private static double[,] CalculateL(double[,] x, ParameterizedCovarianceFunction cov, double sqrSigmaNoise)
{
int n = x.GetLength(0);
var l = new double[n, n];
// calculate covariances
for (int i = 0; i < n; i++)
{
for (int j = i; j < n; j++)
{
l[j, i] = cov.Covariance(x, i, j) / sqrSigmaNoise;
if (j == i)
{
l[j, i] += 1.0;
}
}
}
// cholesky decomposition
var res = alglib.trfac.spdmatrixcholesky(ref l, n, false);
if (!res)
{
throw new ArgumentException("Matrix is not positive semidefinite");
}
return(l);
}
private static IEnumerable <double> GetGradient(double[,] x, int i, int j, IEnumerable <int> columnIndices, double scale, ParameterizedCovarianceFunction cov,
bool fixedScale)
{
if (!fixedScale)
{
yield return(2 * scale * cov.Covariance(x, i, j));
}
foreach (var g in cov.CovarianceGradient(x, i, j))
{
yield return(scale * g);
}
}
private static IList <double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, ParameterizedCovarianceFunction cov,
bool fixedScale)
{
var gr = new List <double>((!fixedScale ? 1 : 0) + cov.CovarianceGradient(x, i, j).Count);
if (!fixedScale)
{
gr.Add(2 * scale * cov.Covariance(x, i, j));
}
foreach (var g in cov.CovarianceGradient(x, i, j))
{
gr.Add(scale * g);
}
return(gr);
}
private static double[,] CalculateL(double[,] x, ParameterizedCovarianceFunction cov, double sqrSigmaNoise) {
int n = x.GetLength(0);
var l = new double[n, n];
// calculate covariances
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
l[j, i] = cov.Covariance(x, i, j) / sqrSigmaNoise;
if (j == i) l[j, i] += 1.0;
}
}
// cholesky decomposition
var res = alglib.trfac.spdmatrixcholesky(ref l, n, false);
if (!res) throw new ArgumentException("Matrix is not positive semidefinite");
return l;
}
private static IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices, double scale, ParameterizedCovarianceFunction cov,
bool fixedScale) {
if (!fixedScale) {
yield return 2 * scale * cov.Covariance(x, i, j);
}
foreach (var g in cov.CovarianceGradient(x, i, j))
yield return scale * g;
}
private static IList<double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, ParameterizedCovarianceFunction cov,
bool fixedScale) {
var gr = new List<double>((!fixedScale ? 1 : 0) + cov.CovarianceGradient(x, i, j).Count);
if (!fixedScale) {
gr.Add(2 * scale * cov.Covariance(x, i, j));
}
foreach (var g in cov.CovarianceGradient(x, i, j))
gr.Add(scale * g);
return gr;
}
