private Tuple<double, double> MinMaxTheta(double[][] data)
{
var features = new BSASFeatures(data);
var clusterData = features.Select();
double MinTheta = 500000;
double MaxTheta = -500000;
for (int i = 0; i < data.Length - 1; ++i)
{
double dist = Distance.Euclidean(clusterData[i], clusterData[i + 1]);
if (dist < MinTheta)
{
MinTheta = dist;
}
if (dist > MaxTheta)
{
MaxTheta = dist;
}
}
return Tuple.Create(MinTheta, MaxTheta);
}
private Tuple <double, double> MinMaxTheta(double[][] data)
{
var features = new BSASFeatures(data);
var clusterData = features.Select();
double MinTheta = 500000;
double MaxTheta = -500000;
for (int i = 0; i < data.Length - 1; ++i)
{
double dist = Distance.Euclidean(clusterData[i], clusterData[i + 1]);
if (dist < MinTheta)
{
MinTheta = dist;
}
if (dist > MaxTheta)
{
MaxTheta = dist;
}
}
return(Tuple.Create(MinTheta, MaxTheta));
}
public KMeansClustering(double[][] data, int iterations, double thetaStepNum)
{
features = new BSASFeatures(data);
m_iterations = iterations;
m_thetaStepNum = thetaStepNum;
}
public KMeansClustering(double[][] data, int iterations, double thetaStepNum)
{
features = new BSASFeatures(data);
m_iterations = iterations;
m_thetaStepNum = thetaStepNum;
}
