public static Metropolis <T> Distribution(
Func <T, float> target,
IDistribution <T> initial,
Func <T, IDistribution <T> > proposal,
IRNG random)
{
var markov = Markov <T> .Distribution(initial, Transition);
var chain = markov.Sample(random);
return(new Metropolis <T>(target, chain.GetEnumerator()));
IDistribution <T> Transition(T item)
{
T candidate = proposal(item).Sample(random);
float probability = target(candidate) / target(candidate);
return(Flip <T> .Distribution(candidate, item, probability));
}
}
public static IWeightedDistribution <T> Distribution(IDictionary <T, float> weights)
{
var weightArray = weights.ToArray();
switch (weightArray.Length)
{
case 0:
return(Empty <T> .Distribution());
case 1:
var item = weights.Keys.First();
return(Singleton <T> .Distribution(item));
case 2:
var item1 = weights.First();
var item2 = weights.Last();
var probability = item1.Value / (item1.Value + item2.Value);
return(Flip <T> .Distribution(item1.Key, item2.Key, probability));
default:
return(new FloatWeighted <T>(weights));
}
}
