/// <summary>
/// Calculates the euclidean distance between the Core Point for every given point
/// </summary>
/// <param name="allPoints">All points you want to know the distance of between the corePoint</param>
/// <param name="corePoint">The point you are using to calculate the distances to</param>
/// <param name="epsilon">The max amount of range around a point to still be considered to be part of a cluster (be a neighbour)</param>
/// <returns></returns>
private static DbscanPoint[] RegionQuery(DbscanPoint[] allPoints, DbscanPoint corePoint, double epsilon)
{
//If the length of Dimensions are not equal to eachother, return an empty array
if (allPoints.Count(x => x.Coordinates.Count() != corePoint.Coordinates.Length) != 0)
{
return(new DbscanPoint[0]);
}
//Return all points where the euclidean distance is within the epsilon range
return(allPoints.Where(point => SimilarityCalculations.CalculateEuclideanDistance(point.Coordinates, corePoint.Coordinates) < epsilon).ToArray());
}
private static void RunCalculationTestMethods()
{
var dataX = new[] { 5.0, 1.0, 3.0 };
var dataY = new[] { 2.0, 4.0, 3.0 };
var eDis = SimilarityCalculations.CalculateEculeanDistanceCoefficient(dataX, dataY);
var mDis = SimilarityCalculations.CalculateManhattanDistanceCoefficient(dataX, dataY);
var r = SimilarityCalculations.CalculatePearsonCoefficient(dataX, dataY);
//Take into account values that haven't been rated by both users (which I skipped in the previous calculations (here 0.0))
var dataCosX = new[] { 5.0, 0.0, 1.0, 3.0, 5.0 };
var dataCosY = new[] { 2.0, 5.0, 4.0, 3.0, 0.0 };
var cosSim = SimilarityCalculations.CalculateCosineSimilarityCoefficient(dataCosX, dataCosY);
}
