public Identifier(Boolean isAnnotated, WhatWhyTrainer wwt)
{
this.isAnnotated = isAnnotated;
this.wwt = wwt;
listWhoCandidates = new List<Candidate>();
listWhenCandidates = new List<Candidate>();
listWhereCandidates = new List<Candidate>();
listWhatCandidates = new List<List<Token>>();
listWhyCandidates = new List<List<Token>>();
listSecondaryWhatCandidates = new List<Candidate>();
listSecondaryWhyCandidates = new List<Candidate>();
fvPOS = new FastVector(Token.PartOfSpeechTags.Length);
foreach (String POS in Token.PartOfSpeechTags)
{
fvPOS.addElement(POS);
}
whoClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\who.model");
whenClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\when.model");
whereClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\where.model");
//whatClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\what.model");
//whyClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\why.model");
initializeAnnotations();
}
private void applyClassificationModelToolStripMenuItem_Click(object sender, EventArgs e)
{
return;
Instances ListInstancesTOClassify = GlobalInfo.CurrentScreen.CellBasedClassification.CreateInstancesWithoutClass(dt);
FastVector attVals = new FastVector();
for (int i = 0; i < GlobalInfo.CurrentScreen.CellBasedClassification.NumClasses; i++)
attVals.addElement(i.ToString());
ListInstancesTOClassify.insertAttributeAt(new weka.core.Attribute("Class", attVals), ListInstancesTOClassify.numAttributes());
ListInstancesTOClassify.setClassIndex(ListInstancesTOClassify.numAttributes() - 1);
List<int> ListIdx = new List<int>();
int Max = int.MinValue;
int Min = int.MaxValue;
for (int i = 0; i < ListInstancesTOClassify.numInstances(); i++)
{
Instance InstToProcess = ListInstancesTOClassify.instance(i);
int Value =(int)GlobalInfo.CurrentScreen.CellBasedClassification.J48Model.classifyInstance(InstToProcess);
if (Value > Max) Max = Value;
if (Value < Min) Min = Value;
ListIdx.Add(Value);
}
byte[][] LUT = GlobalInfo.LUT;
for (int j = 0; j < this.chartForPoints.Series[0].Points.Count; j++)
{
int ConvertedValue = (int)(((ListIdx[j] - Min) * (LUT[0].Length - 1)) / (Max - Min));
this.chartForPoints.Series[0].Points[j].MarkerColor = Color.FromArgb(LUT[0][ConvertedValue], LUT[1][ConvertedValue], LUT[2][ConvertedValue]);
}
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClasses(cInfoClass InfoClass, int NeutralClass)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < InfoClass.NumberOfClass; i++)
attVals.addElement("Class__" + (i).ToString());
atts.addElement(new weka.core.Attribute("Class__", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (CurrentWell.GetCurrentClassIdx() == NeutralClass) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
vals[columnNo] = InfoClass.CorrespondanceTable[CurrentWell.GetCurrentClassIdx()];
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
public MITesDataCollectionForm(string dataDirectory, string arffFile, bool isHierarchical)
{
//where data is being stored
this.dataDirectory = dataDirectory;
//Initialize high resolution unix timer
UnixTime.InitializeTime();
//Initialize and start GUI progress thread
progressMessage = null;
aProgressThread = new Thread(new ThreadStart(ProgressThread));
aProgressThread.Start();
#region Load Configuration files
//load the activity and sensor configuration files
progressMessage = "Loading XML protocol and sensors ...";
AXML.Reader reader = new AXML.Reader(Constants.MASTER_DIRECTORY, dataDirectory);
#if (!PocketPC)
if (reader.validate() == false)
{
throw new Exception("Error Code 0: XML format error - activities.xml does not match activities.xsd!");
}
else
{
#endif
this.annotation = reader.parse();
this.annotation.DataDirectory = dataDirectory;
SXML.Reader sreader = new SXML.Reader(Constants.MASTER_DIRECTORY, dataDirectory);
#if (!PocketPC)
if (sreader.validate() == false)
{
throw new Exception("Error Code 0: XML format error - sensors.xml does not match sensors.xsd!");
}
else
{
#endif
this.sensors = sreader.parse(Constants.MAX_CONTROLLERS);
progressMessage += " Completed\r\n";
//TODO: remove BT components
progressMessage += "Loading configuration file ...";
MITesFeatures.core.conf.ConfigurationReader creader = new MITesFeatures.core.conf.ConfigurationReader(dataDirectory);
this.configuration = creader.parse();
progressMessage += " Completed\r\n";
#if (!PocketPC)
}
}
#endif
#endregion Load Configuration files
#region Initialize External Data Reception Channels
//Initialize 1 master decoder
this.masterDecoder = new MITesDecoder();
//Initialize the software mode
isExtracting = false;
isCollectingDetailedData = false;
isPlotting = true;
isClassifying = true;
#region Initialize Feature Extraction
this.isExtracting = false;
if (this.sensors.TotalReceivers > 0) // if there is at least 1 MIT
//Extractor.Initialize(this.mitesDecoders[0], dataDirectory, this.annotation, this.sensors, this.configuration);
Extractor.Initialize(this.masterDecoder, dataDirectory, this.annotation, this.sensors, this.configuration);
else if (this.sensors.Sensors.Count > 0) // only built in
Extractor.Initialize(this.masterDecoder, dataDirectory, this.annotation, this.sensors, this.configuration);
#endregion Initialize Feature Extraction
labelIndex = new Hashtable();
instances = new Instances(new StreamReader(arffFile));
instances.Class = instances.attribute(Extractor.ArffAttributeLabels.Length);
classifier = new J48();
if (!File.Exists("model.xml"))
{
classifier.buildClassifier(instances);
TextWriter tc = new StreamWriter("model.xml");
classifier.toXML(tc);
tc.Flush();
tc.Close();
}
else
classifier.buildClassifier("model.xml", instances);
fvWekaAttributes = new FastVector(Extractor.ArffAttributeLabels.Length + 1);
for (int i = 0; (i < Extractor.ArffAttributeLabels.Length); i++)
fvWekaAttributes.addElement(new weka.core.Attribute(Extractor.ArffAttributeLabels[i]));
FastVector fvClassVal = new FastVector();
labelCounters = new int[((AXML.Category)this.annotation.Categories[0]).Labels.Count + 1];
activityLabels = new string[((AXML.Category)this.annotation.Categories[0]).Labels.Count + 1];
for (int i = 0; (i < ((AXML.Category)this.annotation.Categories[0]).Labels.Count); i++)
{
labelCounters[i] = 0;
string label = "";
int j = 0;
for (j = 0; (j < this.annotation.Categories.Count - 1); j++)
label += ((AXML.Label)((AXML.Category)this.annotation.Categories[j]).Labels[i]).Name.Replace(' ', '_') + "_";
label += ((AXML.Label)((AXML.Category)this.annotation.Categories[j]).Labels[i]).Name.Replace(' ', '_');
activityLabels[i] = label;
labelIndex.Add(label, i);
fvClassVal.addElement(label);
}
weka.core.Attribute ClassAttribute = new weka.core.Attribute("activity", fvClassVal);
isClassifying = true;
this.aMITesActivityCounters = new Hashtable();
if (!((this.sensors.Sensors.Count == 1) && (this.sensors.HasBuiltinSensors)))
{
//Initialize arrays to store USB and Bluetooth controllers
this.mitesControllers = new MITesReceiverController[this.sensors.TotalWiredReceivers];
#if (PocketPC)
this.bluetoothControllers = new BluetoothController[this.sensors.TotalBluetoothReceivers];
//this.ts = new Thread[this.sensors.TotalBluetoothReceivers];
#endif
//Initialize array to store Bluetooth connection status
//this.bluetoothConnectionStatus = new bool[this.sensors.TotalBluetoothReceivers];
//Initialize a decoder for each sensor
this.mitesDecoders = new MITesDecoder[this.sensors.TotalReceivers];
#if (PocketPC)
#region Bluetooth reception channels initialization
//Initialize and search for wockets connections
progressMessage += "Initializing Bluetooth receivers ... searching " + this.sensors.TotalBluetoothReceivers + " BT receivers\r\n";
//Try to initialize all Bluetooth receivers 10 times then exit
int initializationAttempt = 0;
while (initializationAttempt <= 10)
{
if (InitializeBluetoothReceivers() == false)
{
initializationAttempt++;
if (initializationAttempt == 10)
{
MessageBox.Show("Exiting: Some Bluetooth receivers in your configuration were not initialized.");
Application.Exit();
System.Diagnostics.Process.GetCurrentProcess().Kill();
}
else
progressMessage += "Failed to initialize all BT connections. Retrying (" + initializationAttempt + ")...\r\n";
}
else
break;
Thread.Sleep(2000);
}
#endregion Bluetooth reception channels initialization
#endif
#region USB reception channels initialization
if (InitializeUSBReceivers() == false)
{
MessageBox.Show("Exiting: Some USB receivers in your configuration were not initialized.");
#if (PocketPC)
Application.Exit();
System.Diagnostics.Process.GetCurrentProcess().Kill();
#else
Environment.Exit(0);
#endif
}
#endregion USB reception channels initialization
}
//}
#endregion Initialize External Data Reception Channels
#if (PocketPC)
#region Initialize Builtin Data Reception Channels
if (InitializeBuiltinReceivers() == false)
{
MessageBox.Show("Exiting: A built in receiver channel was not found.");
Application.Exit();
System.Diagnostics.Process.GetCurrentProcess().Kill();
}
#endregion Initialize Builtin Data Reception Channels
#endif
#region Initialize GUI Components
//initialize the interface components
InitializeComponent();
//Initialize GUI timers
progressMessage += "Initializing Timers ...";
InitializeTimers();
progressMessage += " Completed\r\n";
//Initialize different GUI components
progressMessage += "Initializing GUI ...";
InitializeInterface();
progressMessage += " Completed\r\n";
this.isPlotting = true;
//count the number of accelerometers
if (this.sensors.IsHR)
this.maxPlots = this.sensors.Sensors.Count - 1;
else
this.maxPlots = this.sensors.Sensors.Count;
SetFormPositions();
if (this.sensors.TotalReceivers > 0)
aMITesPlotter = new MITesScalablePlotter(this.panel1, MITesScalablePlotter.DeviceTypes.IPAQ, maxPlots, this.masterDecoder, GetGraphSize(false));
else
aMITesPlotter = new MITesScalablePlotter(this.panel1, MITesScalablePlotter.DeviceTypes.IPAQ, maxPlots, this.masterDecoder, GetGraphSize(false));
//Override the resize event
#if (PocketPC)
this.Resize += new EventHandler(OnResize);
#else
this.form1.Resize += new EventHandler(OnResizeForm1);
this.form1.FormClosing += new FormClosingEventHandler(form_FormClosing);
this.form2.Resize += new EventHandler(OnResizeForm2);
this.form2.FormClosing += new FormClosingEventHandler(form_FormClosing);
this.form3.Resize += new EventHandler(OnResizeForm3);
this.form3.FormClosing += new FormClosingEventHandler(form_FormClosing);
this.form4.Resize += new EventHandler(OnResizeForm4);
this.form4.FormClosing += new FormClosingEventHandler(form_FormClosing);
#endif
//Initialize the quality interface
progressMessage += "Initializing MITes Quality GUI ...";
InitializeQualityInterface();
progressMessage += " Completed\r\n";
//Remove classifier tabs
#if (PocketPC)
this.tabControl1.TabPages.RemoveAt(4);
this.tabControl1.SelectedIndex = 0;
#else
this.ShowForms();
#endif
#endregion Initialize GUI Components
#region Initialize Quality Tracking variables
InitializeQuality();
#endregion Initialize Quality Tracking variables
#region Initialize Logging
InitializeLogging(dataDirectory);
#endregion Initialize Logging
#region Initialize CSV Storage (PC Only)
#if (!PocketPC)
//create some counters for activity counts
averageX = new int[this.sensors.MaximumSensorID + 1];
averageY = new int[this.sensors.MaximumSensorID + 1];
averageZ = new int[this.sensors.MaximumSensorID + 1];
averageRawX = new int[this.sensors.MaximumSensorID + 1];
averageRawY = new int[this.sensors.MaximumSensorID + 1];
averageRawZ = new int[this.sensors.MaximumSensorID + 1];
prevX = new int[this.sensors.MaximumSensorID + 1];
prevY = new int[this.sensors.MaximumSensorID + 1];
prevZ = new int[this.sensors.MaximumSensorID + 1];
acCounters = new int[this.sensors.MaximumSensorID + 1];
activityCountWindowSize = 0;
activityCountCSVs = new StreamWriter[this.sensors.MaximumSensorID + 1];
samplingCSVs = new StreamWriter[this.sensors.MaximumSensorID + 1];
averagedRaw = new StreamWriter[this.sensors.MaximumSensorID + 1];
masterCSV = new StreamWriter(dataDirectory + "\\MITesSummaryData.csv");
hrCSV = new StreamWriter(dataDirectory + "\\HeartRate_MITes.csv");
string csv_line1 = "UnixTimeStamp,TimeStamp,X,Y,Z";
string csv_line2 = "UnixTimeStamp,TimeStamp,Sampling";
string hr_csv_header = "UnixTimeStamp,TimeStamp,HR";
string master_csv_header = "UnixTimeStamp,TimeStamp";
foreach (Category category in this.annotation.Categories)
master_csv_header += "," + category.Name;
foreach (Sensor sensor in this.sensors.Sensors)
{
int sensor_id = Convert.ToInt32(sensor.ID);
string location = sensor.Location.Replace(' ', '-');
if (sensor_id > 0) //exclude HR
{
activityCountCSVs[sensor_id] = new StreamWriter(dataDirectory + "\\MITes_" + sensor_id.ToString("00") + "_ActivityCount_" + location + ".csv");
activityCountCSVs[sensor_id].WriteLine(csv_line1);
averagedRaw[sensor_id] = new StreamWriter(dataDirectory + "\\MITes_" + sensor_id.ToString("00") + "_1s-RawMean_" + location + ".csv");
averagedRaw[sensor_id].WriteLine(csv_line1);
samplingCSVs[sensor_id] = new StreamWriter(dataDirectory + "\\MITes_" + sensor_id.ToString("00") + "_SampleRate_" + location + ".csv");
samplingCSVs[sensor_id].WriteLine(csv_line2);
master_csv_header += ",MITes" + sensor_id.ToString("00") + "_SR," + "MITes" + sensor_id.ToString("00") + "_AVRaw_X," +
"MITes" + sensor_id.ToString("00") + "_AVRaw_Y," + "MITes" + sensor_id.ToString("00") + "_AVRaw_Z," + "MITes" + sensor_id.ToString("00") + "_AC_X," +
"MITes" + sensor_id.ToString("00") + "_AC_Y," + "MITes" + sensor_id.ToString("00") + "_AC_Z";
}
}
master_csv_header += ",HR";
this.masterCSV.WriteLine(master_csv_header);
this.hrCSV.WriteLine(hr_csv_header);
#endif
#endregion Initialize CSV Storage (PC Only)
#region Start Collecting Data
//if (this.sensors.TotalReceivers > 0)
// isStartedReceiver = true;
//Start the built in polling thread
#if (PocketPC)
if (this.sensors.HasBuiltinSensors)
{
this.pollingThread = new Thread(new ThreadStart(this.pollingData));
this.pollingThread.Priority = ThreadPriority.Lowest;
this.pollingThread.Start();
}
#endif
//Terminate the progress thread
progressThreadQuit = true;
//Enable all timer functions
this.readDataTimer.Enabled = true;
this.qualityTimer.Enabled = true;
if (this.sensors.IsHR)
this.HRTimer.Enabled = true;
#endregion Start Collecting Data
}
/// <summary>
/// Create a single instance for WEKA
/// </summary>
/// <param name="NClasses">Number of classes</param>
/// <returns>the weka instances</returns>
public Instances CreateInstanceForNClasses(cInfoClass InfoClass)
{
List<double> AverageList = new List<double>();
for (int i = 0; i < Parent.ListDescriptors.Count; i++)
if (Parent.ListDescriptors[i].IsActive()) AverageList.Add(GetAverageValuesList()[i]);
weka.core.FastVector atts = new FastVector();
List<string> NameList = Parent.ListDescriptors.GetListNameActives();
for (int i = 0; i < NameList.Count; i++)
atts.addElement(new weka.core.Attribute(NameList[i]));
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < InfoClass.NumberOfClass; i++)
attVals.addElement("Class" + i);
atts.addElement(new weka.core.Attribute("Class__", attVals));
Instances data1 = new Instances("SingleInstance", atts, 0);
double[] newTable = new double[AverageList.Count + 1];
Array.Copy(AverageList.ToArray(), 0, newTable, 0, AverageList.Count);
//newTable[AverageList.Count] = 1;
data1.add(new DenseInstance(1.0, newTable));
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
/// <summary> Set the output format if the class is nominal.</summary>
private void setOutputFormat()
{
FastVector newAtts;
int newClassIndex;
System.Text.StringBuilder attributeName;
Instances outputFormat;
FastVector vals;
// Compute new attributes
newClassIndex = getInputFormat().classIndex();
newAtts = new FastVector();
for (int j = 0; j < getInputFormat().numAttributes(); j++)
{
weka.core.Attribute att = getInputFormat().attribute(j);
if (!att.Nominal || (j == getInputFormat().classIndex()) || !m_Columns.isInRange(j))
{
newAtts.addElement(att.copy());
}
else
{
if (att.numValues() <= 2)
{
if (m_Numeric)
{
newAtts.addElement(new weka.core.Attribute(att.name()));
}
else
{
newAtts.addElement(att.copy());
}
}
else
{
if (newClassIndex >= 0 && j < getInputFormat().classIndex())
{
newClassIndex += att.numValues() - 1;
}
// Compute values for new attributes
for (int k = 0; k < att.numValues(); k++)
{
attributeName = new System.Text.StringBuilder(att.name() + "=");
attributeName.Append(att.value_Renamed(k));
if (m_Numeric)
{
newAtts.addElement(new weka.core.Attribute(attributeName.ToString()));
}
else
{
vals = new FastVector(2);
vals.addElement("f"); vals.addElement("t");
newAtts.addElement(new weka.core.Attribute(attributeName.ToString(), vals));
}
}
}
}
}
outputFormat = new Instances(getInputFormat().relationName(), newAtts, 0);
outputFormat.ClassIndex = newClassIndex;
setOutputFormat(outputFormat);
}
public void PerformClassification()
{
FormForSingleCellClassifOptions FFSC = new FormForSingleCellClassifOptions();
//cGUI_ListClasses GLC = new cGUI_ListClasses();
//GLC.ClassType = eClassType.PHENOTYPE;
//GLC.IsCheckBoxes = true;
//GLC.IsSelectAll = true;
//GLC.Run(GlobalInfo);
PanelForClassSelection PhenotypeSelectionPanel = new PanelForClassSelection( true, eClassType.PHENOTYPE);
PhenotypeSelectionPanel.Height = FFSC.panelPhenoToBeClassified.Height;
FFSC.panelPhenoToBeClassified.Controls.Add(PhenotypeSelectionPanel);
PanelForClassSelection WellClassSelectionPanel = new PanelForClassSelection( true, eClassType.WELL);
WellClassSelectionPanel.Height = FFSC.panelWellToBeClassified.Height;
FFSC.panelWellToBeClassified.Controls.Add(WellClassSelectionPanel);
PanelForPlatesSelection PlatesSelectionPanel = new PanelForPlatesSelection( true, null, true);
PlatesSelectionPanel.Height = FFSC.panelWellToBeClassified.Height;
FFSC.tabPagePlates.Controls.Add(PlatesSelectionPanel);
if (FFSC.ShowDialog() != DialogResult.OK) return;
// ----------------------- Classification ------------------------------
int DescrCount = cGlobalInfo.CurrentScreening.ListDescriptors.Count;
this.UpDateNumberOfCluster();
if (NumberOfClusters == 0)
{
System.Windows.Forms.MessageBox.Show("Number of cluster is null", "Error !", MessageBoxButtons.OK, MessageBoxIcon.Hand);
return;
}
//if (FFSC.checkBoxGenerationRatio.Checked)
//{
// // first we update the descriptor
// for (int i = 0; i < this.NumberOfClusters; i++)
// GlobalInfo.CurrentScreening.ListDescriptors.AddNew(new cDescriptorType("Ratio_" + GlobalInfo.ListCellularPhenotypes[i].Name, true, 1, GlobalInfo));
//}
FormForProgress ProgressWindow = new FormForProgress();
ProgressWindow.Show();
int IdxProgress = 0;
int MaxProgress = 0;
#region Confusion Matrix init
cListExtendedTable LT = new cListExtendedTable();
cExtendedTable ConfusionMatrix = new cExtendedTable(cGlobalInfo.ListCellularPhenotypes.Count, cGlobalInfo.ListCellularPhenotypes.Count, 0);
ConfusionMatrix.Name = "Confusion Matrix (global)";
ConfusionMatrix.ListRowNames = new List<string>();
for (int i = 0; i < cGlobalInfo.ListCellularPhenotypes.Count; i++)
{
ConfusionMatrix.ListRowNames.Add(cGlobalInfo.ListCellularPhenotypes[i].Name + "*");
ConfusionMatrix[i].Name = cGlobalInfo.ListCellularPhenotypes[i].Name;
}
LT.Add(ConfusionMatrix);
for (int i = 0; i < cGlobalInfo.ListWellClasses.Count; i++)
{
cExtendedTable ConfusionMatrixTmp = new cExtendedTable(cGlobalInfo.ListCellularPhenotypes.Count, cGlobalInfo.ListCellularPhenotypes.Count, 0);
ConfusionMatrixTmp.Name = "Confusion Matrix - " + cGlobalInfo.ListWellClasses[i].Name;
ConfusionMatrixTmp.ListRowNames = new List<string>();
for (int j = 0; j < cGlobalInfo.ListCellularPhenotypes.Count; j++)
{
ConfusionMatrixTmp.ListRowNames.Add(cGlobalInfo.ListCellularPhenotypes[j].Name + "*");
ConfusionMatrixTmp[j].Name =cGlobalInfo.ListCellularPhenotypes[j].Name;
}
LT.Add(ConfusionMatrixTmp);
}
#endregion
cListPlates LP = PlatesSelectionPanel.GetListSelectedPlates();
foreach (cPlate CurrentPlateToProcess in LP /*GlobalInfo.CurrentScreening.ListPlatesAvailable*/)
MaxProgress += (int)CurrentPlateToProcess.ListActiveWells.Count;
ProgressWindow.progressBar.Maximum = MaxProgress;
FastVector attVals = new FastVector();
for (int i = 0; i < this.NumberOfClusters; i++)
attVals.addElement(i.ToString());
cPlate CurrentDispPlate = cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate();
foreach (cPlate CurrentPlateToProcess in LP/* GlobalInfo.CurrentScreening.ListPlatesAvailable*/)
{
foreach (cWell TmpWell in CurrentPlateToProcess.ListActiveWells)
{
ProgressWindow.progressBar.Value = IdxProgress++;
if (TmpWell.GetCurrentClassIdx() == -1) continue;
if (WellClassSelectionPanel.ListCheckBoxes[TmpWell.GetCurrentClassIdx()].Checked == false) continue;
DataTable FinalDataTable = new DataTable();
TmpWell.AssociatedPlate.DBConnection = new cDBConnection(TmpWell.AssociatedPlate, TmpWell.SQLTableName);
TmpWell.AssociatedPlate.DBConnection.AddWellToDataTable(TmpWell, FinalDataTable, this.GlobalInfo);
cListSingleBiologicalObjects LSBO = TmpWell.AssociatedPlate.DBConnection.GetBiologicalPhenotypes(TmpWell);
//TmpWell.AssociatedPlate.DBConnection.AddWellToDataTable(TmpWell, FinalDataTable, checkBoxIncludeWellClassAsDesc.Checked, GlobalInfo);
Instances ListInstancesTOClassify = this.CreateInstancesWithoutClass(FinalDataTable);
ListInstancesTOClassify.insertAttributeAt(new weka.core.Attribute("Class", attVals), ListInstancesTOClassify.numAttributes());
ListInstancesTOClassify.setClassIndex(ListInstancesTOClassify.numAttributes() - 1);
cExtendedList ListNewClasses = new cExtendedList();
int NumInstances = ListInstancesTOClassify.numInstances();
for (int i = 0; i < NumInstances; i++)
{
// ClassId contains the new class
Instance CurrentInst = ListInstancesTOClassify.instance(i);
double classId = this.CurrentClassifier.classifyInstance(CurrentInst);
double[] ClassConfidence = this.CurrentClassifier.distributionForInstance(CurrentInst);
LSBO[i].ClassificationConfidence = ClassConfidence[(int)classId];
ListNewClasses.Add(classId);
if (CurrentPlateToProcess == CurrentDispPlate)
{
LT[0][LSBO[i].GetAssociatedPhenotype().Idx][(int)classId]++;
if (TmpWell.GetCurrentClassIdx() >= 0)
LT[TmpWell.GetCurrentClassIdx() + 1][LSBO[i].GetAssociatedPhenotype().Idx][(int)classId]++;
}
}
ProgressWindow.richTextBoxForComment.AppendText(TmpWell.GetShortInfo().Remove(TmpWell.GetShortInfo().Length - 2) + " : " + NumInstances + " objects\n");
ProgressWindow.Refresh();
// ------------- update class within the database -----------------------------
TmpWell.AssociatedPlate.DBConnection.ChangePhenotypeClass(TmpWell, ListNewClasses);
//if (FFSC.checkBoxGenerationRatio.Checked)
//{
// List<double[]> Histo = ListNewClasses.CreateHistogram(0, ListInstancesTOClassify.numClasses(), ListInstancesTOClassify.numClasses());
// cListSignature LDesc = new cListSignature();
// for (int IdxHisto = 0; IdxHisto < Histo[1].Length - 1; IdxHisto++)
// {
// Histo[1][IdxHisto] = (100.0 * Histo[1][IdxHisto]) / (double)ListInstancesTOClassify.numInstances();
// cSignature NewDesc = new cSignature(Histo[1][IdxHisto], GlobalInfo.CurrentScreening.ListDescriptors[IdxHisto + DescrCount], GlobalInfo.CurrentScreening);
// LDesc.Add(NewDesc);
// }
// TmpWell.AddSignatures(LDesc);
//}
TmpWell.AssociatedPlate.DBConnection.CloseConnection();
}
}
#region Display Report
cDesignerSplitter DS = new cDesignerSplitter();
DS.Orientation = Orientation.Vertical;
cViewertext VTEXT = new cViewertext();
VTEXT.SetInputData(ProgressWindow.richTextBoxForComment.Text);
VTEXT.Run();
cDesignerTab DT = new cDesignerTab();
DT.IsMultiline = false;
foreach (var item in LT)
{
cViewerTable VT = new cViewerTable();
VT.SetInputData(item);
VT.DigitNumber = 0;
VT.Run();
DT.SetInputData(VT.GetOutPut());
}
DT.Run();
cExtendedControl TextEC = DT.GetOutPut();
TextEC.Width = 0;
TextEC.Height = 0;
TextEC.Anchor = (System.Windows.Forms.AnchorStyles)(System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom
| System.Windows.Forms.AnchorStyles.Left
| System.Windows.Forms.AnchorStyles.Right);
DS.SetInputData(VTEXT.GetOutPut());
DS.SetInputData(TextEC);
DS.Run();
ProgressWindow.Close();
cDisplayToWindow CDT = new cDisplayToWindow();
CDT.SetInputData(DS.GetOutPut());
CDT.IsModal = true;
CDT.Title = "Phenotypic Classificaton Report";
CDT.Run();
CDT.Display();
#endregion
//if (IsKeepOriginalDesc == System.Windows.Forms.DialogResult.No)
//{
// // int DescNumToRemove = GlobalInfo.CurrentScreen.ListDescriptors.Count -
// for (int IdxDesc = 0; IdxDesc < DescrCount; IdxDesc++)
// GlobalInfo.CurrentScreening.ListDescriptors.RemoveDesc(GlobalInfo.CurrentScreening.ListDescriptors[0], GlobalInfo.CurrentScreening);
//}
cGlobalInfo.CurrentScreening.ListDescriptors.UpDateDisplay();
cGlobalInfo.CurrentScreening.UpDatePlateListWithFullAvailablePlate();
for (int idxP = 0; idxP < cGlobalInfo.CurrentScreening.ListPlatesActive.Count; idxP++)
cGlobalInfo.CurrentScreening.ListPlatesActive[idxP].UpDataMinMax();
//WindowFormForCellbyCellClassif.Close();
//WindowClusteringInfo.Close();
}
///
/// <summary> * Sets the format of output instances. </summary>
///
private void setOutputFormat()
{
// give names to the new attributes
FastVector newAtts = new FastVector();
string foName = null;
for (int i = 0; i < getInputFormat().numAttributes(); i++)
{
string attName = getInputFormat().attribute(i).name();
foName = "'FT " + attName.Replace('\'', ' ').Trim() + " (re)\'";
Attribute newAttribX = new Attribute(foName);
newAtts.addElement(newAttribX);
foName = "'FT " + attName.Replace('\'', ' ').Trim() + " (im)\'";
Attribute newAttribY = new Attribute(foName);
newAtts.addElement(newAttribY);
}
setOutputFormat(new Instances(getInputFormat().relationName(), newAtts, getNumCoeffs()));
}
/// <summary>
/// Create an instances structure without classes for unsupervised methods
/// </summary>
/// <returns>a weka Instances object</returns>
public Instances CreateInstancesWithoutClass()
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
// Descriptors loop
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
Instances data1 = new Instances("MyRelation", atts, 0);
foreach (cWell CurrentWell in this.ListActiveWells)
{
double[] vals = new double[data1.numAttributes()];
int IdxRealCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxRealCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
data1.add(new DenseInstance(1.0, vals));
}
return data1;
}
private void loadModelToolStripMenuItem_Click(object sender, EventArgs e)
{
OpenFileDialog CurrOpenFileDialog = new OpenFileDialog();
CurrOpenFileDialog.Filter = "Classification Model files (*.model)|*.model";
DialogResult Res = CurrOpenFileDialog.ShowDialog();
if (Res != DialogResult.OK) return;
if (CurrOpenFileDialog.FileName == "") return;
IFormatter formatter = new BinaryFormatter();
Stream stream = new FileStream(CurrOpenFileDialog.FileName,
FileMode.Open,
FileAccess.Read,
FileShare.Read);
cMyClassifierModel obj = (cMyClassifierModel)formatter.Deserialize(stream);
stream.Close();
if (this.comboBoxAxeX.Items.Count != obj.ListDescNames.Count)
{
string ListDescRequired = obj.ListDescNames.Count + " descriptors are required:\n";
foreach (var item in obj.ListDescNames)
{
ListDescRequired += item + "\n";
}
MessageBox.Show(ListDescRequired, "Descriptor list does not match !", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
int IdxDesc = 0;
foreach (var item in this.comboBoxAxeX.Items)
{
if (obj.ListDescNames[IdxDesc++] != item.ToString())
{
MessageBox.Show("Descriptor list does not match !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
}
#region Display associated info and graphs
this.richTextBoxForResults.Clear();
this.richTextBoxForResults.AppendText(obj.ClassifModel.ToString());
if (obj.ClassifModel.GetType() == typeof(J48))
{
GViewer GraphView = MachineLearning.DisplayTree(GlobalInfo, (J48)obj.ClassifModel, true).gViewerForTreeClassif;
GraphView.Size = new System.Drawing.Size(this.panelForGraphicalResults.Width, this.panelForGraphicalResults.Height);
GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right);
this.panelForGraphicalResults.Controls.Clear();
this.panelForGraphicalResults.Controls.Add(GraphView);
}
this.MachineLearning.CurrentClassifier = obj.ClassifModel;
Instances ListInstancesTOClassify = cGlobalInfo.CurrentScreening.CellBasedClassification.CreateInstancesWithoutClass(/*dt*/this.InputTable);
SecondListClassesForValidation = null;
FastVector attVals = new FastVector();
for (int i = 0; i < MachineLearning.NumberOfClusters; i++)
attVals.addElement(i.ToString());
ListInstancesTOClassify.insertAttributeAt(new weka.core.Attribute("Class", attVals), ListInstancesTOClassify.numAttributes());
ListInstancesTOClassify.setClassIndex(ListInstancesTOClassify.numAttributes() - 1);
for (int i = 0; i < ListInstancesTOClassify.numInstances(); i++)
{
this.MachineLearning.Classes[i] = MachineLearning.CurrentClassifier.classifyInstance(ListInstancesTOClassify.instance(i));
}
buttonClassify.Enabled = true;
ReDraw();
#endregion
}
//private void ToolStripMenuItem_CurrentPhenoVisibility(object sender, EventArgs e)
//{
// int SelectedIdx = (int)(((ToolStripMenuItem)sender).Tag);
// if (ListPhenotypeVisible[SelectedIdx]) ListPhenotypeVisible[SelectedIdx] = false;
// else ListPhenotypeVisible[SelectedIdx] = true;
// this.ReFreshMainGraph();
//}
private void ToolStripMenuItem_ApplyCurrentModel(object sender, EventArgs e)
{
Instances InstancesList = cGlobalInfo.CurrentScreening.CellBasedClassification.CreateInstancesWithoutClass(/*dt*/this.InputTable);
FastVector attValsWithoutClasses = new FastVector();
for (int i = 0; i < cGlobalInfo.ListCellularPhenotypes.Count; i++)
attValsWithoutClasses.addElement(cGlobalInfo.ListCellularPhenotypes[i].Name);
InstancesList.insertAttributeAt(new weka.core.Attribute("Class", attValsWithoutClasses), InstancesList.numAttributes());
//int A = Classes.Count;
for (int i = 0; i < MachineLearning.Classes.Count; i++)
InstancesList.get(i).setValue(InstancesList.numAttributes() - 1, MachineLearning.Classes[i]);
InstancesList.setClassIndex(InstancesList.numAttributes() - 1);
// Instances ListInstancesTOClassify = ListInstances;
SecondListClassesForValidation = new double[InstancesList.numInstances()];
//// ListInstances.setClassIndex(ListInstances.numAttributes() - 1);
for (int i = 0; i < InstancesList.numInstances(); i++)
{
SecondListClassesForValidation[i] = MachineLearning.CurrentClassifier.classifyInstance(InstancesList.instance(i));
}
buttonClassify.Enabled = true;
ReDraw();
}
/// <summary> Constructor for nominal attributes and string attributes, where
/// metadata is supplied. If a null vector of attribute values is passed
/// to the method, the attribute is assumed to be a string.
///
/// </summary>
/// <param name="attributeName">the name for the attribute
/// </param>
/// <param name="attributeValues">a vector of strings denoting the
/// attribute values. Null if the attribute is a string attribute.
/// </param>
/// <param name="metadata">the attribute's properties
/// </param>
//@ requires attributeName != null;
//@ requires metadata != null;
/*@ ensures m_Name == attributeName;
ensures m_Index == -1;
ensures attributeValues == null && m_Type == STRING
|| attributeValues != null && m_Type == NOMINAL
&& m_Values.size() == attributeValues.size();
signals (IllegalArgumentException ex)
(* if duplicate strings in attributeValues *);
*/
public Attribute(System.String attributeName, FastVector attributeValues, ProtectedProperties metadata)
{
m_Name = attributeName;
m_Index = - 1;
if (attributeValues == null)
{
m_Values = new FastVector();
m_Hashtable = System.Collections.Hashtable.Synchronized(new System.Collections.Hashtable());
m_Type = STRING;
}
else
{
m_Values = new FastVector(attributeValues.size());
//m_Hashtable = System.Collections.Hashtable.Synchronized(new System.Collections.Hashtable(attributeValues.size()));
m_Hashtable = new System.Collections.Hashtable(attributeValues.size());
for (int i = 0; i < attributeValues.size(); i++)
{
// No serialization
System.Object store = attributeValues.elementAt(i);
//if (((System.String) store).Length > STRING_COMPRESS_THRESHOLD)
//{
// try
// {
// store = new SerializedObject(attributeValues.elementAt(i), true);
// }
// catch (System.Exception ex)
// {
// System.Console.Error.WriteLine("Couldn't compress nominal attribute value -" + " storing uncompressed.");
// }
//}
if (m_Hashtable.ContainsKey(store))
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Object.toString' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.ArgumentException("A nominal attribute (" + attributeName + ") cannot" + " have duplicate labels (" + store + ").");
}
m_Values.addElement(store);
m_Hashtable.Add(store, (System.Int32) i);
}
m_Type = NOMINAL;
}
setMetadata(metadata);
}
private FastVector createWhyFastVector()
{
FastVector fvWhy = new FastVector(8 + whyWordsBefore * 2 + whyWordsAfter * 2);
fvWhy.addElement(new weka.core.Attribute("candidate", (FastVector)null));
fvWhy.addElement(new weka.core.Attribute("wordCount"));
fvWhy.addElement(new weka.core.Attribute("sentence"));
fvWhy.addElement(new weka.core.Attribute("candidateScore"));
fvWhy.addElement(new weka.core.Attribute("numWho"));
fvWhy.addElement(new weka.core.Attribute("numWhen"));
fvWhy.addElement(new weka.core.Attribute("numWhere"));
for (int i = whereWordsBefore; i > 0; i--)
{
fvWhy.addElement(new weka.core.Attribute("word-" + i, (FastVector)null));
}
for (int i = 1; i <= whereWordsAfter; i++)
{
fvWhy.addElement(new weka.core.Attribute("word+" + i, (FastVector)null));
}
for (int i = whyWordsBefore; i > 0; i--)
{
fvWhy.addElement(new weka.core.Attribute("postag-" + i, fvPOS));
}
for (int i = 1; i <= whyWordsAfter; i++)
{
fvWhy.addElement(new weka.core.Attribute("postag+" + i, fvPOS));
}
FastVector fvClass = new FastVector(2);
fvClass.addElement("yes");
fvClass.addElement("no");
fvWhy.addElement(new weka.core.Attribute("why", fvClass));
return fvWhy;
}
private FastVector createWhoFastVector()
{
FastVector fvWho = new FastVector(7 + whoWordsBefore * 2 + whoWordsAfter * 2);
fvWho.addElement(new weka.core.Attribute("word", (FastVector)null));
fvWho.addElement(new weka.core.Attribute("wordCount"));
fvWho.addElement(new weka.core.Attribute("sentence"));
fvWho.addElement(new weka.core.Attribute("position"));
fvWho.addElement(new weka.core.Attribute("sentenceStartProximity"));
fvWho.addElement(new weka.core.Attribute("wordScore"));
for (int i = whoWordsBefore; i > 0; i--)
{
fvWho.addElement(new weka.core.Attribute("word-" + i, (FastVector)null));
}
for (int i = 1; i <= whoWordsAfter; i++)
{
fvWho.addElement(new weka.core.Attribute("word+" + i, (FastVector)null));
}
for (int i = whoWordsBefore; i > 0; i--)
{
fvWho.addElement(new weka.core.Attribute("postag-" + i, fvPOS));
}
for (int i = 1; i <= whoWordsAfter; i++)
{
fvWho.addElement(new weka.core.Attribute("postag+" + i, fvPOS));
}
FastVector fvClass = new FastVector(2);
fvClass.addElement("yes");
fvClass.addElement("no");
fvWho.addElement(new weka.core.Attribute("who", fvClass));
return fvWho;
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClasses(List<bool> ListClassSelected)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
foreach (var item in cGlobalInfo.ListWellClasses)
{
attVals.addElement(item.Name);
}
atts.addElement(new weka.core.Attribute("ClassAttribute", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (!ListClassSelected[CurrentWell.GetCurrentClassIdx()]) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
vals[columnNo] = CurrentWell.GetCurrentClassIdx();
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClassesWithPlateBasedDescriptor(int NumberOfClass)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListPlateBaseddescriptorNames.Count; i++)
{
atts.addElement(new weka.core.Attribute(ParentScreening.ListPlateBaseddescriptorNames[i]));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < NumberOfClass; i++)
attVals.addElement("Class" + (i).ToString());
atts.addElement(new weka.core.Attribute("Class", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (CurrentWell.GetCurrentClassIdx() == -1) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListPlateBaseddescriptorNames.Count; Col++)
{
vals[IdxCol++] = CurrentWell.ListPlateBasedDescriptors[Col].GetValue();
}
vals[columnNo] = CurrentWell.GetCurrentClassIdx();
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
/// <summary> Renames the value of a nominal (or string) attribute value. This
/// change only affects this dataset.
///
/// </summary>
/// <param name="att">the attribute's index
/// </param>
/// <param name="val">the value's index
/// </param>
/// <param name="name">the new name
/// </param>
public virtual void renameAttributeValue(int att, int val, System.String name)
{
Attribute newAtt = (Attribute) attribute(att).copy();
FastVector newVec = new FastVector(numAttributes());
newAtt.setValue(val, name);
for (int i = 0; i < numAttributes(); i++)
{
if (i == att)
{
newVec.addElement(newAtt);
}
else
{
newVec.addElement(attribute(i));
}
}
m_Attributes = newVec;
}
public cInfoForHierarchical CreateInstancesWithUniqueClasse()
{
cInfoForHierarchical InfoForHierarchical = new cInfoForHierarchical();
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < this.ParentScreening.ListDescriptors.Count; i++)
{
if (this.ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(this.ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
atts.addElement(new weka.core.Attribute("Class_____", attVals));
InfoForHierarchical.ListInstances = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (CurrentWell.GetCurrentClassIdx() == -1) continue;
attVals.addElement("Class_____" + (IdxWell).ToString());
InfoForHierarchical.ListIndexedWells.Add(CurrentWell);
double[] vals = new double[InfoForHierarchical.ListInstances.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < this.ParentScreening.ListDescriptors.Count; Col++)
{
if (this.ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
vals[columnNo] = IdxWell;
InfoForHierarchical.ListInstances.add(new DenseInstance(1.0, vals));
IdxWell++;
}
InfoForHierarchical.ListInstances.setClassIndex((InfoForHierarchical.ListInstances.numAttributes() - 1));
return InfoForHierarchical;
}
/// <summary> Reads and stores header of an ARFF file.
///
/// </summary>
/// <param name="tokenizer">the stream tokenizer
/// </param>
/// <exception cref="IOException">if the information is not read
/// successfully
/// </exception>
protected internal virtual void readHeader(StreamTokenizer tokenizer)
{
System.String attributeName;
FastVector attributeValues;
//int i;
Token token=null;
// Get name of relation.
getFirstToken(tokenizer, out token);
//if (tokenizer.ttype == SupportClass.StreamTokenizerSupport.TT_EOF)
if ((token != null) && (token is EofToken))
{
errms(tokenizer, "premature end of file");
}
if (ARFF_RELATION.ToUpper().Equals(token.StringValue.ToUpper()))
{
getNextToken(tokenizer,out token);
m_RelationName = token.StringValue;
getLastToken(tokenizer,out token, false);
}
else
{
errms(tokenizer, "keyword " + ARFF_RELATION + " expected");
}
// Create vectors to hold information temporarily.
m_Attributes = new FastVector();
// Get attribute declarations.
getFirstToken(tokenizer, out token);
//if (tokenizer.ttype == SupportClass.StreamTokenizerSupport.TT_EOF)
if ((token != null) && (token is EofToken))
{
errms(tokenizer, "premature end of file");
}
while (Attribute.ARFF_ATTRIBUTE.ToUpper().Equals(token.StringValue.ToUpper()))
{
// Get attribute name.
getNextToken(tokenizer,out token);
attributeName = token.StringValue;
getNextToken(tokenizer,out token);
// Check if attribute is nominal.
//if (tokenizer.ttype == SupportClass.StreamTokenizerSupport.TT_WORD)
if ((token != null) && (token is WordToken))
{
// Attribute is real, integer, or string.
if (token.StringValue.ToUpper().Equals(Attribute.ARFF_ATTRIBUTE_REAL.ToUpper()) || token.StringValue.ToUpper().Equals(Attribute.ARFF_ATTRIBUTE_INTEGER.ToUpper()) || token.StringValue.ToUpper().Equals(Attribute.ARFF_ATTRIBUTE_NUMERIC.ToUpper()))
{
m_Attributes.addElement(new Attribute(attributeName, numAttributes()));
readTillEOL(tokenizer);
}
else if (token.StringValue.ToUpper().Equals(Attribute.ARFF_ATTRIBUTE_STRING.ToUpper()))
{
m_Attributes.addElement(new Attribute(attributeName, (FastVector) null, numAttributes()));
readTillEOL(tokenizer);
}
else if (token.StringValue.ToUpper().Equals(Attribute.ARFF_ATTRIBUTE_DATE.ToUpper()))
{
System.String format = null;
tokenizer.NextToken(out token);
//if (tokenizer.NextToken() != SupportClass.StreamTokenizerSupport.TT_EOL)
if ((token != null) && (!(token is EofToken)))
{
//if ((tokenizer.ttype != SupportClass.StreamTokenizerSupport.TT_WORD) && (tokenizer.ttype != '\'') && (tokenizer.ttype != '\"'))
if ((token != null) && (!(token is WordToken)) && (token.StringValue!="'") && (token.StringValue!="\"") )
{
errms(tokenizer, "not a valid date format");
}
format = token.StringValue;
readTillEOL(tokenizer);
}
else
{
tokenizer.PushBack(token);
}
m_Attributes.addElement(new Attribute(attributeName, format, numAttributes()));
}
else
{
errms(tokenizer, "no valid attribute type or invalid " + "enumeration");
}
}
else
{
// Attribute is nominal.
attributeValues = new FastVector();
tokenizer.PushBack(token);
// Get values for nominal attribute.
tokenizer.NextToken(out token);
if ( token.StringValue != "{")
{
errms(tokenizer, "{ expected at beginning of enumeration");
}
tokenizer.NextToken(out token);
while ( token.StringValue != "}")
{
//if (tokenizer.ttype == SupportClass.StreamTokenizerSupport.TT_EOL)
if (token is EolToken)
{
errms(tokenizer, "} expected at end of enumeration");
}
else
{
attributeValues.addElement(token.StringValue);
}
tokenizer.NextToken(out token);
}
if (attributeValues.size() == 0)
{
errms(tokenizer, "no nominal values found");
}
m_Attributes.addElement(new Attribute(attributeName, attributeValues, numAttributes()));
}
getLastToken(tokenizer,out token, false);
getFirstToken(tokenizer,out token);
//if (tokenizer.ttype == SupportClass.StreamTokenizerSupport.TT_EOF)
if (token is EofToken)
errms(tokenizer, "premature end of file");
}
// Check if data part follows. We can't easily check for EOL.
if (!ARFF_DATA.ToUpper().Equals(token.StringValue.ToUpper()))
{
errms(tokenizer, "keyword " + ARFF_DATA + " expected");
}
// Check if any attributes have been declared.
if (m_Attributes.size() == 0)
{
errms(tokenizer, "no attributes declared");
}
// Allocate buffers in case sparse instances have to be read
m_ValueBuffer = new double[numAttributes()];
m_IndicesBuffer = new int[numAttributes()];
}
public Instances CreateInstancesWithoutClass(cExtendedTable Input)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
// Descriptors loop
for (int i = 0; i < Input.Count; i++)
{
//if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(Input[i].Name));
columnNo++;
}
// weka.core.FastVector attVals = new FastVector();
Instances data1 = new Instances("MyRelation", atts, 0);
for (int IdxRow = 0; IdxRow < Input[0].Count; IdxRow++)
{
double[] vals = new double[data1.numAttributes()];
for (int Col = 0; Col < columnNo; Col++)
{
// if (Glo .ListDescriptors[Col].IsActive() == false) continue;
vals[Col] = Input[Col][IdxRow];// double.Parse(dt.Rows[IdxRow][Col].ToString());
}
data1.add(new DenseInstance(1.0, vals));
}
return data1;
}
/// <summary> Help function needed for stratification of set.
///
/// </summary>
/// <param name="numFolds">the number of folds for the stratification
/// </param>
protected internal virtual void stratStep(int numFolds)
{
FastVector newVec = new FastVector(m_Instances.capacity());
int start = 0, j;
// create stratified batch
while (newVec.size() < numInstances())
{
j = start;
while (j < numInstances())
{
newVec.addElement(instance(j));
j = j + numFolds;
}
start++;
}
m_Instances = newVec;
}
/// <summary> Split up a string containing options into an array of strings,
/// one for each option.
///
/// </summary>
/// <param name="optionString">the string containing the options
/// </param>
/// <returns> the array of options
/// </returns>
public static System.String[] splitOptions(System.String quotedOptionString)
{
FastVector optionsVec = new FastVector();
System.String str = new System.Text.StringBuilder(quotedOptionString).ToString();
int i;
while (true)
{
//trimLeft
i = 0;
while ((i < str.Length) && (System.Char.IsWhiteSpace(str[i])))
i++;
str = str.Substring(i);
//stop when str is empty
if (str.Length == 0)
break;
//if str start with a double quote
if (str[0] == '"')
{
//find the first not anti-slached double quote
i = 1;
while (i < str.Length)
{
if (str[i] == str[0])
break;
if (str[i] == '\\')
{
i += 1;
if (i >= str.Length)
throw new System.Exception("String should not finish with \\");
if (str[i] != '\\' && str[i] != '"')
throw new System.Exception("Unknow character \\" + str[i]);
}
i += 1;
}
if (i >= str.Length)
throw new System.Exception("Quote parse error.");
//add the founded string to the option vector (without quotes)
System.String optStr = str.Substring(1, (i) - (1));
optStr = unbackQuoteChars(optStr);
optionsVec.addElement(optStr);
str = str.Substring(i + 1);
}
else
{
//find first whiteSpace
i = 0;
while ((i < str.Length) && (!System.Char.IsWhiteSpace(str[i])))
i++;
//add the founded string to the option vector
System.String optStr = str.Substring(0, (i) - (0));
optionsVec.addElement(optStr);
str = str.Substring(i);
}
}
//convert optionsVec to an array of String
System.String[] options = new System.String[optionsVec.size()];
for (i = 0; i < optionsVec.size(); i++)
{
options[i] = ((System.String) optionsVec.elementAt(i));
}
return options;
}
/// <summary> Merges two sets of Instances together. The resulting set will have
/// all the attributes of the first set plus all the attributes of the
/// second set. The number of instances in both sets must be the same.
///
/// </summary>
/// <param name="first">the first set of Instances
/// </param>
/// <param name="second">the second set of Instances
/// </param>
/// <returns> the merged set of Instances
/// </returns>
/// <exception cref="IllegalArgumentException">if the datasets are not the same size
/// </exception>
public static Instances mergeInstances(Instances first, Instances second)
{
if (first.numInstances() != second.numInstances())
{
throw new System.ArgumentException("Instance sets must be of the same size");
}
// Create the vector of merged attributes
FastVector newAttributes = new FastVector();
for (int i = 0; i < first.numAttributes(); i++)
{
newAttributes.addElement(first.attribute(i));
}
for (int i = 0; i < second.numAttributes(); i++)
{
newAttributes.addElement(second.attribute(i));
}
// Create the set of Instances
Instances merged = new Instances(first.relationName() + '_' + second.relationName(), newAttributes, first.numInstances());
// Merge each instance
for (int i = 0; i < first.numInstances(); i++)
{
merged.add(first.instance(i).mergeInstance(second.instance(i)));
}
return merged;
}
private static Instances CreateInstanceOnFly(double[] a, double[] b)
{
FastVector atts;
Instances data;
double[] vals;
// 1. set up attributes
atts = new FastVector();
// - numeric
atts.addElement(new Attribute("att1"));
atts.addElement(new Attribute("att2"));
// 2. create Instances object
data = new Instances("MyRelation", atts, 0);
for (int i = 0; i < a.Length; ++i)
{
// 3. fill with data
// first instance
vals = new double[data.numAttributes()];
// - numeric
vals[0] = a[i];
// - nominal
vals[1] = b[i];
data.add(new weka.core.DenseInstance(1.0, vals));
}
return data;
}
/// <summary> Method for testing this class.
///
/// </summary>
/// <param name="argv">should contain one element: the name of an ARFF file
/// </param>
//@ requires argv != null;
//@ requires argv.length == 1;
//@ requires argv[0] != null;
public static void test(System.String[] argv)
{
Instances instances, secondInstances, train, test, empty;
//Instance instance;
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
System.Random random = new System.Random((System.Int32) 2);
//UPGRADE_ISSUE: Class hierarchy differences between 'java.io.Reader' and 'System.IO.StreamReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1186'"
System.IO.StreamReader reader;
int start, num;
//double newWeight;
FastVector testAtts, testVals;
int i, j;
try
{
if (argv.Length > 1)
{
throw (new System.Exception("Usage: Instances [<filename>]"));
}
// Creating set of instances from scratch
testVals = new FastVector(2);
testVals.addElement("first_value");
testVals.addElement("second_value");
testAtts = new FastVector(2);
testAtts.addElement(new Attribute("nominal_attribute", testVals));
testAtts.addElement(new Attribute("numeric_attribute"));
instances = new Instances("test_set", testAtts, 10);
instances.add(new Instance(instances.numAttributes()));
instances.add(new Instance(instances.numAttributes()));
instances.add(new Instance(instances.numAttributes()));
instances.ClassIndex = 0;
System.Console.Out.WriteLine("\nSet of instances created from scratch:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
if (argv.Length == 1)
{
System.String filename = argv[0];
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
reader = new System.IO.StreamReader(filename, System.Text.Encoding.Default);
// Read first five instances and print them
System.Console.Out.WriteLine("\nFirst five instances from file:\n");
instances = new Instances(reader, 1);
instances.ClassIndex = instances.numAttributes() - 1;
i = 0;
while ((i < 5) && (instances.readInstance(reader)))
{
i++;
}
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
// Read all the instances in the file
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
reader = new System.IO.StreamReader(filename, System.Text.Encoding.Default);
instances = new Instances(reader);
// Make the last attribute be the class
instances.ClassIndex = instances.numAttributes() - 1;
// Print header and instances.
System.Console.Out.WriteLine("\nDataset:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
System.Console.Out.WriteLine("\nClass index: " + instances.classIndex());
}
// Test basic methods based on class index.
System.Console.Out.WriteLine("\nClass name: " + instances.classAttribute().name());
System.Console.Out.WriteLine("\nClass index: " + instances.classIndex());
System.Console.Out.WriteLine("\nClass is nominal: " + instances.classAttribute().Nominal);
System.Console.Out.WriteLine("\nClass is numeric: " + instances.classAttribute().Numeric);
System.Console.Out.WriteLine("\nClasses:\n");
for (i = 0; i < instances.numClasses(); i++)
{
System.Console.Out.WriteLine(instances.classAttribute().value_Renamed(i));
}
System.Console.Out.WriteLine("\nClass values and labels of instances:\n");
for (i = 0; i < instances.numInstances(); i++)
{
Instance inst = instances.instance(i);
System.Console.Out.Write(inst.classValue() + "\t");
System.Console.Out.Write(inst.toString(inst.classIndex()));
if (instances.instance(i).classIsMissing())
{
System.Console.Out.WriteLine("\tis missing");
}
else
{
System.Console.Out.WriteLine();
}
}
// Create random weights.
System.Console.Out.WriteLine("\nCreating random weights for instances.");
for (i = 0; i < instances.numInstances(); i++)
{
instances.instance(i).Weight = random.NextDouble();
}
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
// Insert an attribute
secondInstances = new Instances(instances);
Attribute testAtt = new Attribute("Inserted");
secondInstances.insertAttributeAt(testAtt, 0);
System.Console.Out.WriteLine("\nSet with inserted attribute:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(secondInstances);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Delete the attribute
secondInstances.deleteAttributeAt(0);
System.Console.Out.WriteLine("\nSet with attribute deleted:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(secondInstances);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Test if headers are equal
System.Console.Out.WriteLine("\nHeaders equal: " + instances.equalHeaders(secondInstances) + "\n");
// Print data in internal format.
System.Console.Out.WriteLine("\nData (internal values):\n");
for (i = 0; i < instances.numInstances(); i++)
{
for (j = 0; j < instances.numAttributes(); j++)
{
if (instances.instance(i).isMissing(j))
{
System.Console.Out.Write("? ");
}
else
{
System.Console.Out.Write(instances.instance(i).value_Renamed(j) + " ");
}
}
System.Console.Out.WriteLine();
}
// Just print header
System.Console.Out.WriteLine("\nEmpty dataset:\n");
empty = new Instances(instances, 0);
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(empty);
System.Console.Out.WriteLine("\nClass name: " + empty.classAttribute().name());
// Create copy and rename an attribute and a value (if possible)
if (empty.classAttribute().Nominal)
{
Instances copy = new Instances(empty, 0);
copy.renameAttribute(copy.classAttribute(), "new_name");
copy.renameAttributeValue(copy.classAttribute(), copy.classAttribute().value_Renamed(0), "new_val_name");
System.Console.Out.WriteLine("\nDataset with names changed:\n" + copy);
System.Console.Out.WriteLine("\nOriginal dataset:\n" + empty);
}
// Create and prints subset of instances.
start = instances.numInstances() / 4;
num = instances.numInstances() / 2;
System.Console.Out.Write("\nSubset of dataset: ");
System.Console.Out.WriteLine(num + " instances from " + (start + 1) + ". instance");
secondInstances = new Instances(instances, start, num);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(secondInstances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(secondInstances.sumOfWeights());
// Create and print training and test sets for 3-fold
// cross-validation.
System.Console.Out.WriteLine("\nTrain and test folds for 3-fold CV:");
if (instances.classAttribute().Nominal)
{
instances.stratify(3);
}
for (j = 0; j < 3; j++)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
train = instances.trainCV(3, j, new System.Random((System.Int32) 1));
test = instances.testCV(3, j);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nTrain: ");
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(train.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(train.sumOfWeights());
System.Console.Out.WriteLine("\nClass name: " + train.classAttribute().name());
System.Console.Out.WriteLine("\nTest: ");
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(test.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(test.sumOfWeights());
System.Console.Out.WriteLine("\nClass name: " + test.classAttribute().name());
}
// Randomize instances and print them.
System.Console.Out.WriteLine("\nRandomized dataset:");
instances.randomize(random);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
// Sort instances according to first attribute and
// print them.
System.Console.Out.Write("\nInstances sorted according to first attribute:\n ");
instances.sort(0);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
}
catch (System.Exception)
{
//.WriteStackTrace(e, Console.Error);
}
}
/// <summary> Removes all instances with missing values for a particular
/// attribute from the dataset.
///
/// </summary>
/// <param name="attIndex">the attribute's index
/// </param>
//@ requires 0 <= attIndex && attIndex < numAttributes();
public virtual void deleteWithMissing(int attIndex)
{
FastVector newInstances = new FastVector(numInstances());
for (int i = 0; i < numInstances(); i++)
{
if (!instance(i).isMissing(attIndex))
{
newInstances.addElement(instance(i));
}
}
m_Instances = newInstances;
}
private void buttonStartCluster_Click(object sender, EventArgs e)
{
FormSingleCellClusteringInfo WindowClusteringInfo = new FormSingleCellClusteringInfo(GlobalInfo);
if (WindowClusteringInfo.ShowDialog() != System.Windows.Forms.DialogResult.OK) return;
Instances ListInstances = GlobalInfo.CurrentScreen.CellBasedClassification.CreateInstancesWithoutClass(dt);
if ((WindowClusteringInfo.radioButtonAutomated.Checked) && (WindowClusteringInfo.radioButtonEM.Checked))
{
ClusterEvaluation eval;
Classes = new cExtendedList();
weka.clusterers.EM EMCluster = new EM();
if(WindowClusteringInfo.checkBoxEMAutomated.Checked)
EMCluster.setNumClusters(-1);
else
EMCluster.setNumClusters((int)WindowClusteringInfo.numericUpDownClassNumber.Value);
EMCluster.buildClusterer(ListInstances);
EMCluster.getClusterModelsNumericAtts();
eval = new ClusterEvaluation();
eval.setClusterer(EMCluster);
eval.evaluateClusterer(ListInstances);
Classes.AddRange(eval.getClusterAssignments());
NumClusters= eval.getNumClusters();
ReDraw();
FormForCellByCellClusteringResults WindowFormForCellByCellClusteringResults = new FormForCellByCellClusteringResults();
WindowFormForCellByCellClusteringResults.richTextBoxResults.Clear();
WindowFormForCellByCellClusteringResults.richTextBoxResults.AppendText(eval.clusterResultsToString());
if (WindowFormForCellByCellClusteringResults.ShowDialog() != System.Windows.Forms.DialogResult.OK) return;
}
else if (WindowClusteringInfo.radioButtonDescriptorBased.Checked)
{
Classes = new cExtendedList();
DataTable FinalDataTable = new DataTable();
int IdxDescForClassSelect = WindowClusteringInfo.comboBoxDescriptorForClass.SelectedIndex;
for (int IdxWell = 0; IdxWell < GlobalInfo.ListSelectedWell.Count; IdxWell++)
{
cWell TmpWell = GlobalInfo.ListSelectedWell[IdxWell];
// if (IdxWell == 0)
if (TmpWell.ListDescriptors[IdxDescForClassSelect].GetAssociatedType().DataType == eDataType.HISTOGRAM)
{
Classes.AddRange(TmpWell.ListDescriptors[IdxDescForClassSelect].GetOriginalValues());
}
else
{
double ClasseValue = TmpWell.ListDescriptors[IdxDescForClassSelect].GetValue();
for (int IdxCell = 0; IdxCell < TmpWell.CellNumber; IdxCell++)
Classes.Add(ClasseValue);
//TmpWell.AddDescriptors
}
}
List<double> ListClassValues = new List<double>();
foreach (var item in Classes.Distinct())
{
ListClassValues.Add(item);
}
//(List<double>)Classes.Distinct();
NumClusters = ListClassValues.Count();
//Classes = new cExtendedList();
for (int IdxClust = 0; IdxClust < Classes.Count; IdxClust++)
{
for (int IdxCl = 0; IdxCl < ListClassValues.Count; IdxCl++)
{
if (ListClassValues[IdxCl] == Classes[IdxClust])
{
Classes[IdxClust] = IdxCl;
break;
}
}
//Classes[IdxClust] = ListClassValues.Find(Classes[IdxClust]);
}
//int NumClusters =
ReDraw();
}
// ReDraw();
//if (MessageBox.Show("Do you want perform a j48 training process ?", "Question", MessageBoxButtons.YesNo, MessageBoxIcon.Question) != System.Windows.Forms.DialogResult.Yes) return;
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < NumClusters; i++)
attVals.addElement("Class__" + (i).ToString());
ListInstances.insertAttributeAt(new weka.core.Attribute("Class__", attVals), ListInstances.numAttributes());
for (int i = 0; i < Classes.Count; i++)
{
ListInstances.get(i).setValue(ListInstances.numAttributes() - 1, Classes[i]);
}
ListInstances.setClassIndex(ListInstances.numAttributes() - 1);
GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased = new weka.classifiers.trees.J48();
GlobalInfo.CurrentScreen.CellBasedClassification.SetJ48Tree((weka.classifiers.trees.J48)GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased,Classes.Count);
GlobalInfo.CurrentScreen.CellBasedClassification.J48Model.setMinNumObj((int)GlobalInfo.OptionsWindow.numericUpDownJ48MinNumObjects.Value);
weka.core.Instances train = new weka.core.Instances(ListInstances, 0, ListInstances.numInstances());
GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased.buildClassifier(train);
GlobalInfo.ConsoleWriteLine(GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased.ToString());
GlobalInfo.CurrentScreen.CellBasedClassification.evaluation = new weka.classifiers.Evaluation(ListInstances);
GlobalInfo.CurrentScreen.CellBasedClassification.evaluation.crossValidateModel(GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased, ListInstances, 2, new java.util.Random(1));
GlobalInfo.CurrentScreen.CellBasedClassification.DisplayTree(GlobalInfo).Show();
FormForCellbyCellClassif WindowFormForCellbyCellClassif = new FormForCellbyCellClassif();
if (WindowFormForCellbyCellClassif.ShowDialog() != System.Windows.Forms.DialogResult.OK) return;
int DescrCount = GlobalInfo.CurrentScreen.ListDescriptors.Count;
// first we update the descriptor
for (int i = 0; i < ListInstances.numClasses(); i++)
GlobalInfo.CurrentScreen.ListDescriptors.AddNew(new cDescriptorsType("Ratio_Class " + i, true, 1, GlobalInfo));
FormForProgress ProgressWindow = new FormForProgress();
ProgressWindow.Show();
int IdxProgress = 0;
int MaxProgress = 0;
foreach (cPlate CurrentPlateToProcess in GlobalInfo.CurrentScreen.ListPlatesAvailable)
MaxProgress += CurrentPlateToProcess.ParentScreening.Columns * CurrentPlateToProcess.ParentScreening.Rows;
ProgressWindow.progressBar.Maximum = MaxProgress;
attVals = new FastVector();
for (int i = 0; i < NumClusters; i++)
attVals.addElement(i.ToString());
//ParallelOptions options = new ParallelOptions();
//options.MaxDegreeOfParallelism = -1; // -1 is for unlimited. 1 is for sequential.
//Stopwatch stopwatch = new Stopwatch();
//stopwatch.Start();
//////for (int PlateIdx = 0; PlateIdx < NumberOfPlates; PlateIdx++)
//int NumberOfPlates = CompleteScreening.ListPlatesAvailable.Count;
//Parallel.For(0, NumberOfPlates, options, (PlateIdx) =>
//{
// cPlate CurrentPlateToProcess = CompleteScreening.ListPlatesActive.GetPlate((string)Parent.GlobalInfo.PlateListWindow.listBoxPlateNameToProcess.Items[PlateIdx]);
// for (int row = 0; row < Parent.Rows; row++)
// for (int col = 0; col < Parent.Columns; col++)
// {
// TempWell = CurrentPlateToProcess.GetWell(col, row, false);
// if (TempWell == null) continue;
// else
// {
// if (TempWell.GetClass() == this.ClassForClassif)
// Pos.Add(TempWell.ListDescriptors[Parent.ListDescriptors.CurrentSelectedDescriptor].GetValue());
// }
// }
//}
//);
foreach (cPlate CurrentPlateToProcess in GlobalInfo.CurrentScreen.ListPlatesAvailable)
//Parallel.ForEach(GlobalInfo.CurrentScreen.ListPlatesActive, options, CurrentPlateToProcess =>
{
//Parallel.ForEach(CurrentPlateToProcess.ListActiveWells, options, TmpWell =>
for(int j=0;j<CurrentPlateToProcess.ParentScreening.Rows;j++)
for (int k = 0; k < CurrentPlateToProcess.ParentScreening.Columns; k++)
{
cWell TmpWell = CurrentPlateToProcess.GetWell(k, j, false);
if (TmpWell == null) continue;
ProgressWindow.progressBar.Value = IdxProgress++;
//DataTable FinalDataTable = new DataTable();
//TmpWell.AssociatedPlate.DBConnection = new cDBConnection(TmpWell.AssociatedPlate, TmpWell.SQLTableName);
//TmpWell.AssociatedPlate.DBConnection.AddWellToDataTable(TmpWell, FinalDataTable, false);
DataTable FinalDataTable = TmpWell.GetDescDataTable(true);
Instances ListInstancesTOClassify = GlobalInfo.CurrentScreen.CellBasedClassification.CreateInstancesWithoutClass(FinalDataTable);
ListInstancesTOClassify.insertAttributeAt(new weka.core.Attribute("Class", attVals), ListInstancesTOClassify.numAttributes());
ListInstancesTOClassify.setClassIndex(ListInstancesTOClassify.numAttributes() - 1);
cExtendedList ListClasses = new cExtendedList();
for (int i = 0; i < ListInstancesTOClassify.numInstances(); i++)
{
double classId = GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased.classifyInstance(ListInstancesTOClassify.instance(i));
ListClasses.Add(classId);
}
List<double[]> Histo = ListClasses.CreateHistogram(0, ListInstances.numClasses() - 1, ListInstances.numClasses() - 1);
List<cDescriptor> LDesc = new List<cDescriptor>();
for (int IdxHisto = 0; IdxHisto < Histo[1].Length; IdxHisto++)
{
double Value = (100.0 * Histo[1][IdxHisto]) / (double)ListInstancesTOClassify.numInstances();
cDescriptor NewDesc = new cDescriptor(Value, GlobalInfo.CurrentScreen.ListDescriptors[IdxHisto + DescrCount], GlobalInfo.CurrentScreen);
LDesc.Add(NewDesc);
}
TmpWell.AddDescriptors(LDesc);
//TmpWell.AssociatedPlate.DBConnection.DB_CloseConnection();
}//);
}
ProgressWindow.Close();
if (WindowFormForCellbyCellClassif.checkBoxKeepOriginalDesc.Checked == false)
{
// int DescNumToRemove = GlobalInfo.CurrentScreen.ListDescriptors.Count -
for (int IdxDesc = 0; IdxDesc < DescrCount; IdxDesc++)
GlobalInfo.CurrentScreen.ListDescriptors.RemoveDesc(GlobalInfo.CurrentScreen.ListDescriptors[0], GlobalInfo.CurrentScreen);
}
GlobalInfo.CurrentScreen.ListDescriptors.UpDateDisplay();
GlobalInfo.CurrentScreen.UpDatePlateListWithFullAvailablePlate();
for (int idxP = 0; idxP < GlobalInfo.CurrentScreen.ListPlatesActive.Count; idxP++)
GlobalInfo.CurrentScreen.ListPlatesActive[idxP].UpDataMinMax();
if (WindowFormForCellbyCellClassif.checkBoxKeepOriginalDesc.Checked == false)
GlobalInfo.CurrentScreen.GetCurrentDisplayPlate().DisplayDistribution(0, false);
//WindowFormForCellbyCellClassif.Close();
//WindowClusteringInfo.Close();
}
