// 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 Run()
{
// Config
var rect = new Rectangle
{
XMin = -200,
XMax = 200,
YMin = -100,
YMax = 100,
MaxDistance = 20,
};
rect.Validate();
var conf = new KnnConfiguration { K = 100 };
// Random points
IPoints points = new Points();
var rand = new Random();
for (var i = 0; i < 500000; i++)
{
var x = rect.XMin + rand.NextDouble() * rect.Width;
var y = rect.YMin + rand.NextDouble() * rect.Height;
points.Data.Add(new P
{
X = x,
Y = y,
});
}
points.Round(3);
// Init algo
IAlgorithm algo =
new Algorithm(points, rect, StrategyType.Grid);
// Use algo
var origin = new P { X = 0, Y = 0 };
var duration = algo.UpdateKnn(origin, conf);
// Print result
WL(string.Format("{0} msec. {1}:", algo.Strategy.Name, duration));
WL("K Nearest Neighbors:");
WL(string.Format("Origin: {0}", origin));
WL(string.Format("Distance sum: {0}", algo.Knn.GetDistanceSum()));
algo.Knn.NNs.OrderBy(i => i.Distance).ToList().ForEach(WL);
// Update strategy
algo.SetAlgorithmStrategy(new NaiveStrategy());
// Use algo
duration = algo.UpdateKnn(origin, conf);
// Print result
WL(string.Format("\n{0} msec. {1}:", algo.Strategy.Name, duration));
WL("K Nearest Neighbors:");
WL(string.Format("Distance sum: {0}", algo.Knn.GetDistanceSum()));
algo.Knn.NNs.OrderBy(i => i.Distance).ToList().ForEach(WL);
}
static void Run()
{
// Config
var rect = new Rectangle
{
XMin = -300, XMax = 300,
YMin = -200, YMax = 200,
MaxDistance = 10,
};
rect.Validate();
// Random points
IPoints points = new Points();
var rand = new Random();
for (var i = 0; i < 5000; i++)
{
var x = rect.XMin + rand.NextDouble() * rect.Width;
var y = rect.YMin + rand.NextDouble() * rect.Height;
points.Data.Add(new P
{
X = x,
Y = y,
});
}
points.Round(3);
// Init algo
IAlgorithm algo =
new Algorithm(points, rect, StrategyType.Grid);
// Use algo
var duration = algo.UpdateSingles();
// Print result
WL(string.Format("{0} msec. {1}:", algo.Strategy.Name, duration));
WL("Singles:\n");
algo.Singles.OrderBy(i => i.Uid).ToList().ForEach(WL);
// Update strategy
algo.SetAlgorithmStrategy(new NaiveStrategy());
// Use algo
duration = algo.UpdateSingles();
// Print result
WL(string.Format("\n{0} msec. {1}:", algo.Strategy.Name, duration));
WL("Singles:\n");
algo.Singles.OrderBy(i => i.Uid).ToList().ForEach(WL);
}
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);
}
// 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;
}
