C# Instances.randomize方法代码示例

		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> 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> 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.........