public void TestDan()
{
var r = new RandomMath();
int N = 50;
// Generate an array of independent estimates of whether a signal
// is high or low
Uncertain <bool>[] data = (from i in Enumerable.Range(0, N)
let noise = r.NextGaussian(0, 0.01)
let vad = i > 15 && i < 30 ? 0.9 : 0.1
let param = Math.Abs(vad + noise)
let f = new Bernoulli(param > 1 ? 1 : param)
select f).ToArray();
// history operator we chatted about
Uncertain <bool[]> history = data.USeq(N);
// Inference computes a weighted bool[] object: effectively a histogram
// The call to SampledInference needs to know (i) how many samples to take and how to compare bool[]
Uncertain <bool[]> posterior = history.SampledInference(10000, new SequenceComparer <bool>());
// now inspect by materializing a list
List <Weighted <bool[]> > top5 = posterior
.Support() // enumerate the histogram
.OrderByDescending(k => k.Probability) // sorted by probability
.Take(5) // just top 5
.ToList(); // produce list
//var program = from bools in history
// let sum = bools.Select(Convert.ToInt32).Sum()
// from prior in new Gaussian(20, 0.01)
// where sum == (int) prior
// select bools;
//Uncertain<bool[]> posterior1 = program.SampledInference(10000, new BoolArrayEqualityComparer());
Func <bool[], bool[]> Intervalize = _ => _;
var program = from bools in data.USeq(N)
select Intervalize(bools);
// now inspect by materializing a list
List <Weighted <bool[]> > top51 = posterior
.Support() // enumerate the histogram
.OrderByDescending(k => k.Probability) // sorted by probability
.Take(5) // just top 5
.ToList(); // produce list
// set breakpoint
int x = 10;
}