public void Test_UpdateMin()
{
double[] min = new double[2].Fill(double.MaxValue);
VectorS.UpdateMin(new double[] { 1, 3 }, min);
Assert.AreEqual(new double[] { 1, 3 }, min);
VectorS.UpdateMin(new double[] { 5, 0.4 }, min);
Assert.AreEqual(new double[] { 1, 0.4 }, min);
}
public void Test_UpdateMax()
{
double[] max = new double[2].Fill(double.MinValue);
VectorS.UpdateMax(new double[] { 1, 3 }, max);
Assert.AreEqual(new double[] { 1, 3 }, max);
VectorS.UpdateMax(new double[] { 5, 0.4 }, max);
Assert.AreEqual(new double[] { 5, 3 }, max);
}
public void Test_NormalizeByDiff()
{
double[] min = new double[2] {
1, 2
};
double[] diff = new double[2] {
4, 5
};
double[] v = new double[2] {
3, 4
};
VectorS.NormalizeByDiff(v, min, diff);
Assert.AreEqual(new double[] { 0.5, 0.4 }, v);
}
public void Test_Normalize()
{
double[] min = new double[2] {
1, 2
};
double[] max = new double[2] {
5, 7
};
double[] v = new double[2] {
3, 4
};
VectorS.Normalize(v, min, max);
Assert.AreEqual(new double[] { 0.5, 0.4 }, v);
}
private static void MainCatch(ref DictStatstic DICT, ref List <string> Blong, int startPo, int endPo, string[,] data, ref string[,] Table2)
{
MatrixS Mdata = new MatrixS(data);
int kk = 0;
for (int i = startPo; i <= endPo; i++)
{
/// 欲處理資料
string[] content = data[i, 10].Split(' ');
/// 至原始資料之字串矩陣取除目標列數，並將原始之Work與Stone工時與加班轉換並儲存至向量oriVWS中
VectorS Vcontent = Mdata.GetVector(i, 1);
Vector oriVWS = new Vector(new double[] { Convert.ToDouble(Vcontent.Data[1]), Convert.ToDouble(Vcontent.Data[2]), Convert.ToDouble(Vcontent.Data[3]), Convert.ToDouble(Vcontent.Data[4]) });
/// 用來統計每一天所有之Work與Stone工時與加班
Vector VWS = new Vector(new double[4]);
/// 用來儲存每一天之歸屬資料
string blong = "";
foreach (string ff in content)
{
if (ff.IndexOf(")") != -1)
{
Table2[kk, 0] = i.ToString();
Table2[kk, 0] = data[i, 0];
Table2[kk, 1] = ff.Split('.')[1];
string catData = ff.Substring(ff.IndexOf("(") + 1, ff.IndexOf(")") - ff.IndexOf("(") - 1);
string CompanyName = null;
double[] WorkAndStone = CatDataProcess(catData, ref CompanyName, ref blong);
Vector vws = new Vector(WorkAndStone);
DICT.Add(CompanyName, WorkAndStone);
VWS += vws;
kk++;
}
}
Vector ResVWS = oriVWS - VWS;
blong = OriComBlongProcess(ResVWS, blong);
Blong.Add(blong);
DICT.Add("利晉", ResVWS.Data);
}
}
/// <summary>
/// Returns index of the child closest to the point.
/// </summary>
public int FindClosestChild(double [] point, bool normalize)
{
double[] normalizedPoint = point.ShallowCopy();
if (normalize)
{
VectorS.NormalizeByDiff(normalizedPoint, ValueMin, ValueBounds);
}
double minDist = double.MaxValue;
int best = -1;
for (int c = 0; c < Children.Length; ++c)
{
double dist = VectorS.SquaredDistance(Children[c].Center, normalizedPoint);
if (dist < minDist)
{
minDist = dist;
best = c;
}
}
return(best);
}
/// <summary>Returns the singular values as a <see cref="Vector{T}"/>.</summary>
/// <returns>the singular values as a <see cref="Vector{T}"/>.</returns>
public Vector <T> S()
{
return(VectorS.Clone());
}
/// <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_Sub()
{
Assert.AreEqual(new double[] { 2, 3 }, VectorS.Sub(new double[] { -1, 2 }, new double[] { -3, -1 }));
}
public void Test_Add()
{
Assert.AreEqual(new double[] { 2, 3 }, VectorS.Add(new double[] { -1, 2 }, new double[] { 3, 1 }));
}
public void Test_Distance()
{
Assert.AreEqual(5, VectorS.Distance(new double[] { -1, 2 }, new double[] { 2, 6 }));
}
public void Test_SquaredDistance()
{
Assert.AreEqual(2, VectorS.SquaredDistance(new double[] { 1, 3 }, new double[] { 2, 4 }));
}
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 }));
}
public void Test_Volume()
{
Assert.AreEqual(2, VectorS.Volume(new double[] { 2 }));
Assert.AreEqual(6, VectorS.Volume(new double[] { 2, 3 }));
Assert.AreEqual(30, VectorS.Volume(new double[] { 2, 5, 3 }));
}