private Hashtable worst = new Hashtable(); // trakcs the worst
#endregion Fields
#region Constructors
public NRA(NearestNeighbor.NearestNeighbor in1, NearestNeighbor.NearestNeighbor in2)
{
numAccess = 0;
obj1 = in1;
obj2 = in2;
op = new COMP(avg);
}
public Algorithm(
IPoints points, IRectangle rect, StrategyType type = StrategyType.Grid, ILog2 log = null)
{
_log = log ?? new NoLog();
Rectangle = rect;
Points = points.Data;
Singles = new List <IP>();
Knn = new NearestNeighbor();
GridContainer = new GridContainer(Rectangle, Points);
switch (type)
{
case StrategyType.Naive:
Strategy = new NaiveStrategy(_log);
break;
case StrategyType.Grid:
Strategy = new GridStrategy(_log);
break;
case StrategyType.KdTree:
Strategy = new KdTreeStrategy(Points);
break;
default:
throw new NotImplementedException("Unknown strategy");
}
//_log.Info(MethodBase.GetCurrentMethod(), "object init");
}
/// <summary>
/// Create the concrete instance of the Algorithm with a specified Neighborhood as parameter.
/// </summary>
/// <param name="currentAlgorithm"> Algorithm type to create. </param>
/// <param name="neighborhood"> Neighborhood type to pass to the Algorithm. </param>
/// <returns> The Algorithm's instance to run. </returns>
internal static Algorithm CreateAlgorithm(AlgorithmType algorithmType, Neighborhood <ToSolution> neighborhood)
{
Algorithm algorithm = default;
switch (algorithmType)
{
case AlgorithmType.None:
break;
case AlgorithmType.NearestNeighbor:
algorithm = new NearestNeighbor();
break;
case AlgorithmType.NearestNeighborKnapsack:
algorithm = new NearestNeighborKnapsack();
break;
case AlgorithmType.CheapestInsertion:
algorithm = new CheapestInsertion();
break;
case AlgorithmType.TwoOpt:
if (neighborhood == null)
{
neighborhood = NeighborhoodFactory.CreateNeighborhood(algorithmType);
}
algorithm = new LocalSearchTemplate(neighborhood);
break;
case AlgorithmType.LinKernighan:
algorithm = new LinKernighan();
break;
case AlgorithmType.HybridCustomInsertion:
algorithm = new HybridCustomInsertion();
break;
case AlgorithmType.HybridCustomUpdate:
algorithm = new HybridCustomUpdate();
break;
case AlgorithmType.TabuSearch:
if (neighborhood == null)
{
neighborhood = NeighborhoodFactory.CreateNeighborhood(algorithmType);
}
algorithm = new TabuSearch(neighborhood);
break;
// Add new Algorithm types here ...
default:
throw new ArgumentOutOfRangeException(nameof(algorithmType), algorithmType, null);
}
return(algorithm);
}
public void TestDoubleTreeFailOnIncompleteGraph()
{
Graph <int, double> graph = new GraphBuilder <int, double>()
.AddVerteces(5, v => v)
.AddEdge(0, 2)
.AddEdge(2, 1)
.AddEdge(2, 3)
.AddEdge(2, 4)
.Build();
NearestNeighbor.FindTour(graph);
}
static void Main(string[] args)
{
List <Vector> positives = new List <Vector>();
List <Vector> negatives = new List <Vector>();
// Vectorの各要素 0:無料 1:出会い 2:登録 3:参加 4:講義
positives.Add(new Vector(new double[] { 1.0, 0, 0, 1.0, 1.0 }));
negatives.Add(new Vector(new double[] { 2.0, 1.0, 1.0, 1.0, 0 }));
NearestNeighbor nearestNeighbor = new NearestNeighbor(positives, negatives);
int result = nearestNeighbor.Classify(new Vector(new double[] { 1.0, 1.0, 1.0, 0, 0 }), 1);
Console.WriteLine(result);
Console.ReadKey();
}
static void Main1(string[] args)
{
// Usage:
// NearestNeighbour [indexFolder queryfile pagesize=6000 outputfile=results.txt]
NearestNeighbor nnObj1 = new NearestNeighbor();
nnObj1.setFolderDir(@"C:\cse515\idx\sift_k50_l16\");
nnObj1.setQueryFile(@"C:\cse515\sift-query.txt");
NearestNeighbor nnObj2 = new NearestNeighbor();
nnObj2.setFolderDir(@"C:\cse515\idx\shape_k8_l5\");
nnObj2.setQueryFile(@"C:\cse515\query.txt");
NRA merge = new NRA(nnObj1, nnObj2);
List<string> images = merge.mergeAndReturn(2);
foreach (string i in images)
{
Console.WriteLine("image: " + i);
}
//resultSet = nnObj1.getNext(7);
//resultSet = nnObj.getNext(10);
//resultSet = nnObj.getNext(10);
//resultSet = nnObj.getNext(10);
//resultSet = nnObj.getNext(10);
//resultSet = nnObj.getNext(10);
//resultSet = nnObj.getNext(10);
//resultSet = nnObj.getNext(10);
//resultSet = nnObj.getNext(10);
//feature index starts from 0, so if you wanna change the 5th feature, pass 4 here...
//nnObj.setFeatureWeight(2, 4.0);
//MyResultSet newResults = nnObj.getFirst(10);
//newResults = nnObj.getNext(10);
//newResults = nnObj.getNext(10);
//newResults = nnObj.getNext(10);
//newResults = nnObj.getNext(10);
//newResults = nnObj.getNext(10);
//newResults = nnObj.getNext(10);
//newResults = nnObj.getNext(10);
//newResults = nnObj.getNext(10);
//newResults = nnObj.getNext(10);
//nnObj.setQueryFile(@"E:\CG\NearestNeighborCS\NearestNeighborCS\bin\Debug\query1.txt");
//newResults = nnObj.getFirst(10);
}
private void TestTspAlgorithm(IWeightedGraph <int, double> graph,
TspAlgorithm algorithm,
int expectedVerteces,
int expectedEdges,
double optimalTourCosts = 0.0,
double precision = 0.01)
{
IWeightedGraph <int, double> tour = null;
// Compute tour with the chosen algorithm
switch (algorithm)
{
case TspAlgorithm.NearestNeighbor:
tour = NearestNeighbor.FindTour(graph);
break;
case TspAlgorithm.DoubleTree:
tour = DoubleTree.FindTour(graph);
break;
case TspAlgorithm.BruteForce:
tour = BruteForce.FindOptimalTour(graph, 0.0, double.MaxValue, (w1, w2) => w1 + w2);
break;
default:
throw new NotSupportedException($"Testing TSP with the {algorithm} algorithm is currently not supported.");
}
// Check route for component count
Assert.AreEqual(expectedVerteces, tour.VertexCount);
Assert.AreEqual(expectedEdges, tour.GetAllEdges().Count());
// For algorithms that find the optimal tour, check the optmial tour costs
if (algorithm == TspAlgorithm.BruteForce)
{
AssertDoublesNearlyEqual(optimalTourCosts, tour.GetAllEdges().Sum(e => e.Weight), precision);
}
}
void Awake()
{
_pawn = GetComponent <IControllable>();
_nearestNeighbor = GetComponent <NearestNeighbor>();
}
