static void Knn()
{
IPoints points = Dataset.LoadDataset(@"c:\temp\points.csv");
// Used for testing K nearest neighbor
IPointsKnn dataset = new PointsKnn();
dataset.Data.AddRange(points.Data.Select(i => i as IPKnn));
// Used for testing K nearest neighbor
var rect = new SingleDetectLibrary.Code.Data.Rectangle
{
XMin = -180,
XMax = 180,
YMin = -90,
YMax = 90,
MaxDistance = 20,
};
rect.Validate();
const int k = 3;
IAlgorithm algo = new Algorithm(dataset, rect, StrategyType.Grid);
var origin = new SingleDetectLibrary.Code.Data.P { X = 0, Y = 0 };
algo.UpdateIndex(origin);
var duration = algo.UpdateKnn(origin, new KnnConfiguration{K = k});
// Print result
CW(string.Format("{0} msec. {1}:", algo.Strategy.Name, duration));
CW("K Nearest Neighbors:");
CW(string.Format("Origin: {0}", origin));
CW(string.Format("Distance sum: {0}", algo.Knn.GetDistanceSum()));
algo.Knn.NNs.OrderBy(i => i.Distance).ToList().ForEach(CW);
// Update strategy
algo.SetAlgorithmStrategy(new NaiveStrategy());
// Use algo
duration = algo.UpdateKnn(origin, new KnnConfiguration{K = k});
// Print result
CW(string.Format("\n{0} msec. {1}:", algo.Strategy.Name, duration));
CW("K Nearest Neighbors:");
CW(string.Format("Distance sum: {0}", algo.Knn.GetDistanceSum()));
algo.Knn.NNs.OrderBy(i => i.Distance).ToList().ForEach(CW);
}