/// <summary>
/// Calculate the sum of squares.
/// </summary>
public void CalcSumOfSquares()
{
int size = _data.Count;
double temp = 0;
for (int i = 0; i < size; i++)
{
temp += KMeansClustering.CalculateEuclideanDistance(_centroid,
(_data[i]));
}
_sumSqr = temp;
}
public void TestCluster()
{
var set = new BasicMLDataSet();
int i;
for (i = 0; i < Data.Length; i++)
{
set.Add(new BasicMLData(Data[i]));
}
var kmeans = new KMeansClustering(2, set);
kmeans.Iteration();
i = 1;
foreach (IMLCluster cluster in kmeans.Clusters)
{
IMLDataSet ds = cluster.CreateDataSet();
IMLDataPair pair;
pair = ds[0];
double t = pair.Input[0];
for (int j = 0; j < ds.Count; j++)
{
pair = ds[j];
for (j = 0; j < pair.Input.Count; j++)
{
if (t > 10)
{
Assert.IsTrue(pair.Input[j] > 10);
}
else
{
Assert.IsTrue(pair.Input[j] < 10);
}
}
}
i++;
}
}
public void TestCluster()
{
var set = new BasicMLDataSet();
int i;
for (i = 0; i < Data.Length; i++)
{
set.Add(new BasicMLData(Data[i]));
}
var kmeans = new KMeansClustering(2, set);
kmeans.Iteration();
i = 1;
foreach (IMLCluster cluster in kmeans.Clusters)
{
IMLDataSet ds = cluster.CreateDataSet();
IMLDataPair pair = BasicMLDataPair.CreatePair(ds.InputSize, ds.IdealSize);
ds.GetRecord(0, pair);
double t = pair.InputArray[0];
for (int j = 0; j < ds.Count; j++)
{
ds.GetRecord(j, pair);
for (j = 0; j < pair.InputArray.Length; j++)
{
if (t > 10)
{
Assert.IsTrue(pair.InputArray[j] > 10);
}
else
{
Assert.IsTrue(pair.InputArray[j] < 10);
}
}
}
i++;
}
}
/// <summary>
/// Process the file and cluster.
/// </summary>
/// <param name="outputFile">The output file.</param>
/// <param name="clusters">The number of clusters.</param>
/// <param name="theAnalyst">The analyst to use.</param>
/// <param name="iterations">The number of iterations to use.</param>
public void Process(FileInfo outputFile, int clusters,
EncogAnalyst theAnalyst, int iterations)
{
StreamWriter tw = PrepareOutputFile(outputFile);
ResetStatus();
var cluster = new KMeansClustering(clusters,
_data);
cluster.Iteration(iterations);
int clusterNum = 0;
foreach (IMLCluster cl in cluster.Clusters)
{
foreach (IMLData item in cl.Data)
{
int clsIndex = item.Count;
var lr = new LoadedRow(Format, item, 1);
lr.Data[clsIndex] = "" + clusterNum;
WriteRow(tw, lr);
}
clusterNum++;
}
ReportDone(false);
tw.Close();
}
public void Process(FileInfo outputFile, int clusters, EncogAnalyst theAnalyst, int iterations)
{
KMeansClustering clustering;
int num;
int num2;
IMLCluster[] clusterArray;
int num3;
StreamWriter tw = this.xf911a8958011bd6d(outputFile);
goto Label_014D;
Label_001F:
if (num3 >= clusterArray.Length)
{
if ((((uint) num2) | 0x80000000) != 0)
{
if ((((uint) num) + ((uint) num)) <= uint.MaxValue)
{
base.ReportDone(false);
tw.Close();
return;
}
goto Label_014D;
}
goto Label_00F7;
}
IMLCluster cluster = clusterArray[num3];
Label_004F:
using (IEnumerator<IMLData> enumerator = cluster.Data.GetEnumerator())
{
LoadedRow row2;
goto Label_0090;
Label_005E:
row2.Data[num2] = num;
if ((((uint) num3) & 0) != 0)
{
goto Label_00AD;
}
base.WriteRow(tw, row2);
Label_0090:
if (!enumerator.MoveNext())
{
goto Label_00D6;
}
IMLData current = enumerator.Current;
ClusterRow row = (ClusterRow) current;
Label_00AD:
num2 = row.Input.Count - 1;
row2 = row.Row;
goto Label_005E;
}
Label_00D6:
num++;
num3++;
if (((uint) num) < 0)
{
goto Label_004F;
}
goto Label_001F;
Label_00F7:
clustering = new KMeansClustering(clusters, this._x4a3f0a05c02f235f);
if (((uint) num2) < 0)
{
goto Label_00F7;
}
clustering.Iteration(iterations);
num = 0;
clusterArray = clustering.Clusters;
num3 = 0;
goto Label_001F;
Label_014D:
base.ResetStatus();
goto Label_00F7;
}