public override object Part1()
{
var bots = Parse();
var best = bots.MaxBy(x => x.R);
return(bots.Count(b => Dist(best, (b.X, b.Y, b.Z)) <= best.R));
}
/// <summary>
/// Draws n samples from a distribution.
/// This will throw an exception if the distribution contains any conditionals.
/// </summary>
public static IEnumerable <A> SampleN <A>(this Dist <A> dist, int n)
{
for (int i = 0; i < n; ++i)
{
yield return(dist.Sample());
}
}
public Dist <Samples <A> > Bind <B>(Dist <B> dist, Func <B, Dist <A> > bind)
{
return(from ps in dist.Run(new Smc <B>(numParticles))
let unzipped = ps.Unzip()
from ys in unzipped.Item1.Select(bind).Sequence()
select Samples(ys.Zip(unzipped.Item2, ItemProb)));
}
public static string RandomSample(int samples, int sampleSize, Dist f)
{
List <List <double> > allSamples = new List <List <double> >();
for (int s = 1; s < samples; ++s)
{
List <double> sample = new List <double>();
for (int e = 0; e < sampleSize; ++e)
{
sample.Add(f());
}
allSamples.Add(sample);
}
List <double> Avgs = new List <double>();
List <double> Stds = new List <double>();
foreach (List <double> sample in allSamples)
{
Avgs.Add(sample.Average());
Stds.Add(MathUtils.StandardDeviation(sample, false));
}
double finalAvg = Avgs.Average();
double finalStd = MathUtils.StandardDeviation(Avgs, false);
double elementStd = Stds.Average();
return(String.Format($"Avg {finalAvg} , Std {finalStd}, ElementStd {elementStd}"));
}
/// <summary>
/// Generates data for the hidden markov model
/// </summary>
public static Dist <List <Tuple <A, B> > > Sample <A, B>(
Func <A, Dist <B> > emissionDist,
Dist <List <A> > startDist,
Func <A, Dist <A> > transitionDist,
int numSamples
)
{
Dist <List <Tuple <A, B> > > samples =
from list in startDist
let initial = list.First()
from emit in emissionDist(initial)
select new List <Tuple <A, B> > {
Tuple(initial, emit)
};
while (numSamples > 0)
{
samples = from prev in samples
from next in transitionDist(prev.Last().Item1)
from emit in emissionDist(next)
select prev.Append(Tuple(next, emit)).ToList();
numSamples -= 1;
}
return(samples);
}
public RunIndependent3(Dist <T1> first, Dist <T2> second, Dist <T3> third, Func <T1, T2, T3, Dist <A> > run)
{
this.first = first;
this.second = second;
this.third = third;
this.run = run;
}
public void Nu_WrongValues(double d)
{
var i = (int)d;
Assert.False(StudentsTDistribution.IsValidParam(i));
Assert.False(Dist.IsValidNu(i));
Assert.Throws <ArgumentOutOfRangeException>(() => { Dist.Nu = i; });
}
public A RunIndependent <B, C>(Dist <B> distB, Dist <C> distC, Func <B, C, Dist <A> > run)
{
var taskB = Task.Run(() => distB.RunParallel(new ParallelSampler <B>()));
var taskC = Task.Run(() => distC.RunParallel(new ParallelSampler <C>()));
Task.WaitAll(taskB, taskC);
return(run(taskB.Result, taskC.Result).RunParallel(new ParallelSampler <A>()));
}
public void Next_ManyRand()
{
for (var i = 0; i < Iterations; ++i)
{
Results[i] = Dist.Next();
}
AssertDist(Dist);
}
public void Alpha_WrongValues(double d)
{
var i = (int)d;
Assert.False(ErlangDistribution.AreValidParams(i, 1));
Assert.False(Dist.IsValidAlpha(i));
Assert.Throws <ArgumentOutOfRangeException>(() => { Dist.Alpha = i; });
}
public void Beta_WrongValues(double d)
{
var i = (int)d;
Assert.False(BinomialDistribution.AreValidParams(0.5, i));
Assert.False(Dist.IsValidBeta(i));
Assert.Throws <ArgumentOutOfRangeException>(() => { Dist.Beta = i; });
}
public bool UpdateRange(Object CurObj)
{
float Distance = CurObj.GetDistance(CurObj.LastRangeCheck);
if (Distance > 100)
{
CurObj.LastRangeCheck.X = CurObj.X;
CurObj.LastRangeCheck.Y = CurObj.Y;
}
else
{
return(false);
}
List <Object> Objects = GetRangedObject(CurObj, 1);
foreach (Object DistObj in Objects) // Ici on check tous les objets visibles
{
if (CurObj == DistObj)
{
continue;
}
if (DistObj.IsPlayer() && !DistObj.GetPlayer().Client.IsPlaying())
{
continue;
}
if (!CurObj.HasInRange(DistObj))
{
CurObj.AddInRange(DistObj);
DistObj.AddInRange(CurObj);
if (CurObj.IsPlayer())
{
DistObj.SendMeTo(CurObj.GetPlayer());
}
if (DistObj.IsPlayer())
{
CurObj.SendMeTo(DistObj.GetPlayer());
}
}
else
{
}
}
List <Object> ToDel = CurObj._ObjectRanged.FindAll(dist => dist != null && dist.GetDistance(CurObj) > MAX_VISIBILITY_RANGE);
foreach (Object Dist in ToDel)
{
CurObj.RemoveInRange(Dist);
Dist.RemoveInRange(CurObj);
}
return(true);
}
//For Debugging
//private static void DrawMap(Dist[,] map)
//{
// var width = map.GetLength(0);
// var height = map.GetLength(1);
// for (var x = 0; x < width; x++)
// {
// for (var y = 0; y < height; y++)
// {
// Console.Write($"{map[x, y].Id}");
// }
// Console.WriteLine();
// }
//}
private static (char, int) FillMap(Dist[,] map, Dictionary <char, Point> points)
{
/*Naive initial impl. went points first, and then multiple iterations of the map
*/
var width = map.GetLength(0);
var height = map.GetLength(1);
foreach (var p in points.Values)
{
for (var x = 0; x < width; x++)
{
for (var y = 0; y < height; y++)
{
var d = GetManhattanDistance(p, x, y);
var isInf = IsInfinite(x, y, width, height);
if ((map[x, y] == null) || //unmapped
(map[x, y].Id != p.Id && map[x, y].D > d)) //someone else further away
{
map[x, y] = new Dist {
Id = p.Id, D = d, IsInfinite = isInf
};
}
else if (map[x, y].Id != p.Id && map[x, y].D == d)
{
map[x, y] = new Dist {
Id = '.', D = d, IsInfinite = isInf
};
}
}
}
}
//Flatten It
var tmp = new Dist[width * height];
for (var y = 0; y < height; y++)
{
for (var x = 0; x < width; x++)
{
tmp[y * width + x] = map[x, y];
}
}
//Group By Cell Id & coun't the ones that are not touching infinity
var r = tmp.GroupBy(k => k.Id)
.Where(w => !w.Any(d => d.IsInfinite))
.Select(s => new
{
Id = s.Key,
Count = s.Count()
})
.OrderByDescending(o => o.Count)
.First();
return(r.Id, r.Count);
}
public static Prob Pdf(Dist <Matrix <double> > dist, Matrix <double> y)
{
if (dist is Primitive <Matrix <double> > )
{
var primitive = dist as Primitive <Matrix <double> >;
return(Pdf(primitive.dist, y));
}
throw new ArgumentException("Can only calculate PDF for primitive distributions");
}
/// <summary>
/// The metropolis algorithm with prior as proposal dist.
/// It's a really slow mixing chain if the prior is too different from the posterior.
/// </summary>
/// <returns>A distribution of Markov chains</returns>
public static Dist <IEnumerable <A> > MHPrior <A>(Dist <A> dist, int n)
{
var initial = new List <ItemProb <A> > {
dist.WeightedPrior().Sample()
};
var chain = Iterate(n, dist.WeightedPrior, initial);
return(chain.Select(ipList => ipList.Select(ip => ip.Item)));
}
public A RunIndependent3 <B, C, D>(Dist <B> distB, Dist <C> distC, Dist <D> distD, Func <B, C, D, Dist <A> > run)
{
var taskB = StartTask(() => distB.RunParallel(new ParallelSampler <B>()));
var taskC = StartTask(() => distC.RunParallel(new ParallelSampler <C>()));
var taskD = StartTask(() => distD.RunParallel(new ParallelSampler <D>()));
Task.WaitAll(taskB, taskC, taskD);
return(run(taskB.Result, taskC.Result, taskD.Result).RunParallel(new ParallelSampler <A>()));
}
public static Prob Pmf(Dist <int> dist, int y)
{
if (dist is Primitive <int> )
{
var primitive = dist as Primitive <int>;
return(Pmf(primitive.dist, y));
}
throw new ArgumentException("Can only calculate pmf for primitive distributions");
}
public static Prob Pmf <T>(Dist <T> dist, T k)
{
if (dist is Primitive <T> )
{
var primitive = dist as Primitive <T>;
return(Pmf(primitive.dist, k));
}
throw new ArgumentException("Can only calculate PDF for primitive distributions");
}
private void InitHatas(ResponseObservation observation)
{
FindBaseLocations(observation);
SetBasePosionsByMinerals();
var hatas = observation.Observation.RawData.Units.Where(d => d.UnitType == UnitTypes.HATCHERY && d.Alliance == Alliance.Self).ToList();
if (hatas.Count() != Bases.Where(b => b.Taken).Count())
{
foreach (var h in hatas)
{
foreach (var b in Bases)
{
if (Dist.Distance(b.Position, h.Pos) < 5)
{
b.Taken = true;
b.Position = h.Pos;
b.BaseId = h.Tag;
}
}
}
}
if (MainBase == 0 && hatas != null)
{
MainBase = hatas.FirstOrDefault().Tag;
}
//if (hatas.Count() > 1 || hatas.Count() == 0)
// return;
var minDist = float.MaxValue;
var bas = Bases[0];
foreach (var b in Bases)
{
var dist = Dist.Distance(b.Position, hatas[0].Pos);
if (dist < minDist)
{
minDist = dist;
bas = b;
}
}
bas.BaseId = hatas[0].Tag;
foreach (var b in Bases)
{
if (b.BaseId == MainBase)
{
continue;
}
b.DistanceToMain = Dist.Distance(hatas[0].Pos, b.Position);
}
}
public void AlphaBeta_WrongValues(double a, double b)
{
Assert.Throws <ArgumentOutOfRangeException>(() =>
{
Assert.False(ContinuousUniformDistribution.AreValidParams(a, b));
Dist.Alpha = Dist.Beta = a;
Assert.False(Dist.IsValidBeta(b));
Dist.Beta = b;
});
}
public void Weights_WrongValues(double d1, double d2, double d3)
{
var w = new List <double> {
d1, d2, d3
};
Assert.False(CategoricalDistribution.IsValidParam(w));
Assert.False(Dist.AreValidWeights(w));
Assert.Throws <ArgumentOutOfRangeException>(() => { Dist.Weights = w; });
}
public void Integers_Next_SameOutput()
{
var integers = Dist.DistributedIntegers().GetEnumerator();
for (var i = 0; i < Iterations; ++i)
{
integers.MoveNext();
Assert.AreEqual(integers.Current, OtherDist.Next());
}
}
public void Integers_SameOutput()
{
var integers = Dist.DistributedIntegers().GetEnumerator();
integers.MoveNext();
for (var i = 0; i < Iterations; ++i, integers.MoveNext())
{
OtherDist.Next().ShouldBe(integers.Current);
}
}
public void Doubles_SameOutput()
{
var doubles = Dist.DistributedDoubles().GetEnumerator();
doubles.MoveNext();
for (var i = 0; i < Iterations; ++i, doubles.MoveNext())
{
Assert.AreEqual(OtherDist.NextDouble(), doubles.Current);
}
}
public void Reset_AfterOneRand()
{
if (!Dist.CanReset)
{
Assert.Pass();
}
var d = Dist.Next();
Assert.True(Dist.Reset());
Assert.AreEqual(d, Dist.Next());
}
async Task BuildIndexType()
{
await BuildPaging(Data.Where(x => x.Raw.Type == PostType.Article).ToList(),
Dist.CreateSubdirectory("articles"));
await BuildPaging(Data.Where(x => x.Raw.Type == PostType.Slides).ToList(),
Dist.CreateSubdirectory("slides"));
await BuildPaging(Data.Where(x => x.Raw.Type == PostType.Note).ToList(),
Dist.CreateSubdirectory("notes"));
}
public void Integers_ManyRand()
{
var integers = Dist.DistributedIntegers().GetEnumerator();
integers.MoveNext();
for (var i = 0; i < Iterations; ++i, integers.MoveNext())
{
Results[i] = integers.Current;
}
AssertDist(Dist);
}
public void Doubles_ManyRand()
{
var doubles = Dist.DistributedDoubles().GetEnumerator();
doubles.MoveNext();
for (var i = 0; i < Iterations; ++i, doubles.MoveNext())
{
Results[i] = doubles.Current;
}
AssertDist(Dist);
}
public int CompareTo(Node other)
{
int res = Dist.CompareTo(other.Dist);
//if res and other is equal then apply different CompareTo() value (OrderedSet deletes any State if
if (res == 0)
{
return(uniqueIndex.CompareTo(other.uniqueIndex));
}
return(res);
}
public Dist <Samples <A> > Conditional(Func <A, Prob> lik, Dist <A> dist)
{
var updated = new Conditional <Samples <A> >(
samples => samples.SumProbs(),
from ps in dist.Run(new Smc <A>(numParticles))
select ps.Select(ip => ItemProb(ip.Item, lik(ip.Item).Mult(ip.Prob)))
);
return(updated.Select(Importance.Normalize)
.SelectMany(Importance.Resample));
}
public XSub(Ctx parent, int tid, int sid)
: base(parent, tid, sid)
{
m_options.SocketType = ZmqSocketType.Xsub;
m_hasMessage = false;
m_more = false;
m_options.Linger = 0;
m_fq = new FQ();
m_dist = new Dist();
m_subscriptions = new Trie();
}
public FormMain()
{
InitializeComponent();
dist = new Dist();
}
public XPub(Ctx parent, int tid, int sid)
: base(parent, tid, sid)
{
m_options.SocketType = ZmqSocketType.Xpub;
m_verbose = false;
m_more = false;
m_subscriptions = new Mtrie();
m_dist = new Dist();
m_pending = new Deque<Blob>();
}
