[10465] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16662] | 3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[10465] | 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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[10646] | 31 | [TestCategory("General")]
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[10465] | 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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[10646] | 40 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
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[10465] | 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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[10646] | 50 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
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[10465] | 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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[10646] | 59 | var zeroSelected = 0;
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[10465] | 60 | for (int i = 0; i < 1000; i++) {
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[10646] | 61 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
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[10465] | 62 | Assert.AreEqual(sample.Count(), 1);
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[10646] | 63 | if (sample[0] == 0) zeroSelected++;
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[10465] | 64 | }
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[10646] | 65 | Assert.IsTrue(zeroSelected > 0 && zeroSelected < 1000);
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[10465] | 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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[10646] | 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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[10465] | 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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[10646] | 83 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 2, weights, false, false).ToArray();
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[10465] | 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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[10646] | 93 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, false, false).ToArray();
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[10465] | 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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[10646] | 105 | var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 100, weights, false, false).ToArray();
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[10465] | 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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[10646] | 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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[10465] | 122 |
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[10646] | 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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[10465] | 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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[10646] | 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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[10465] | 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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