Changeset 15783

Timestamp:

02/16/18 11:35:54 (6 years ago)

Author:

fholzing

Message:

#2902: Changed from Cast to Iterator and adapted all occurrences.

Location:

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4

Files:

• DoubleArrayExtensions.cs (added)
• HeuristicLab.Algorithms.DataAnalysis-3.4.csproj (modified) (2 diffs)
• Linear/LinearDiscriminantAnalysis.cs (modified) (1 diff)
• Linear/LinearRegression.cs (modified) (2 diffs)
• Linear/MultinomialLogitClassification.cs (modified) (1 diff)
• NearestNeighbour/NearestNeighbourModel.cs (modified) (1 diff)
• NeuralNetwork/NeuralNetworkClassification.cs (modified) (2 diffs)
• NeuralNetwork/NeuralNetworkEnsembleClassification.cs (modified) (1 diff)
• NeuralNetwork/NeuralNetworkEnsembleRegression.cs (modified) (2 diffs)
• NeuralNetwork/NeuralNetworkRegression.cs (modified) (2 diffs)
• RandomForest/RandomForestModel.cs (modified) (2 diffs)
• TimeSeries/AutoregressiveModeling.cs (modified) (2 diffs)
• kMeans/KMeansClustering.cs (modified) (1 diff)

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj

@@ -131 +131 @@
       <SubType>Code</SubType>
     </Compile>
+    <Compile Include="DoubleArrayExtensions.cs" />
     <Compile Include="FixedDataAnalysisAlgorithm.cs" />
     <Compile Include="GaussianProcess\CovarianceFunctions\CovarianceSpectralMixture.cs" />
@@ -320 +321 @@
     <Compile Include="TSNE\Distances\IndexedItemDistance.cs" />
     <Compile Include="TSNE\Distances\ManhattanDistance.cs" />
-  <Compile Include="TSNE\Distances\WeightedEuclideanDistance.cs" />
+    <Compile Include="TSNE\Distances\WeightedEuclideanDistance.cs" />
     <Compile Include="TSNE\Distances\IDistance.cs" />
     <Compile Include="TSNE\PriorityQueue.cs" />

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

@@ -80 +80 @@
       inputMatrix = factorMatrix.HorzCat(inputMatrix);
 
-      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
 
@@ -100 +100 @@
       int nVarCoeff = doubleVariables.Count();
       var tree = LinearModelToTreeConverter.CreateTree(factorVariables, coefficients.Take(nFactorCoeff).ToArray(),
-        doubleVariables.ToArray(), coefficients.Skip(nFactorCoeff).Take(nVarCoeff).ToArray(), 
+        doubleVariables.ToArray(), coefficients.Skip(nFactorCoeff).Take(nVarCoeff).ToArray(),
         @const: coefficients[nFeatures]);
-      
+
       SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(problemData.TargetVariable, tree, new SymbolicDataAnalysisExpressionTreeLinearInterpreter()), (IRegressionProblemData)problemData.Clone());
       solution.Model.Name = "Linear Regression Model";

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

@@ -78 +78 @@
       inputMatrix = factorMatrix.HorzCat(inputMatrix);
 
-      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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

@@ -115 +115 @@
     public NeuralNetworkClassification()
       : base() {
-      var validHiddenLayerValues = new ItemSet<IntValue>(new IntValue[] { 
-        (IntValue)new IntValue(0).AsReadOnly(), 
-        (IntValue)new IntValue(1).AsReadOnly(), 
+      var validHiddenLayerValues = new ItemSet<IntValue>(new IntValue[] {
+        (IntValue)new IntValue(0).AsReadOnly(),
+        (IntValue)new IntValue(1).AsReadOnly(),
         (IntValue)new IntValue(2).AsReadOnly() });
       var selectedHiddenLayerValue = (from v in validHiddenLayerValues
@@ -185 +185 @@
       IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = dataset.ToArray(allowedInputVariables.Concat(new string[] { targetVariable }), rows);
-      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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

@@ -125 +125 @@
     public NeuralNetworkEnsembleRegression()
       : base() {
-      var validHiddenLayerValues = new ItemSet<IntValue>(new IntValue[] { 
-        (IntValue)new IntValue(0).AsReadOnly(), 
-        (IntValue)new IntValue(1).AsReadOnly(), 
+      var validHiddenLayerValues = new ItemSet<IntValue>(new IntValue[] {
+        (IntValue)new IntValue(0).AsReadOnly(),
+        (IntValue)new IntValue(1).AsReadOnly(),
         (IntValue)new IntValue(2).AsReadOnly() });
       var selectedHiddenLayerValue = (from v in validHiddenLayerValues
@@ -170 +170 @@
       IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = dataset.ToArray(allowedInputVariables.Concat(new string[] { targetVariable }), rows);
-      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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

@@ -115 +115 @@
     public NeuralNetworkRegression()
       : base() {
-      var validHiddenLayerValues = new ItemSet<IntValue>(new IntValue[] { 
-        (IntValue)new IntValue(0).AsReadOnly(), 
-        (IntValue)new IntValue(1).AsReadOnly(), 
+      var validHiddenLayerValues = new ItemSet<IntValue>(new IntValue[] {
+        (IntValue)new IntValue(0).AsReadOnly(),
+        (IntValue)new IntValue(1).AsReadOnly(),
         (IntValue)new IntValue(2).AsReadOnly() });
       var selectedHiddenLayerValue = (from v in validHiddenLayerValues
@@ -186 +186 @@
       IEnumerable<int> rows = problemData.TrainingIndices;
       double[,] inputMatrix = dataset.ToArray(allowedInputVariables.Concat(new string[] { targetVariable }), rows);
-      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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

@@ -310 +310 @@
     public static RandomForestModel CreateClassificationModel(IClassificationProblemData problemData, int nTrees, double r, double m, int seed,
       out double rmsError, out double outOfBagRmsError, out double relClassificationError, out double outOfBagRelClassificationError) {
-      return CreateClassificationModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed, 
+      return CreateClassificationModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed,
         out rmsError, out outOfBagRmsError, out relClassificationError, out outOfBagRelClassificationError);
     }
@@ -370 +370 @@
 
     private static void AssertInputMatrix(double[,] inputMatrix) {
-      if (inputMatrix.Cast<double>().Any(x => Double.IsNaN(x) || Double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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

@@ -26 +26 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
@@ -97 +96 @@
         inputMatrix[i, timeOffset] = targetValues[i + problemData.TrainingPartition.Start];
 
-      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         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.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanInf())
         throw new NotSupportedException("k-Means clustering does not support NaN or infinity values in the input dataset.");