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source: branches/2708_ScopedAlgorithms/HeuristicLab.Tests/HeuristicLab.Random-3.3/RandomEnumerableSampleTest.cs @ 17230

Last change on this file since 17230 was 12012, checked in by ascheibe, 10 years ago

#2212 merged r12008, r12009, r12010 back into trunk

File size: 8.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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;
23using System.Linq;
24using System.Text;
25using Microsoft.VisualStudio.TestTools.UnitTesting;
26namespace HeuristicLab.Random.Tests {
27
28  [TestClass()]
29  public class RandomEnumerableSampleTest {
30    [TestMethod]
31    [TestCategory("General")]
32    [TestProperty("Time", "short")]
33    public void SampleProportionalWithoutRepetitionTest() {
34      {
35        // select 1 of 100 uniformly (weights = 0)
36        var items = Enumerable.Range(0, 100);
37        var random = new MersenneTwister(31415);
38        var weights = Enumerable.Repeat(0.0, 100);
39        for (int i = 0; i < 1000; i++) {
40          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
41          Assert.AreEqual(sample.Count(), 1);
42        }
43      }
44      {
45        // select 1 of 1 uniformly (weights = 0)
46        var items = Enumerable.Range(0, 1);
47        var random = new MersenneTwister(31415);
48        var weights = Enumerable.Repeat(0.0, 1);
49        for (int i = 0; i < 1000; i++) {
50          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
51          Assert.AreEqual(sample.Count(), 1);
52        }
53      }
54      {
55        // select 1 of 2 non-uniformly (weights = 1, 2)
56        var items = Enumerable.Range(0, 2);
57        var random = new MersenneTwister(31415);
58        var weights = new double[] { 1.0, 2.0 };
59        var zeroSelected = 0;
60        for (int i = 0; i < 1000; i++) {
61          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
62          Assert.AreEqual(sample.Count(), 1);
63          if (sample[0] == 0) zeroSelected++;
64        }
65        Assert.IsTrue(zeroSelected > 0 && zeroSelected < 1000);
66      }
67      {
68        // select 2 of 2 non-uniformly (weights = 1, 1000)
69        var items = Enumerable.Range(0, 2);
70        var random = new MersenneTwister(31415);
71        var weights = new double[] { 1.0, 1000.0 };
72        for (int i = 0; i < 1000; i++) {
73          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 2, weights, false, false).ToArray();
74          Assert.AreEqual(sample.Count(), 2);
75          Assert.AreEqual(sample.Distinct().Count(), 2);
76        }
77      }
78      {
79        // select 2 from 1 uniformly (weights = 0), this does not throw an exception but instead returns a sample with 1 element!
80        var items = Enumerable.Range(0, 1);
81        var random = new MersenneTwister(31415);
82        var weights = Enumerable.Repeat(0.0, 1);
83        var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 2, weights, false, false).ToArray();
84        Assert.AreEqual(sample.Count(), 1);
85      }
86
87      {
88        // select 10 of 100 uniformly (weights = 0)
89        var items = Enumerable.Range(0, 100);
90        var random = new MersenneTwister(31415);
91        var weights = Enumerable.Repeat(0.0, 100);
92        for (int i = 0; i < 1000; i++) {
93          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, false, false).ToArray();
94          Assert.AreEqual(sample.Count(), 10);
95          Assert.AreEqual(sample.Distinct().Count(), 10);
96        }
97      }
98
99      {
100        // select 100 of 100 uniformly (weights = 0)
101        var items = Enumerable.Range(0, 100);
102        var random = new MersenneTwister(31415);
103        var weights = Enumerable.Repeat(0.0, 100);
104        for (int i = 0; i < 1000; i++) {
105          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 100, weights, false, false).ToArray();
106          Assert.AreEqual(sample.Count(), 100);
107          Assert.AreEqual(sample.Distinct().Count(), 100);
108        }
109      }
110
111      {
112        // select 10 of 10 uniformly (weights = 1)
113        var items = Enumerable.Range(0, 10);
114        var random = new MersenneTwister(31415);
115        var weights = Enumerable.Repeat(1.0, 10);
116        for (int i = 0; i < 1000; i++) {
117          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, false, false).ToArray();
118          Assert.AreEqual(sample.Count(), 10);
119          Assert.AreEqual(sample.Distinct().Count(), 10);
120        }
121      }
122
123      {
124        // select 10 of 10 uniformly (weights = 1)
125        var items = Enumerable.Range(0, 10);
126        var random = new MersenneTwister(31415);
127        var weights = Enumerable.Repeat(1.0, 10);
128        for (int i = 0; i < 1000; i++) {
129          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, true, false).ToArray();
130          Assert.AreEqual(sample.Count(), 10);
131          Assert.AreEqual(sample.Distinct().Count(), 10);
132        }
133      }
134
135      {
136        // select 10 of 10 uniformly (weights = 1)
137        var items = Enumerable.Range(0, 10);
138        var random = new MersenneTwister(31415);
139        var weights = Enumerable.Repeat(1.0, 10);
140        for (int i = 0; i < 1000; i++) {
141          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, true, true).ToArray();
142          Assert.AreEqual(sample.Count(), 10);
143          Assert.AreEqual(sample.Distinct().Count(), 10);
144        }
145      }
146
147      {
148        // select 5 of 10 uniformly (weights = 0..n)
149        var items = Enumerable.Range(0, 10);
150        var random = new MersenneTwister(31415);
151        var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
152        for (int i = 0; i < 1000; i++) {
153          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, false, false).ToArray();
154          Assert.AreEqual(sample.Count(), 5);
155          Assert.AreEqual(sample.Distinct().Count(), 5);
156        }
157      }
158
159      {
160        // select 5 of 10 uniformly (weights = 0..n)
161        var items = Enumerable.Range(0, 10);
162        var random = new MersenneTwister(31415);
163        var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
164        for (int i = 0; i < 1000; i++) {
165          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, true, false).ToArray();
166          Assert.AreEqual(sample.Count(), 5);
167          Assert.AreEqual(sample.Distinct().Count(), 5);
168        }
169      }
170
171      {
172        // select 5 of 10 uniformly (weights = 0..n)
173        var items = Enumerable.Range(0, 10);
174        var random = new MersenneTwister(31415);
175        var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
176        for (int i = 0; i < 1000; i++) {
177          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, true, true).ToArray();
178          Assert.AreEqual(sample.Count(), 5);
179          Assert.AreEqual(sample.Distinct().Count(), 5);
180        }
181      }
182
183      {
184        // select 10 of 100 uniformly (weights = 1)
185        // repeat 1000000 times and calculate statistics
186        var items = Enumerable.Range(0, 100);
187        var random = new MersenneTwister(31415);
188        var weights = Enumerable.Repeat(1.0, 100);
189        var selectionCount = new int[100, 100]; // frequency of selecting item at pos
190        for (int i = 0; i < 1000000; i++) {
191          var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 100, weights, false, false).ToArray();
192          Assert.AreEqual(sample.Count(), 100);
193          Assert.AreEqual(sample.Distinct().Count(), 100);
194
195          int pos = 0;
196          foreach (var item in sample) {
197            selectionCount[item, pos]++;
198            pos++;
199          }
200        }
201        var sb = new StringBuilder();
202        for (int item = 0; item < 100; item++) {
203          for (int pos = 0; pos < 100; pos++) {
204            sb.AppendFormat("{0} ", selectionCount[item, pos]);
205          }
206          sb.AppendLine();
207        }
208        Console.WriteLine(sb.ToString());
209      }
210    }
211  }
212}
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