public void PerformScreeningClustering(List<cPlate> ListPlatesToProcess, bool IsOneByOne)
{
this.Cursor = Cursors.WaitCursor;
cMachineLearning MachineLearning = new cMachineLearning(this.GlobalInfo);
cParamAlgo ParamAlgoForClustering = MachineLearning.AskAndGetClusteringAlgo();
if (ParamAlgoForClustering == null)
{
this.Cursor = Cursors.Default;
return;
}
//DataTable dt = new DataTable();
cExtendedTable dt = new cExtendedTable();
for (int IdxDesc = 0; IdxDesc < cGlobalInfo.CurrentScreening.ListDescriptors.Count; IdxDesc++)
{
if (cGlobalInfo.CurrentScreening.ListDescriptors[IdxDesc].IsActive())
dt.Add(new cExtendedList(cGlobalInfo.CurrentScreening.ListDescriptors[IdxDesc].GetName()));
}
if (IsOneByOne)
{
foreach (cPlate itemPlate in ListPlatesToProcess)
{
cListWells ListWell = new cListWells(null);
foreach (var item in itemPlate.ListActiveWells)
ListWell.Add(item);
dt = ListWell.GetAverageDescriptorValues(cGlobalInfo.CurrentScreening.ListDescriptors.GetActiveDescriptors(), false, false);
MachineLearning.SelectedClusterer = MachineLearning.BuildClusterer(ParamAlgoForClustering, dt);
if (MachineLearning.SelectedClusterer != null)
{
double[] Assign = MachineLearning.EvaluteAndDisplayClusterer(richTextBoxInfoClustering,
null,
MachineLearning.CreateInstancesWithoutClass(dt)).getClusterAssignments();
MachineLearning.Classes = new cExtendedList();
MachineLearning.Classes.AddRange(Assign);
}
if (MachineLearning.Classes.Max() >= cGlobalInfo.ListWellClasses.Count)
{
MessageBox.Show("The number of clusters is higher than the supported number of classes. Operation cancelled !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
this.Cursor = Cursors.Default;
return;
}
if (MachineLearning.Classes.IsContainNegative() || (MachineLearning.Classes.Count == 0))
{
MessageBox.Show("Negative or null cluster index identified. Operation cancelled !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
this.Cursor = Cursors.Default;
return;
}
// ----- update well classes ------
int IdxWell = 0;
foreach (cWell item in itemPlate.ListActiveWells)
item.SetClass((int)MachineLearning.Classes[IdxWell++]);
}
}
else
{
cListWells ListWell = new cListWells(null);
foreach (cPlate itemPlate in ListPlatesToProcess)
{
foreach (cWell item in itemPlate.ListActiveWells)
{
//cExtendedList ListValues = item.GetAverageValuesList(false);
//dt.Rows.Add();
//int RealIdx = 0;
//for (int IdxDesc = 0; IdxDesc < CompleteScreening.ListDescriptors.Count; IdxDesc++)
//{
// if (CompleteScreening.ListDescriptors[IdxDesc].IsActive())
// dt.Rows[dt.Rows.Count - 1][RealIdx++] = ListValues[IdxDesc];
ListWell.Add(item);
//}
}
}
dt = ListWell.GetAverageDescriptorValues(cGlobalInfo.CurrentScreening.ListDescriptors.GetActiveDescriptors(), false, false);
MachineLearning.SelectedClusterer = MachineLearning.BuildClusterer(ParamAlgoForClustering, dt);
if (MachineLearning.SelectedClusterer != null)
{
double[] Assign = MachineLearning.EvaluteAndDisplayClusterer(richTextBoxInfoClustering,
null,
MachineLearning.CreateInstancesWithoutClass(dt)).getClusterAssignments();
MachineLearning.Classes = new cExtendedList();
MachineLearning.Classes.AddRange(Assign);
}
if (MachineLearning.Classes.Max() >= cGlobalInfo.ListWellClasses.Count)
{
MessageBox.Show("The number of cluster is higher than the supported number of classes. Operation cancelled !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
this.Cursor = Cursors.Default;
return;
}
if (MachineLearning.Classes.IsContainNegative() || (MachineLearning.Classes.Count == 0))
{
MessageBox.Show("Negative or null cluster index identified. Operation cancelled !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
this.Cursor = Cursors.Default;
return;
}
int IdxWell = 0;
// ----- update well classes ------
foreach (cPlate itemPlate in ListPlatesToProcess)
{
foreach (cWell item in itemPlate.ListActiveWells)
item.SetClass((int)MachineLearning.Classes[IdxWell++]);
}
}
cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate().DisplayDistribution(cGlobalInfo.CurrentScreening.ListDescriptors.GetActiveDescriptor(), false);
this.Cursor = Cursors.Default;
}
public cFeedBackMessage Run()
{
base.Start();
if (!FeedBackMessage.IsSucceed) return FeedBackMessage;
if (this.Input.Count > 1)
{
this.FeedBackMessage = new cFeedBackMessage(false, this);
return this.FeedBackMessage;
}
#region parameters initialization
//object _firstValue = base.ListProperties.FindByName("Number of Clusters");
//int NumOfClusters = 0;
//if (_firstValue == null)
//{
// base.GenerateError("-Number of Clusters- not found !");
// return base.FeedBackMessage;
//}
//try
//{
// cProperty TmpProp = (cProperty)_firstValue;
// NumOfClusters = (int)TmpProp.GetValue();
//}
//catch (Exception)
//{
// base.GenerateError("-Number of Clusters- cast didn't work");
// return base.FeedBackMessage;
//}
#endregion
cMachineLearning MachineLearning = new cMachineLearning(/*cWell.GlobalInfo*/null);
if (ParamAlgoForClustering == null)
ParamAlgoForClustering = MachineLearning.AskAndGetClusteringAlgo();
if (ParamAlgoForClustering == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "Invalid Parameters";
return FeedBackMessage;
}
MachineLearning.SelectedClusterer = MachineLearning.BuildClusterer(ParamAlgoForClustering, this.Input);
if (MachineLearning.SelectedClusterer != null)
{
cLinearize L = new cLinearize();
L.SetInputData(this.Input);
L.Run();
cExtendedTable Test = L.GetOutPut();
ClusterEvaluation CE = MachineLearning.EvaluteAndDisplayClusterer(/*richTextBoxInfoClustering*/null,
null,
MachineLearning.CreateInstancesWithoutClass(this.Input));
double[] Assign = CE.getClusterAssignments();
base.Output = new cExtendedTable(this.Input);
cExtendedList CL = new cExtendedList("Class ID");
CL.SetInfo(CE.clusterResultsToString());
base.Output.Add(CL);
for (int i = 0; i < Assign.Length; i++)
base.Output[base.Output.Count-1].Add(Assign[i]);
}
base.End();
return FeedBackMessage;
}
