1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using System.IO;


23  using System;


24  using HeuristicLab.Random;


25  using HeuristicLab.Common;


26  using System.Collections.Generic;


27  using System.Diagnostics;


28  using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;


29  using HeuristicLab.Problems.DataAnalysis.Symbolic;


30  using Microsoft.VisualStudio.TestTools.UnitTesting;


31  using System.Linq;


32  using System.Globalization;


33  using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;


34  using HeuristicLab.Problems.DataAnalysis.Evaluators;


35  namespace HeuristicLab.Problems.DataAnalysis.Tests {


36 


37  [TestClass()]


38  public class StatisticCalculatorsTest {


39  private double[,] testData = new double[,] {


40  {5,1,1,1,2,1,3,1,1,2},


41  {5,4,4,5,7,10,3,2,1,2},


42  {3,1,1,1,2,2,3,1,1,2},


43  {6,8,8,1,3,4,3,7,1,2},


44  {4,1,1,3,2,1,3,1,1,2},


45  {8,10,10,8,7,10,9,7,1,4},


46  {1,1,1,1,2,10,3,1,1,2},


47  {2,1,2,1,2,1,3,1,1,2},


48  {2,1,1,1,2,1,1,1,5,2},


49  {4,2,1,1,2,1,2,1,1,2},


50  {1,1,1,1,1,1,3,1,1,2},


51  {2,1,1,1,2,1,2,1,1,2},


52  {5,3,3,3,2,3,4,4,1,4},


53  {8,7,5,10,7,9,5,5,4,4},


54  {7,4,6,4,6,1,4,3,1,4},


55  {4,1,1,1,2,1,2,1,1,2},


56  {4,1,1,1,2,1,3,1,1,2},


57  {10,7,7,6,4,10,4,1,2,4},


58  {6,1,1,1,2,1,3,1,1,2},


59  {7,3,2,10,5,10,5,4,4,4},


60  {10,5,5,3,6,7,7,10,1,4}


61  };


62 


63  [TestMethod]


64  public void CalculateMeanAndVarianceTest() {


65  System.Random random = new System.Random(31415);


66 


67  int n = testData.GetLength(0);


68  int cols = testData.GetLength(1);


69  {


70  for (int col = 0; col < cols; col++) {


71  double scale = random.NextDouble() * 1E7;


72  IEnumerable<double> x = from rows in Enumerable.Range(0, n)


73  select testData[rows, col] * scale;


74  double[] xs = x.ToArray();


75  double mean_alglib, variance_alglib;


76  mean_alglib = variance_alglib = 0.0;


77  double tmp = 0;


78 


79  alglib.descriptivestatistics.calculatemoments(ref xs, n, ref mean_alglib, ref variance_alglib, ref tmp, ref tmp);


80 


81  var calculator = new OnlineMeanAndVarianceCalculator();


82  for (int i = 0; i < n; i++) {


83  calculator.Add(xs[i]);


84  }


85  double mean = calculator.Mean;


86  double variance = calculator.Variance;


87 


88  Assert.AreEqual(mean_alglib, mean, 1E6 * scale);


89  Assert.AreEqual(variance_alglib, variance, 1E6 * scale);


90  }


91  }


92  }


93 


94  [TestMethod]


95  public void CalculatePearsonsRSquaredTest() {


96  System.Random random = new System.Random(31415);


97  int n = testData.GetLength(0);


98  int cols = testData.GetLength(1);


99  for (int c1 = 0; c1 < cols; c1++) {


100  for (int c2 = c1 + 1; c2 < cols; c2++) {


101  {


102  double c1Scale = random.NextDouble() * 1E7;


103  double c2Scale = random.NextDouble() * 1E7;


104  IEnumerable<double> x = from rows in Enumerable.Range(0, n)


105  select testData[rows, c1] * c1Scale;


106  IEnumerable<double> y = from rows in Enumerable.Range(0, n)


107  select testData[rows, c2] * c2Scale;


108  double[] xs = x.ToArray();


109  double[] ys = y.ToArray();


110  double r2_alglib = alglib.correlation.pearsoncorrelation(ref xs, ref ys, n);


111  r2_alglib *= r2_alglib;


112 


113  var r2Calculator = new OnlinePearsonsRSquaredEvaluator();


114  for (int i = 0; i < n; i++) {


115  r2Calculator.Add(xs[i], ys[i]);


116  }


117  double r2 = r2Calculator.RSquared;


118 


119  Assert.AreEqual(r2_alglib, r2, 1E6 * Math.Max(c1Scale, c2Scale));


120  }


121  }


122  }


123  }


124  }


125  }

