private static (double[], double[], double[]) AdaptiveParzenNormal(double[] mus, double priorMu, double priorSigma)
{
int[] order = ArrayMath.ArgSort(mus);
double[] sortedMus = ArrayMath.Index(mus, order);
int priorPos = ArrayMath.SearchSorted(sortedMus, priorMu);
sortedMus = ArrayMath.Insert(sortedMus, priorPos, priorMu);
int length = mus.Length;
double[] sigma = new double[length + 1];
if (length == 0)
{
sigma[0] = priorSigma;
}
else if (length == 1)
{
sigma[priorPos] = priorSigma;
sigma[1 - priorPos] = priorSigma * 0.5;
}
else
{
sigma[0] = sortedMus[1] - sortedMus[0];
sigma[length] = sortedMus[length] - sortedMus[length - 1];
for (int i = 1; i < length; i++)
{
sigma[i] = Math.Max(sortedMus[i] - sortedMus[i - 1], sortedMus[i + 1] - sortedMus[i]);
}
}
double maxSigma = priorSigma / 1.0;
double minSigma = priorSigma / Math.Min(100.0, length + 2);
sigma = ArrayMath.Clip(sigma, minSigma, maxSigma);
sigma[priorPos] = priorSigma;
double[] sortedWeights;
if (lf < length)
{
sortedWeights = ArrayMath.Index(LinearForgettingWeights(length), order);
}
else
{
sortedWeights = Enumerable.Repeat(1.0, length).ToArray();
}
sortedWeights = ArrayMath.Insert(sortedWeights, priorPos, priorWeight);
sortedWeights = ArrayMath.DivSum(sortedWeights);
return(sortedWeights, sortedMus, sigma);
}
private static int SuggestCategorical(List <Result> history, string tag, int size)
{
if (history.Count < nStartupJobs)
{
return(rng.Integer(size));
}
var(obsBelow, obsAbove) = ApSplitTrials(history, tag);
double[] weights = LinearForgettingWeights(obsBelow.Length);
double[] counts = Bincount(obsBelow, weights, size);
double[] p = ArrayMath.DivSum(ArrayMath.Add(counts, priorWeight));
int[] sample = rng.Categorical(p, nEiCandidates);
double[] belowLLik = ArrayMath.Log(ArrayMath.Index(p, sample));
weights = LinearForgettingWeights(obsAbove.Length);
counts = Bincount(obsAbove, weights, size);
p = ArrayMath.DivSum(ArrayMath.Add(counts, priorWeight));
double[] aboveLLik = ArrayMath.Log(ArrayMath.Index(p, sample));
return(FindBest(sample, belowLLik, aboveLLik));
}