Changeset 11594 for branches/Breadcrumbs/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/FeatureSelection/FeatureSelection.cs
- Timestamp:
- 11/27/14 11:23:37 (10 years ago)
- Location:
- branches/Breadcrumbs
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- 3 edited
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branches/Breadcrumbs
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old new 8 8 FxCopResults.txt 9 9 Google.ProtocolBuffers-0.9.1.dll 10 Google.ProtocolBuffers-2.4.1.473.dll 10 11 HeuristicLab 3.3.5.1.ReSharper.user 11 12 HeuristicLab 3.3.6.0.ReSharper.user 12 13 HeuristicLab.4.5.resharper.user 13 14 HeuristicLab.ExtLibs.6.0.ReSharper.user 15 HeuristicLab.Scripting.Development 14 16 HeuristicLab.resharper.user 15 17 ProtoGen.exe … … 17 19 _ReSharper.HeuristicLab 18 20 _ReSharper.HeuristicLab 3.3 21 _ReSharper.HeuristicLab 3.3 Tests 19 22 _ReSharper.HeuristicLab.ExtLibs 20 23 bin 21 24 protoc.exe 22 _ReSharper.HeuristicLab 3.3 Tests23 Google.ProtocolBuffers-2.4.1.473.dll
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- Property svn:mergeinfo changed
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branches/Breadcrumbs/HeuristicLab.Problems.Instances.DataAnalysis
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branches/Breadcrumbs/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/FeatureSelection/FeatureSelection.cs
r9456 r11594 1 1 #region License Information 2 2 /* HeuristicLab 3 * Copyright (C) 2002-201 3Heuristic and Evolutionary Algorithms Laboratory (HEAL)3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL) 4 4 * 5 5 * This file is part of HeuristicLab. … … 48 48 + "Where is the S is a N x d matrix containing the selected columns from N x k the matrix of all features X" + Environment.NewLine 49 49 + "For each feature the probability that it is selected is " + selectionProbability + "%" + Environment.NewLine 50 + "X(i,j) ~ N(0, 1) iid, w(i) ~ U(0, 10) iid, n ~ N(0, sigma(w*S) * SQRT(" + noiseRatio + "))" + Environment.NewLine50 + "X(i,j) ~ N(0, 1) iid, w(i) ~ U(0, 10) iid, n ~ N(0, sigma(w*S) * SQRT(" + noiseRatio / (1 - noiseRatio) + "))" + Environment.NewLine 51 51 + "The noise level is " + noiseRatio + " * sigma, thus an optimal model has R² = " 52 52 + Math.Round(optimalRSquared, 2) + " (or equivalently: NMSE = " + noiseRatio + ")" + Environment.NewLine … … 131 131 } 132 132 var targetSigma = target.StandardDeviation(); 133 var noisePrng = new NormalDistributedRandom(random, 0, targetSigma * Math.Sqrt(noiseRatio ));133 var noisePrng = new NormalDistributedRandom(random, 0, targetSigma * Math.Sqrt(noiseRatio / (1.0 - noiseRatio))); 134 134 135 135 data.Add(target.Select(t => t + noisePrng.NextDouble()).ToList());
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