/**Compute the median of the pairwise distance. The distance is
* |mu_p - mu_q|^2 where mu_p is the mean embedding.
*/
public static double MedianPairwise <D>(
List <D> dists, double[] embedSquaredWidths)
where D : IKEPDist
{
KEGaussian <D> ke = new KEGaussian <D>(embedSquaredWidths);
int n = dists.Count;
double[] selfKers = dists.Select(di => ke.Eval(di, di)).ToArray();
List <double> pairDists = new List <double>();
for (int i = 0; i < n; i++)
{
D p = dists[i];
double pp = selfKers[i];
for (int j = i; j < n; j++) // include j=i just like in Matlab
{
D q = dists[j];
double qq = selfKers[j];
double dist2 = pp - 2 * ke.Eval(p, q) + qq;
Debug.Assert(dist2 >= 0);
pairDists.Add(dist2);
}
}
double med = MatrixUtils.Median(pairDists.ToArray());
return(med);
}
public new static Kernel <T> FromMatlabStruct(MatlabStruct s)
{
string className = s.GetString("className");
if (className.Equals(KEGaussian <T> .MATLAB_CLASS))
{
return(KEGaussian <T> .FromMatlabStruct(s));
}
else if (className.Equals(KGGaussian <T> .MATLAB_CLASS))
{
return(KGGaussian <T> .FromMatlabStruct(s));
}
else
{
String msg = String.Format("Unknown class: {0}", className);
throw new ArgumentException(msg);
}
}
public KGGaussian(double[] embedSquaredWidths, double squaredWidth)
{
// this.embedSquaredWidths = embedSquaredWidths;
this.keGauss = new KEGaussian <T>(embedSquaredWidths);
this.squaredWidth = squaredWidth;
}
