst] = dist[src] + w;
prev[dst] = src;
}
}
}
return(dist);
}
internal HeapArray(HeapArray <T> heap)
{
_compare = heap._compare;
_heap = new T[heap._heap.Length];
heap._heap.CopyTo(_heap);
_minimumCapacity = heap._minimumCapacity;
_count = heap._count;
}
/// <summary>
/// Inserts a new value into the Max Heap.
/// </summary>
/// <param name="value">The new value to be inserted in the tree.</param>
/// <param name="heapArrayLength">The length/size of the heap array. </param>
public override void Insert(KeyValuePair <TKey, TValue> value, int heapArrayLength)
{
HeapArray.Add(value);// means gets added to the end of the list.
// Bubble up this element/node
int nodeIndex = heapArrayLength;
BubbleUp_Iteratively(nodeIndex, heapArrayLength + 1); // Notice that the size of the array is grown by one now.
}
public void Add()
{
IHeap <Person> heap = new HeapArray <Person>();
foreach (Person person in RandomTestData)
{
heap.Enqueue(person);
}
}
/// <summary>
/// Inserts a new value into the Min Heap.
/// </summary>
/// <param name="newValue">The new key-value pair to be inserted in the tree.</param>
/// <param name="heapArrayLength">The length of the heap array. </param>
public override void Insert(KeyValuePair <TKey, TValue> newValue, int heapArrayLength)
{
/* Add the new value to the end of the array. List is a dynamic array and grows in size automatically. */
HeapArray.Add(newValue);
/* Bubble up the new value, and stop when the parent is no longer bigger than the new value, or when new value is bubbled up to the root's position. */
int nodeIndex = heapArrayLength;
BubbleUp_Iteratively(nodeIndex, heapArrayLength + 1);
}
public void Enqueue()
{
IHeap <int> heap = new HeapArray <int>();
int enqueueCount = EnqueueCount;
for (int i = 0; i < enqueueCount; i++)
{
heap.Enqueue(i);
}
}
/// <summary>
/// This method is for finding the root of the heap, without removing it.
/// </summary>
/// <param name="keyValue">The key-value of the root.</param>
/// <param name="heapArrayLength">The length of the heap array. </param>
/// <returns>True in case of success, and false in case of failure.</returns>
public override bool TryFindRoot(out KeyValuePair <TKey, TValue> keyValue, int heapArrayLength)
{
if (HeapArray.Any())
{
keyValue = HeapArray[0];
return(true);
}
keyValue = new KeyValuePair <TKey, TValue>((TKey)typeof(TKey).GetField("MaxValue").GetValue(null), default(TValue));
return(false);
}
public void InsertKey(T key)
{
if (HeapSize == HeapArray.Length)
{
T[] newHeap = new T[HeapArray.Length * 2];
HeapArray.CopyTo(newHeap, 0);
HeapArray = newHeap;
}
HeapArray[HeapSize] = key;
FixUp(HeapSize++);
}
/// <summary>Runs the A* search algorithm algorithm on a graph.</summary>
/// <param name="start">The node to start at.</param>
/// <param name="neighbors">Step function for all neigbors of a given node.</param>
/// <param name="heuristic">Computes the heuristic value of a given node in a graph.</param>
/// <param name="cost">Computes the cost of moving from the current node to a specific neighbor.</param>
/// <param name="goal">Predicate for determining if we have reached the goal node.</param>
/// <returns>Stepper of the shortest path or null if no path exists.</returns>
public static Stepper <T> Astar(T start, Neighbors neighbors, Heuristic heuristic, Cost cost, Goal goal)
{
// using a heap (aka priority queue) to store nodes based on their computed A* f(n) value
Heap <Astar_Node> fringe = new HeapArray <Astar_Node>(
// NOTE: Typical A* implementations prioritize smaller values
(Astar_Node left, Astar_Node right) =>
{
Comparison comparison = Compute.Compare <Math>(right.Priority, left.Priority);
return(comparison);
});
// using a map (aka dictionary) to store costs from start to current nodes
Map <Math, Astar_Node> computed_costs = new MapHashArray <Math, Astar_Node>();
// construct the f(n) for this A* execution
Astar_function function = (T node, Astar_Node previous) =>
{
Math previousCost = computed_costs.Get(previous);
Math currentCost = cost(previous.Value, node);
Math costFromStart = Compute.Add <Math>(previousCost, currentCost);
Math hueristic = heuristic(node);
return(Compute.Add <Math>(costFromStart, hueristic));
};
// push starting node
Astar_Node start_node = new Astar_Node(null, start, default(Math));
fringe.Enqueue(start_node);
computed_costs.Add(start_node, default(Math));
// run the algorithm
while (fringe.Count != 0)
{
Astar_Node current = fringe.Dequeue();
if (goal(current.Value))
{
return(Astar_BuildPath(current));
}
else
{
neighbors(current.Value,
(T neighbor) =>
{
Astar_Node newNode = new Astar_Node(current, neighbor, function(neighbor, current));
Math costValue = Compute.Add <Math>(computed_costs.Get(current), cost(current.Value, neighbor));
computed_costs.Add(newNode, costValue);
fringe.Enqueue(newNode);
});
}
}
return(null); // goal node was not reached (no path exists)
}
/// <summary>Runs the A* search algorithm algorithm on a graph.</summary>
/// <typeparam name="Node">The node type of the graph being searched.</typeparam>
/// <typeparam name="Numeric">The numeric to use when performing calculations.</typeparam>
/// <param name="start">The node to start at.</param>
/// <param name="neighbors">Step function for all neigbors of a given node.</param>
/// <param name="heuristic">Computes the heuristic value of a given node in a graph.</param>
/// <param name="cost">Computes the cost of moving from the current node to a specific neighbor.</param>
/// <param name="goal">Predicate for determining if we have reached the goal node.</param>
/// <returns>Stepper of the shortest path or null if no path exists.</returns>
public static Stepper <Node> Graph <Node, Numeric>(Node start, Neighbors <Node> neighbors, Heuristic <Node, Numeric> heuristic, Cost <Node, Numeric> cost, Goal <Node> goal)
{
// using a heap (aka priority queue) to store nodes based on their computed A* f(n) value
IHeap <AstarNode <Node, Numeric> > fringe = new HeapArray <AstarNode <Node, Numeric> >(
// NOTE: Typical A* implementations prioritize smaller values
(a, b) => Compute.Compare(b.Priority, a.Priority));
// push starting node
fringe.Enqueue(
new AstarNode <Node, Numeric>()
{
Previous = null,
Value = start,
Priority = default,
public static void HeapSort <T>(T[] data, int size) where T : IComparable
{
var heapArray = new HeapArray <T>(size);
heapArray.BulidHeapBottomUp(data, size);
while (size > 1)
{
var maxValue = data[1];
data[1] = data[size];
data[size] = maxValue;
size--;
RestoreDown(1, data, size);
}
}
[TestMethod] public void Dequeue_Testing()
{
void Test <T>(T[] values, Compare <T> compare)
{
T[] clonedValues = (T[])values.Clone();
Towel.Sort.Shuffle(clonedValues);
IHeap <T> heap = new HeapArray <T>(compare);
clonedValues.Stepper(x => heap.Enqueue(x));
foreach (T value in values)
{
T dequeue = heap.Dequeue();
Assert.IsTrue(value.Equals(dequeue));
}
}
{ // int compare
const int count = 100;
int[] values = new int[count];
Stepper.Iterate(count, i => values[i] = i);
Array.Sort(values, (a, b) => - a.CompareTo(b));
Test(values, (a, b) => Compare.Wrap(a.CompareTo(b)));
}
{ // string compare
const int count = 100;
string[] values = new string[count];
Stepper.Iterate(count, i => values[i] = i.ToString());
Array.Sort(values, (a, b) => - a.CompareTo(b));
Test(values, (a, b) => Compare.Wrap(a.CompareTo(b)));
}
{ // int reverse compare
const int count = 100;
int[] values = new int[count];
Stepper.Iterate(count, i => values[i] = i);
Array.Sort(values);
Test(values, (a, b) => Compare.Wrap(-a.CompareTo(b)));
}
{ // string reverse compare
const int count = 100;
string[] values = new string[count];
Stepper.Iterate(count, i => values[i] = i.ToString());
Array.Sort(values);
Test(values, (a, b) => Compare.Wrap(-a.CompareTo(b)));
}
}
/// <summary>
/// Removes the min element from the heap.
/// </summary>
/// <param name="keyValue">If the operation is successful, contains the minimum element in the array.</param>
/// <param name="heapArrayLength">The length of the heap array. </param>
/// <returns></returns>
public override bool TryRemoveRoot(out KeyValuePair <TKey, TValue> keyValue, int heapArrayLength)
{
keyValue = new KeyValuePair <TKey, TValue>((TKey)typeof(TKey).GetField("MinValue").GetValue(null), default(TValue));
if (heapArrayLength == 0)
{
return(false);
}
if (heapArrayLength == 1)
{
keyValue = HeapArray[0];
HeapArray.Clear();
return(true);
}
keyValue = HeapArray[0];
HeapArray[0] = HeapArray[heapArrayLength - 1];
HeapArray.RemoveAt(heapArrayLength - 1);
BubbleDownMin_Recursively(0, heapArrayLength - 1); /* Calling this method, because this is a min-max heap and 0 is expected to be on a min level.*/ /* Also notice that the array is shorter by one value now, thus the new array length is one smaller. */
return(true);
}
/// <summary>Runs the A* search algorithm algorithm on a graph.</summary>
/// <param name="start">The node to start at.</param>
/// <param name="neighbors">Step function for all neigbors of a given node.</param>
/// <param name="heuristic">Computes the heuristic value of a given node in a graph.</param>
/// <param name="cost">Computes the cost of moving from the current node to a specific neighbor.</param>
/// <param name="goal">Predicate for determining if we have reached the goal node.</param>
/// <returns>Stepper of the shortest path or null if no path exists.</returns>
public static Stepper <NODE> Graph <NODE, NUMERIC>(NODE start, Neighbors <NODE> neighbors, Heuristic <NODE, NUMERIC> heuristic, Cost <NODE, NUMERIC> cost, Goal <NODE> goal)
{
// using a heap (aka priority queue) to store nodes based on their computed A* f(n) value
IHeap <AstarNode <NODE, NUMERIC> > fringe = new HeapArray <AstarNode <NODE, NUMERIC> >(
// NOTE: Typical A* implementations prioritize smaller values
(a, b) => Compute.Compare(b.Priority, a.Priority));
// push starting node
fringe.Enqueue(
new AstarNode <NODE, NUMERIC>(
null,
start,
default(NUMERIC),
Constant <NUMERIC> .Zero));
// run the algorithm
while (fringe.Count != 0)
{
AstarNode <NODE, NUMERIC> current = fringe.Dequeue();
if (goal(current.Value))
{
return(BuildPath(current));
}
else
{
neighbors(current.Value,
(NODE neighbor) =>
{
NUMERIC costValue = Compute.Add(current.Cost, cost(current.Value, neighbor));
fringe.Enqueue(
new AstarNode <NODE, NUMERIC>(
current,
neighbor,
Compute.Add(heuristic(neighbor), costValue),
costValue));
});
}
}
return(null); // goal node was not reached (no path exists)
}
/// <summary>
/// Removes the max element from the heap.
/// </summary>
/// <param name="keyValue">If the operation is successful, contains the maximum element in the array.</param>
/// <param name="heapArrayLength">The length of the heap array. </param>
/// <returns>True in case of success, and false otherwise</returns>
public override bool TryRemoveRoot(out KeyValuePair <TKey, TValue> keyValue, int heapArrayLength)
{
keyValue = new KeyValuePair <TKey, TValue>((TKey)typeof(TKey).GetField("MaxValue").GetValue(null), default(TValue));
if (heapArrayLength == 0)
{
return(false);
}
if (heapArrayLength == 1)
{
keyValue = HeapArray[0];
HeapArray.Clear();
return(true);
}
keyValue = HeapArray[0];
HeapArray[0] = HeapArray[heapArrayLength - 1];
HeapArray.RemoveAt(heapArrayLength - 1);
BubbleDown_Recursively(0, heapArrayLength - 1); /* notice that the array is shorter by one value now, thus the new array length is one smaller. */
return(true);
}
public void Dequeue_Testing()
{
void Test <T>(T[] values, Func <T, T, CompareResult> compare)
{
T[] clonedValues = (T[])values.Clone();
Shuffle <T>(clonedValues);
IHeap <T> heap = HeapArray.New <T>(compare);
clonedValues.Stepper(x => heap.Enqueue(x));
foreach (T value in values)
{
T dequeue = heap.Dequeue();
Assert.IsTrue(value !.Equals(dequeue));
}
}
{ // int compare
int[] values = (..100).ToArray();
Array.Sort(values, (a, b) => - a.CompareTo(b));
Test(values, Compare);
}
{ // string compare
string[] values = (..100).ToArray(i => i.ToString());
Array.Sort(values, (a, b) => - a.CompareTo(b));
Test(values, Compare);
}
{ // int reverse compare
int[] values = (..100).ToArray();
Array.Sort(values);
Test(values, (a, b) => Compare(b, a));
}
{ // string reverse compare
string[] values = (..100).ToArray(i => i.ToString());
Array.Sort(values);
Test(values, (a, b) => Compare(b, a));
}
}
/// <summary>Runs the Greedy search algorithm algorithm on a graph.</summary>
/// <param name="start">The node to start at.</param>
/// <param name="neighbors">Step function for all neigbors of a given node.</param>
/// <param name="heuristic">Computes the heuristic value of a given node in a graph.</param>
/// <param name="goal">Predicate for determining if we have reached the goal node.</param>
/// <returns>Stepper of the shortest path or null if no path exists.</returns>
public static Stepper <NODE> Graph <NODE, NUMERIC>(NODE start, Neighbors <NODE> neighbors, Heuristic <NODE, NUMERIC> heuristic, Goal <NODE> goal)
{
// using a heap (aka priority queue) to store nodes based on their computed heuristic value
IHeap <GreedyNode <NODE, NUMERIC> > fringe = new HeapArray <GreedyNode <NODE, NUMERIC> >(
// NOTE: Typical graph search implementations prioritize smaller values
(a, b) => Compute.Compare(b.Priority, a.Priority));
// push starting node
fringe.Enqueue(
new GreedyNode <NODE, NUMERIC>(
null,
start,
default(NUMERIC)));
// run the algorithm
while (fringe.Count != 0)
{
GreedyNode <NODE, NUMERIC> current = fringe.Dequeue();
if (goal(current.Value))
{
return(BuildPath(current));
}
else
{
neighbors(current.Value,
(NODE neighbor) =>
{
fringe.Enqueue(
new GreedyNode <NODE, NUMERIC>(
current,
neighbor,
heuristic(neighbor)));
});
}
}
return(null); // goal node was not reached (no path exists)
}
static void Main(string[] args)
{
Random random = new Random();
int test = 10;
Console.WriteLine("You are runnning the Data Structures example.");
Console.WriteLine("======================================================");
Console.WriteLine();
#region Link (aka Tuple)
Console.WriteLine(" Link------------------------------------");
Console.WriteLine();
Console.WriteLine(" A \"Link\" is like a System.Tuple that implements");
Console.WriteLine(" Towel.DataStructures.DataStructure. A Link/Tuple is");
Console.WriteLine(" used when you have a small, known-sized set of objects");
Console.WriteLine(" that you want to bundle together without making a custom");
Console.WriteLine(" custom class.");
Console.WriteLine();
Link link = new Link <int, int, int, int, int, int>(0, 1, 2, 3, 4, 5);
Console.Write(" Traversal: ");
link.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" Size: " + link.Size);
Console.WriteLine();
#endregion
#region Indexed (aka Array)
Console.WriteLine(" Indexed---------------------------------");
Console.WriteLine();
Console.WriteLine(" An \"Indexed\" is just a wrapper for arrays that implements");
Console.WriteLine(" Towel.DataStructures.DataStructure. An array is used when");
Console.WriteLine(" dealing with static-sized, known-sized sets of data. Arrays");
Console.WriteLine(" can be sorted along 1 dimensions for binary searching algorithms.");
Console.WriteLine();
IIndexed <int> indexed = new IndexedArray <int>(test);
Console.Write(" Filling in (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
indexed[i] = i;
}
Console.WriteLine();
Console.Write(" Traversal: ");
indexed.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" Length: " + indexed.Length);
Console.WriteLine();
#endregion
#region Addable (aka List)
Console.WriteLine(" Addable---------------------------------");
Console.WriteLine();
Console.WriteLine(" An \"Addable\" is like an IList that implements");
Console.WriteLine(" Towel.DataStructures.DataStructure. \"AddableArray\" is");
Console.WriteLine(" the array implementation while \"AddableLinked\" is the");
Console.WriteLine(" the linked-list implementation. An Addable/List is used");
Console.WriteLine(" when dealing with an unknown quantity of data that you");
Console.WriteLine(" will likely have to enumerate/step through everything. The");
Console.WriteLine(" AddableArray shares the properties of an Indexed/Array in");
Console.WriteLine(" that it can be relateively quickly sorted along 1 dimensions");
Console.WriteLine(" for binary search algorithms.");
Console.WriteLine();
// AddableArray ---------------------------------------
IAddable <int> addableArray = new AddableArray <int>(test);
Console.Write(" [AddableArray] Adding (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
addableArray.Add(i);
}
Console.WriteLine();
Console.Write(" [AddableArray] Traversal: ");
addableArray.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" [AddableArray] Count: " + addableArray.Count);
addableArray.Clear(); // Clears the addable
Console.WriteLine();
// AddableLinked ---------------------------------------
IAddable <int> addableLinked = new AddableLinked <int>();
Console.Write(" [AddableLinked] Adding (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
addableLinked.Add(i);
}
Console.WriteLine();
Console.Write(" [AddableLinked] Traversal: ");
addableLinked.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" [AddableLinked] Count: " + addableLinked.Count);
addableLinked.Clear(); // Clears the addable
Console.WriteLine();
#endregion
#region FirstInLastOut (aka stack)
{
Console.WriteLine(" FirstInLastOut---------------------------------");
Console.WriteLine();
Console.WriteLine(" An \"FirstInLastOut\" is a Stack that implements");
Console.WriteLine(" Towel.DataStructures.DataStructure. \"FirstInLastOutArray\" is");
Console.WriteLine(" the array implementation while \"FirstInLastOutLinked\" is the");
Console.WriteLine(" the linked-list implementation. A FirstInLastOut/Stack is used");
Console.WriteLine(" specifically when you need the algorithm provided by the Push");
Console.WriteLine(" and Pop functions.");
Console.WriteLine();
IFirstInLastOut <int> firstInLastOutArray = new FirstInLastOutArray <int>();
Console.Write(" [FirstInLastOutArray] Pushing (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
firstInLastOutArray.Push(i);
}
Console.WriteLine();
Console.Write(" [FirstInLastOutArray] Traversal: ");
firstInLastOutArray.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" [FirstInLastOutArray] Pop: " + firstInLastOutArray.Pop());
Console.WriteLine(" [FirstInLastOutArray] Pop: " + firstInLastOutArray.Pop());
Console.WriteLine(" [FirstInLastOutArray] Peek: " + firstInLastOutArray.Peek());
Console.WriteLine(" [FirstInLastOutArray] Pop: " + firstInLastOutArray.Pop());
Console.WriteLine(" [FirstInLastOutArray] Count: " + firstInLastOutArray.Count);
firstInLastOutArray.Clear(); // Clears the firstInLastOut
Console.WriteLine();
IFirstInLastOut <int> firstInLastOutLinked = new FirstInLastOutLinked <int>();
Console.Write(" [FirstInLastOutLinked] Pushing (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
firstInLastOutLinked.Push(i);
}
Console.WriteLine();
Console.Write(" [FirstInLastOutLinked] Traversal: ");
firstInLastOutLinked.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" [FirstInLastOutLinked] Pop: " + firstInLastOutLinked.Pop());
Console.WriteLine(" [FirstInLastOutLinked] Pop: " + firstInLastOutLinked.Pop());
Console.WriteLine(" [FirstInLastOutLinked] Peek: " + firstInLastOutLinked.Peek());
Console.WriteLine(" [FirstInLastOutLinked] Pop: " + firstInLastOutLinked.Pop());
Console.WriteLine(" [FirstInLastOutLinked] Count: " + firstInLastOutLinked.Count);
firstInLastOutLinked.Clear(); // Clears the firstInLastOut
Console.WriteLine();
}
#endregion
#region FirstInFirstOut (aka Queue)
{
Console.WriteLine(" FirstInFirstOut---------------------------------");
Console.WriteLine();
Console.WriteLine(" An \"FirstInFirstOut\" is a Queue that implements");
Console.WriteLine(" Towel.DataStructures.DataStructure. \"FirstInFirstOutArray\" is");
Console.WriteLine(" the array implementation while \"FirstInFirstOutLinked\" is the");
Console.WriteLine(" the linked-list implementation. A FirstInFirstOut/Stack is used");
Console.WriteLine(" specifically when you need the algorithm provided by the Queue");
Console.WriteLine(" and Dequeue functions.");
Console.WriteLine();
IFirstInFirstOut <int> firstInFirstOutArray = new FirstInFirstOutArray <int>();
Console.Write(" [FirstInFirstOutArray] Enqueuing (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
firstInFirstOutArray.Enqueue(i);
}
Console.WriteLine();
Console.Write(" [FirstInFirstOutArray] Traversal: ");
firstInFirstOutArray.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" [FirstInFirstOutArray] Dequeue: " + firstInFirstOutArray.Dequeue());
Console.WriteLine(" [FirstInFirstOutArray] Dequeue: " + firstInFirstOutArray.Dequeue());
Console.WriteLine(" [FirstInFirstOutArray] Peek: " + firstInFirstOutArray.Peek());
Console.WriteLine(" [FirstInFirstOutArray] Dequeue: " + firstInFirstOutArray.Dequeue());
Console.WriteLine(" [FirstInFirstOutArray] Count: " + firstInFirstOutArray.Count);
firstInFirstOutArray.Clear(); // Clears the firstInLastOut
Console.WriteLine();
IFirstInFirstOut <int> firstInFirstOutLinked = new FirstInFirstOutLinked <int>();
Console.Write(" [FirstInFirstOutLinked] Enqueuing (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
firstInFirstOutLinked.Enqueue(i);
}
Console.WriteLine();
Console.Write(" [FirstInFirstOutLinked] Traversal: ");
firstInFirstOutLinked.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" [FirstInFirstOutLinked] Pop: " + firstInFirstOutLinked.Dequeue());
Console.WriteLine(" [FirstInFirstOutLinked] Pop: " + firstInFirstOutLinked.Dequeue());
Console.WriteLine(" [FirstInFirstOutLinked] Peek: " + firstInFirstOutLinked.Peek());
Console.WriteLine(" [FirstInFirstOutLinked] Pop: " + firstInFirstOutLinked.Dequeue());
Console.WriteLine(" [FirstInFirstOutLinked] Count: " + firstInFirstOutLinked.Count);
firstInFirstOutLinked.Clear(); // Clears the firstInLastOut
Console.WriteLine();
}
#endregion
#region Heap
{
Console.WriteLine(" Heap---------------------------------");
Console.WriteLine();
Console.WriteLine(" An \"Heap\" is a binary tree that stores items based on priorities.");
Console.WriteLine(" It implements Towel.DataStructures.DataStructure like the others.");
Console.WriteLine(" It uses sifting algorithms to move nodes vertically through itself.");
Console.WriteLine(" It is often the best data structure for standard priority queues.");
Console.WriteLine(" \"HeapArray\" is an implementation where the tree has been flattened");
Console.WriteLine(" into an array.");
Console.WriteLine();
Console.WriteLine(" Let's say the priority is how close a number is to \"5\".");
Console.WriteLine(" So \"Dequeue\" will give us the next closest value to \"5\".");
Comparison Priority(int a, int b)
{
int _a = Compute.AbsoluteValue(a - 5);
int _b = Compute.AbsoluteValue(b - 5);
Comparison comparison = Compare.Wrap(_b.CompareTo(_a));
return(comparison);
}
Console.WriteLine();
IHeap <int> heapArray = new HeapArray <int>(Priority);
Console.Write(" [HeapArray] Enqueuing (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
heapArray.Enqueue(i);
}
Console.WriteLine();
Console.WriteLine(" [HeapArray] Dequeue: " + heapArray.Dequeue());
Console.WriteLine(" [HeapArray] Dequeue: " + heapArray.Dequeue());
Console.WriteLine(" [HeapArray] Peek: " + heapArray.Peek());
Console.WriteLine(" [HeapArray] Dequeue: " + heapArray.Dequeue());
Console.WriteLine(" [HeapArray] Count: " + heapArray.Count);
heapArray.Clear(); // Clears the heapArray
Console.WriteLine();
}
#endregion
#region Tree
//Console.WriteLine(" Tree-----------------------------");
//Tree<int> tree_Map = new TreeMap<int>(0, Compute.Equal, Hash.Default);
//for (int i = 1; i < test; i++)
//{
// tree_Map.Add(i, i / Compute.SquareRoot(i));
//}
//Console.Write(" Children of 0 (root): ");
//tree_Map.Children(0, (int i) => { Console.Write(i + " "); });
//Console.WriteLine();
//Console.Write(" Children of " + ((int)System.Math.Sqrt(test) - 1) + " (root): ");
//tree_Map.Children(((int)System.Math.Sqrt(test) - 1), (int i) => { Console.Write(i + " "); });
//Console.WriteLine();
//Console.Write(" Traversal: ");
//tree_Map.Stepper((int i) => { Console.Write(i + " "); });
//Console.WriteLine();
//Console.WriteLine();
#endregion
#region AVL Tree
{
Console.WriteLine(" AvlTree------------------------------------------------");
Console.WriteLine();
Console.WriteLine(" An AVL Tree is a sorted binary tree.");
Console.WriteLine(" It implements Towel.DataStructures.DataStructure like the others.");
Console.WriteLine(" It allows for very fast 1D ranged queries/traversals.");
Console.WriteLine(" It is very similar to an Red Black tree, but uses a different sorting algorithm.");
Console.WriteLine();
IAvlTree <int> avlTree = new AvlTreeLinked <int>();
Console.Write(" Adding (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
avlTree.Add(i);
}
Console.WriteLine();
Console.Write(" Traversal: ");
avlTree.Stepper(i => Console.Write(i));
Console.WriteLine();
//// Note: Because the nodes in AVL Tree linked do not have
//// a parent pointer, the IEnumerable "foreach" iteration
//// is extremely slow and should be avoided. It requires
//// a stack for it's iteration.
//
//Console.Write(" Traversal Foreach: ");
//foreach (int i in avlTree)
//{
// Console.Write(i);
//}
//Console.WriteLine();
int minimum = random.Next(1, test / 2);
int maximum = random.Next(1, test / 2) + test / 2;
Console.Write(" Ranged Traversal [" + minimum + "-" + maximum + "]: ");
avlTree.Stepper(i => Console.Write(i), minimum, maximum);
Console.WriteLine();
int removal = random.Next(0, test);
Console.Write(" Remove(" + removal + "): ");
avlTree.Remove(removal);
avlTree.Stepper(i => Console.Write(i));
Console.WriteLine();
int contains = random.Next(0, test);
Console.WriteLine(" Contains(" + contains + "): " + avlTree.Contains(contains));
Console.WriteLine(" Current Least: " + avlTree.CurrentLeast);
Console.WriteLine(" Current Greatest: " + avlTree.CurrentGreatest);
Console.WriteLine(" Count: " + avlTree.Count);
avlTree.Clear(); // Clears the AVL tree
Console.WriteLine();
}
#endregion
#region Red-Black Tree
{
Console.WriteLine(" Red-Black Tree------------------------------------------------");
Console.WriteLine();
Console.WriteLine(" An Red-Black Tree is a sorted binary tree.");
Console.WriteLine(" It implements Towel.DataStructures.DataStructure like the others.");
Console.WriteLine(" It allows for very fast 1D ranged queries/traversals.");
Console.WriteLine(" It is very similar to an AVL tree, but uses a different sorting algorithm.");
Console.WriteLine();
IRedBlackTree <int> redBlackTree = new RedBlackTreeLinked <int>();
Console.Write(" Adding (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
redBlackTree.Add(i);
}
Console.WriteLine();
Console.Write(" Traversal: ");
redBlackTree.Stepper(i => Console.Write(i));
Console.WriteLine();
int minimum = random.Next(1, test / 2);
int maximum = random.Next(1, test / 2) + test / 2;
Console.Write(" Ranged Traversal [" + minimum + "-" + maximum + "]: ");
redBlackTree.Stepper(i => Console.Write(i), minimum, maximum);
Console.WriteLine();
int removal = random.Next(0, test);
Console.Write(" Remove(" + removal + "): ");
redBlackTree.Remove(removal);
redBlackTree.Stepper(i => Console.Write(i));
Console.WriteLine();
int contains = random.Next(0, test);
Console.WriteLine(" Contains(" + contains + "): " + redBlackTree.Contains(contains));
Console.WriteLine(" Current Least: " + redBlackTree.CurrentLeast);
Console.WriteLine(" Current Greatest: " + redBlackTree.CurrentGreatest);
Console.WriteLine(" Count: " + redBlackTree.Count);
redBlackTree.Clear(); // Clears the Red Black tree
Console.WriteLine();
}
#endregion
#region BTree
{
Console.WriteLine(" B Tree------------------------------------------------");
Console.WriteLine();
Console.WriteLine(" A B Tree is a sorted binary tree that allows multiple values to");
Console.WriteLine(" be stored per node. This makes it sort of a hybrid between a");
Console.WriteLine(" binary tree and an array. Because multiple values are stored ");
Console.WriteLine(" per node, it means less nodes must be traversed to completely");
Console.WriteLine(" traverse the values in the B tree.");
Console.WriteLine();
Console.WriteLine(" The generic B Tree in Towel is still in development.");
Console.WriteLine();
}
#endregion
#region Set
{
Console.WriteLine(" Set------------------------------------------------");
Console.WriteLine();
Console.WriteLine(" A Set is like an Addable/List, but it does not allow duplicates. Sets are");
Console.WriteLine(" usually implemented using hash codes. Implementations with hash codes");
Console.WriteLine(" usually have very fast \"Contains\" checks to see if a value has already");
Console.WriteLine(" been added to the set.");
Console.WriteLine();
ISet <int> setHashLinked = new SetHashLinked <int>();
Console.Write(" Adding (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
setHashLinked.Add(i);
}
Console.WriteLine();
Console.Write(" Traversal: ");
setHashLinked.Stepper(i => Console.Write(i));
Console.WriteLine();
int a = random.Next(0, test);
setHashLinked.Remove(a);
Console.Write(" Remove(" + a + "): ");
setHashLinked.Stepper(i => Console.Write(i));
Console.WriteLine();
int b = random.Next(0, test);
Console.WriteLine(" Contains(" + b + "): " + setHashLinked.Contains(b));
Console.WriteLine(" Count: " + setHashLinked.Count);
Console.WriteLine();
}
#endregion
#region Map (aka Dictionary)
{
Console.WriteLine(" Map------------------------------------------------");
Console.WriteLine();
Console.WriteLine(" A Map (aka Dictionary) is similar to a Set, but it stores two values (a ");
Console.WriteLine(" key and a value). Maps do not allow duplicate keys much like Sets don't");
Console.WriteLine(" allow duplicate values. When provided with the key, the Map uses that key");
Console.WriteLine(" to look up the value that it is associated with. Thus, it allows you to ");
Console.WriteLine(" \"map\" one object to another. As with Sets, Maps are usually implemented");
Console.WriteLine(" using hash codes.");
Console.WriteLine();
// Note: the first generic is the value, the second is the key
IMap <string, int> mapHashLinked = new MapHashLinked <string, int>();
Console.WriteLine(" Let's map each int to its word representation (ex 1 -> One).");
Console.Write(" Adding (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
mapHashLinked.Add(i, ((decimal)i).ToEnglishWords());
}
Console.WriteLine();
Console.WriteLine(" Traversal: ");
mapHashLinked.Keys(i => Console.WriteLine(" " + i + "->" + mapHashLinked[i]));
Console.WriteLine();
int a = random.Next(0, test);
mapHashLinked.Remove(a);
Console.Write(" Remove(" + a + "): ");
mapHashLinked.Keys(i => Console.Write(i));
Console.WriteLine();
int b = random.Next(0, test);
Console.WriteLine(" Contains(" + b + "): " + mapHashLinked.Contains(b));
Console.WriteLine(" Count: " + mapHashLinked.Count);
Console.WriteLine();
}
#endregion
#region OmnitreePoints
{
Console.WriteLine(" OmnitreePoints--------------------------------------");
Console.WriteLine();
Console.WriteLine(" An Omnitree is an ND SPT that allows for");
Console.WriteLine(" multidimensional sorting. Any time you need to look");
Console.WriteLine(" items up based on multiple fields/properties, then");
Console.WriteLine(" you might want to use an Omnitree. If you need to");
Console.WriteLine(" perform ranged queries on multiple dimensions, then");
Console.WriteLine(" the Omnitree is the data structure for you.");
Console.WriteLine();
Console.WriteLine(" The \"OmnitreePoints\" stores individual points (vectors),");
Console.WriteLine(" and the \"OmnitreeBounds\" stores bounded objects (spaces).");
Console.WriteLine();
IOmnitreePoints <int, double, string, decimal> omnitree =
new OmnitreePointsLinked <int, double, string, decimal>(
// This is a location delegate. (how to locate the item along each dimension)
(int index, out double a, out string b, out decimal c) =>
{
a = index;
b = index.ToString();
c = index;
});
Console.Write(" Adding (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
omnitree.Add(i);
}
Console.WriteLine();
Console.Write(" Traversal: ");
omnitree.Stepper(i => Console.Write(i));
Console.WriteLine();
int minimumXZ = random.Next(1, test / 2);
int maximumXZ = random.Next(1, test / 2) + test / 2;
string minimumY = minimumXZ.ToString();
string maximumY = maximumXZ.ToString();
Console.Write(" Spacial Traversal [" +
"(" + minimumXZ + ", \"" + minimumY + "\", " + minimumXZ + ")->" +
"(" + maximumXZ + ", \"" + maximumY + "\", " + maximumXZ + ")]: ");
omnitree.Stepper(i => Console.Write(i),
minimumXZ, maximumXZ,
minimumY, maximumY,
minimumXZ, maximumXZ);
Console.WriteLine();
// Note: this "look up" is just a very narrow spacial query that (since we know the data)
// wil only give us one result.
int lookUp = random.Next(0, test);
string lookUpToString = lookUp.ToString();
Console.Write(" Look Up (" + lookUp + ", \"" + lookUpToString + "\", " + lookUp + "): ");
omnitree.Stepper(i => Console.Write(i),
lookUp, lookUp,
lookUp.ToString(), lookUp.ToString(),
lookUp, lookUp);
Console.WriteLine();
// Ignoring dimensions on traversals example.
// If you want to ignore a column on a traversal, you can do so like this:
omnitree.Stepper(i => { /*Do Nothing*/ },
lookUp, lookUp,
Omnitree.Bound <string> .None, Omnitree.Bound <string> .None,
Omnitree.Bound <decimal> .None, Omnitree.Bound <decimal> .None);
Console.Write(" Counting Items In a Space [" +
"(" + minimumXZ + ", \"" + minimumY + "\", " + minimumXZ + ")->" +
"(" + maximumXZ + ", \"" + maximumY + "\", " + maximumXZ + ")]: ");
omnitree.CountSubSpace(
minimumXZ, maximumXZ,
minimumY, maximumY,
minimumXZ, maximumXZ);
Console.WriteLine();
int removalMinimum = random.Next(1, test / 2);
int removalMaximum = random.Next(1, test / 2) + test / 2;
string removalMinimumY = removalMinimum.ToString();
string removalMaximumY = removalMaximum.ToString();
Console.Write(" Remove (" + removalMinimum + "-" + removalMaximum + "): ");
omnitree.Remove(
removalMinimum, removalMaximum,
removalMinimumY, removalMaximumY,
removalMinimum, removalMaximum);
omnitree.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" Dimensions: " + omnitree.Dimensions);
Console.WriteLine(" Count: " + omnitree.Count);
omnitree.Clear(); // Clears the Omnitree
Console.WriteLine();
}
#endregion
#region OmnitreeBounds
{
Console.WriteLine(" OmnitreeBounds--------------------------------------");
Console.WriteLine();
Console.WriteLine(" An Omnitree is an ND SPT that allows for");
Console.WriteLine(" multidimensional sorting. Any time you need to look");
Console.WriteLine(" items up based on multiple fields/properties, then");
Console.WriteLine(" you might want to use an Omnitree. If you need to");
Console.WriteLine(" perform ranged queries on multiple dimensions, then");
Console.WriteLine(" the Omnitree is the data structure for you.");
Console.WriteLine();
Console.WriteLine(" The \"OmnitreePoints\" stores individual points (vectors),");
Console.WriteLine(" and the \"OmnitreeBounds\" stores bounded objects (spaces).");
Console.WriteLine();
IOmnitreeBounds <int, double, string, decimal> omnitree =
new OmnitreeBoundsLinked <int, double, string, decimal>(
// This is a location delegate. (how to locate the item along each dimension)
(int index,
out double min1, out double max1,
out string min2, out string max2,
out decimal min3, out decimal max3) =>
{
string indexToString = index.ToString();
min1 = index; max1 = index;
min2 = indexToString; max2 = indexToString;
min3 = index; max3 = index;
});
Console.Write(" Adding (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
omnitree.Add(i);
}
Console.WriteLine();
Console.Write(" Traversal: ");
omnitree.Stepper(i => Console.Write(i));
Console.WriteLine();
int minimumXZ = random.Next(1, test / 2);
int maximumXZ = random.Next(1, test / 2) + test / 2;
string minimumY = minimumXZ.ToString();
string maximumY = maximumXZ.ToString();
Console.Write(" Spacial Traversal [" +
"(" + minimumXZ + ", \"" + minimumY + "\", " + minimumXZ + ")->" +
"(" + maximumXZ + ", \"" + maximumY + "\", " + maximumXZ + ")]: ");
omnitree.StepperOverlapped(i => Console.Write(i),
minimumXZ, maximumXZ,
minimumY, maximumY,
minimumXZ, maximumXZ);
Console.WriteLine();
// Note: this "look up" is just a very narrow spacial query that (since we know the data)
// wil only give us one result.
int lookUpXZ = random.Next(0, test);
string lookUpY = lookUpXZ.ToString();
Console.Write(" Look Up (" + lookUpXZ + ", \"" + lookUpY + "\", " + lookUpXZ + "): ");
omnitree.StepperOverlapped(i => Console.Write(i),
lookUpXZ, lookUpXZ,
lookUpY, lookUpY,
lookUpXZ, lookUpXZ);
Console.WriteLine();
// Ignoring dimensions on traversals example.
// If you want to ignore a dimension on a traversal, you can do so like this:
omnitree.StepperOverlapped(i => { /*Do Nothing*/ },
lookUpXZ, lookUpXZ,
// The "None" means there is no bound, so all values are valid
Omnitree.Bound <string> .None, Omnitree.Bound <string> .None,
Omnitree.Bound <decimal> .None, Omnitree.Bound <decimal> .None);
Console.Write(" Counting Items In a Space [" +
"(" + minimumXZ + ", \"" + minimumY + "\", " + minimumXZ + ")->" +
"(" + maximumXZ + ", \"" + maximumY + "\", " + maximumXZ + ")]: " +
omnitree.CountSubSpaceOverlapped(
minimumXZ, maximumXZ,
minimumY, maximumY,
minimumXZ, maximumXZ));
Console.WriteLine();
int removalMinimumXZ = random.Next(1, test / 2);
int removalMaximumXZ = random.Next(1, test / 2) + test / 2;
string removalMinimumY = removalMinimumXZ.ToString();
string removalMaximumY = removalMaximumXZ.ToString();
Console.Write(" Remove (" + removalMinimumXZ + "-" + removalMaximumXZ + "): ");
omnitree.RemoveOverlapped(
removalMinimumXZ, removalMaximumXZ,
removalMinimumY, removalMaximumY,
removalMinimumXZ, removalMaximumXZ);
omnitree.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" Dimensions: " + omnitree.Dimensions);
Console.WriteLine(" Count: " + omnitree.Count);
omnitree.Clear(); // Clears the Omnitree
Console.WriteLine();
}
#endregion
#region KD Tree
{
Console.WriteLine(" KD Tree------------------------------------------------");
Console.WriteLine();
Console.WriteLine(" A KD Tree binary tree that stores points sorted along along an");
Console.WriteLine(" arbitrary number of dimensions. So it performs multidimensional");
Console.WriteLine(" sorting similar to the Omnitree (Quadtree/Octree) in Towel, but");
Console.WriteLine(" it uses a completely different algorithm and format.");
Console.WriteLine();
Console.WriteLine(" The generic KD Tree in Towel is still in development.");
Console.WriteLine();
}
#endregion
#region Graph
{
Console.WriteLine(" Graph------------------------------------------------");
Console.WriteLine();
Console.WriteLine(" A Graph is a data structure of nodes and edges. Nodes are values");
Console.WriteLine(" and edges are connections between those values. Graphs are often");
Console.WriteLine(" used to model real world data such as maps, and are often used in");
Console.WriteLine(" path finding algoritms. See the \"Algorithms\" example for path");
Console.WriteLine(" finding examples. This is just an example of how to make a graph.");
Console.WriteLine(" A \"GraphSetOmnitree\" is an implementation where nodes are stored.");
Console.WriteLine(" in a Set and edges are stored in an Omnitree (aka Quadtree).");
Console.WriteLine();
IGraph <int> graphSetOmnitree = new GraphSetOmnitree <int>();
Console.WriteLine(" Adding Nodes (0-" + (test - 1) + ")...");
for (int i = 0; i < test; i++)
{
graphSetOmnitree.Add(i);
}
int edgesPerNode = 3;
Console.WriteLine(" Adding Random Edges (0-3 per node)...");
for (int i = 0; i < test; i++)
{
// lets use a heap to randomize the edges using random priorities
IHeap <(int, int)> heap = new HeapArray <(int, int)>((x, y) => Compare.Wrap(x.Item2.CompareTo(y.Item2)));
for (int j = 0; j < test; j++)
{
if (j != i)
{
heap.Enqueue((j, random.Next()));
}
}
// dequeue some random edges from the heap and add them to the graph
int randomEdgeCount = random.Next(edgesPerNode + 1);
for (int j = 0; j < randomEdgeCount; j++)
{
graphSetOmnitree.Add(i, heap.Dequeue().Item1);
}
}
Console.Write(" Nodes (Traversal): ");
graphSetOmnitree.Stepper(i => Console.Write(i));
Console.WriteLine();
Console.WriteLine(" Edges (Traversal): ");
graphSetOmnitree.Stepper((from, to) => Console.WriteLine(" " + from + "->" + to));
Console.WriteLine();
int a = random.Next(0, test);
Console.Write(" Neighbors (" + a + "):");
graphSetOmnitree.Neighbors(a, i => Console.Write(" " + i));
Console.WriteLine();
int b = random.Next(0, test / 2);
int c = random.Next(test / 2, test);
Console.WriteLine(" Are Adjacent (" + b + ", " + c + "): " + graphSetOmnitree.Adjacent(b, c));
Console.WriteLine(" Node Count: " + graphSetOmnitree.NodeCount);
Console.WriteLine(" Edge Count: " + graphSetOmnitree.EdgeCount);
graphSetOmnitree.Clear(); // Clears the graph
Console.WriteLine();
}
#endregion
#region Trie
{
Console.WriteLine(" Trie------------------------------------------------");
Console.WriteLine();
Console.WriteLine(" A Trie is a tree where portions of the data are stored in each node");
Console.WriteLine(" such that when you traverse the tree to a leaf, you have read the contents");
Console.WriteLine(" of that leaf along the way. Because of this, a Trie allows for its values");
Console.WriteLine(" to share data, which is a form of compression. So a Trie may be used to save");
Console.WriteLine(" memory. A trie may also be a very useful tool in pattern matching, because it");
Console.WriteLine(" it allows for culling based are portions of the data.");
Console.WriteLine();
Console.WriteLine(" The generic Trie in Towel is still in development.");
Console.WriteLine();
}
#endregion
Console.WriteLine("============================================");
Console.WriteLine("Examples Complete...");
Console.ReadLine();
}
static void Main(string[] args)
{
Random random = new Random();
int test = 10;
Console.WriteLine("You are runnning the Data Structures example.");
Console.WriteLine("======================================================");
Console.WriteLine();
#region Link
Console.WriteLine(" Testing Link-------------------------------");
Console.WriteLine(" Size: 6");
Link link = new Link <int, int, int, int, int, int>(0, 1, 2, 3, 4, 5);
Console.Write(" Traversal: ");
link.Stepper((dynamic current) => { Console.Write(current); });
Console.WriteLine();
// Saving to a file
//string linklink_file = "link." + ToExtension(link.GetType());
//Console.WriteLine(" File: \"" + linklink_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(linklink_file, link));
//Link<int, int, int, int, int, int> deserialized_linklink;
//Console.WriteLine(" Deserialized: " + Deserialize(linklink_file, out deserialized_linklink));
Console.WriteLine();
#endregion
#region Array
Console.WriteLine(" Testing Array_Array<int>-------------------");
Indexed <int> array = new IndexedArray <int>(test);
for (int i = 0; i < test; i++)
{
array[i] = i;
}
Console.Write(" Traversal: ");
array.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
// Saving to a file
//string arrayarray_file = "array." + ToExtension(array.GetType());
//Console.WriteLine(" File: \"" + arrayarray_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(arrayarray_file, array));
//ArrayArray<int> deserialized_arrayarray;
//Console.WriteLine(" Deserialized: " + Deserialize(arrayarray_file, out deserialized_arrayarray));
Console.WriteLine();
#endregion
#region List
Console.WriteLine(" Testing List_Array<int>--------------------");
Addable <int> list_array = new AddableArray <int>(test);
for (int i = 0; i < test; i++)
{
list_array.Add(i);
}
Console.Write(" Traversal: ");
list_array.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
//string list_array_serialization = (list_array as ListArray<int>).Serialize(x => x.ToString());
//using (StreamWriter writer = new StreamWriter("ListArray.ListArray"))
//{
// writer.WriteLine(list_array_serialization);
//}
//using (StreamReader reader = new StreamReader("ListArray.ListArray"))
//{
// list_array = ListArray<int>.Deserialize(reader.ReadToEnd(), x => Int16.Parse(x.Trim()));
//}
//Console.Write(" Serialization/Deserialization is possible.");
list_array.Add(11);
list_array.Remove(7);
Console.WriteLine();
Console.WriteLine();
//ListArray<ListArray<int>> list_array2 = new ListArray<ListArray<int>>(test);
//for (int i = 0; i < test; i++)
//{
// ListArray<int> nested_list = new ListArray<int>();
// for (int j = 0; j < test; j++)
// {
// nested_list.Add(j);
// }
// list_array2.Add(nested_list);
//}
//string list_array2_serialization = list_array2.Serialize(x => x.Serialize(y => y.ToString()));
//using (StreamWriter writer = new StreamWriter("ListArray2.ListArray"))
//{
// writer.WriteLine(list_array2_serialization);
//}
//using (StreamReader reader = new StreamReader("ListArray2.ListArray"))
//{
// list_array2 = ListArray<ListArray<int>>.Deserialize(reader.ReadToEnd(), x => ListArray<int>.Deserialize(x, y => Int16.Parse(y.Trim())));
//}
Console.WriteLine(" Testing List_Linked<int>-------------------");
Addable <int> list_linked = new AddableLinked <int>();
for (int i = 0; i < test; i++)
{
list_linked.Add(i);
}
Console.Write(" Traversal: ");
list_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
// Saving to a file
//string listlinked_file = "list_linked." + ToExtension(list_linked.GetType());
//Console.WriteLine(" File: \"" + listlinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(listlinked_file, list_linked));
//ListLinked<int> deserialized_listlinked;
//Console.WriteLine(" Deserialized: " + Deserialize(listlinked_file, out deserialized_listlinked));
Console.WriteLine();
#endregion
#region Stack
Console.WriteLine(" Testing Stack_Linked<int>------------------");
FirstInLastOut <int> stack_linked = new FirstInLastOutLinked <int>();
for (int i = 0; i < test; i++)
{
stack_linked.Push(i);
}
Console.Write(" Traversal: ");
stack_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
// Saving to a file
//string stacklinked_file = "stack_linked." + ToExtension(stack_linked.GetType());
//Console.WriteLine(" File: \"" + stacklinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(stacklinked_file, stack_linked));
//StackLinked<int> deserialized_stacklinked;
//Console.WriteLine(" Deserialized: " + Deserialize(stacklinked_file, out deserialized_stacklinked));
Console.WriteLine();
#endregion
#region Queue
Console.WriteLine(" Testing Queue_Linked<int>------------------");
FirstInFirstOut <int> queue_linked = new FirstInFirstOutLinked <int>();
for (int i = 0; i < test; i++)
{
queue_linked.Enqueue(i);
}
Console.Write(" Traversal: ");
queue_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
// Saving to a file
//string queuelinked_file = "queue_linked." + ToExtension(queue_linked.GetType());
//Console.WriteLine(" File: \"" + queuelinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(queuelinked_file, queue_linked));
//QueueLinked<int> deserialized_queuelinked;
//Console.WriteLine(" Deserialized: " + Deserialize(queuelinked_file, out deserialized_queuelinked));
Console.WriteLine();
#endregion
#region Heap
Console.WriteLine(" Testing Heap_Array<int>--------------------");
Heap <int> heap_array = new HeapArray <int>(Compute.Compare);
for (int i = 0; i < test; i++)
{
heap_array.Enqueue(i);
}
Console.Write(" Delegate: ");
heap_array.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
// Saving to a file
//string heaplinked_file = "heap_array." + ToExtension(heap_array.GetType());
//Console.WriteLine(" File: \"" + heaplinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(heaplinked_file, heap_array));
//HeapArray<int> deserialized_heaplinked;
//Console.WriteLine(" Deserialized: " + Deserialize(heaplinked_file, out deserialized_heaplinked));
Console.WriteLine();
#endregion
#region Tree
Console.WriteLine(" Testing Tree_Map<int>----------------------");
Tree <int> tree_Map = new TreeMap <int>(0, Compute.Equal, Hash.Default);
for (int i = 1; i < test; i++)
{
tree_Map.Add(i, i / (int)System.Math.Sqrt(test));
}
Console.Write(" Children of 0 (root): ");
tree_Map.Children(0, (int i) => { Console.Write(i + " "); });
Console.WriteLine();
Console.Write(" Children of " + ((int)System.Math.Sqrt(test) - 1) + " (root): ");
tree_Map.Children(((int)System.Math.Sqrt(test) - 1), (int i) => { Console.Write(i + " "); });
Console.WriteLine();
Console.Write(" Traversal: ");
tree_Map.Stepper((int i) => { Console.Write(i + " "); });
Console.WriteLine();
// Saving to a file
//string treelinked_file = "tree_Map." + ToExtension(tree_Map.GetType());
//Console.WriteLine(" File: \"" + treelinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(treelinked_file, tree_Map));
//TreeMap<int> deserialized_treelinked;
//Console.WriteLine(" Deserialized: " + Deserialize(treelinked_file, out deserialized_treelinked));
Console.WriteLine();
#endregion
#region AVL Tree
//Console.WriteLine(" Testing AvlTree_Linked<int>----------------");
//// Construction
//AvlTree<int> avlTree_linked = new AvlTree_Linked<int>(Logic.compare);
//// Adding Items
//Console.Write(" Adding (0-" + test + ")...");
//for (int i = 0; i < test; i++)
// avlTree_linked.Add(i);
//Console.WriteLine();
//// Iteration
//Console.Write(" Traversal: ");
//avlTree_linked.Stepper((int current) => { Console.Write(current); });
//Console.WriteLine();
//// Removal
//int avl_tree_linked_removal = random.Next(0, test);
//avlTree_linked.Remove(avl_tree_linked_removal);
//Console.Write(" Remove(" + avl_tree_linked_removal + "): ");
//avlTree_linked.Stepper((int current) => { Console.Write(current); });
//Console.WriteLine();
//// Look Up Items
//int avl_tree_linked_lookup = random.Next(0, test);
//while (avl_tree_linked_lookup == avl_tree_linked_removal)
// avl_tree_linked_lookup = random.Next(0, test);
//Console.WriteLine(" Look Up (" + avl_tree_linked_lookup + "): " + avlTree_linked.TryGet(avl_tree_linked_lookup, Logic.compare, out temp));
//Console.WriteLine(" Look Up (" + avl_tree_linked_removal + "): " + avlTree_linked.TryGet(avl_tree_linked_removal, Logic.compare, out temp));
//avlTree_linked.Get(avl_tree_linked_lookup, Logic.compare);
//// Current Min-Max Values
//Console.WriteLine(" Least: " + avlTree_linked.CurrentLeast + " Greatest: " + avlTree_linked.CurrentGreatest);
//// Saving to a file
//string avltreelinked_file = "avlTree_linked." + ToExtension(avlTree_linked.GetType());
//Console.WriteLine(" File: \"" + avltreelinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(avltreelinked_file, avlTree_linked));
//AvlTree_Linked<int> deserialized_avltreelinked;
//Console.WriteLine(" Deserialized: " + Deserialize(avltreelinked_file, out deserialized_avltreelinked));
//Console.WriteLine();
#endregion
#region Red-Black Tree
Console.WriteLine(" Testing RedBlack_Linked<int>---------------");
RedBlackTree <int> redBlackTree_linked = new RedBlackTreeLinked <int>(Compute.Compare);
for (int i = 0; i < test; i++)
{
redBlackTree_linked.Add(i);
}
Console.Write(" Traversal: ");
redBlackTree_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
// Saving to a file
//string redblacktreelinked_file = "redBlackTree_linked." + ToExtension(redBlackTree_linked.GetType());
//Console.WriteLine(" File: \"" + redblacktreelinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(redblacktreelinked_file, redBlackTree_linked));
//RedBlackTreeLinked<int> deserialized_redblacktreelinked;
//Console.WriteLine(" Deserialized: " + Deserialize(redblacktreelinked_file, out deserialized_redblacktreelinked));
Console.WriteLine();
#endregion
#region BTree
//Console.WriteLine(" Testing BTree_LinkedArray<int>-------------");
//BTree<int> btree_linked = new BTree_LinkedArray<int>(Logic.compare, 3);
//for (int i = 0; i < test; i++)
// btree_linked.Add(i);
//Console.Write(" Delegate: ");
//btree_linked.Stepper((int current) => { Console.Write(current); });
//Console.WriteLine();
//Console.Write(" IEnumerator: ");
//foreach (int current in btree_linked)
// Console.Write(current);
//Console.WriteLine();
//Console.WriteLine(" Press Enter to continue...");
//string maplinked_file = "maplinked.quad";
//Console.WriteLine(" File: \"" + maplinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(maplinked_file, hashTable_linked));
//Omnitree_LinkedLinkedLists<int, double> deserialized_maplinked;
//Console.WriteLine(" Deserialized: " + Deserialize(maplinked_file, out deserialized_maplinked));
//Console.ReadLine();
//Console.WriteLine();
#endregion
#region Set
Console.WriteLine(" Testing Set_Hash<int>----------------------");
Set <int> set_linked = new SetHashList <int>(Compute.Equal, Hash.Default);
for (int i = 0; i < test; i++)
{
set_linked.Add(i);
}
// Traversal
Console.Write(" Traversal: ");
set_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.Write(" Table Size: " + (set_linked as SetHashList <int>).TableSize);
Console.WriteLine();
Console.WriteLine();
#endregion
#region Map
Console.WriteLine(" Testing MapHashList<int, int>--------------");
Map <int, int> map_sethash = new MapHashLinked <int, int>(Compute.Equal, Hash.Default);
for (int i = 0; i < test; i++)
{
map_sethash.Add(i, i);
}
Console.Write(" Look Ups: ");
for (int i = 0; i < test; i++)
{
Console.Write(map_sethash[i]);
}
Console.WriteLine();
// Traversal
Console.Write(" Traversal: ");
map_sethash.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.Write(" Table Size: " + (map_sethash as MapHashLinked <int, int>).TableSize);
Console.WriteLine();
Console.WriteLine();
#endregion
#region OmnitreePoints
{
Console.WriteLine(" Testing OmnitreeLinkedLinked<int, double>-------");
// Construction
OmnitreePoints <int, double, double, double> omnitree_linked = new OmnitreePointsLinked <int, double, double, double>(
(int index, out double a, out double b, out double c) => { a = index; b = index; c = index; }); // axis average function
// Properties
Console.WriteLine(" Dimensions: " + omnitree_linked.Dimensions);
Console.WriteLine(" Count: " + omnitree_linked.Count);
// Addition
Console.Write(" Adding 0-" + test + ": ");
for (int i = 0; i < test; i++)
{
omnitree_linked.Add(i);
}
omnitree_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.WriteLine(" Count: " + omnitree_linked.Count);
// Traversal
Console.Write(" Traversal [ALL]: ");
omnitree_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
// Look Up 1
Console.Write(" Traversal [(" + (test / 2) + ", " + (test / 2) + ", " + (test / 2) + ")->(" + test + ", " + test + ", " + test + ")]: ");
omnitree_linked.Stepper((int current) => { Console.Write(current); },
test / 2, test,
test / 2, test,
test / 2, test);
Console.WriteLine();
// Look Up 2
Console.Write(" Look Up [" + (test / 3) + ", " + (test / 3) + ", " + (test / 3) + "]: ");
omnitree_linked[(test / 3), (test / 3), (test / 3)]((int current) => { Console.Write(current); });
Console.WriteLine();
// Removal
Console.Write(" Remove 0-" + test / 3 + ": ");
omnitree_linked.Remove(
0, test / 3,
0, test / 3,
0, test / 3);
omnitree_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.WriteLine(" Count: " + omnitree_linked.Count);
// Clear
Console.Write(" Clear: ");
omnitree_linked.Clear();
omnitree_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.WriteLine(" Count: " + omnitree_linked.Count);
// Saving to a file
//string omnitreelinked_file = "omnitree_linkedlinkedlists." + ToExtension(omnitree_linked.GetType());
//Console.WriteLine(" File: \"" + omnitreelinked_file + "\"");
//Console.WriteLine(" Serialized: " + Serialize(omnitreelinked_file, omnitree_linked));
//OmnitreeLinkedLinkedLists<int, double> deserialized_omnitreeLinked;
//Console.WriteLine(" Deserialized: " + Deserialize(omnitreelinked_file, out deserialized_omnitreeLinked));
Console.WriteLine();
//Console.WriteLine(" Testing Omnitree_LinkedArrayLists<int, double>--------");
//// Construction
//Omnitree<int, double> omnitree_array = new OmnitreeLinkedArray<int, double>(
// new double[] { -test - 1, -test - 1, -test - 1 }, // minimum dimensions of the omnitree
// new double[] { test + 1, test + 1, test + 1 }, // maximum dimensions of the omnitree
// (int index) => { return Accessor.Get(new double[] { index, index, index }); }, // "N-D" location function
// Compute<double>.Compare, // comparison function
// (double a, double b) => { return (a + b) / 2; }); // average function
//// Properties
//Console.WriteLine(" Origin: [" + omnitree_array.Origin(0) + ", " + omnitree_array.Origin(1) + ", " + omnitree_array.Origin(2) + "]");
//Console.WriteLine(" Minimum: [" + omnitree_array.Min(0) + ", " + omnitree_array.Min(1) + ", " + omnitree_array.Min(2) + "]");
//Console.WriteLine(" Maximum: [" + omnitree_array.Max(0) + ", " + omnitree_array.Max(1) + ", " + omnitree_array.Max(2) + "]");
//Console.WriteLine(" Dimensions: " + omnitree_array.Dimensions);
//Console.WriteLine(" Count: " + omnitree_array.Count);
//// Addition
//Console.Write(" Adding 0-" + test + ": ");
//for (int i = 0; i < test; i++)
// omnitree_array.Add(i);
//omnitree_array.Stepper((int current) => { Console.Write(current); });
//Console.WriteLine();
//Console.WriteLine(" Count: " + omnitree_array.Count);
//// Traversal
//Console.Write(" Traversal [ALL]: ");
// omnitree_array.Stepper((int current) => { Console.Write(current); });
//Console.WriteLine();
//// Look Up
//Console.Write(" Traversal [" + (test / 2) + "-" + test + "]: ");
// omnitree_array.Stepper((int current) => { Console.Write(current); },
// new double[] { test / 2, test / 2, test / 2 },
// new double[] { test, test, test });
//Console.WriteLine();
//// Removal
//Console.Write(" Remove 0-" + test / 3 + ": ");
//omnitree_array.Remove(
// new double[] { 0, 0, 0 },
// new double[] { test / 3, test / 3, test / 3 });
//omnitree_array.Stepper((int current) => { Console.Write(current); });
//Console.WriteLine();
//Console.WriteLine(" Count: " + omnitree_array.Count);
//// Clear
//Console.Write(" Clear: ");
//omnitree_array.Clear();
// omnitree_array.Stepper((int current) => { Console.Write(current); });
//Console.WriteLine();
//Console.WriteLine(" Count: " + omnitree_array.Count);
//// Saving to a file
////string omnitreearray_file = "omnitree_linkedarraylists." + ToExtension(omnitree_array.GetType());
////Console.WriteLine(" File: \"" + omnitreearray_file + "\"");
////Console.WriteLine(" Serialized: " + Serialize(omnitreearray_file, omnitree_array));
////OmnitreeLinkedLinkedLists<int, double> deserialized_omnitreearray;
////Console.WriteLine(" Deserialized: " + Deserialize(omnitreearray_file, out deserialized_omnitreearray));
//Console.WriteLine();
}
#endregion
#region OmnitreeBounds
{
Console.WriteLine(" Testing OmnitreeBoundsLinked<int, double>-------");
// Construction
OmnitreeBounds <int, double, double, double> omnitreeBounds_linked = new OmnitreeBoundsLinked <int, double, double, double>(
(int index,
out double min1, out double max1,
out double min2, out double max2,
out double min3, out double max3) =>
{
min1 = index; max1 = index;
min2 = index; max2 = index;
min3 = index; max3 = index;
});
// Properties
Console.WriteLine(" Dimensions: " + omnitreeBounds_linked.Dimensions);
Console.WriteLine(" Count: " + omnitreeBounds_linked.Count);
// Addition
Console.Write(" Adding 0-" + test + ": ");
for (int i = 0; i < test; i++)
{
omnitreeBounds_linked.Add(i);
}
omnitreeBounds_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.WriteLine(" Count: " + omnitreeBounds_linked.Count);
// Traversal
Console.Write(" Traversal [ALL]: ");
omnitreeBounds_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
// Look Up 1
//Console.Write(" Traversal [(" + (test / 2) + ", " + (test / 2) + ", " + (test / 2) + ")->(" + test + ", " + test + ", " + test + ")]: ");
//omnitreeBounds_linked.Stepper((int current) => { Console.Write(current); },
// test / 2, test,
// test / 2, test,
// test / 2, test);
//Console.WriteLine();
// Removal
Console.Write(" Remove 0-" + test / 3 + ": ");
omnitreeBounds_linked.RemoveOverlapped(
0, test / 3,
0, test / 3,
0, test / 3);
omnitreeBounds_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.WriteLine(" Count: " + omnitreeBounds_linked.Count);
// Clear
Console.Write(" Clear: ");
omnitreeBounds_linked.Clear();
omnitreeBounds_linked.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.WriteLine(" Count: " + omnitreeBounds_linked.Count);
Console.WriteLine();
}
#endregion
#region KD Tree
////List<KdTreeNode<float, string>> testNodes = new List_Linked<KdTreeNode<float, string>>();
//KdTree_Linked<string, float> tree = new KdTree_Linked<string, float>(
// 2,
// Logic.compare,
// float.MinValue,
// float.MaxValue,
// 0,
// Arithmetic.Add,
// Arithmetic.Subtract,
// Arithmetic.Multiply);
//List<KdTree_Linked<string, float>.Node> testNodes =
// new List_Linked<KdTree_Linked<string, float>.Node>
//{
// new KdTree_Linked<string, float>.Node(new float[] { 5, 5 }, "Root"),
// new KdTree_Linked<string, float>.Node(new float[] { 2.5f, 2.5f }, "Root-Left"),
// new KdTree_Linked<string, float>.Node(new float[] { 7.5f, 7.5f }, "Root-Right"),
// new KdTree_Linked<string, float>.Node(new float[] { 1, 10 }, "Root-Left-Left"),
// new KdTree_Linked<string, float>.Node(new float[] { 10, 10 }, "Root-Right-Right")
//};
//foreach (var node in testNodes)
// if (!tree.Add(node.Point, node.Value))
// throw new Exception("Failed to add node to tree");
//var nodesToRemove = new KdTreeNode<float, string>[] {
// testNodes[1], // Root-Left
// testNodes[0] // Root
//};
//foreach (var nodeToRemove in nodesToRemove)
//{
// tree.RemoveAt(nodeToRemove.Point);
// testNodes.Remove(nodeToRemove);
// Assert.IsNull(tree.FindValue(nodeToRemove.Value));
// Assert.IsNull(tree.FindValueAt(nodeToRemove.Point));
// foreach (var testNode in testNodes)
// {
// Assert.AreEqual(testNode.Value, tree.FindValueAt(testNode.Point));
// Assert.AreEqual(testNode.Point, tree.FindValue(testNode.Value));
// }
// Assert.AreEqual(testNodes.Count, tree.Count);
//}
#endregion
#region Graph
Console.WriteLine(" Testing Graph_SetOmnitree<int>-------------");
Graph <int> graph = new GraphSetOmnitree <int>(Compute.Equal, Compute.Compare, Hash.Default);
// add nodes
for (int i = 0; i < test; i++)
{
graph.Add(i);
}
// add edges
for (int i = 0; i < test - 1; i++)
{
graph.Add(i, i + 1);
}
Console.Write(" Traversal: ");
graph.Stepper((int current) => { Console.Write(current); });
Console.WriteLine();
Console.WriteLine(" Edges: ");
//((Graph_SetQuadtree<int>)graph)._edges.Foreach((Graph_SetQuadtree<int>.Edge e) => { Console.WriteLine(" " + e.Start + " " + e.End); });
graph.Stepper(
(int current) =>
{
Console.Write(" " + current + ": ");
graph.Neighbors(current,
(int a) =>
{
Console.Write(a);
});
Console.WriteLine();
});
Console.WriteLine();
#endregion
Console.WriteLine("============================================");
Console.WriteLine("Examples Complete...");
Console.ReadLine();
}
IEnumerator Search(Vector3 beginning, Vector3 end)
{
HeapArray <State> openSet = new HeapArray <State>(gridMap.LargestSize);
State goal = gridMap.RetrieveState(end);
State start = gridMap.RetrieveState(beginning);
openSet.Enqueue(start);
HashSet <State> cameFrom = new HashSet <State>();
Vector3[] intermediates = new Vector3[0];
bool foundPath = false;
while (openSet.Count > 0)
{
State present = openSet.Dequeue();
cameFrom.Add(present);
if (present == goal)
{
foundPath = true;
intermediates = ReconstructPath(start, goal);
break;
}
else
{
List <State> adjacents = gridMap.RetrieveAdjacentStates(present);
foreach (State s in adjacents)
{
if (cameFrom.Contains(s) || !(s.unblocked))
{
continue;
}
else
{
int yWeight = Mathf.Abs(present.yCoordinate - s.yCoordinate);
int xWeight = Mathf.Abs(present.xCoordinate - s.xCoordinate);
int edgeWeight = 0;
if (xWeight < yWeight)
{
edgeWeight = 10 * (yWeight - xWeight) + 14 * xWeight;
}
else
{
edgeWeight = 10 * (xWeight - yWeight) + 14 * yWeight;
}
int tentativeCost = present.gOfN + edgeWeight;
if (!(openSet.Contains(s)) || tentativeCost < s.gOfN)
{
s.parent = present;
s.gOfN = tentativeCost;
yWeight = Mathf.Abs(s.yCoordinate - goal.yCoordinate);
xWeight = Mathf.Abs(s.xCoordinate - goal.xCoordinate);
if (xWeight < yWeight)
{
s.hOfN = 10 * (yWeight - xWeight) + 14 * xWeight;
}
else
{
s.hOfN = 10 * (xWeight - yWeight) + 14 * yWeight;
}
if (!openSet.Contains(s))
{
openSet.Enqueue(s);
}
else
{
openSet.UpdateState(s);
}
}
}
}
}
}
yield return(null);
searchHandler.FinishedPresentSearch(intermediates, foundPath);
}
public override Structuring BuildStructuring()
{
try
{
if (Set == null)
throw new NullReferenceException();
int _current = 1;
int _max = Set.ElementsCount;
if (IContainerProgressBar != null)
{
IContainerProgressBar.ResetProgressBar(1, _max, true);
IContainerProgressBar.UpdateProgressBar(1, "Running Hierarchical agglomerative algorithm with Lifetime...", true);
}
double AlfaI = 0, AlfaJ = 0, Beta = 0, Gamma = 0;
//Al inicio cada elemento es un cluster
double[,] DMatrix = new double[Set.Elements.Count, Set.Elements.Count];
List<Cluster> clusters = new List<Cluster>();
List<Cluster> best_clusters = null;
List<double> distances = new List<double>();
bool[] des = new bool[Set.Elements.Count];//true si ese elemento ya no es representativo
for (int i = 0; i < Set.Elements.Count; i++)
{
List<Element> l = new List<Element>();
l.Add(Set[i]);
clusters.Add(new Cluster("C-" + i, l));
distances.Add(0);
}
//Construir para cada cluster una cola con prioridad
//con las disimilitudes de el con el resto de los clusters
//y la matriz de todas las disimilitudes O(n2*log n)
List<HeapArray<Container>> lh = new List<HeapArray<Container>>();
for (int i = 0; i < Set.Elements.Count; i++)
{
lh.Add(new HeapArray<Container>(Set.Elements.Count - 1));//No se pone la diss de un elemento con el mismo
}
for (int i = 0; i < Set.Elements.Count; i++)
{
for (int j = i + 1; j < Set.Elements.Count; j++)
{
double temp_diss = Proximity.CalculateProximity(Set.Elements[i], Set.Elements[j]);
DMatrix[i, j] = temp_diss;
DMatrix[j, i] = temp_diss;
lh[i].Add(new Container { Rank = temp_diss, Name = i, Cluster = j });
lh[j].Add(new Container { Rank = temp_diss, Name = j, Cluster = i });
}
}
//Variables de las stopping rules
double step = Set.ElementsCount - 1, bestST = double.MinValue;
double stoprule = 1;
//Algoritmo O(n2*log n)
for (int i = Set.Elements.Count; i > 2; i--)
{
if (IContainerProgressBar != null)
IContainerProgressBar.UpdateProgressBar(_current++, "Running Hierarchical agglomerative algorithm with Lifetime...", false);
//Seleccionar los 2 clusters mas parecidos O(n)
double min = double.MaxValue;
int cluster_i = 0, cluster_j = 0;
int pos_cluster_i = 0, pos_cluster_j = 0;
for (int j = lh.Count - 1; j >= 0; j--)
{
if (lh[j].First.Rank < min)
{
min = lh[j].First.Rank;
cluster_i = lh[j].First.Name;
cluster_j = lh[j].First.Cluster;
pos_cluster_i = j;
}
}
for (int j = 0; j < lh.Count; j++)
{
if (lh[j].First != null && lh[j].First.Name == cluster_j)
{
pos_cluster_j = j;
break;
}
}
//Calcular posiciones para borrar y guardar
int erase_pos = 0, final_pos = 0;
erase_pos = pos_cluster_i > pos_cluster_j ? pos_cluster_i : pos_cluster_j;
final_pos = pos_cluster_i < pos_cluster_j ? pos_cluster_i : pos_cluster_j;
lh.RemoveAt(erase_pos);
//Actualizar los parametros AlfaI, AlfaJ y Beta
double cluster_i_count = clusters[erase_pos].ElementsCount;
double cluster_j_count = clusters[final_pos].ElementsCount;
AlfaI = UpdateAlfaI(cluster_i_count, cluster_j_count);
AlfaJ = UpdateAlfaJ(cluster_i_count, cluster_j_count);
Beta = UpdateBeta(cluster_i_count, cluster_j_count);
Gamma = UpdateGamma(cluster_i_count, cluster_j_count);
//Llamado al Stopping Rule
stoprule = LifeTimeStoppingRule(distances, final_pos, erase_pos, min);
//stoprule = CHStoppingRule(clusters, step, final_pos);
if (stoprule > bestST)
{
bestST = stoprule;
best_clusters = new List<Cluster>();
foreach (Cluster item in clusters)
{
Cluster temp = new Cluster(item.Name);
foreach (var e in item.Elements)
{
temp.Elements.Add(e);
}
best_clusters.Add(temp);
}
}
step--;
//Unir los clusters
foreach (Element item in clusters[erase_pos].Elements)
{
clusters[final_pos].AddElement(item);
}
clusters.RemoveAt(erase_pos);
//Actualizar DMatrix con la disimilitud del nuevo cluster
//y el resto de los clusters O(n)
//Formula que se usa segun el algoritmo
//La posicion del cluster i en Dmatrix es cluster_i
//La posicion del cluster j en Dmatrix es cluster_j
int pos_h = -1;
int pos_i = cluster_i;
int pos_j = cluster_j;
if (erase_pos == pos_cluster_i)
{
des[cluster_i] = true;
pos_h = cluster_j;
}
else
{
des[cluster_j] = true;
pos_h = cluster_i;
}
for (int k = 0; k < DMatrix.GetLength(0); k++)
{
DMatrix[pos_h, k] = AlfaI * DMatrix[pos_i, k] + AlfaJ * DMatrix[pos_j, k] + Beta * DMatrix[pos_i, pos_j] + Gamma * Math.Abs(DMatrix[pos_i, k] - DMatrix[pos_j, k]);
DMatrix[k, pos_h] = DMatrix[pos_h, k];
}
//Actualizar array de Heaps con la disimilitud del nuevo cluster
//y el resto de los clusters O(n*log n)
lh[final_pos] = new HeapArray<Container>(Set.Elements.Count - 1);
for (int j = 0; j < lh.Count; j++)
{
Container[] lc = lh[j].ToArray;
lh[j] = new HeapArray<Container>(Set.Elements.Count - 1);
for (int k = 1; k < lc.Length; k++)
{
if (lc[k] == null)
break;
if (lc[k].Cluster != cluster_i && lc[k].Cluster != cluster_j)
lh[j].Add(lc[k]);
}
if (j != final_pos && lh[j].First != null)
lh[j].Add(new Container { Rank = DMatrix[pos_h, lh[j].First.Name], Name = lh[j].First.Name, Cluster = pos_h });
}
for (int j = 0; j < DMatrix.GetLength(0); j++)
{
if (pos_h != j && pos_j != j && pos_i != j && !des[j])
lh[final_pos].Add(new Container { Rank = DMatrix[pos_h, j], Name = pos_h, Cluster = j });
}
}
//Crear Dictionary<string,Cluster> para construir la particion
Dictionary<string, Cluster> dic_clusters = new Dictionary<string, Cluster>();
int cont = 0;
for (int i = 0; i < best_clusters.Count; i++)
{
if (best_clusters[i] != null)
{
best_clusters[i].Name = "C-" + cont;
dic_clusters.Add(best_clusters[i].Name, best_clusters[i]);
cont++;
}
}
Structuring = new Partition() { Clusters = dic_clusters, Proximity = Proximity };
if (IContainerProgressBar != null)
IContainerProgressBar.FinishProgressBar();
return Structuring;
}
catch
{
if (IContainerProgressBar != null)
IContainerProgressBar.ShowError("Error occurred in Hierarchical agglomerative algorithm with Lifetime.");
return null;
}
}
public override Structuring BuildStructuring()
{
try
{
if (Set == null)
{
throw new NullReferenceException();
}
int _current = 1;
int _max = Set.ElementsCount;
if (IContainerProgressBar != null)
{
IContainerProgressBar.ResetProgressBar(1, _max, true);
IContainerProgressBar.UpdateProgressBar(1, "Running Hierarchical agglomerative algorithm...", true);
}
if (ClustersCount > Set.ElementsCount)
{
ClustersCount = Set.ElementsCount;
}
if (ClustersCount <= 0)
{
throw new Exception("La cantidad de clusters debe ser mayor que cero");
}
if (ClustersCount == 1)
{
Dictionary <string, Cluster> dic_clus = new Dictionary <string, Cluster>();
string name = "C-0";
List <Element> temp = new List <Element>();
for (int i = 0; i < Set.ElementsCount; i++)
{
if (IContainerProgressBar != null)
{
IContainerProgressBar.UpdateProgressBar(_current++, "Running Hierarchical agglomerative algorithm...", false);
}
temp.Add(Set[i]);
}
dic_clus.Add(name, new Cluster(name)
{
Elements = temp
});
Structuring = new Partition()
{
Clusters = dic_clus, Proximity = Proximity
};
if (IContainerProgressBar != null)
{
IContainerProgressBar.FinishProgressBar();
}
return(Structuring);
}
double AlfaI = 0, AlfaJ = 0, Beta = 0, Gamma = 0;
//Al inicio cada elemento es un cluster
double[,] DMatrix = new double[Set.Elements.Count, Set.Elements.Count];
List <Cluster> clusters = new List <Cluster>();
bool[] des = new bool[Set.Elements.Count];//true si ese elemento ya no es representativo
for (int i = 0; i < Set.Elements.Count; i++)
{
List <Element> l = new List <Element>();
l.Add(Set[i]);
clusters.Add(new Cluster("C-" + i, l));
}
//Construir para cada cluster una cola con prioridad
//con las disimilitudes de el con el resto de los clusters
//y la matriz de todas las disimilitudes O(n2*log n)
List <HeapArray <Container> > lh = new List <HeapArray <Container> >();
for (int i = 0; i < Set.Elements.Count; i++)
{
lh.Add(new HeapArray <Container>(Set.Elements.Count - 1));//No se pone la diss de un elemento con el mismo
}
for (int i = 0; i < Set.Elements.Count; i++)
{
for (int j = i + 1; j < Set.Elements.Count; j++)
{
double temp_diss = Proximity.CalculateProximity(Set.Elements[i], Set.Elements[j]);
DMatrix[i, j] = temp_diss;
DMatrix[j, i] = temp_diss;
lh[i].Add(new Container {
Rank = temp_diss, Name = i, Cluster = j
});
lh[j].Add(new Container {
Rank = temp_diss, Name = j, Cluster = i
});
}
}
//Algoritmo O(n2*log n)
for (int i = Set.Elements.Count; i > ClustersCount; i--)
{
if (IContainerProgressBar != null)
{
IContainerProgressBar.UpdateProgressBar(_current++, "Running Hierarchical agglomerative algorithm...", false);
}
//Seleccionar los 2 clusters mas similares O(n)
double min = double.MaxValue;
int cluster_i = 0, cluster_j = 0;
int pos_cluster_i = 0, pos_cluster_j = 0;
for (int j = lh.Count - 1; j >= 0; j--)
{
if (lh[j].First.Rank < min)
{
min = lh[j].First.Rank;
cluster_i = lh[j].First.Name;
cluster_j = lh[j].First.Cluster;
pos_cluster_i = j;
}
}
for (int j = 0; j < lh.Count; j++)
{
if (lh[j].First != null && lh[j].First.Name == cluster_j)
{
pos_cluster_j = j;
break;
}
}
//Calcular posiciones para borrar y guardar
int erase_pos = 0, final_pos = 0;
erase_pos = pos_cluster_i > pos_cluster_j ? pos_cluster_i : pos_cluster_j;
final_pos = pos_cluster_i < pos_cluster_j ? pos_cluster_i : pos_cluster_j;
lh.RemoveAt(erase_pos);
//Actualizar los parametros AlfaI, AlfaJ, Beta y Gamma
double cluster_i_count = clusters[erase_pos].ElementsCount;
double cluster_j_count = clusters[final_pos].ElementsCount;
AlfaI = UpdateAlfaI(cluster_i_count, cluster_j_count);
AlfaJ = UpdateAlfaJ(cluster_i_count, cluster_j_count);
Beta = UpdateBeta(cluster_i_count, cluster_j_count);
Gamma = UpdateGamma(cluster_i_count, cluster_j_count);
//Unir los clusters
foreach (Element item in clusters[erase_pos].Elements)
{
clusters[final_pos].AddElement(item);
}
clusters.RemoveAt(erase_pos);
//Actualizar DMatrix con la disimilitud del nuevo cluster
//y el resto de los clusters O(n)
//Formula que se usa segun el algoritmo
//La posicion del cluster i en Dmatrix es cluster_i
//La posicion del cluster j en Dmatrix es cluster_j
int pos_h = -1;
int pos_i = cluster_i;
int pos_j = cluster_j;
if (erase_pos == pos_cluster_i)
{
des[cluster_i] = true;
pos_h = cluster_j;
}
else
{
des[cluster_j] = true;
pos_h = cluster_i;
}
for (int k = 0; k < DMatrix.GetLength(0); k++)
{
DMatrix[pos_h, k] = AlfaI * DMatrix[pos_i, k] + AlfaJ * DMatrix[pos_j, k] + Beta * DMatrix[pos_i, pos_j] + Gamma * Math.Abs(DMatrix[pos_i, k] - DMatrix[pos_j, k]);
DMatrix[k, pos_h] = DMatrix[pos_h, k];
}
//Actualizar array de Heaps con la disimilitud del nuevo cluster
//y el resto de los clusters O(n*log n)
lh[final_pos] = new HeapArray <Container>(Set.Elements.Count - 1);
for (int j = 0; j < lh.Count; j++)
{
Container[] lc = lh[j].ToArray;
lh[j] = new HeapArray <Container>(Set.Elements.Count - 1);
for (int k = 1; k < lc.Length; k++)
{
if (lc[k] == null)
{
break;
}
if (lc[k].Cluster != cluster_i && lc[k].Cluster != cluster_j)
{
lh[j].Add(lc[k]);
}
}
if (j != final_pos && lh[j].First != null)
{
lh[j].Add(new Container {
Rank = DMatrix[pos_h, lh[j].First.Name], Name = lh[j].First.Name, Cluster = pos_h
});
}
}
for (int j = 0; j < DMatrix.GetLength(0); j++)
{
if (pos_h != j && pos_j != j && pos_i != j && !des[j])
{
lh[final_pos].Add(new Container {
Rank = DMatrix[pos_h, j], Name = pos_h, Cluster = j
});
}
}
}
//Crear Dictionary<string,Cluster> para construir la particion
Dictionary <string, Cluster> dic_clusters = new Dictionary <string, Cluster>();
int cont = 0;
for (int i = 0; i < clusters.Count; i++)
{
if (clusters[i] != null)
{
clusters[i].Name = "C-" + cont;
dic_clusters.Add(clusters[i].Name, clusters[i]);
cont++;
}
}
Structuring = new Partition()
{
Clusters = dic_clusters, Proximity = Proximity
};
if (IContainerProgressBar != null)
{
IContainerProgressBar.FinishProgressBar();
}
return(Structuring);
}
catch
{
if (IContainerProgressBar != null)
{
IContainerProgressBar.ShowError("Error occurred in Hierarchical agglomerative algorithm.");
}
return(null);
}
}