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;
}
public cFeedBackMessage Run(bool IsDisplayResults)
{
cMachineLearning MachineLearning = new cMachineLearning(/*cWell.GlobalInfo*/null);
ParamAlgoForClustering = MachineLearning.AskAndGetClusteringAlgo();
if (MinObjectsNumber == -1)
MinObjectsNumber = (cGlobalInfo.CurrentScreening.Columns * cGlobalInfo.CurrentScreening.Rows * (int)cGlobalInfo.OptionsWindow.numericUpDownSystemMinWellRatio.Value) / 100;
foreach (cPlate CurrentPlate in PlatesToProcess)
{
cExtendedTable CurrentPlateResult = null;
foreach (var item in this.DescriptorsToProcess)
{
cExtendedTable ET = CurrentPlate.GetAverageValueList(item, false);
ET.Name = item.GetName();
cExtendedTable ResultTable = this.GenerateArtifactMessage(ET, CurrentPlate);
ResultTable.ListRowNames = new List<string>();
ResultTable.ListRowNames.Add(item.GetName());
if (CurrentPlateResult == null) CurrentPlateResult = new cExtendedTable(ResultTable);
else
{
cMerge M = new cMerge();
M.IsHorizontal = false;
M.SetInputData(CurrentPlateResult, ResultTable);
M.Run();
CurrentPlateResult = M.GetOutPut();
}
}
CurrentPlateResult.Name = CurrentPlate.GetName();
CurrentPlateResult.Tag = CurrentPlate;
this.Output.Add(CurrentPlateResult);
}
this.Output.Name = "Systematic Errors (" + PlatesToProcess.Count + " plate(s))";
if (IsDisplayResults)
{
cDisplayToWindow DTW = new cDisplayToWindow();
cDesignerMultiChoices DMC = new cDesignerMultiChoices();
foreach (var item in this.Output)
{
cDesignerSplitter DS = new cDesignerSplitter();
DS.Orientation = System.Windows.Forms.Orientation.Horizontal;
DS.Title = item.Name;
cViewerTable VT = new cViewerTable();
VT.SetInputData(item);
VT.DigitNumber = 0;
VT.Run();
DS.SetInputData(VT.GetOutPut());
cViewerHeatMap VHM = new cViewerHeatMap();
VHM.SetInputData(item);
VHM.IsAutomatedMinMax = false;
VHM.Min = 0;
VHM.Max = 1;
VHM.Run();
DS.SetInputData(VHM.GetOutPut());
DS.Run();
cExtendedControl TmpXCtrl = DS.GetOutPut();
TmpXCtrl.Tag = item.Tag;
DMC.SetInputData(TmpXCtrl);
}
DMC.Run();
DTW.SetInputData(DMC.GetOutPut());
DTW.Title = "Systematic Errors [" + PlatesToProcess.Count + " plate(s)]";
DTW.Run();
DTW.Display();
//cDisplayListExtendedTable DLET = new cDisplayListExtendedTable();
//DLET.SetInputData(this.Output);
//return DLET.Run();
}
return FeedBackMessage;
}
private void buttonNewClassificationProcess_Click(object sender, EventArgs e)
{
cMachineLearning MachineLearningForClassif = new cMachineLearning(this.GlobalInfo);
//cParamAlgo AlgoAndParameters = MachineLearningForClassif.AskAndGetClassifAlgo();
// List<bool> ListClassSelected = ((PanelForClassSelection)panelForClassSelectionClustering.Controls[0]).GetListSelectedClass();
// cInfoClass InfoClass = new cInfoClass();
//Instances ListInstances = CompleteScreening.GetCurrentDisplayPlate().CreateInstancesWithClasses(ListClassSelected);
cInfoClass InfoClass = cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate().GetNumberOfClassesBut(comboBoxNeutralClassForClassif.SelectedIndex);
Instances ListInstances = cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate().CreateInstancesWithClasses(InfoClass, comboBoxNeutralClassForClassif.SelectedIndex);
//panelForClassSelectionClustering
weka.classifiers.Evaluation EvalClassif = new weka.classifiers.Evaluation(ListInstances);
MachineLearningForClassif.PerformTraining(MachineLearningForClassif.AskAndGetClassifAlgo(),
ListInstances,
//InfoClass.NumberOfClass,
richTextBoxInfoClustering,
panelTMPForFeedBack,
out EvalClassif,
false);
}
public FormForSingleCellsDisplay(cExtendedTable InputTable, cExtendedList ListClasses)
{
InitializeComponent();
SliderForOpacity.numericUpDown.Maximum = 255;
SliderForOpacity.numericUpDown.Value = this.Opacity;
this.InputTable = InputTable;
// build the panel for the phenotypic selection
PanelPhenotypeSelection = new PanelForClassSelection(true, eClassType.PHENOTYPE);
PanelPhenotypeSelection.SelectionChanged += new PanelForClassSelection.ChangedEventHandler(PanelPhenotypeSelection_SelectionChanged);
PanelPhenotypeSelection.Height = this.panelForClasses.Height;
this.panelForClasses.Controls.Add(PanelPhenotypeSelection);
#region initialize histograms display
splitContainerHorizontal.Panel1Collapsed = true;
splitContainerVertical.Panel2Collapsed = true;
System.Drawing.Image ImageOriginal = (System.Drawing.Image)(Properties.Resources.Arrow);
if (splitContainerVertical.Panel2Collapsed)
ImageOriginal.RotateFlip(RotateFlipType.Rotate270FlipNone);
else
ImageOriginal.RotateFlip(RotateFlipType.Rotate90FlipNone);
buttonCollapseVertical.BackgroundImage = ImageOriginal;
#endregion
MachineLearning = new cMachineLearning(GlobalInfo);
MachineLearning.Classes.AddRange(ListClasses);
WindowForModelHistory.Show();
WindowForModelHistory.Visible = false;
ToolTipForX.AutoPopDelay = ToolTipForY.AutoPopDelay = 5000;
ToolTipForX.InitialDelay = ToolTipForY.InitialDelay = 500;
ToolTipForX.ReshowDelay = ToolTipForY.ReshowDelay = 500;
ToolTipForX.ShowAlways = ToolTipForY.ShowAlways = true;
ToolTipForX.SetToolTip(comboBoxAxeX, comboBoxAxeX.Text);
ToolTipForY.SetToolTip(comboBoxAxeY, comboBoxAxeY.Text);
ToolTipForVolume.SetToolTip(comboBoxVolume, comboBoxVolume.Text);
this.chartForPoints.ChartAreas[0].CursorX.Interval = double.Epsilon;
this.chartForPoints.ChartAreas[0].CursorY.Interval = double.Epsilon;
this.chartForPoints.MouseClick += new System.Windows.Forms.MouseEventHandler(this.AssociatedChart_MouseClick);
for (int i = 0; i < cGlobalInfo.ListCellularPhenotypes.Count; i++)
{
this.ListPhenotypeVisible.Add(true);
}
}
