Index: branches/3022-FastFunctionExtraction/FFX/BFUtils.cs
===================================================================
--- branches/3022-FastFunctionExtraction/FFX/BFUtils.cs	(revision 17739)
+++ branches/3022-FastFunctionExtraction/FFX/BFUtils.cs	(revision 17740)
@@ -1,3 +1,4 @@
 ﻿using HeuristicLab.Algorithms.DataAnalysis.Glmnet;
+using HeuristicLab.Common;
 using HeuristicLab.Data;
 using HeuristicLab.Problems.DataAnalysis;
@@ -18,5 +19,5 @@
             if (approach.AllowHinge) {
                 // only allow hinge functions for features with exponent = 1 (deemed too complex otherwise)
-                var linearSimpleBasisFuncs = simpleBasisFuncs.Where(simpleBf => simpleBf.Exponent == 1);
+                var linearSimpleBasisFuncs = simpleBasisFuncs.Where(simpleBf => simpleBf.Exponent == 1 && simpleBf.Operator.Equals(NonlinearOperator.None));
                 simpleBasisFuncs = simpleBasisFuncs.Concat(CreateHingeBases(data, linearSimpleBasisFuncs, approach.MinHingeThr, approach.MaxHingeThr, approach.NumHingeThrs));
             }
@@ -88,5 +89,5 @@
         // the importance of a basis function is measured by the absolute value of its coefficient when optimized on the data
         public static IEnumerable<ISimpleBasisFunction> OrderBasisFuncsByImportance(IRegressionProblemData data, IList<ISimpleBasisFunction> candidateFunctions) {
-            var elnetData = PrepareData(Normalize(data), candidateFunctions);
+            var elnetData = PrepareData(Normalize(data, out _, out _, out _, out _), candidateFunctions);
             var coeff = ElasticNetLinearRegression.CalculateModelCoefficients(elnetData, 0, 0, out var trainNMSE, out var testNMSE); // LS-fit
             var intercept = coeff.Last();
@@ -177,6 +178,23 @@
         }
 
-        private static IRegressionProblemData Normalize(IRegressionProblemData data)
-            => new RegressionProblemData(Normalize(data.Dataset), data.AllowedInputVariables, data.TargetVariable);
+        public static IRegressionProblemData Normalize(IRegressionProblemData data, out double[] X_avgs, out double[] X_stds, out double y_avg, out double y_std) {
+            X_avgs = data.AllowedInputVariables
+                .Select(varname => data.Dataset.GetDoubleValues(varname)
+                    .Average())
+                .ToArray();
+            X_stds = data.AllowedInputVariables
+                .Select(varname => data.Dataset.GetDoubleValues(varname)
+                    .StandardDeviationPop())
+                .ToArray();
+            for (int i = 0; i < X_stds.Length; i++) {
+                if (X_stds[i] == 0) X_stds[i] = 1;
+            }
+            y_avg = data.TargetVariableValues.Average();
+            y_std = data.TargetVariableValues.StandardDeviationPop();
+            if (y_std == 0) y_std = 1;
+            var temp = Normalize(data.Dataset);
+            var ans = new RegressionProblemData(Normalize(data.Dataset), data.AllowedInputVariables, data.TargetVariable);
+            return ans;
+        }
 
         // return a normalized version of IDataset ds
Index: branches/3022-FastFunctionExtraction/FFX/FastFunctionExtraction.cs
===================================================================
--- branches/3022-FastFunctionExtraction/FFX/FastFunctionExtraction.cs	(revision 17739)
+++ branches/3022-FastFunctionExtraction/FFX/FastFunctionExtraction.cs	(revision 17740)
@@ -12,4 +12,5 @@
 using System.Collections.Generic;
 using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
+using HeuristicLab.Problems.DataAnalysis.Symbolic;
 
 namespace HeuristicLab.Algorithms.DataAnalysis.FastFunctionExtraction {
@@ -24,5 +25,5 @@
         private static readonly HashSet<NonlinearOperator> nonlinFuncs = new HashSet<NonlinearOperator> { NonlinearOperator.Abs, NonlinearOperator.Log, NonlinearOperator.None };
         private static readonly double minHingeThr = 0.2;
-        private static readonly double maxHingeThr = 0.8;
+        private static readonly double maxHingeThr = 0.9;
         private static readonly int numHingeThrs = 5;
 
@@ -192,25 +193,45 @@
             var basisFunctions = BFUtils.CreateBasisFunctions(data, approach).ToArray();
 
+
+            // FFX Step 2
             var funcsArr = basisFunctions.ToArray();
             var elnetData = BFUtils.PrepareData(data, funcsArr);
-
-            // FFX Step 2
-            ElasticNetLinearRegression.RunElasticNetLinearRegression(elnetData, approach.ElnetPenalty, out var _, out var _, out var _, out var candidateCoeffs, out var intercept, maxVars: approach.MaxNumBases);
+            var normalizedElnetData = BFUtils.Normalize(elnetData, out var X_avgs, out var X_stds, out var y_avg, out var y_std);
+            
+            ElasticNetLinearRegression.RunElasticNetLinearRegression(normalizedElnetData, approach.ElnetPenalty, out var _, out var _, out var _, out var candidateCoeffsNorm, out var interceptNorm, maxVars: approach.MaxNumBases);
+
+            var coefs = RebiasCoefs(candidateCoeffsNorm, interceptNorm, X_avgs, X_stds, y_avg, y_std, out var intercept);
 
             // create models out of the learned coefficients
-            var ffxModels = GetUniqueModelsFromCoeffs(candidateCoeffs, intercept, funcsArr, approach);
-             
+            var ffxModels = GetModelsFromCoeffs(coefs, intercept, funcsArr, approach);
+
             // one last LS-optimization step on the training data
             foreach (var ffxModel in ffxModels) {
                 if (ffxModel.NumBases > 0) ffxModel.OptimizeCoefficients(data);
             }
-
             return ffxModels;
         }
 
+        private static double[,] RebiasCoefs(double[,] unbiasedCoefs, double[] unbiasedIntercepts, double [] X_avgs, double[] X_stds, double y_avg, double y_std, out double[] rebiasedIntercepts) {
+            var rows = unbiasedIntercepts.Length;
+            var cols = X_stds.Length;
+            var rebiasedCoefs = new double[rows,cols];
+            rebiasedIntercepts = new double[rows];
+
+            for (int i = 0; i < rows; i++) {
+                var unbiasedIntercept = unbiasedIntercepts[i];
+                rebiasedIntercepts[i] = unbiasedIntercept * y_std + y_avg;
+
+                for (int j = 0; j < cols; j++) {
+                    rebiasedCoefs[i, j] = unbiasedCoefs[i, j] * y_std / X_stds[j];
+                    rebiasedIntercepts[i] -= rebiasedCoefs[i, j] * X_avgs[j];
+                }
+            }
+            return rebiasedCoefs;
+        }
+
         // finds all models with unique combinations of basis functions
-        private static IEnumerable<FFXModel> GetUniqueModelsFromCoeffs(double[,] candidateCoeffs, double[] intercept, IBasisFunction[] funcsArr, Approach approach) {
+        private static IEnumerable<FFXModel> GetModelsFromCoeffs(double[,] candidateCoeffs, double[] intercept, IBasisFunction[] funcsArr, Approach approach) {
             List<FFXModel> ffxModels = new List<FFXModel>();
-            List<int[]> bfCombinations = new List<int[]>();
 
             for (int i = 0; i < intercept.Length; i++) {
@@ -219,7 +240,5 @@
                 if (nonzeroIndices.Count() > approach.MaxNumBases) continue;
                 // ignore duplicate models (models with same combination of basis functions)
-                if (bfCombinations.Any(arr => Enumerable.SequenceEqual(arr, nonzeroIndices))) continue;
                 var ffxModel = new FFXModel(intercept[i], nonzeroIndices.Select(idx => (row[idx], funcsArr[idx])));
-                bfCombinations.Add(nonzeroIndices);
                 ffxModels.Add(ffxModel);
             }
Index: branches/3022-FastFunctionExtraction/TestFFX/Program.cs
===================================================================
--- branches/3022-FastFunctionExtraction/TestFFX/Program.cs	(revision 17739)
+++ branches/3022-FastFunctionExtraction/TestFFX/Program.cs	(revision 17740)
@@ -24,4 +24,5 @@
             // foreach dataset
             foreach (var fInfo in dInfo.GetFiles()) {
+                //if (fInfo.Name != "1089_USCrime.csv") continue;
                 Console.Write($"{++idx}/{len}\t{fInfo.Name,-35}:");
                 RunFFXOnDataset(fInfo, targetPath, out var elapsedSeconds);
@@ -56,5 +57,5 @@
             Stopwatch sw = new Stopwatch();
             sw.Start();
-            var regressionModels = FastFunctionExtraction.Fit(data, 0.95, out var numBases, true, true, true, true, true, maxNumBases: 20).ToArray();
+            var regressionModels = FastFunctionExtraction.Fit(data, 0.95, out var numBases, true, true, true, true, true, maxNumBases: 10).ToArray();
             sw.Stop();
             var regressionSolutions = regressionModels.Select(model => new SymbolicRegressionSolution(model, data)).ToArray();
Index: branches/3022-FastFunctionExtraction/TestFFX/TestFFX.csproj
===================================================================
--- branches/3022-FastFunctionExtraction/TestFFX/TestFFX.csproj	(revision 17739)
+++ branches/3022-FastFunctionExtraction/TestFFX/TestFFX.csproj	(revision 17740)
@@ -169,5 +169,4 @@
   <ItemGroup />
   <ItemGroup>
-    <Content Include="results\lengths.txt" />
     <Content Include="results\results.txt" />
   </ItemGroup>
Index: branches/3022-FastFunctionExtraction/TestFFX/results/results.txt
===================================================================
--- branches/3022-FastFunctionExtraction/TestFFX/results/results.txt	(revision 17739)
+++ branches/3022-FastFunctionExtraction/TestFFX/results/results.txt	(revision 17740)
@@ -1,1179 +1,685 @@
 dataset,algorithm,num_bases,train_mse,train_mae,test_mse,test_mae,runtime
-1027_ESL,hl_ffx,0,2.21921190540177,1.2006517065305,1.82046156947057,1.11727358237033,1.2627732
-1027_ESL,hl_ffx,1,1.85496573674532,1.08948205747595,1.54460467160375,1.02874565246501,1.2627732
-1027_ESL,hl_ffx,2,1.51984265250861,0.976201968022541,1.2865939159192,0.933754674559429,1.2627732
-1027_ESL,hl_ffx,3,1.1814006930044,0.853864287729073,0.942957925730236,0.785400880412206,1.2627732
-1027_ESL,hl_ffx,4,0.820178445817836,0.702579620668778,0.650275030049176,0.646307223512914,1.2627732
-1027_ESL,hl_ffx,5,0.320856395576467,0.424180671799732,0.305694603721764,0.420514490477614,1.2627732
-1027_ESL,hl_ffx,6,0.248978513565686,0.385060646373953,0.284976609369674,0.410495454348272,1.2627732
-1027_ESL,hl_ffx,11,0.248105375569678,0.381679298874508,0.282585145166553,0.403689293002266,1.2627732
-1027_ESL,hl_ffx,12,0.255216616893734,0.385837025589503,0.282508028615252,0.40366974110695,1.2627732
-1028_SWD,hl_ffx,0,0.636656000000002,0.665872000000007,0.678384000000001,0.687360000000008,2.128796
-1028_SWD,hl_ffx,1,0.592932317616271,0.641648666099552,0.631767181728749,0.662467977630744,2.128796
-1028_SWD,hl_ffx,2,0.567676470499729,0.620733305015546,0.606285648361,0.6404706747681,2.128796
-1028_SWD,hl_ffx,3,0.510173485696301,0.592081355952582,0.549761408144058,0.610615157646714,2.128796
-1028_SWD,hl_ffx,4,0.479811705118018,0.580319203210735,0.513666386062213,0.596978642568589,2.128796
-1028_SWD,hl_ffx,5,0.438821440716981,0.555494190672456,0.470875119564631,0.568551181344911,2.128796
-1028_SWD,hl_ffx,6,0.42813804129214,0.548144532350457,0.459196336269358,0.560961590517629,2.128796
-1028_SWD,hl_ffx,7,0.40518151957839,0.529145924331657,0.434467552478969,0.542498296329828,2.128796
-1028_SWD,hl_ffx,8,0.388589442482424,0.511264394432213,0.415032568680417,0.525531716092011,2.128796
-1028_SWD,hl_ffx,9,0.381092942583327,0.504474365390688,0.405790500261395,0.518637244049736,2.128796
-1028_SWD,hl_ffx,11,0.375174287702892,0.498142777099804,0.398182284164681,0.512147075158408,2.128796
-1028_SWD,hl_ffx,12,0.371306281578616,0.492421587644481,0.39264790977947,0.506843882082517,2.128796
-1028_SWD,hl_ffx,13,0.37018227953927,0.490126886485287,0.390675659066499,0.505559443117481,2.128796
-1028_SWD,hl_ffx,14,0.367032162405632,0.487735013385344,0.389295624977466,0.503662317364352,2.128796
-1028_SWD,hl_ffx,15,0.364009569564073,0.485755906251904,0.387540020681971,0.502259006151651,2.128796
-1028_SWD,hl_ffx,16,0.360533509651228,0.483852520217539,0.38643509858396,0.501544054568926,2.128796
-1028_SWD,hl_ffx,18,0.359883163308365,0.483499087087804,0.386356660684938,0.501518629278771,2.128796
-1028_SWD,hl_ffx,19,0.35949937823785,0.483274766126267,0.386319924349887,0.501489365836209,2.128796
-1028_SWD,hl_ffx,20,0.358619943248345,0.482809548316216,0.386266621283937,0.501520710917455,2.128796
-1029_LEV,hl_ffx,0,0.926475999999998,0.781448000000001,0.895091999999999,0.763296,1.2134017
-1029_LEV,hl_ffx,1,0.866324153335936,0.752065421784597,0.819805467666223,0.729622896294112,1.2134017
-1029_LEV,hl_ffx,2,0.774948701487943,0.703675928582297,0.706852049314019,0.674675141085467,1.2134017
-1029_LEV,hl_ffx,3,0.627592070335985,0.635390099719034,0.554312549141319,0.601621603371594,1.2134017
-1029_LEV,hl_ffx,4,0.578748890257134,0.608978119312922,0.5092158896375,0.5758109765397,1.2134017
-1029_LEV,hl_ffx,5,0.40993370055332,0.485169133869925,0.385313307926541,0.483019935449457,1.2134017
-1029_LEV,hl_ffx,9,0.41732519420722,0.496676263887013,0.38374773862595,0.489809266119741,1.2134017
-1029_LEV,hl_ffx,10,0.412180991524898,0.490684756309508,0.38217103228576,0.485725153868844,1.2134017
-1029_LEV,hl_ffx,12,0.408941223862732,0.491244925634102,0.381159367366734,0.486370070424148,1.2134017
-1030_ERA,hl_ffx,0,4.18617600000004,1.714912,3.68990400000002,1.59182399999999,1.2013925
-1030_ERA,hl_ffx,1,3.83927908075954,1.64769548835825,3.38382928883988,1.5261509767165,1.2013925
-1030_ERA,hl_ffx,2,3.01406818473822,1.46578160495649,2.69452896702493,1.3575678873187,1.2013925
-1030_ERA,hl_ffx,3,2.80621617048149,1.40729273461977,2.52200417491753,1.30203246923954,1.2013925
-1030_ERA,hl_ffx,4,2.76299692270536,1.37828767627959,2.4390207672461,1.26255144033337,1.2013925
-1030_ERA,hl_ffx,5,2.64201687684598,1.32709248763592,2.29705193454577,1.21540093108658,1.2013925
-1030_ERA,hl_ffx,7,2.58695978290774,1.29665355332284,2.29687685137965,1.20513665878065,1.2013925
-1030_ERA,hl_ffx,8,2.57117929847121,1.28883760701133,2.28738862355291,1.1996929889526,1.2013925
-1089_USCrime,hl_ffx,0,1695.86389413989,34.8052930056711,1446.92958412098,35.0507246376812,0.2389534
-1089_USCrime,hl_ffx,1,1368.19003950652,31.1203450891589,1231.7086530973,32.3400526858661,0.2389534
-1089_USCrime,hl_ffx,2,285.7950553919,13.425080968293,453.849901512573,17.4784977558349,0.2389534
-1089_USCrime,hl_ffx,3,265.239966975582,13.0208496561499,433.274228590792,16.8565408370487,0.2389534
-1089_USCrime,hl_ffx,4,223.869927586863,12.1700520463138,365.358533998151,15.2672031753223,0.2389534
-1089_USCrime,hl_ffx,5,175.933510471616,10.7276370380729,344.155262516737,14.0601355738973,0.2389534
-1089_USCrime,hl_ffx,6,149.211639879306,10.0003419965696,317.206769481538,13.1451551384108,0.2389534
-1089_USCrime,hl_ffx,7,139.077896448652,9.70231202133502,306.951656655507,12.8253323144941,0.2389534
-1089_USCrime,hl_ffx,8,124.727043152427,9.18453936574502,292.017168072195,12.6550575200081,0.2389534
-1089_USCrime,hl_ffx,9,133.612829154786,9.62826396543537,230.958346052984,10.9751944805641,0.2389534
-1089_USCrime,hl_ffx,12,83.0507393927492,7.38116881018409,220.655545572531,12.3165383031039,0.2389534
-1089_USCrime,hl_ffx,13,114.003867364938,8.96094631382618,205.750733012765,10.7082542898409,0.2389534
-1089_USCrime,hl_ffx,14,95.9949274979287,8.31742781886829,192.281057398515,11.1475235653535,0.2389534
-1089_USCrime,hl_ffx,15,93.459110487685,8.23302739637125,191.294178318997,11.2263090010516,0.2389534
-1089_USCrime,hl_ffx,16,91.1763867320312,8.15583708859322,188.756784789442,11.2461850759821,0.2389534
-1089_USCrime,hl_ffx,17,85.1728587544972,7.87555218082298,184.422582190323,11.3454061613458,0.2389534
-1089_USCrime,hl_ffx,18,84.6385698854118,7.83786166339477,184.365077174812,11.358982140435,0.2389534
-1089_USCrime,hl_ffx,19,83.7750407738003,7.76032329037561,184.218397017001,11.3698693330555,0.2389534
-1096_FacultySalaries,hl_ffx,0,20.8715850773349,3.4379521484375,23.9053807631944,4.30703994750977,0.1317426
-1096_FacultySalaries,hl_ffx,1,16.6605406795473,3.05780052577047,19.3556873148141,3.88127313324717,0.1317426
-1096_FacultySalaries,hl_ffx,2,4.45120670154169,1.45467513749071,5.49158487323299,1.96479559603797,0.1317426
-1096_FacultySalaries,hl_ffx,3,3.22652411211049,1.44113365766156,2.04832556702813,1.17161153696469,0.1317426
-1096_FacultySalaries,hl_ffx,4,3.25342424685175,1.44101259295172,1.98979403765695,1.14274163673407,0.1317426
-192_vineyard,hl_ffx,0,8.57581360946745,1.94230769230769,42.6838017751477,5.22337278106507,0.1197636
-192_vineyard,hl_ffx,1,7.13332483805537,1.826995,30.9842770286965,4.42137032762782,0.1197636
-192_vineyard,hl_ffx,2,4.0080476045338,1.51823812625161,11.4162986352515,2.65603260366349,0.1197636
-192_vineyard,hl_ffx,3,3.2417417884662,1.43698920321465,8.74629609698807,2.57493772364124,0.1197636
-192_vineyard,hl_ffx,4,3.27412093571332,1.44888571399639,8.72083029118088,2.24757592575119,0.1197636
-195_auto_price,hl_ffx,0,47659464.6591892,5275.21871494951,21172799.7580576,3901.03496835442,0.6460301
-195_auto_price,hl_ffx,1,37072233.0095957,4699.82229649169,16797592.8494074,3527.19565888694,0.6460301
-195_auto_price,hl_ffx,2,18475458.2990545,3232.05373626954,8832493.59353004,2530.31046793292,0.6460301
-195_auto_price,hl_ffx,3,12670581.7716669,2622.65981746071,6761362.03696773,2162.65465503816,0.6460301
-195_auto_price,hl_ffx,4,8626240.92177789,2065.06920430122,5028461.5586477,1840.76111891418,0.6460301
-195_auto_price,hl_ffx,5,8582394.03185763,2061.43431733834,4930458.30325255,1816.69494357941,0.6460301
-195_auto_price,hl_ffx,6,7848037.8070657,1918.39253715701,4544727.02318675,1655.03175742646,0.6460301
-195_auto_price,hl_ffx,7,7716243.8565321,1881.69099980235,4392242.46657281,1610.43382797183,0.6460301
-195_auto_price,hl_ffx,9,7202121.71856325,1821.61384852219,4217829.65243529,1574.8881945632,0.6460301
-207_autoPrice,hl_ffx,0,47659464.6591892,5275.21871494951,21172799.7580576,3901.03496835442,0.5857331
-207_autoPrice,hl_ffx,1,37072233.0095957,4699.82229649169,16797592.8494074,3527.19565888694,0.5857331
-207_autoPrice,hl_ffx,2,18475458.2990545,3232.05373626954,8832493.59353004,2530.31046793292,0.5857331
-207_autoPrice,hl_ffx,3,12670581.7716669,2622.65981746071,6761362.03696773,2162.65465503816,0.5857331
-207_autoPrice,hl_ffx,4,8626240.92177789,2065.06920430122,5028461.5586477,1840.76111891418,0.5857331
-207_autoPrice,hl_ffx,5,8582394.03185763,2061.43431733834,4930458.30325255,1816.69494357941,0.5857331
-207_autoPrice,hl_ffx,6,7848037.8070657,1918.39253715701,4544727.02318675,1655.03175742646,0.5857331
-207_autoPrice,hl_ffx,7,7716243.8565321,1881.69099980235,4392242.46657281,1610.43382797183,0.5857331
-207_autoPrice,hl_ffx,9,7202121.71856325,1821.61384852219,4217829.65243529,1574.8881945632,0.5857331
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