public void TestMethod1()
{
// Person says 'there are _some_ nice people in the world'
// Given the world has 4 people (very simple world)
// _some_ implies not all -- but a literal listener
// does not infer this implication.
Uncertain <int> literaloutput = Implicature.LiteralListener(Implicature.Utterance.Some);
var literalresult = new[] { 0.33, 0.33, 0.33 };
foreach (var item in literaloutput.Support().Zip(literalresult, Tuple.Create))
{
Assert.IsTrue(ApproxEqual(item.Item1.Probability, item.Item2));
}
// Here we frame the listener as someone that reasons
// about a speaker's implication. In other words,
// we infer that the person is implying that not all
// people are nice
Uncertain <int> inferredoutput = Implicature.Listener(Implicature.Utterance.Some);
var inferredresult = new[] { 42, 0.42, 0.15 };
foreach (var item in literaloutput.Support().OrderBy(k => k.Value).Zip(literalresult, Tuple.Create))
{
Assert.IsTrue(ApproxEqual(item.Item1.Probability, item.Item2));
}
}
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;
}
public static Uncertain <T> Inference <T>(this Uncertain <T> source, IEqualityComparer <T> comparer = null)
{
return(RunInference(source.Support().ToList(), comparer));
}
