private INeuron CalculateBmu(IVector input)
{
var bmu = _lattice[0, 0];
var bestDist = input.EuclideanDistance(bmu.Weights);
for (var i = 0; i < _width; i++)
{
for (var j = 0; j < _height; j++)
{
var distance = input.EuclideanDistance(_lattice[i, j].Weights);
if (distance < bestDist)
{
bmu = _lattice[i, j];
bestDist = distance;
}
}
}
return(bmu);
}
/// <summary>
/// Calculates the distance between two vectors
/// </summary>
/// <param name="distance"></param>
/// <param name="vector1"></param>
/// <param name="vector2"></param>
public static float Calculate(this DistanceMetric distance, IVector vector1, IVector vector2)
{
switch (distance)
{
case DistanceMetric.Cosine:
return(vector1.CosineDistance(vector2));
case DistanceMetric.Euclidean:
return(vector1.EuclideanDistance(vector2));
case DistanceMetric.Manhattan:
return(vector1.ManhattanDistance(vector2));
case DistanceMetric.SquaredEuclidean:
return(vector1.SquaredEuclidean(vector2));
default:
return(vector1.MeanSquaredDistance(vector2));
}
}
public double Distance(IVector <double> x, double[] y, int offy)
{
return(x.EuclideanDistance(y, offy));
}
public float Distance(IVector <float> x, float[] y, int offy)
{
return(x.EuclideanDistance(y, offy));
}
