[5574] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[11171] | 3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5574] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System.Collections.Generic;
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| 23 | using System.Linq;
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[6880] | 24 | using HeuristicLab.Common;
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[5574] | 25 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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[9764] | 26 | namespace HeuristicLab.Problems.DataAnalysis.Tests {
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[5574] | 27 |
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| 28 | [TestClass()]
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| 29 | public class StatisticCalculatorsTest {
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| 30 | private double[,] testData = new double[,] {
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| 31 | {5,1,1,1,2,1,3,1,1,2},
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| 32 | {5,4,4,5,7,10,3,2,1,2},
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| 33 | {3,1,1,1,2,2,3,1,1,2},
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| 34 | {6,8,8,1,3,4,3,7,1,2},
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| 35 | {4,1,1,3,2,1,3,1,1,2},
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| 36 | {8,10,10,8,7,10,9,7,1,4},
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| 37 | {1,1,1,1,2,10,3,1,1,2},
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| 38 | {2,1,2,1,2,1,3,1,1,2},
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| 39 | {2,1,1,1,2,1,1,1,5,2},
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| 40 | {4,2,1,1,2,1,2,1,1,2},
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| 41 | {1,1,1,1,1,1,3,1,1,2},
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| 42 | {2,1,1,1,2,1,2,1,1,2},
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| 43 | {5,3,3,3,2,3,4,4,1,4},
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| 44 | {8,7,5,10,7,9,5,5,4,4},
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| 45 | {7,4,6,4,6,1,4,3,1,4},
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| 46 | {4,1,1,1,2,1,2,1,1,2},
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| 47 | {4,1,1,1,2,1,3,1,1,2},
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| 48 | {10,7,7,6,4,10,4,1,2,4},
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| 49 | {6,1,1,1,2,1,3,1,1,2},
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| 50 | {7,3,2,10,5,10,5,4,4,4},
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| 51 | {10,5,5,3,6,7,7,10,1,4}
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| 52 | };
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| 53 |
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| 54 | [TestMethod]
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[9785] | 55 | [TestCategory("Problems.DataAnalysis")]
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| 56 | [TestProperty("Time", "short")]
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[5574] | 57 | public void CalculateMeanAndVarianceTest() {
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| 58 | System.Random random = new System.Random(31415);
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| 59 |
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| 60 | int n = testData.GetLength(0);
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| 61 | int cols = testData.GetLength(1);
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| 62 | {
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| 63 | for (int col = 0; col < cols; col++) {
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[6880] | 64 | double scale = random.NextDouble();
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[5574] | 65 | IEnumerable<double> x = from rows in Enumerable.Range(0, n)
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| 66 | select testData[rows, col] * scale;
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| 67 | double[] xs = x.ToArray();
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| 68 | double mean_alglib, variance_alglib;
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| 69 | mean_alglib = variance_alglib = 0.0;
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| 70 | double tmp = 0;
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| 71 |
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| 72 | alglib.samplemoments(xs, n, out mean_alglib, out variance_alglib, out tmp, out tmp);
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| 73 |
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| 74 | var calculator = new OnlineMeanAndVarianceCalculator();
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| 75 | for (int i = 0; i < n; i++) {
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| 76 | calculator.Add(xs[i]);
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| 77 | }
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| 78 | double mean = calculator.Mean;
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| 79 | double variance = calculator.Variance;
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| 80 |
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[6880] | 81 | Assert.IsTrue(mean_alglib.IsAlmost(mean));
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| 82 | Assert.IsTrue(variance_alglib.IsAlmost(variance));
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[5574] | 83 | }
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| 84 | }
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| 85 | }
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| 86 |
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| 87 | [TestMethod]
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[9785] | 88 | [TestCategory("Problems.DataAnalysis")]
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| 89 | [TestProperty("Time", "short")]
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[5574] | 90 | public void CalculatePearsonsRSquaredTest() {
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| 91 | System.Random random = new System.Random(31415);
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| 92 | int n = testData.GetLength(0);
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| 93 | int cols = testData.GetLength(1);
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| 94 | for (int c1 = 0; c1 < cols; c1++) {
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| 95 | for (int c2 = c1 + 1; c2 < cols; c2++) {
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| 96 | {
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| 97 | double c1Scale = random.NextDouble() * 1E7;
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| 98 | double c2Scale = random.NextDouble() * 1E7;
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| 99 | IEnumerable<double> x = from rows in Enumerable.Range(0, n)
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| 100 | select testData[rows, c1] * c1Scale;
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| 101 | IEnumerable<double> y = from rows in Enumerable.Range(0, n)
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| 102 | select testData[rows, c2] * c2Scale;
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| 103 | double[] xs = x.ToArray();
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| 104 | double[] ys = y.ToArray();
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| 105 | double r2_alglib = alglib.pearsoncorrelation(xs, ys, n);
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| 106 | r2_alglib *= r2_alglib;
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| 107 |
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[5944] | 108 | var r2Calculator = new OnlinePearsonsRSquaredCalculator();
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[5574] | 109 | for (int i = 0; i < n; i++) {
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| 110 | r2Calculator.Add(xs[i], ys[i]);
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| 111 | }
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| 112 | double r2 = r2Calculator.RSquared;
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| 113 |
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[6880] | 114 | Assert.IsTrue(r2_alglib.IsAlmost(r2));
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[5574] | 115 | }
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| 116 | }
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| 117 | }
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| 118 | }
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[9785] | 119 |
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[6184] | 120 | [TestMethod]
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[9785] | 121 | [TestCategory("Problems.DataAnalysis")]
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| 122 | [TestProperty("Time", "short")]
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[6184] | 123 | public void CalculatePearsonsRSquaredOfConstantTest() {
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| 124 | System.Random random = new System.Random(31415);
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| 125 | int n = 12;
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| 126 | int cols = testData.GetLength(1);
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| 127 | for (int c1 = 0; c1 < cols; c1++) {
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| 128 | double c1Scale = random.NextDouble() * 1E7;
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| 129 | IEnumerable<double> x = from rows in Enumerable.Range(0, n)
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| 130 | select testData[rows, c1] * c1Scale;
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[6738] | 131 | IEnumerable<double> y = (new List<double>() { 150494407424305.47 })
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[6184] | 132 | .Concat(Enumerable.Repeat(150494407424305.47, n - 1));
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| 133 | double[] xs = x.ToArray();
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| 134 | double[] ys = y.ToArray();
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| 135 | double r2_alglib = alglib.pearsoncorrelation(xs, ys, n);
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| 136 | r2_alglib *= r2_alglib;
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| 137 |
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| 138 | var r2Calculator = new OnlinePearsonsRSquaredCalculator();
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| 139 | for (int i = 0; i < n; i++) {
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| 140 | r2Calculator.Add(xs[i], ys[i]);
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| 141 | }
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| 142 | double r2 = r2Calculator.RSquared;
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| 143 |
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| 144 | Assert.AreEqual(r2_alglib.ToString(), r2.ToString());
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| 145 | }
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| 146 | }
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[8355] | 147 |
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| 148 | [TestMethod]
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[9785] | 149 | [TestCategory("Problems.DataAnalysis")]
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| 150 | [TestProperty("Time", "short")]
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[8355] | 151 | public void CalculateHoeffdingsDTest() {
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| 152 | OnlineCalculatorError error;
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| 153 | // direct perfect dependency
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| 154 | var xs = new double[] { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };
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| 155 | var ys = new double[] { 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
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[8834] | 156 | var d = HoeffdingsDependenceCalculator.CalculateHoeffdings(xs, ys, out error);
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[8355] | 157 | Assert.AreEqual(error, OnlineCalculatorError.None);
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| 158 | Assert.AreEqual(d, 1.0, 1E-5);
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| 159 |
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| 160 | // perfect negative dependency
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| 161 | ys = xs.Select(x => -x).ToArray();
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[8834] | 162 | d = HoeffdingsDependenceCalculator.CalculateHoeffdings(xs, ys, out error);
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[8355] | 163 | Assert.AreEqual(error, OnlineCalculatorError.None);
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| 164 | Assert.AreEqual(d, 1.0, 1E-5);
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| 165 |
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| 166 | // ties
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| 167 | xs = new double[] { 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 5.0, 5.0, 5.0 };
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| 168 | ys = new double[] { 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 5.0, 5.0, 6.0, 6.0, 6.0 };
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[8834] | 169 | d = HoeffdingsDependenceCalculator.CalculateHoeffdings(xs, ys, out error);
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[8355] | 170 | Assert.AreEqual(error, OnlineCalculatorError.None);
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| 171 | Assert.AreEqual(d, 0.6783, 1E-5);
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| 172 |
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| 173 | // ties
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| 174 | xs = new double[] { 1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0, 6.0 };
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| 175 | ys = xs.Select(x => x * x).ToArray();
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[8834] | 176 | d = HoeffdingsDependenceCalculator.CalculateHoeffdings(xs, ys, out error);
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[8355] | 177 | Assert.AreEqual(error, OnlineCalculatorError.None);
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| 178 | Assert.AreEqual(d, 0.75, 1E-5);
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| 179 |
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| 180 | // degenerate
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| 181 | xs = new double[] { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
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| 182 | ys = new double[] { 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0 };
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[8834] | 183 | d = HoeffdingsDependenceCalculator.CalculateHoeffdings(xs, ys, out error);
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[8355] | 184 | Assert.AreEqual(error, OnlineCalculatorError.None);
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| 185 | Assert.AreEqual(d, -0.3516, 1E-4);
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| 186 |
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| 187 |
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| 188 | var normal = new HeuristicLab.Random.NormalDistributedRandom(new HeuristicLab.Random.MersenneTwister(31415), 0, 1);
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| 189 |
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| 190 | xs = Enumerable.Range(0, 1000).Select(i => normal.NextDouble()).ToArray();
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| 191 | ys = Enumerable.Range(0, 1000).Select(i => normal.NextDouble()).ToArray();
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| 192 |
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| 193 | // independent
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[8834] | 194 | d = HoeffdingsDependenceCalculator.CalculateHoeffdings(xs, ys, out error);
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[8355] | 195 | Assert.AreEqual(error, OnlineCalculatorError.None);
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| 196 | Assert.AreEqual(d, -0.00023, 1E-5);
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| 197 |
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| 198 |
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| 199 | xs = Enumerable.Range(0, 1000).Select(i => normal.NextDouble()).ToArray();
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| 200 | ys = xs.Select(x => x * x).ToArray();
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| 201 |
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[8834] | 202 | d = HoeffdingsDependenceCalculator.CalculateHoeffdings(xs, ys, out error);
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[8355] | 203 | Assert.AreEqual(error, OnlineCalculatorError.None);
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| 204 | Assert.AreEqual(d, 0.25071, 1E-5);
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| 205 |
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| 206 | // symmetric?
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[8834] | 207 | d = HoeffdingsDependenceCalculator.CalculateHoeffdings(ys, xs, out error);
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[8355] | 208 | Assert.AreEqual(error, OnlineCalculatorError.None);
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| 209 | Assert.AreEqual(d, 0.25071, 1E-5);
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| 210 |
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| 211 | }
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[5574] | 212 | }
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| 213 | }
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