C# Instances.numAttributes方法代码示例

        public void EndTrainingSession()
        {
            Stream s = new MemoryStream ();
            TextWriter tw = new StreamWriter (s);
            AbstractBasicTextVector.WriteInstancesArff (tw, vectors, "c45recommender", tags, results);
            tw.Flush ();
            s.Position = 0;
            Instances source = new Instances (new InputStreamReader (new InputStreamWrapper (s)));
            tw.Close ();
            s.Close ();

            Instances[] derived = new Instances[this.not];
            classifiers = new AbstractClassifier[this.not];
            int[] args = new int[this.not - 1];
            int l = source.numAttributes () - this.not;
            for (int i = 0; i < this.not-1; i++) {
                args [i] = i + l + 1;
            }
            for (int i = 0; i < this.not; i++) {
                Remove rem = new Remove ();
                rem.setAttributeIndicesArray (args);
                rem.setInputFormat (source);
                derived [i] = Filter.useFilter (source, rem);
                classifiers [i] = GenerateClassifier ();
                derived [i].setClassIndex (derived [i].numAttributes () - 1);
                classifiers [i].buildClassifier (derived [i]);
                if (i < this.not - 1) {
                    args [i] = l + i;
                }
            }
            datasets = derived;
        }

  public static void Main(string[] args) {
    try {
      int runs = 1;
      string algo = "";
      string data = "";
      if(args.Length>0) runs = Convert.ToInt32(args[0]);
      if(args.Length>1) algo = args[1];
      if(args.Length>2) data = args[2];

      Stopwatch read = new Stopwatch(), 
        build = new Stopwatch(), 
        classify = new Stopwatch();
      for (int cnt=0; cnt<runs; cnt++) {
        read.Start();
        Instances train = new Instances(new java.io.FileReader(data+"train.arff"));
        train.setClassIndex(train.numAttributes() - 1);
        Instances test = new Instances(new java.io.FileReader(data+"test.arff"));
        test.setClassIndex(test.numAttributes() - 1);
        read.Stop();

        Classifier[] clList = {
          new weka.classifiers.bayes.NaiveBayes(),
          new weka.classifiers.trees.RandomForest(),
          new weka.classifiers.trees.J48(),
          new weka.classifiers.functions.MultilayerPerceptron(),
          new weka.classifiers.rules.ConjunctiveRule(),
          new weka.classifiers.functions.SMO()
        };

        build.Start();
        foreach (Classifier classifier in clList) {
          if(algo.Equals("") || algo.Equals("All") || classifier.getClass().getSimpleName().Equals(algo))
              classifier.buildClassifier(train);
        }
        build.Stop();

        classify.Start();
        foreach (Classifier classifier in clList) {
          if(algo.Equals("") || algo.Equals("All") || classifier.getClass().getSimpleName().Equals(algo)) {
              int numCorrect = 0;
              for (int i = 0; i < test.numInstances(); i++)
              {
                  if (classifier.classifyInstance(test.instance(i)) == test.instance(i).classValue())
                      numCorrect++;
              }
              //Console.Write(classifier.getClass().getSimpleName() + "\t" + numCorrect + " out of " + test.numInstances() + " correct (" +(100.0 * numCorrect / test.numInstances()) + "%)");
          }
        }
        classify.Stop();
      }
      Console.WriteLine("{\""+ algo + "\"," + read.ElapsedMilliseconds + "," + build.ElapsedMilliseconds + "," + classify.ElapsedMilliseconds + "," + (read.ElapsedMilliseconds+build.ElapsedMilliseconds+classify.ElapsedMilliseconds)+"};");
      if(args.Length>3) Console.ReadLine();
    } catch (java.lang.Exception e){
      e.printStackTrace();
    }
  }

    // Test the classification result of each map that a user played,
    // with the data available as if they were playing through it
    public static void classifyTest(String dataString, String playerID)
    {
        String results = "";
        try {
            java.io.StringReader stringReader = new java.io.StringReader(dataString);
            java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);

            /* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
            //weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
            weka.core.Instances data = new weka.core.Instances(buffReader); //source.getDataSet();
            // setting class attribute if the data format does not provide this information
            // For example, the XRFF format saves the class attribute information as well
            if (data.classIndex() == -1)
                data.setClassIndex(data.numAttributes() - 1);

            weka.classifiers.Classifier cl;
            for (int i = 3; i < data.numInstances(); i++) {
                cl = new weka.classifiers.bayes.NaiveBayes();
                //cl = new weka.classifiers.trees.J48();
                //cl = new weka.classifiers.lazy.IB1();
                //cl = new weka.classifiers.functions.MultilayerPerceptron();
                ((weka.classifiers.functions.MultilayerPerceptron)cl).setHiddenLayers("12");

                weka.core.Instances subset = new weka.core.Instances(data,0,i);
                cl.buildClassifier(subset);

                weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(subset);
         		eval.crossValidateModel(cl, subset, 3, new java.util.Random(1));
                results = results + eval.pctCorrect(); // For accuracy measurement
                /* For Mathews Correlation Coefficient */
                //double TP = eval.numTruePositives(1);
                //double FP = eval.numFalsePositives(1);
                //double TN = eval.numTrueNegatives(1);
                //double FN = eval.numFalseNegatives(1);
                //double correlationCoeff = ((TP*TN)-(FP*FN))/Math.Sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN));
                //results = results + correlationCoeff;
                if (i != data.numInstances()-1)
                    results = results + ", ";
                if(i == data.numInstances()-1)
                    Debug.Log("Player: " + playerID + ", Num Maps: " + data.numInstances() + ", AUC: " + eval.areaUnderROC(1));
            }
        } catch (java.lang.Exception ex)
        {
            Debug.LogError(ex.getMessage());
        }
        // Write values to file for a matlab read
        // For accuracy
         	StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldValidations_NeuralNet.txt");

        //StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldCorrCoeff.txt"); // For mathews cc
        writer.WriteLine(results);
        writer.Close();
        Debug.Log(playerID + " has been written to file");
    }

		/// <summary> Sets the format of the input instances.
		/// 
		/// </summary>
		/// <param name="instanceInfo">an Instances object containing the input 
		/// instance structure (any instances contained in the object are 
		/// ignored - only the structure is required).
		/// </param>
		/// <returns> true if the outputFormat may be collected immediately
		/// </returns>
		/// <exception cref="Exception">if the input format can't be set 
		/// successfully
		/// </exception>
		public override bool setInputFormat(Instances instanceInfo)
		{
			
			base.setInputFormat(instanceInfo);
			
			m_Columns.Upper = instanceInfo.numAttributes() - 1;
			
			setOutputFormat();
			m_Indices = null;
			return true;
		}

    /* Use when the player logs in to initially create the classifier with data from server */
    public void InitializeClassifier(String dataString)
    {
        try {
            java.io.StringReader stringReader = new java.io.StringReader(dataString);
            java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);

            playerData = new weka.core.Instances(buffReader);

            /* State where in each Instance the class attribute is, if its not already specified by the file */
            if (playerData.classIndex() == -1)
                playerData.setClassIndex(playerData.numAttributes() - 1);

            /* NAIVE BAYES */
            //classifier = new weka.classifiers.bayes.NaiveBayes();

            /* NEURAL NET */
            //classifier = new weka.classifiers.functions.MultilayerPerceptron();
            //((weka.classifiers.functions.MultilayerPerceptron)classifier).setHiddenLayers("12");

            /* J48 TREE */
            //classifier = new weka.classifiers.trees.J48();

            /* IB1 NEAREST NEIGHBOUR */
            //classifier = new weka.classifiers.lazy.IB1();

            /* RANDOM FOREST */
            classifier = new weka.classifiers.trees.RandomForest();

            classifier.buildClassifier(playerData);
            Debug.Log("Initialized Classifier");
        }
        catch (java.lang.Exception ex)
        {
            Debug.LogError(ex.getMessage());
        }
    }

		/// <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;
		}

		/// <summary> Prints the predictions for the given dataset into a String variable.
		/// 
		/// </summary>
		/// <param name="classifier		the">classifier to use
		/// </param>
		/// <param name="train		the">training data
		/// </param>
		/// <param name="testFileName	the">name of the test file
		/// </param>
		/// <param name="classIndex		the">class index
		/// </param>
		/// <param name="attributesToOutput	the">indices of the attributes to output
		/// </param>
		/// <returns>			the generated predictions for the attribute range
		/// </returns>
		/// <throws>  Exception 		if test file cannot be opened </throws>
		protected internal static System.String toPrintClassifications(Classifier classifier, Instances train, System.String testFileName, int classIndex, Range attributesToOutput)
		{
			
			System.Text.StringBuilder text = new System.Text.StringBuilder();
			if (testFileName.Length != 0)
			{
				System.IO.StreamReader testReader = null;
				try
				{
					//UPGRADE_TODO: The differences in the expected value  of parameters for constructor 'java.io.BufferedReader.BufferedReader'  may cause compilation errors.  "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
					//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
					//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'"
					testReader = new System.IO.StreamReader(new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).CurrentEncoding);
				}
				catch (System.Exception e)
				{
					//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
					throw new System.Exception("Can't open file " + e.Message + '.');
				}
				Instances test = new Instances(testReader, 1);
				if (classIndex != - 1)
				{
					test.ClassIndex = classIndex - 1;
				}
				else
				{
					test.ClassIndex = test.numAttributes() - 1;
				}
				int i = 0;
				while (test.readInstance(testReader))
				{
					Instance instance = test.instance(0);
					Instance withMissing = (Instance) instance.copy();
					withMissing.Dataset = test;
					double predValue = ((Classifier) classifier).classifyInstance(withMissing);
					if (test.classAttribute().Numeric)
					{
						if (Instance.isMissingValue(predValue))
						{
							text.Append(i + " missing ");
						}
						else
						{
							text.Append(i + " " + predValue + " ");
						}
						if (instance.classIsMissing())
						{
							text.Append("missing");
						}
						else
						{
							text.Append(instance.classValue());
						}
						text.Append(" " + attributeValuesString(withMissing, attributesToOutput) + "\n");
					}
					else
					{
						if (Instance.isMissingValue(predValue))
						{
							text.Append(i + " missing ");
						}
						else
						{
							//UPGRADE_WARNING: Data types in Visual C# might be different.  Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
							text.Append(i + " " + test.classAttribute().value_Renamed((int) predValue) + " ");
						}
						if (Instance.isMissingValue(predValue))
						{
							text.Append("missing ");
						}
						else
						{
							//UPGRADE_WARNING: Data types in Visual C# might be different.  Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
							text.Append(classifier.distributionForInstance(withMissing)[(int) predValue] + " ");
						}
						text.Append(instance.toString(instance.classIndex()) + " " + attributeValuesString(withMissing, attributesToOutput) + "\n");
					}
					test.delete(0);
					i++;
				}
				testReader.Close();
			}
			return text.ToString();
		}

//.........这里部分代码省略.........
					{
						//UPGRADE_TODO: The differences in the expected value  of parameters for constructor 'java.io.BufferedReader.BufferedReader'  may cause compilation errors.  "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
						//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
						//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'"
						testReader = new System.IO.StreamReader(new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).CurrentEncoding);
					}
					if (objectInputFileName.Length != 0)
					{
						//UPGRADE_TODO: Constructor 'java.io.FileInputStream.FileInputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileInputStreamFileInputStream_javalangString'"
						objectStream= new System.IO.FileStream(objectInputFileName, System.IO.FileMode.Open, System.IO.FileAccess.Read);
						if (objectInputFileName.EndsWith(".gz"))
						{
							//UPGRADE_ISSUE: Constructor 'java.util.zip.GZIPInputStream.GZIPInputStream' was not converted. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1000_javautilzipGZIPInputStream'"
							objectStream= new ICSharpCode.SharpZipLib.GZip.GZipInputStream(objectStream);
						}
						//UPGRADE_TODO: Class 'java.io.ObjectInputStream' was converted to 'System.IO.BinaryReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectInputStream'"
						objectInputStream = new System.IO.BinaryReader(objectStream);
					}
				}
				catch (System.Exception e)
				{
					//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
					throw new System.Exception("Can't open file " + e.Message + '.');
				}
				if (testFileName.Length != 0)
				{
					template = test = new Instances(testReader, 1);
					if (classIndex != - 1)
					{
						test.ClassIndex = classIndex - 1;
					}
					else
					{
						test.ClassIndex = test.numAttributes() - 1;
					}
					if (classIndex > test.numAttributes())
					{
						throw new System.Exception("Index of class attribute too large.");
					}
				}
				if (trainFileName.Length != 0)
				{
					if ((classifier is UpdateableClassifier) && (testFileName.Length != 0))
					{
						train = new Instances(trainReader, 1);
					}
					else
					{
						train = new Instances(trainReader);
					}
					template = train;
					if (classIndex != - 1)
					{
						train.ClassIndex = classIndex - 1;
					}
					else
					{
						train.ClassIndex = train.numAttributes() - 1;
					}
					if ((testFileName.Length != 0) && !test.equalHeaders(train))
					{
						throw new System.ArgumentException("Train and test file not compatible!");
					}
					if (classIndex > train.numAttributes())
					{
						throw new System.Exception("Index of class attribute too large.");

 private LabelsMetaData loadLabelsMeta(Instances data, int numLabels)
 {
     LabelsMetaDataImpl labelsData = new LabelsMetaDataImpl();
     int numAttributes = data.numAttributes();
     for (int index = numAttributes - numLabels; index < numAttributes; index++) {
         String attrName = data.attribute(index).name();
         labelsData.addRootNode(new LabelNodeImpl(attrName));
     }
     return labelsData;
 }

        /// <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;
        }

        /// <summary>
        /// Evalute and display a WEKA clusterer
        /// </summary>
        /// <param name="SelectedClusterer">weka clusterer</param>
        /// <param name="InstancesList">list of instances for the validation</param>
        /// <param name="RichTextBoxToDisplayResults">Text box for the results (can be NULL)</param>
        /// <param name="PanelTodisplayGraphicalResults">Panel to display visual results if avalaible (can be NULL)</param>
        /// <returns></returns>
        public ClusterEvaluation EvaluteAndDisplayClusterer(RichTextBox RichTextBoxToDisplayResults,
                                                            Panel PanelTodisplayGraphicalResults, Instances ListInstanceForValid)
        {
            ClusterEvaluation eval = new ClusterEvaluation();
            eval.setClusterer(SelectedClusterer);
            eval.evaluateClusterer(ListInstanceForValid);

            if (RichTextBoxToDisplayResults != null)
            {

                if ((RichTextBoxToDisplayResults != null) && (eval.getNumClusters() > cGlobalInfo.ListCellularPhenotypes.Count))
                {
                    RichTextBoxToDisplayResults.Clear();
                    RichTextBoxToDisplayResults.AppendText("Error: " + eval.getNumClusters() + " clusters identifed.");
                    RichTextBoxToDisplayResults.AppendText("The maximum number of cluster is " + cGlobalInfo.ListCellularPhenotypes.Count + ".");
                    return null;

                }
                if (RichTextBoxToDisplayResults != null)
                {
                    RichTextBoxToDisplayResults.Clear();
                    RichTextBoxToDisplayResults.AppendText(eval.clusterResultsToString());
                }

                RichTextBoxToDisplayResults.AppendText("\n" + ListInstanceForValid.numAttributes() + " attributes:\n\n");
                for (int IdxAttributes = 0; IdxAttributes < ListInstanceForValid.numAttributes(); IdxAttributes++)
                {
                    RichTextBoxToDisplayResults.AppendText(IdxAttributes + "\t: " + ListInstanceForValid.attribute(IdxAttributes).name() + "\n");
                }
            }

            if (PanelTodisplayGraphicalResults != null) PanelTodisplayGraphicalResults.Controls.Clear();

            if ((PanelTodisplayGraphicalResults != null) && (SelectedClusterer.GetType().Name == "HierarchicalClusterer"))
            {
                Button ButtonToDisplayHierarchicalClustering = new Button();
                ButtonToDisplayHierarchicalClustering.Text = "Display Hierarchical Tree";
                ButtonToDisplayHierarchicalClustering.Width *= 2;
                ButtonToDisplayHierarchicalClustering.Location = new System.Drawing.Point((PanelTodisplayGraphicalResults.Width - ButtonToDisplayHierarchicalClustering.Width) / 2,
                    (PanelTodisplayGraphicalResults.Height - ButtonToDisplayHierarchicalClustering.Height) / 2);

                ButtonToDisplayHierarchicalClustering.Anchor = AnchorStyles.None;
                ButtonToDisplayHierarchicalClustering.Click += new EventHandler(ClickToDisplayHierarchicalTree);
                PanelTodisplayGraphicalResults.Controls.Add(ButtonToDisplayHierarchicalClustering);
            }

            return eval;
        }

        /// <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;
        }

        // ---- OPERATIONS ----
        ///    
        ///     <summary> * Sets the format of the input instances. If the filter is able to
        ///     * determine the output format before seeing any input instances, it
        ///     * does so here. This default implementation clears the output format
        ///     * and output queue, and the new batch flag is set. Overriders should
        ///     * call <code>super.setInputFormat(Instances)</code>
        ///     * </summary>
        ///     * <param name="instanceInfo"> an Instances object containing the input instance
        ///     * structure (any instances contained in the object are ignored - only the
        ///     * structure is required). </param>
        ///     * <returns> true if the outputFormat may be collected immediately </returns>
        ///     * <exception cref="Exception"> if the inputFormat can't be set successfully  </exception>
        ///     
        //JAVA TO VB & C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
        //ORIGINAL LINE: public boolean setInputFormat(Instances instanceInfo) throws Exception
        public override bool setInputFormat(Instances instanceInfo)
        {
            base.setInputFormat(instanceInfo);

            for (int i = 0; i < instanceInfo.numAttributes(); ++i)
            {
                if (!instanceInfo.attribute(i).isNumeric())
                {
                    throw new UnsupportedAttributeTypeException("All attributes must be numeric");
                }
            }

            // Create the output buffer
            setOutputFormat();
            return true;
        }

        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>
        /// 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;
        }