/// <summary>
/// Run k-means.
/// Returns hand values.
/// </summary>
protected double[][] CalcValuesAndKMeans(KMeansNode parentKmNode, int round, McHand[] hands, out double[][] centers)
{
double [][] values = new double[hands.Length][].Fill(i => new double[Dim]);
double[] min = new double[Dim].Fill(double.MaxValue);
double[] max = new double[Dim].Fill(double.MinValue);
for (int i = 0; i < hands.Length; ++i)
{
CalculateValue(hands[i].Cards, hands[i].Length, values[i]);
VectorS.UpdateMin(values[i], min);
VectorS.UpdateMax(values[i], max);
}
double [] delta = VectorS.Sub(max, min);
if (_normalizeHandValues)
{
for (int i = 0; i < values.Length; ++i)
{
VectorS.NormalizeByDiff(values[i], min, delta);
}
min.CopyTo(parentKmNode.ValueMin, 0);
delta.CopyTo(parentKmNode.ValueBounds, 0);
}
if (_printHandValues || _printHands)
{
for (int i = 0; i < values.Length; ++i)
{
if (_printHands)
{
Console.Write("{0} ", StdDeck.Descriptor.GetCardNames(hands[i].Cards, 0, hands[i].Length));
}
if (_printHandValues)
{
PrintVector(values[i]);
}
Console.WriteLine();
}
}
// Calcualate number of clusters.
int k = MaxBucketCounts[round];
int adaptingDim = -1;
for (int d = 0; d < Dim; ++d)
{
double clusterSize = ClusterSizes[d][round];
if (clusterSize != 0)
{
int clCount = (int)Math.Round(delta[d] / clusterSize, 0);
if (clCount < k)
{
adaptingDim = d;
k = clCount;
}
}
}
// Make sure number of clusters is in the given range.
k = Math.Max(MinBucketCounts[round], k);
if (IsVerbose)
{
Console.Write("Min: ");
PrintVector(min);
Console.Write(" Max: ");
PrintVector(max);
Console.Write(" Delta: ");
PrintVector(delta);
if (k < MaxBucketCounts[round])
{
Console.Write(" K adapted to {0} by dim: {1}", k, adaptingDim);
}
Console.WriteLine();
}
double [][] differentValues = new double[k][].Fill(i => new double[Dim]);
int differentValuesCount = 0;
for (int i = 0; i < values.Length; ++i)
{
for (int j = 0; j < differentValuesCount; ++j)
{
if (VectorS.AreEqual(values[i], differentValues[j]))
{
goto RepeatedValue;
}
}
values[i].CopyTo(differentValues[differentValuesCount], 0);
differentValuesCount++;
if (differentValuesCount == k)
{
break;
}
RepeatedValue :;
}
if (differentValuesCount < k)
{
// Too few different values to build k clusters. Do not run k-means, it may hang.
centers = differentValues;
Array.Resize(ref centers, differentValuesCount);
if (IsVerbose)
{
Console.WriteLine("Only {0} different values found. Set cluster count, do not run kmeans", differentValuesCount);
}
return(values);
}
_kmParameters.k = k;
_kmParameters.n = values.Length;
_kmParameters.Allocate();
for (int i = 0; i < values.Length; ++i)
{
for (int d = 0; d < Dim; ++d)
{
*_kmParameters.GetPoint(i, d) = values[i][d];
}
}
fixed(Kml.Parameters *kmlp = &_kmParameters)
{
Kml.KML_Hybrid(kmlp);
}
centers = new double[_kmParameters.k][].Fill(i => new double[Dim]);
for (int c = 0; c < _kmParameters.k; ++c)
{
for (int d = 0; d < Dim; ++d)
{
centers[c][d] = *_kmParameters.GetCenter(c, d);
}
}
_kmParameters.Free();
return(values);
}
public void Test_AreEqual()
{
Assert.IsTrue(VectorS.AreEqual(new double[] { 1, 3 }, new double[] { 1, 3 }));
Assert.IsFalse(VectorS.AreEqual(new double[] { 1, 3 }, new double[] { 1, 3, 3 }));
Assert.IsFalse(VectorS.AreEqual(new double[] { 1, 3 }, new double[] { 1, 2 }));
}