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));
}
private static double SuggestNumerical(List <Result> history, string tag, double low, double high, bool log, bool integer)
{
if (history.Count < nStartupJobs)
{
double x = rng.Uniform(low, high);
if (log)
{
x = Math.Exp(x);
}
if (integer)
{
x = Math.Round(x);
}
return(x);
}
var(obsBelow, obsAbove) = ApSplitTrials(history, tag);
if (log)
{
obsBelow = ArrayMath.Log(obsBelow);
obsAbove = ArrayMath.Log(obsAbove);
}
double priorMu = 0.5 * (high + low);
double priorSigma = high - low;
var(weights, mus, sigmas) = AdaptiveParzenNormal(obsBelow, priorMu, priorSigma);
double[] samples = Gmm1(weights, mus, sigmas, low, high, log, integer);
double[] belowLLik = Gmm1Lpdf(samples, weights, mus, sigmas, low, high, log, integer);
(weights, mus, sigmas) = AdaptiveParzenNormal(obsAbove, priorMu, priorSigma);
double[] aboveLLik = Gmm1Lpdf(samples, weights, mus, sigmas, low, high, log, integer);
return(FindBest(samples, belowLLik, aboveLLik));
}