C# Instances.add方法代码示例

		/// <summary> Sets distribution associated with model.</summary>
		public override void  resetDistribution(Instances data)
		{
			
			Instances insts = new Instances(data, data.numInstances());
			for (int i = 0; i < data.numInstances(); i++)
			{
				if (whichSubset(data.instance(i)) > - 1)
				{
					insts.add(data.instance(i));
				}
			}
			Distribution newD = new Distribution(insts, this);
			newD.addInstWithUnknown(data, m_attIndex);
			m_distribution = newD;
		}

        public void TrainandTest(CandidateClassifier classifierInfo, CandidateParameter cp)
        {
            //string dealType = classifierInfo.DealType;
            Classifier cls = null;

            if (TestParameters.UseTrain)
            {
                string modelFileName = GetModelFileName(classifierInfo.Name);

                if (TestParameters.SaveModel)
                {
                    cls = WekaUtils.TryLoadClassifier(modelFileName);
                }

                Instances trainInstancesNew, trainInstances;
                trainInstances = m_trainInstances;
                trainInstancesNew = m_trainInstancesNew;

                if (cls == null)
                {
                    if (classifierInfo.Classifier == null)
                    {
                        classifierInfo.Classifier = WekaUtils.CreateClassifier(cp.ClassifierType, m_currentTp, m_currentSl);
                    }
                    cls = classifierInfo.Classifier;
                }
                else
                {
                    if (TestParameters.EnableDetailLog)
                    {
                        System.Console.WriteLine("Model is loaded.");
                    }
                }

                if (m_enableTrainSplitTest)
                {
                    Instances trainTrainInst, trainTestInst;
                    DateTime splitTrainTimeEnd;
                    if (m_trainSplitTestNums != -1)
                    {
                        int trainTrainSize = trainInstancesNew.numInstances() - m_trainSplitTestNums;
                        int trainTestSize = m_trainSplitTestNums;
                        trainTrainInst = new Instances(trainInstancesNew, 0, trainTrainSize);
                        trainTestInst = new Instances(trainInstancesNew, trainTrainSize, trainTestSize);
                        splitTrainTimeEnd = WekaUtils.GetDateValueFromInstances(trainInstances, 0, trainTrainSize);
                    }
                    else if (m_trainSplitPercent != -1)
                    {
                        if (m_trainSplitPercent == 100.0)
                        {
                            int trainTrainSize = trainInstancesNew.numInstances();
                            trainTrainInst = new Instances(trainInstancesNew, 0, trainTrainSize);
                            trainTestInst = new Instances(trainInstancesNew, 0, trainTrainSize);
                            splitTrainTimeEnd = WekaUtils.GetDateValueFromInstances(trainInstances, 0, trainTrainSize);
                        }
                        else
                        {
                            int trainTrainSize = (int)Math.Round(trainInstancesNew.numInstances() * m_trainSplitPercent / 100);
                            int trainTestSize = trainInstancesNew.numInstances() - trainTrainSize;

                            trainTrainInst = new Instances(trainInstancesNew, 0, trainTrainSize);
                            trainTestInst = new Instances(trainInstancesNew, trainTrainSize, trainTestSize);
                            splitTrainTimeEnd = WekaUtils.GetDateValueFromInstances(trainInstances, 0, trainTrainSize);
                        }
                    }
                    else
                    {
                        trainTrainInst = new Instances(trainInstancesNew, 0);
                        trainTestInst = new Instances(trainInstancesNew, 0);
                        DateTime dt = WekaUtils.GetDateValueFromInstances(trainInstances, 0, trainInstances.numInstances() - 1);
                        splitTrainTimeEnd = m_trainTimeEnd.AddMinutes(-TestParameters.BatchTestMinutes);
                        while (splitTrainTimeEnd > dt)
                        {
                            splitTrainTimeEnd = splitTrainTimeEnd.AddMinutes(-TestParameters.BatchTestMinutes);
                        }
                        for (int i = 0; i < trainInstances.numInstances(); ++i)
                        {
                            dt = WekaUtils.GetDateValueFromInstances(trainInstances, 0, i);
                            if (dt <= splitTrainTimeEnd)
                            {
                                var ins = new DenseInstance(trainInstancesNew.instance(i));
                                trainTrainInst.add(ins);
                            }
                            else
                            {
                                var ins = new DenseInstance(trainInstancesNew.instance(i));
                                trainTestInst.add(ins);
                            }
                        }
                    }
                    cls = WekaUtils.TrainInstances(trainTrainInst, TestParameters.SaveModel ? modelFileName : null, cls);

                    //m_classifierQueue[dealType].Enqueue(new ClassifierInfo(cls, splitTrainTimeEnd));
                    //foreach (var i in m_classifierQueue[dealType])
                    //{
                    //    var e = WekaUtils.TestInstances(trainTestInst, i.Cls);
                    //    i.TotalCost = i.TotalCost * m_classifierQueueFactor + e.totalCost();
                    //    i.TotalNum = (int)(i.TotalNum * m_classifierQueueFactor) + (int)e.numInstances();
                    //}

//.........这里部分代码省略.........

        /// <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;
        }

        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 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> 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
					{
//.........这里部分代码省略.........

 private Instances createWhyInstances()
 {
     FastVector fvWhy = createWhyFastVector();
     Instances whyInstances = new Instances("WhyInstances", fvWhy, listSecondaryWhyCandidates.Count);
     foreach (Token candidate in listSecondaryWhyCandidates)
     {
         if (candidate.Value == null) continue;
         Instance whyInstance = createSingleWhyInstance(fvWhy, candidate);
         whyInstance.setDataset(whyInstances);
         whyInstances.add(whyInstance);
     }
     whyInstances.setClassIndex(fvWhy.size() - 1);
     return whyInstances;
 }

		/// <summary> Copies instances from one set to the end of another 
		/// one.
		/// 
		/// </summary>
		/// <param name="source">the source of the instances
		/// </param>
		/// <param name="from">the position of the first instance to be copied
		/// </param>
		/// <param name="dest">the destination for the instances
		/// </param>
		/// <param name="num">the number of instances to be copied
		/// </param>
		//@ requires 0 <= from && from <= numInstances() - num;
		//@ requires 0 <= num;
		protected internal virtual void  copyInstances(int from, Instances dest, int num)
		{
			
			for (int i = 0; i < num; i++)
			{
				dest.add(instance(from + i));
			}
		}

		/// <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> Creates a new dataset of the same size using random sampling
		/// with replacement according to the given weight vector. The
		/// weights of the instances in the new dataset are set to one.
		/// The length of the weight vector has to be the same as the
		/// number of instances in the dataset, and all weights have to
		/// be positive.
		/// 
		/// </summary>
		/// <param name="random">a random number generator
		/// </param>
		/// <param name="weights">the weight vector
		/// </param>
		/// <returns> the new dataset
		/// </returns>
		/// <exception cref="IllegalArgumentException">if the weights array is of the wrong
		/// length or contains negative weights.
		/// </exception>
		public virtual Instances resampleWithWeights(System.Random random, double[] weights)
		{
			
			if (weights.Length != numInstances())
			{
				throw new System.ArgumentException("weights.length != numInstances.");
			}
			Instances newData = new Instances(this, numInstances());
			if (numInstances() == 0)
			{
				return newData;
			}
			double[] probabilities = new double[numInstances()];
			double sumProbs = 0, sumOfWeights = Utils.sum(weights);
			for (int i = 0; i < numInstances(); i++)
			{
				sumProbs += random.NextDouble();
				probabilities[i] = sumProbs;
			}
			Utils.normalize(probabilities, sumProbs / sumOfWeights);
			
			// Make sure that rounding errors don't mess things up
			probabilities[numInstances() - 1] = sumOfWeights;
			int k = 0; int l = 0;
			sumProbs = 0;
			while ((k < numInstances() && (l < numInstances())))
			{
				if (weights[l] < 0)
				{
					throw new System.ArgumentException("Weights have to be positive.");
				}
				sumProbs += weights[l];
				while ((k < numInstances()) && (probabilities[k] <= sumProbs))
				{
					newData.add(instance(l));
					newData.instance(k).Weight = 1;
					k++;
				}
				l++;
			}
			return newData;
		}

		/// <summary> Creates a new dataset of the same size using random sampling
		/// with replacement.
		/// 
		/// </summary>
		/// <param name="random">a random number generator
		/// </param>
		/// <returns> the new dataset
		/// </returns>
		public virtual Instances resample(System.Random random)
		{
			
			Instances newData = new Instances(this, numInstances());
			while (newData.numInstances() < numInstances())
			{
				newData.add(instance(random.Next(numInstances())));
			}
			return newData;
		}

    // 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)
    {
        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 thisData = new weka.core.Instances(buffReader); //source.getDataSet();
            if (thisData.classIndex() == -1)
                thisData.setClassIndex(thisData.numAttributes() - 1);

            weka.core.Instances thisUniqueData = new weka.core.Instances(thisData);
            if (thisUniqueData.classIndex() == -1)
                thisUniqueData.setClassIndex(thisUniqueData.numAttributes() - 1);
            thisUniqueData.delete();

            if (allUniqueData == null) {
                allUniqueData = new weka.core.Instances(thisData);
                if (allUniqueData.classIndex() == -1)
                    allUniqueData.setClassIndex(allUniqueData.numAttributes() - 1);
                allUniqueData.delete();
            }

            weka.core.InstanceComparator com = new weka.core.InstanceComparator(false);

            for (int i = 0; i < thisData.numInstances(); i++)
            {
                bool dup = false;
                for (int j = 0; j < allUniqueData.numInstances(); j++)
                {
                    if (com.compare(thisData.instance(i),allUniqueData.instance(j)) == 0)
                    {
                        Debug.Log("Duplicate found!");
                        dup = true;
                        break;
                    }
                }
                if (!dup)
                    allUniqueData.add(thisData.instance(i));
                else
                    dupInstances++;
            }

            for (int i = 0; i < thisData.numInstances(); i++)
            {
                bool dup = false;
                for (int j = 0; j < thisUniqueData.numInstances(); j++)
                {
                    if (com.compare(thisData.instance(i),thisUniqueData.instance(j)) == 0)
                    {
                        Debug.Log("Duplicate found!");
                        dup = true;
                        break;
                    }
                }
                if (!dup)
                    thisUniqueData.add(thisData.instance(i));
                else
                    dupInstancesSamePlayer++;
            }

            //Debug.Log("All Data Instance Count = " + thisData.numInstances());
            //Debug.Log("Unique Data Instance Count = " + thisUniqueData.numInstances());
            //Debug.Log("Done!");

        } catch (java.lang.Exception ex)
        {
            Debug.LogError(ex.getMessage());
        }
    }

        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>
        /// 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;
        }