Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs	(revision 8139)
@@ -68,5 +68,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       int nClasses = problemData.ClassNames.Count();
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs	(revision 8139)
@@ -72,5 +72,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs	(revision 8139)
@@ -69,5 +69,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
@@ -81,10 +81,10 @@
       int nClasses = classValues.Count();
       // map original class values to values [0..nClasses-1]
-      Dictionary<double, double> classIndizes = new Dictionary<double, double>();
+      Dictionary<double, double> classIndices = new Dictionary<double, double>();
       for (int i = 0; i < nClasses; i++) {
-        classIndizes[classValues[i]] = i;
+        classIndices[classValues[i]] = i;
       }
       for (int row = 0; row < nRows; row++) {
-        inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
+        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
       }
       int info;
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs	(revision 8139)
@@ -87,5 +87,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
@@ -99,10 +99,10 @@
       int nClasses = classValues.Count();
       // map original class values to values [0..nClasses-1]
-      Dictionary<double, double> classIndizes = new Dictionary<double, double>();
+      Dictionary<double, double> classIndices = new Dictionary<double, double>();
       for (int i = 0; i < nClasses; i++) {
-        classIndizes[classValues[i]] = i;
+        classIndices[classValues[i]] = i;
       }
       for (int row = 0; row < nRows; row++) {
-        inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
+        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
       }
       alglib.nearestneighbor.kdtreebuild(inputMatrix, nRows, inputMatrix.GetLength(1) - 1, 1, 2, kdtree);
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs	(revision 8139)
@@ -87,5 +87,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs	(revision 8139)
@@ -185,5 +185,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
@@ -195,10 +195,10 @@
       int nClasses = classValues.Count();
       // map original class values to values [0..nClasses-1]
-      Dictionary<double, double> classIndizes = new Dictionary<double, double>();
+      Dictionary<double, double> classIndices = new Dictionary<double, double>();
       for (int i = 0; i < nClasses; i++) {
-        classIndizes[classValues[i]] = i;
+        classIndices[classValues[i]] = i;
       }
       for (int row = 0; row < nRows; row++) {
-        inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
+        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
       }
 
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs	(revision 8139)
@@ -171,5 +171,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
@@ -181,10 +181,10 @@
       int nClasses = classValues.Count();
       // map original class values to values [0..nClasses-1]
-      Dictionary<double, double> classIndizes = new Dictionary<double, double>();
+      Dictionary<double, double> classIndices = new Dictionary<double, double>();
       for (int i = 0; i < nClasses; i++) {
-        classIndizes[classValues[i]] = i;
+        classIndices[classValues[i]] = i;
       }
       for (int row = 0; row < nRows; row++) {
-        inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
+        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
       }
 
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs	(revision 8139)
@@ -170,5 +170,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkRegression.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkRegression.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkRegression.cs	(revision 8139)
@@ -186,5 +186,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestClassification.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestClassification.cs	(revision 8139)
@@ -97,5 +97,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
@@ -111,10 +111,10 @@
       int nClasses = classValues.Count();
       // map original class values to values [0..nClasses-1]
-      Dictionary<double, double> classIndizes = new Dictionary<double, double>();
+      Dictionary<double, double> classIndices = new Dictionary<double, double>();
       for (int i = 0; i < nClasses; i++) {
-        classIndizes[classValues[i]] = i;
+        classIndices[classValues[i]] = i;
       }
       for (int row = 0; row < nRows; row++) {
-        inputMatrix[row, nCols - 1] = classIndizes[inputMatrix[row, nCols - 1]];
+        inputMatrix[row, nCols - 1] = classIndices[inputMatrix[row, nCols - 1]];
       }
       // execute random forest algorithm
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs	(revision 8139)
@@ -97,5 +97,5 @@
       string targetVariable = problemData.TargetVariable;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorClassification.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorClassification.cs	(revision 8139)
@@ -132,5 +132,5 @@
       Dataset dataset = problemData.Dataset;
       string targetVariable = problemData.TargetVariable;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
 
       //extract SVM parameters from scope and set them
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorRegression.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorRegression.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorRegression.cs	(revision 8139)
@@ -140,5 +140,5 @@
       Dataset dataset = problemData.Dataset;
       string targetVariable = problemData.TargetVariable;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
 
       //extract SVM parameters from scope and set them
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClustering.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClustering.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClustering.cs	(revision 8139)
@@ -85,5 +85,5 @@
       Dataset dataset = problemData.Dataset;
       IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndizes;
+      IEnumerable<int> rows = problemData.TrainingIndices;
       int info;
       double[,] centers;
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClusteringSolution.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClusteringSolution.cs	(revision 8137)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClusteringSolution.cs	(revision 8139)
@@ -52,6 +52,6 @@
     public KMeansClusteringSolution(KMeansClusteringModel model, IClusteringProblemData problemData)
       : base(model, problemData) {
-      double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndizes);
-      double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndizes);
+      double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndices);
+      double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndices);
       this.Add(new Result(TrainingIntraClusterSumOfSquaresResultName, "The sum of squared distances of points of the training partition to the cluster center (is minimized by k-Means).", new DoubleValue(trainingIntraClusterSumOfSquares)));
       this.Add(new Result(TestIntraClusterSumOfSquaresResultName, "The sum of squared distances of points of the test partition to the cluster center (is minimized by k-Means).", new DoubleValue(testIntraClusterSumOfSquares)));
