/// <summary>
/// Flattens nested samples
/// </summary>
public static Samples <A> Flatten <A>(Samples <Samples <A> > samples)
{
return(Samples(from outer in Normalize(samples).Weights
from inner in outer.Item.Weights
select ItemProb(inner.Item, inner.Prob.Mult(outer.Prob))));
}
/// <summary>
/// Normalizes a list of samples
/// </summary>
public static Samples <A> Normalize <A>(Samples <A> samples)
{
var normConst = samples.SumProbs();
return(Samples(samples.Weights.Select(s => ItemProb(s.Item, s.Prob.Div(normConst)))));
}
/// <summary>
/// Generates a weighted histogram from some samples
/// </summary>
/// <param name="scale">Scale factor. Defaults to 40</param>
public static string Weighted(Samples <double> nums, int numBuckets = 10, double scale = DEFAULT_SCALE)
{
return(Weighted(nums.Weights, numBuckets, scale));
}
/// <summary>
/// Calculates KL divergence from list of samples and a finite distribution
/// </summary>
/// <param name="keyFunc">Groups identical samples</param>
public static double KLDivergence <A, Key>(Samples <A> samples, FiniteDist <A> dist, Func <A, Key> keyFunc) where A : IComparable <A>
{
var sampleDist = CategoricalF(samples);
return(KLDivergenceF(sampleDist, dist, keyFunc));
}
public static Samples <A> Normalize <A>(this Samples <A> self)
{
return(Importance.Normalize(self));
}
/// <summary>
/// Unzip samples into items and weights
/// </summary>
public static Tuple <IEnumerable <A>, IEnumerable <Prob> > Unzip <A>(this Samples <A> samples)
{
return(new Tuple <IEnumerable <A>, IEnumerable <Prob> >(samples.Weights.Select(ip => ip.Item), samples.Weights.Select(ip => ip.Prob)));
}
public static Samples <B> MapSample <A, B>(this Samples <A> self, Func <A, B> f)
{
return(Samples(self.Weights.Select(ip => ItemProb(f(ip.Item), ip.Prob))));
}
/// <summary>
/// Sum all probabilities in samples
/// </summary>
public static Prob SumProbs <A>(this Samples <A> self)
{
return(Prob(self.Weights.Select(ip => ip.Prob.Value).Sum()));
}
public static Samples <B> Select <A, B>(this Samples <A> self, Func <ItemProb <A>, ItemProb <B> > f)
{
return(Samples(self.Weights.Select(f)));
}
/// <summary>
/// Aggregates probabilities of samples with identical values
/// The samples are arranged in ascending order
/// </summary>
/// <param name="keyFunc">Used to identify identical values</param>
public static Samples <A> Compact <A, Key>(Samples <A> samples, Func <A, Key> keyFunc) where A : IComparable <A>
{
return(Samples(CompactUnordered(samples, keyFunc).Weights.OrderBy(w => w.Item)));
}
/// <summary>
/// Categorical distribution
/// </summary>
public static Dist <A> Categorical <A>(Samples <A> samples)
{
return(Primitive(CategoricalF(samples).ToSampleDist()));
}
/// <summary>
/// Categorical distribution
/// Only composable with other finite distributions
/// </summary>
public static FiniteDist <A> CategoricalF <A>(Samples <A> samples)
{
return(new FiniteDist <A>(samples));
}
public FiniteDist(Samples <A> samples)
{
Explicit = samples;
}