public Bucket[] BucketizeHands(int round, McHand[] hands, IClusterNode parentNode)
{
KMeansNode parentKmNode = (KMeansNode)parentNode;
if (hands == null || hands.Length == 0)
{
parentKmNode.AllocateChildren(1);
parentKmNode.Children[0] = new KMeansNode(Dim, 0);
return(new Bucket[] { new Bucket() });
}
double[][] centers;
double[][] values = CalcValuesAndKMeans(parentKmNode, round, hands, out centers);
if (IsVerbose)
{
Console.Write("Centers:");
PrintCenters(centers);
Console.WriteLine();
}
Array.Sort(centers, new CenterComparer());
if (IsVerbose)
{
Console.Write("Sorted centers:");
PrintCenters(centers);
Console.WriteLine();
}
parentKmNode.AllocateChildren(centers.Length);
for (int c = 0; c < centers.Length; ++c)
{
KMeansNode node = new KMeansNode(Dim, 0);
centers[c].CopyTo(node.Center, 0);
parentKmNode.Children[c] = node;
}
Bucket[] buckets = new Bucket[parentKmNode.Children.Length].Fill(i => new Bucket());
for (int i = 0; i < hands.Length; ++i)
{
// Never normalize values here, because it is either not necessary or already done (for k-means).
int abstrCard = parentKmNode.FindClosestChild(values[i], false);
buckets[abstrCard].Hands.Add(hands[i]);
}
return(buckets);
}
public int GetAbstractCard(int[] hand, int handLength)
{
int round = HeHelper.HandSizeToRound[handLength];
int abstrCard = GetPreflopAbstrCard(hand);
if (round == 0)
{
return(abstrCard);
}
KMeansNode kn = (KMeansNode)_clusterTree.Root.GetChild(abstrCard);
double[] point = new double[Dim];
for (int r = 1; r <= round; ++r)
{
CalculateValue(hand, HeHelper.RoundToHandSize[r], point);
abstrCard = kn.FindClosestChild(point, _normalizeHandValues);
kn = kn.Children[abstrCard];
}
Debug.Assert(abstrCard >= 0 && abstrCard < MaxBucketCounts[round]);
return(abstrCard);
}
