1 | #region License Information |
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2 | /* HeuristicLab |
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3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL) |
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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; |
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23 | using System.Linq; |
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24 | using System.Text; |
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25 | using Microsoft.VisualStudio.TestTools.UnitTesting; |
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26 | namespace HeuristicLab.Random.Tests { |
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27 | |
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28 | [TestClass()] |
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29 | public class RandomEnumerableSampleTest { |
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30 | [TestMethod] |
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31 | [TestCategory("General")] |
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32 | [TestProperty("Time", "short")] |
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33 | public void SampleProportionalWithoutRepetitionTest() { |
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34 | { |
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35 | // select 1 of 100 uniformly (weights = 0) |
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36 | var items = Enumerable.Range(0, 100); |
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37 | var random = new MersenneTwister(31415); |
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38 | var weights = Enumerable.Repeat(0.0, 100); |
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39 | for (int i = 0; i < 1000; i++) { |
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40 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray(); |
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41 | Assert.AreEqual(sample.Count(), 1); |
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42 | } |
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43 | } |
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44 | { |
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45 | // select 1 of 1 uniformly (weights = 0) |
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46 | var items = Enumerable.Range(0, 1); |
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47 | var random = new MersenneTwister(31415); |
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48 | var weights = Enumerable.Repeat(0.0, 1); |
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49 | for (int i = 0; i < 1000; i++) { |
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50 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray(); |
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51 | Assert.AreEqual(sample.Count(), 1); |
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52 | } |
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53 | } |
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54 | { |
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55 | // select 1 of 2 non-uniformly (weights = 1, 2) |
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56 | var items = Enumerable.Range(0, 2); |
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57 | var random = new MersenneTwister(31415); |
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58 | var weights = new double[] { 1.0, 2.0 }; |
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59 | var zeroSelected = 0; |
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60 | for (int i = 0; i < 1000; i++) { |
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61 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray(); |
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62 | Assert.AreEqual(sample.Count(), 1); |
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63 | if (sample[0] == 0) zeroSelected++; |
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64 | } |
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65 | Assert.IsTrue(zeroSelected > 0 && zeroSelected < 1000); |
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66 | } |
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67 | { |
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68 | // select 2 of 2 non-uniformly (weights = 1, 1000) |
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69 | var items = Enumerable.Range(0, 2); |
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70 | var random = new MersenneTwister(31415); |
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71 | var weights = new double[] { 1.0, 1000.0 }; |
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72 | for (int i = 0; i < 1000; i++) { |
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73 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 2, weights, false, false).ToArray(); |
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74 | Assert.AreEqual(sample.Count(), 2); |
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75 | Assert.AreEqual(sample.Distinct().Count(), 2); |
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76 | } |
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77 | } |
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78 | { |
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79 | // select 2 from 1 uniformly (weights = 0), this does not throw an exception but instead returns a sample with 1 element! |
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80 | var items = Enumerable.Range(0, 1); |
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81 | var random = new MersenneTwister(31415); |
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82 | var weights = Enumerable.Repeat(0.0, 1); |
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83 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 2, weights, false, false).ToArray(); |
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84 | Assert.AreEqual(sample.Count(), 1); |
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85 | } |
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86 | |
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87 | { |
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88 | // select 10 of 100 uniformly (weights = 0) |
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89 | var items = Enumerable.Range(0, 100); |
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90 | var random = new MersenneTwister(31415); |
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91 | var weights = Enumerable.Repeat(0.0, 100); |
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92 | for (int i = 0; i < 1000; i++) { |
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93 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, false, false).ToArray(); |
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94 | Assert.AreEqual(sample.Count(), 10); |
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95 | Assert.AreEqual(sample.Distinct().Count(), 10); |
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96 | } |
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97 | } |
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98 | |
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99 | { |
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100 | // select 100 of 100 uniformly (weights = 0) |
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101 | var items = Enumerable.Range(0, 100); |
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102 | var random = new MersenneTwister(31415); |
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103 | var weights = Enumerable.Repeat(0.0, 100); |
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104 | for (int i = 0; i < 1000; i++) { |
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105 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 100, weights, false, false).ToArray(); |
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106 | Assert.AreEqual(sample.Count(), 100); |
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107 | Assert.AreEqual(sample.Distinct().Count(), 100); |
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108 | } |
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109 | } |
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110 | |
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111 | { |
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112 | // select 10 of 10 uniformly (weights = 1) |
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113 | var items = Enumerable.Range(0, 10); |
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114 | var random = new MersenneTwister(31415); |
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115 | var weights = Enumerable.Repeat(1.0, 10); |
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116 | for (int i = 0; i < 1000; i++) { |
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117 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, false, false).ToArray(); |
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118 | Assert.AreEqual(sample.Count(), 10); |
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119 | Assert.AreEqual(sample.Distinct().Count(), 10); |
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120 | } |
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121 | } |
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122 | |
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123 | { |
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124 | // select 10 of 10 uniformly (weights = 1) |
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125 | var items = Enumerable.Range(0, 10); |
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126 | var random = new MersenneTwister(31415); |
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127 | var weights = Enumerable.Repeat(1.0, 10); |
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128 | for (int i = 0; i < 1000; i++) { |
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129 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, true, false).ToArray(); |
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130 | Assert.AreEqual(sample.Count(), 10); |
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131 | Assert.AreEqual(sample.Distinct().Count(), 10); |
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132 | } |
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133 | } |
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134 | |
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135 | { |
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136 | // select 10 of 10 uniformly (weights = 1) |
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137 | var items = Enumerable.Range(0, 10); |
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138 | var random = new MersenneTwister(31415); |
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139 | var weights = Enumerable.Repeat(1.0, 10); |
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140 | for (int i = 0; i < 1000; i++) { |
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141 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, true, true).ToArray(); |
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142 | Assert.AreEqual(sample.Count(), 10); |
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143 | Assert.AreEqual(sample.Distinct().Count(), 10); |
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144 | } |
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145 | } |
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146 | |
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147 | { |
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148 | // select 5 of 10 uniformly (weights = 0..n) |
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149 | var items = Enumerable.Range(0, 10); |
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150 | var random = new MersenneTwister(31415); |
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151 | var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }; |
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152 | for (int i = 0; i < 1000; i++) { |
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153 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, false, false).ToArray(); |
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154 | Assert.AreEqual(sample.Count(), 5); |
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155 | Assert.AreEqual(sample.Distinct().Count(), 5); |
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156 | } |
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157 | } |
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158 | |
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159 | { |
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160 | // select 5 of 10 uniformly (weights = 0..n) |
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161 | var items = Enumerable.Range(0, 10); |
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162 | var random = new MersenneTwister(31415); |
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163 | var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }; |
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164 | for (int i = 0; i < 1000; i++) { |
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165 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, true, false).ToArray(); |
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166 | Assert.AreEqual(sample.Count(), 5); |
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167 | Assert.AreEqual(sample.Distinct().Count(), 5); |
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168 | } |
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169 | } |
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170 | |
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171 | { |
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172 | // select 5 of 10 uniformly (weights = 0..n) |
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173 | var items = Enumerable.Range(0, 10); |
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174 | var random = new MersenneTwister(31415); |
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175 | var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }; |
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176 | for (int i = 0; i < 1000; i++) { |
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177 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, true, true).ToArray(); |
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178 | Assert.AreEqual(sample.Count(), 5); |
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179 | Assert.AreEqual(sample.Distinct().Count(), 5); |
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180 | } |
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181 | } |
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182 | |
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183 | { |
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184 | // select 10 of 100 uniformly (weights = 1) |
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185 | // repeat 1000000 times and calculate statistics |
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186 | var items = Enumerable.Range(0, 100); |
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187 | var random = new MersenneTwister(31415); |
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188 | var weights = Enumerable.Repeat(1.0, 100); |
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189 | var selectionCount = new int[100, 100]; // frequency of selecting item at pos |
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190 | for (int i = 0; i < 1000000; i++) { |
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191 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 100, weights, false, false).ToArray(); |
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192 | Assert.AreEqual(sample.Count(), 100); |
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193 | Assert.AreEqual(sample.Distinct().Count(), 100); |
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194 | |
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195 | int pos = 0; |
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196 | foreach (var item in sample) { |
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197 | selectionCount[item, pos]++; |
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198 | pos++; |
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199 | } |
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200 | } |
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201 | var sb = new StringBuilder(); |
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202 | for (int item = 0; item < 100; item++) { |
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203 | for (int pos = 0; pos < 100; pos++) { |
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204 | sb.AppendFormat("{0} ", selectionCount[item, pos]); |
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205 | } |
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206 | sb.AppendLine(); |
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207 | } |
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208 | Console.WriteLine(sb.ToString()); |
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209 | } |
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210 | } |
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211 | } |
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212 | } |
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