private void writeGaussianData(System.IO.StreamWriter sw)
{
GaussianClusterCollection gCol = (GaussianClusterCollection)clusterCollection;
for (int i = 0; i < gCol.Count; i++)
{
GaussianCluster gClust = gCol[i];
sw.WriteLine(String.Join(",", (from double d in gClust.Mean select d.ToString()).ToArray()));
sw.WriteLine(String.Join(",", (from double d in gClust.Covariance select d.ToString()).ToArray()));
sw.WriteLine(gClust.Proportion.ToString());
}
}
/// <summary>
/// Estimates Gaussian distributions from the data.
/// </summary>
///
private void btnCompute_Click(object sender, EventArgs e)
{
// Create a new Gaussian Mixture Model
var gmm = new GaussianMixtureModel(k);
// If available, initialize with k-means
if (kmeans != null)
{
gmm.Initialize(kmeans);
}
// Compute the model
GaussianClusterCollection clustering = gmm.Learn(observations);
// Classify all instances in mixture data
int[] classifications = clustering.Decide(observations);
// Draw the classifications
updateGraph(classifications);
}
private void gaussianReport(Forms.RunningProcess.frmRunningProcessDialog rd)
{
GaussianClusterCollection gCol = (GaussianClusterCollection)clusterCollection;
for (int i = 0; i < gCol.Count; i++)
{
GaussianCluster gClust = gCol[i];
double[] mns = gClust.Mean;
double[,] cov = gClust.Covariance;
rd.addMessage("\n\nCluster " + Labels[i] + ":\nMeans: " + String.Join(", ", (from double d in mns select d.ToString()).ToArray()) + "\nCovariance:");
for (int j = 0; j < VariableFieldNames.Length; j++)
{
string[] covStrArr = new string[VariableFieldNames.Length];
for (int l = 0; l < covStrArr.Length; l++)
{
covStrArr[l] = cov[l, j].ToString();
}
rd.addMessage("\n" + String.Join(",", covStrArr));
}
}
}
