C# Instances.classAttribute方法代码示例

		/// <summary> Generates the classifier.
		/// 
		/// </summary>
		/// <param name="instances">set of instances serving as training data 
		/// </param>
		/// <exception cref="Exception">if the classifier has not been generated successfully
		/// </exception>
		public override void  buildClassifier(Instances instances)
		{
			
			double sumOfWeights = 0;
			
			m_Class = instances.classAttribute();
			m_ClassValue = 0;
			switch (instances.classAttribute().type())
			{
				
				case weka.core.Attribute.NUMERIC: 
					m_Counts = null;
					break;

                case weka.core.Attribute.NOMINAL: 
					m_Counts = new double[instances.numClasses()];
					for (int i = 0; i < m_Counts.Length; i++)
					{
						m_Counts[i] = 1;
					}
					sumOfWeights = instances.numClasses();
					break;
				
				default: 
					throw new System.Exception("ZeroR can only handle nominal and numeric class" + " attributes.");
				
			}
			System.Collections.IEnumerator enu = instances.enumerateInstances();
			//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
			while (enu.MoveNext())
			{
				//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
				Instance instance = (Instance) enu.Current;
				if (!instance.classIsMissing())
				{
					if (instances.classAttribute().Nominal)
					{
						//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'"
						m_Counts[(int) instance.classValue()] += instance.weight();
					}
					else
					{
						m_ClassValue += instance.weight() * instance.classValue();
					}
					sumOfWeights += instance.weight();
				}
			}
			if (instances.classAttribute().Numeric)
			{
				if (Utils.gr(sumOfWeights, 0))
				{
					m_ClassValue /= sumOfWeights;
				}
			}
			else
			{
				m_ClassValue = Utils.maxIndex(m_Counts);
				Utils.normalize(m_Counts, sumOfWeights);
			}
		}

		/// <summary> Method for building a pruneable classifier tree.
		/// 
		/// </summary>
		/// <exception cref="Exception">if something goes wrong
		/// </exception>
		public override void  buildClassifier(Instances data)
		{
			
			if (data.classAttribute().Numeric)
				throw new Exception("Class is numeric!");
			if (data.checkForStringAttributes())
			{
				throw new Exception("Cannot handle string attributes!");
			}
			data = new Instances(data);
			data.deleteWithMissingClass();
			buildTree(data, m_subtreeRaising);
			collapse();
			if (m_pruneTheTree)
			{
				prune();
			}
			if (m_cleanup)
			{
				cleanup(new Instances(data, 0));
			}
		}

		/// <summary> Initializes all the counters for the evaluation and also takes a
		/// cost matrix as parameter.
		/// Use <code>useNoPriors()</code> if the dataset is the test set and you
		/// can't initialize with the priors from the training set via 
		/// <code>setPriors(Instances)</code>.
		/// 
		/// </summary>
		/// <param name="data">	set of training instances, to get some header 
		/// information and prior class distribution information
		/// </param>
		/// <param name="costMatrix">	the cost matrix---if null, default costs will be used
		/// </param>
		/// <throws>  Exception 	if cost matrix is not compatible with  </throws>
		/// <summary> 			data, the class is not defined or the class is numeric
		/// </summary>
		/// <seealso cref="useNoPriors()">
		/// </seealso>
		/// <seealso cref="setPriors(Instances)">
		/// </seealso>
		public Evaluation(Instances data, CostMatrix costMatrix)
		{
			
			m_NumClasses = data.numClasses();
			m_NumFolds = 1;
			m_ClassIsNominal = data.classAttribute().Nominal;
			
			if (m_ClassIsNominal)
			{
				double[][] tmpArray = new double[m_NumClasses][];
				for (int i = 0; i < m_NumClasses; i++)
				{
					tmpArray[i] = new double[m_NumClasses];
				}
				m_ConfusionMatrix = tmpArray;
				m_ClassNames = new System.String[m_NumClasses];
				for (int i = 0; i < m_NumClasses; i++)
				{
					m_ClassNames[i] = data.classAttribute().value_Renamed(i);
				}
			}
			m_CostMatrix = costMatrix;
			if (m_CostMatrix != null)
			{
				if (!m_ClassIsNominal)
				{
					throw new System.Exception("Class has to be nominal if cost matrix " + "given!");
				}
				if (m_CostMatrix.size() != m_NumClasses)
				{
					throw new System.Exception("Cost matrix not compatible with data!");
				}
			}
			m_ClassPriors = new double[m_NumClasses];
			Priors = data;
			m_MarginCounts = new double[k_MarginResolution + 1];
		}

		/// <summary> Performs a (stratified if class is nominal) cross-validation 
		/// for a classifier on a set of instances. Now performs
		/// a deep copy of the classifier before each call to 
		/// buildClassifier() (just in case the classifier is not
		/// initialized properly).
		/// 
		/// </summary>
		/// <param name="classifier">the classifier with any options set.
		/// </param>
		/// <param name="data">the data on which the cross-validation is to be 
		/// performed 
		/// </param>
		/// <param name="numFolds">the number of folds for the cross-validation
		/// </param>
		/// <param name="random">random number generator for randomization 
		/// </param>
		/// <throws>  Exception if a classifier could not be generated  </throws>
		/// <summary> successfully or the class is not defined
		/// </summary>
		public virtual void  crossValidateModel(Classifier classifier, Instances data, int numFolds, System.Random random)
		{
			
			// Make a copy of the data we can reorder
			data = new Instances(data);
			data.randomize(random);
			if (data.classAttribute().Nominal)
			{
				data.stratify(numFolds);
			}
			// Do the folds
			for (int i = 0; i < numFolds; i++)
			{
				Instances train = data.trainCV(numFolds, i, random);
				Priors = train;
				Classifier copiedClassifier = Classifier.makeCopy(classifier);
				copiedClassifier.buildClassifier(train);
				Instances test = data.testCV(numFolds, i);
				evaluateModel(copiedClassifier, test);
			}
			m_NumFolds = numFolds;
		}

		/// <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();
		}

		public override void  buildClassifier(Instances insts)
		{
			
			if (insts.checkForStringAttributes())
			{
				throw new Exception("Cannot handle string attributes!");
			}
			if (insts.numClasses() > 2)
			{
				throw new System.Exception("Can only handle two-class datasets!");
			}
			if (insts.classAttribute().Numeric)
			{
				throw new Exception("Can't handle a numeric class!");
			}
			
			// Filter data
			m_Train = new Instances(insts);
			m_Train.deleteWithMissingClass();
			m_ReplaceMissingValues = new ReplaceMissingValues();
			m_ReplaceMissingValues.setInputFormat(m_Train);
			m_Train = Filter.useFilter(m_Train, m_ReplaceMissingValues);
			
			m_NominalToBinary = new NominalToBinary();
			m_NominalToBinary.setInputFormat(m_Train);
			m_Train = Filter.useFilter(m_Train, m_NominalToBinary);
			
			/** Randomize training data */
			//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'"
			m_Train.randomize(new System.Random((System.Int32) m_Seed));
			
			/** Make space to store perceptrons */
			m_Additions = new int[m_MaxK + 1];
			m_IsAddition = new bool[m_MaxK + 1];
			m_Weights = new int[m_MaxK + 1];
			
			/** Compute perceptrons */
			m_K = 0;
			for (int it = 0; it < m_NumIterations; it++)
			{
				for (int i = 0; i < m_Train.numInstances(); i++)
				{
					Instance inst = m_Train.instance(i);
					if (!inst.classIsMissing())
					{
						int prediction = makePrediction(m_K, inst);
						//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'"
						int classValue = (int) inst.classValue();
						if (prediction == classValue)
						{
							m_Weights[m_K]++;
						}
						else
						{
							m_IsAddition[m_K] = (classValue == 1);
							m_Additions[m_K] = i;
							m_K++;
							m_Weights[m_K]++;
						}
						if (m_K == m_MaxK)
						{
							//UPGRADE_NOTE: Labeled break statement was changed to a goto statement. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1012'"
							goto out_brk;
						}
					}
				}
			}
			//UPGRADE_NOTE: Label 'out_brk' was added. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1011'"

out_brk: ;
			
		}

		/// <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.........

		/// <summary> Builds the boosted classifier</summary>
		public virtual void  buildClassifier(Instances data)
		{
			m_RandomInstance = new Random(m_Seed);
			Instances boostData;
			int classIndex = data.classIndex();
			
			if (data.classAttribute().Numeric)
			{
				throw new Exception("LogitBoost can't handle a numeric class!");
			}
			if (m_Classifier == null)
			{
				throw new System.Exception("A base classifier has not been specified!");
			}
			
			if (!(m_Classifier is WeightedInstancesHandler) && !m_UseResampling)
			{
				m_UseResampling = true;
			}
			if (data.checkForStringAttributes())
			{
				throw new Exception("Cannot handle string attributes!");
			}
			if (m_Debug)
			{
				System.Console.Error.WriteLine("Creating copy of the training data");
			}
			
			m_NumClasses = data.numClasses();
			m_ClassAttribute = data.classAttribute();
			
			// Create a copy of the data 
			data = new Instances(data);
			data.deleteWithMissingClass();
			
			// Create the base classifiers
			if (m_Debug)
			{
				System.Console.Error.WriteLine("Creating base classifiers");
			}
			m_Classifiers = new Classifier[m_NumClasses][];
			for (int j = 0; j < m_NumClasses; j++)
			{
				m_Classifiers[j] = Classifier.makeCopies(m_Classifier, this.NumIterations);
			}
			
			// Do we want to select the appropriate number of iterations
			// using cross-validation?
			int bestNumIterations = this.NumIterations;
			if (m_NumFolds > 1)
			{
				if (m_Debug)
				{
					System.Console.Error.WriteLine("Processing first fold.");
				}
				
				// Array for storing the results
				double[] results = new double[this.NumIterations];
				
				// Iterate throught the cv-runs
				for (int r = 0; r < m_NumRuns; r++)
				{
					
					// Stratify the data
					data.randomize(m_RandomInstance);
					data.stratify(m_NumFolds);
					
					// Perform the cross-validation
					for (int i = 0; i < m_NumFolds; i++)
					{
						
						// Get train and test folds
						Instances train = data.trainCV(m_NumFolds, i, m_RandomInstance);
						Instances test = data.testCV(m_NumFolds, i);
						
						// Make class numeric
						Instances trainN = new Instances(train);
						trainN.ClassIndex = - 1;
						trainN.deleteAttributeAt(classIndex);
						trainN.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
						trainN.ClassIndex = classIndex;
						m_NumericClassData = new Instances(trainN, 0);
						
						// Get class values
						int numInstances = train.numInstances();
						double[][] tmpArray = new double[numInstances][];
						for (int i2 = 0; i2 < numInstances; i2++)
						{
							tmpArray[i2] = new double[m_NumClasses];
						}
						double[][] trainFs = tmpArray;
						double[][] tmpArray2 = new double[numInstances][];
						for (int i3 = 0; i3 < numInstances; i3++)
						{
							tmpArray2[i3] = new double[m_NumClasses];
						}
						double[][] trainYs = tmpArray2;
						for (int j = 0; j < m_NumClasses; j++)
						{
//.........这里部分代码省略.........

		/// <summary> Generates the classifier.
		/// 
		/// </summary>
		/// <param name="instances">set of instances serving as training data 
		/// </param>
		/// <exception cref="Exception">if the classifier has not been generated successfully
		/// </exception>
		public override void  buildClassifier(Instances instances)
		{
			//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
			double bestVal = System.Double.MaxValue, currVal;
			//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
			double bestPoint = - System.Double.MaxValue, sum;
			int bestAtt = - 1, numClasses;
			
			if (instances.checkForStringAttributes())
			{
				throw new Exception("Can't handle string attributes!");
			}
			
			double[][] bestDist = new double[3][];
			for (int i = 0; i < 3; i++)
			{
				bestDist[i] = new double[instances.numClasses()];
			}
			
			m_Instances = new Instances(instances);
			m_Instances.deleteWithMissingClass();
			
			if (m_Instances.numInstances() == 0)
			{
				throw new System.ArgumentException("No instances without missing " + "class values in training file!");
			}
			
			if (instances.numAttributes() == 1)
			{
				throw new System.ArgumentException("Attribute missing. Need at least one " + "attribute other than class attribute!");
			}
			
			if (m_Instances.classAttribute().Nominal)
			{
				numClasses = m_Instances.numClasses();
			}
			else
			{
				numClasses = 1;
			}
			
			// For each attribute
			bool first = true;
			for (int i = 0; i < m_Instances.numAttributes(); i++)
			{
				if (i != m_Instances.classIndex())
				{
					
					// Reserve space for distribution.
					double[][] tmpArray = new double[3][];
					for (int i2 = 0; i2 < 3; i2++)
					{
						tmpArray[i2] = new double[numClasses];
					}
					m_Distribution = tmpArray;
					
					// Compute value of criterion for best split on attribute
					if (m_Instances.attribute(i).Nominal)
					{
						currVal = findSplitNominal(i);
					}
					else
					{
						currVal = findSplitNumeric(i);
					}
					if ((first) || (currVal < bestVal))
					{
						bestVal = currVal;
						bestAtt = i;
						bestPoint = m_SplitPoint;
						for (int j = 0; j < 3; j++)
						{
							Array.Copy(m_Distribution[j], 0, bestDist[j], 0, numClasses);
						}
					}
					
					// First attribute has been investigated
					first = false;
				}
			}
			
			// Set attribute, split point and distribution.
			m_AttIndex = bestAtt;
			m_SplitPoint = bestPoint;
			m_Distribution = bestDist;
			if (m_Instances.classAttribute().Nominal)
			{
				for (int i = 0; i < m_Distribution.Length; i++)
				{
					double sumCounts = Utils.sum(m_Distribution[i]);
					if (sumCounts == 0)
					{
						// This means there were only missing attribute values
//.........这里部分代码省略.........