public GP(IMatrixkernel kernel, double length, double noise)
{
this._kernel = kernel;
this._length = length;
this._noise = noise;
this._Kte = kernel.Main(DistanceMatrix.Instance.Matrix, length);
}
public GPDynamic(IMatrixkernel kernel, double length, double noise)
{
this.kernel = kernel;
this.length = length;
this.noise = noise;
//this.results = results;
//Mte = Kte.RowCount;
//z = RandomHelper.randnArray(Mte);
//p = RandomHelper.randnArray(Mte);
}
public static (Vector <double>, Svd <double>) Evaluate(double[] testPointsX, double[] trainingPointsX, double[] trainingPointsY, IMatrixkernel kernel, double length, Svd <double> svd1, Matrix <double> Kte)
{
var gpOut = new GPOut();
Svd <double> svd;
var dmTeTr = MathHelper.ComputeDistanceMatrix(testPointsX, trainingPointsX.ToArray());
var tmp = kernel.Main(dmTeTr, length) * svd1.U;
Vector <double> v = Vector <double> .Build.DenseOfEnumerable(trainingPointsY);
var mu = tmp * (svd1.S.PointwiseMultiply(svd1.U.Transpose() * v));
var cov = tmp * Matrix <double> .Build.DenseOfDiagonalVector(svd1.S.PointwiseSqrt());
cov = cov * cov.Transpose();
cov = Kte - cov;
svd = cov.Svd();
for (int i = 0; i < svd.S.Count; i++)
{
svd.S[i] = svd.S[i] < Double.Epsilon ? 0 : svd.S[i];
}
return(mu, svd);
}
//public void Reset()
//{
// TrainingPointsX.Clear();
// TrainingPointsY.Clear();
//}
public GP2(IMatrixkernel kernel, double length, double noise)
{
this.kernel = kernel;
this.length = length;
this.noise = noise;
}
