// Read about this at github.com/kunukn/single-detect
static void SetKnnAlgo(IPoints points)
{
IPointsKnn dataset = new PointsKnn();
dataset.Data.AddRange(points.Data.Select(i => i as IPKnn));
var rect = new SingleDetectLibrary.Code.Data.Rectangle
{
XMin = -190,
XMax = 190,
YMin = -100,
YMax = 100,
MaxDistance = 20,
};
rect.Validate();
// Naive stratey works with all points on Earth.
// Grid strategy runs much faster and can be used as approx algo
// but only works on certain local areas, not wrapped world. e.g. from lon -90 to lon 90,
// e.g. Europe only areas or US only areas etc. but not New Zealand due to being near lon 180.
// All points must be within rect boundary
IKnnAlgorithm algo = new KnnAlgorithm(dataset, rect, StrategyType.Naive);
Data = algo;
}
static void SaveCsvData(IPoints dataset, FileInfo filepath)
{
//var dataset = FileUtil.LoadDataSetFromFile();
var save = dataset.Data.Select(p => string.Format("{0};{1};{2};{3}",
p.X, p.Y, p.I, p.T)).ToList();
//foreach (var s in save) WL(s);
FileUtil.WriteFile(save, filepath);
}
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);
}
static void SaveSerData(IPoints dataset)
{
FileUtil.SaveDataSetToFile(dataset);
var list = FileUtil.LoadDataSetFromFile();
}
static void SaveCsvData(IPoints dataset, FileInfo filepath)
{
//var dataset = FileUtil.LoadDataSetFromFile();
var save = dataset.Data.Select(p => string.Format("{0};{1};{2};{3}",
p.X, p.Y, p.I, p.T)).ToList();
//foreach (var s in save) WL(s);
FileUtil.WriteFile(save, filepath);
}
static void SaveSerData(IPoints dataset)
{
FileUtil.SaveDataSetToFile(dataset);
var list = FileUtil.LoadDataSetFromFile();
}
// Read about this at github.com/kunukn/single-detect
static void SetKnnAlgo(IPoints points)
{
IPointsKnn dataset = new PointsKnn();
dataset.Data.AddRange(points.Data.Select(i => i as IPKnn));
var rect = new SingleDetectLibrary.Code.Data.Rectangle
{
XMin = -190,
XMax = 190,
YMin = -100,
YMax = 100,
MaxDistance = 20,
};
rect.Validate();
// Naive stratey works with all points on Earth.
// Grid strategy runs much faster and can be used as approx algo
// but only works on certain local areas, not wrapped world. e.g. from lon -90 to lon 90,
// e.g. Europe only areas or US only areas etc. but not New Zealand due to being near lon 180.
// All points must be within rect boundary
IKnnAlgorithm algo = new KnnAlgorithm(dataset, rect, StrategyType.Naive);
Data = algo;
}
