private async Task Run()
{
var results = Clustering.Cluster.RunIndividual(fixedKAlgorithmComboBox.SelectedAlgorithm,
myDatasetComboBox.SelectedDataset, myKSelector.K, true);
var individualResults = results.Where(c => c.group_count == myKSelector.K).Where(c => c.significant_rate > results.First().significant_rate - 0.05).ToArray();
if (individualResults.Length == 0)
{
MessageBox.Show("Failed to find any clustering that meets the criterion");
}
if (individualResults.Length == 1)
{
Report.Run(individualResults[0]);
return;
}
var ensembleRusults = CSEnsemble.Run(GetEnsembleFolderName(), "HE", myKSelector.K, 5, individualResults.ToArray());
List <StatisticalResultCsv> all = new List <StatisticalResultCsv>();
all.AddRange(individualResults);
all.AddRange(ensembleRusults);
all = all.OrderBy(c => c.significant_rate).ThenBy(c => c.Compactness).ToList();
Report.Run(all.First());
}
private async Task EnsembleTask()
{
List <StatisticalResultCsv> selectedMembers = new List <StatisticalResultCsv>();
for (int i = 0; i < candidateDataGridView.SelectedRows.Count; i++)
{
StatisticalResultCsv item = (StatisticalResultCsv)candidateDataGridView.SelectedRows[i].DataBoundItem;
selectedMembers.Add(item);
}
int keep = 0;
int.TryParse(keepNumericUpDown.Text, out keep);
NameAlias na = (NameAlias)ensembleMethodcomboBox.SelectedItem;
CSEnsemble.Run(ensembleNameTextBox.Text, na.Alias, keSelector.K, keep, selectedMembers);
}
