Changeset 15786 for trunk/HeuristicLab.Algorithms.DataAnalysis

Timestamp:

02/20/18 08:02:17 (7 years ago)

Author:

fholzing

Message:

#2902: Renamed ContainsNanInf to ContainsNanOrInfinity

Location:

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4

Files:

• DoubleArrayExtensions.cs (modified) (1 diff)
• Linear/LinearDiscriminantAnalysis.cs (modified) (1 diff)
• Linear/LinearRegression.cs (modified) (1 diff)
• Linear/MultinomialLogitClassification.cs (modified) (1 diff)
• NearestNeighbour/NearestNeighbourModel.cs (modified) (1 diff)
• NeuralNetwork/NeuralNetworkClassification.cs (modified) (1 diff)
• NeuralNetwork/NeuralNetworkEnsembleClassification.cs (modified) (1 diff)
• NeuralNetwork/NeuralNetworkEnsembleRegression.cs (modified) (1 diff)
• NeuralNetwork/NeuralNetworkRegression.cs (modified) (1 diff)
• RandomForest/RandomForestModel.cs (modified) (1 diff)
• TimeSeries/AutoregressiveModeling.cs (modified) (1 diff)
• kMeans/KMeansClustering.cs (modified) (1 diff)

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/DoubleArrayExtensions.cs

@@ -1 +1 @@
 namespace HeuristicLab.Algorithms.DataAnalysis {
   public static class DoubleArrayExtensions {
-    public static bool ContainsNanInf(this double[,] array) {
+    public static bool ContainsNanOrInfinity(this double[,] array) {
       foreach (var entry in array) {
         if (double.IsNaN(entry) || double.IsInfinity(entry)) {

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs

@@ -80 +80 @@
       inputMatrix = factorMatrix.HorzCat(inputMatrix);
 
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Linear discriminant analysis does not support NaN or infinity values in the input dataset.");
 

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

@@ -80 +80 @@
       var inputMatrix = binaryMatrix.HorzCat(doubleVarMatrix);
 
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
 

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs

@@ -78 +78 @@
       inputMatrix = factorMatrix.HorzCat(inputMatrix);
 
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Multinomial logit classification does not support NaN or infinity values in the input dataset.");
 

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourModel.cs

@@ -142 +142 @@
       }
 
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException(
           "Nearest neighbour model does not support NaN or infinity values in the input dataset.");

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs

@@ -185 +185 @@
       IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = dataset.ToArray(allowedInputVariables.Concat(new string[] { targetVariable }), rows);
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Neural network classification does not support NaN or infinity values in the input dataset.");
 

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs

@@ -171 +171 @@
       IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = dataset.ToArray(allowedInputVariables.Concat(new string[] { targetVariable }), rows);
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Neural network ensemble classification does not support NaN or infinity values in the input dataset.");
 

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs

@@ -170 +170 @@
       IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = dataset.ToArray(allowedInputVariables.Concat(new string[] { targetVariable }), rows);
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Neural network ensemble regression does not support NaN or infinity values in the input dataset.");
 

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkRegression.cs

@@ -186 +186 @@
       IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = dataset.ToArray(allowedInputVariables.Concat(new string[] { targetVariable }), rows);
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Neural network regression does not support NaN or infinity values in the input dataset.");
 

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestModel.cs

@@ -370 +370 @@
 
     private static void AssertInputMatrix(double[,] inputMatrix) {
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Random forest modeling does not support NaN or infinity values in the input dataset.");
     }

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/TimeSeries/AutoregressiveModeling.cs

@@ -96 +96 @@
         inputMatrix[i, timeOffset] = targetValues[i + problemData.TrainingPartition.Start];
 
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
 

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClustering.cs

@@ -91 +91 @@
       int[] xyc;
       double[,] inputMatrix = dataset.ToArray(allowedInputVariables, rows);
-      if (inputMatrix.ContainsNanInf())
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("k-Means clustering does not support NaN or infinity values in the input dataset.");