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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Tests/StatisticCalculatorsTest.cs @ 4459

Last change on this file since 4459 was 4459, checked in by gkronber, 12 years ago

Fixed data analysis test cases. #1142

File size: 4.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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
22using System.IO;
23using System;
24using HeuristicLab.Random;
25using HeuristicLab.Common;
26using System.Collections.Generic;
27using System.Diagnostics;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Problems.DataAnalysis.Symbolic;
30using Microsoft.VisualStudio.TestTools.UnitTesting;
31using System.Linq;
32using System.Globalization;
33using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
34using HeuristicLab.Problems.DataAnalysis.Evaluators;
35namespace 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, 1E-6 * scale);
89          Assert.AreEqual(variance_alglib, variance, 1E-6 * 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, 1E-6 * Math.Max(c1Scale, c2Scale));
120          }
121        }
122      }
123    }
124  }
125}
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