1 | #region License Information
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2 | /* SimSharp - A .NET port of SimPy, discrete event simulation framework
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3 | Copyright (C) 2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 |
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5 | This program is free software: you can redistribute it and/or modify
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6 | it under the terms of the GNU General Public License as published by
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7 | the Free Software Foundation, either version 3 of the License, or
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8 | (at your option) any later version.
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9 |
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10 | This program is distributed in the hope that it will be useful,
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11 | but WITHOUT ANY WARRANTY; without even the implied warranty of
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12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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13 | GNU General Public License for more details.
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14 |
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15 | You should have received a copy of the GNU General Public License
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16 | along with this program. If not, see <http://www.gnu.org/licenses/>.*/
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17 | #endregion
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18 |
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19 | using System;
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20 | using System.Collections.Generic;
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21 | using System.IO;
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22 |
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23 | namespace SimSharp {
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24 | /// <summary>
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25 | /// Simulation hold the event queues, schedule and process events.
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26 | /// </summary>
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27 | /// <remarks>
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28 | /// This class is not thread-safe against manipulation of the event queue. If you supply a termination
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29 | /// event that is set outside the simulation, please use the <see cref="ThreadSafeSimulation"/> environment.
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30 | ///
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31 | /// For most purposes <see cref="Simulation"/> is however the better and faster choice.
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32 | /// </remarks>
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33 | public class Simulation {
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34 | private const int InitialMaxEvents = 1024;
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35 |
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36 | /// <summary>
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37 | /// Describes the number of seconds that a logical step of 1 in the *D-API takes.
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38 | /// </summary>
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39 | protected double DefaultTimeStepSeconds { get; private set; }
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40 |
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41 | /// <summary>
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42 | /// Calculates the logical date of the simulation by the amount of default steps
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43 | /// that have passed.
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44 | /// </summary>
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45 | public double NowD {
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46 | get { return (Now - StartDate).TotalSeconds / DefaultTimeStepSeconds; }
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47 | }
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48 |
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49 | /// <summary>
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50 | /// The current simulation time as a calendar date.
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51 | /// </summary>
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52 | public DateTime Now { get; protected set; }
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53 |
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54 | /// <summary>
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55 | /// The calendar date when the simulation started. This defaults to 1970-1-1 if
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56 | /// no other date has been specified in the overloaded constructor.
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57 | /// </summary>
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58 | public DateTime StartDate { get; protected set; }
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59 |
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60 | /// <summary>
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61 | /// The random number generator that is to be used in all events in
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62 | /// order to produce reproducible results.
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63 | /// </summary>
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64 | protected IRandom Random { get; set; }
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65 |
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66 | protected EventQueue ScheduleQ;
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67 | public Process ActiveProcess { get; set; }
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68 |
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69 | public TextWriter Logger { get; set; }
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70 | public int ProcessedEvents { get; protected set; }
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71 |
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72 | public Simulation() : this(new DateTime(1970, 1, 1)) { }
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73 | public Simulation(TimeSpan? defaultStep) : this(new DateTime(1970, 1, 1), defaultStep) { }
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74 | public Simulation(int randomSeed, TimeSpan? defaultStep = null) : this(new DateTime(1970, 1, 1), randomSeed, defaultStep) { }
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75 | public Simulation(DateTime initialDateTime, TimeSpan? defaultStep = null) : this(new PcgRandom(), initialDateTime, defaultStep) { }
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76 | public Simulation(DateTime initialDateTime, int randomSeed, TimeSpan? defaultStep = null) : this(new PcgRandom(randomSeed), initialDateTime, defaultStep) { }
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77 | public Simulation(IRandom random, DateTime initialDateTime, TimeSpan? defaultStep = null) {
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78 | DefaultTimeStepSeconds = (defaultStep ?? TimeSpan.FromSeconds(1)).Duration().TotalSeconds;
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79 | StartDate = initialDateTime;
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80 | Now = initialDateTime;
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81 | Random = random;
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82 | ScheduleQ = new EventQueue(InitialMaxEvents);
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83 | Logger = Console.Out;
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84 | }
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85 |
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86 | public double ToDouble(TimeSpan span) {
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87 | return span.TotalSeconds / DefaultTimeStepSeconds;
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88 | }
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89 |
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90 | public TimeSpan ToTimeSpan(double span) {
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91 | return TimeSpan.FromSeconds(DefaultTimeStepSeconds * span);
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92 | }
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93 |
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94 | /// <summary>
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95 | /// Creates a new process from an event generator. The process is automatically
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96 | /// scheduled to be started at the current simulation time.
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97 | /// </summary>
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98 | /// <param name="generator">The generator function that represents the process.</param>
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99 | /// <param name="priority">The priority to rank events at the same time (smaller value = higher priority).</param>
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100 | /// <returns>The scheduled process that was created.</returns>
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101 | public Process Process(IEnumerable<Event> generator, int priority = 0) {
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102 | return new Process(this, generator, priority);
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103 | }
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104 |
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105 | /// <summary>
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106 | /// Creates and returns a new timeout.
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107 | /// </summary>
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108 | /// <param name="delay">The time after which the timeout is fired.</param>
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109 | /// <param name="priority">The priority to rank events at the same time (smaller value = higher priority).</param>
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110 | /// <returns>The scheduled timeout event that was created.</returns>
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111 | public Timeout TimeoutD(double delay, int priority = 0) {
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112 | return Timeout(TimeSpan.FromSeconds(DefaultTimeStepSeconds * delay), priority);
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113 | }
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114 |
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115 | /// <summary>
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116 | /// Creates and returns a new timeout.
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117 | /// </summary>
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118 | /// <param name="delay">The time after which the timeout is fired.</param>
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119 | /// <param name="priority">The priority to rank events at the same time (smaller value = higher priority).</param>
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120 | /// <returns>The scheduled timeout event that was created.</returns>
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121 | public Timeout Timeout(TimeSpan delay, int priority = 0) {
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122 | return new Timeout(this, delay, priority: priority);
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123 | }
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124 |
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125 | public virtual void Reset(int randomSeed) {
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126 | ProcessedEvents = 0;
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127 | Now = StartDate;
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128 | Random = new PcgRandom(randomSeed);
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129 | ScheduleQ = new EventQueue(InitialMaxEvents);
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130 | useSpareNormal = false;
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131 | }
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132 |
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133 | public virtual void ScheduleD(double delay, Event @event) {
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134 | Schedule(TimeSpan.FromSeconds(DefaultTimeStepSeconds * delay), @event);
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135 | }
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136 |
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137 | /// <summary>
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138 | /// Schedules an event to occur at the same simulation time as the call was made.
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139 | /// </summary>
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140 | /// <param name="event">The event that should be scheduled.</param>
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141 | /// <param name="priority">The priority to rank events at the same time (smaller value = higher priority).</param>
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142 | public virtual void Schedule(Event @event, int priority = 0) {
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143 | DoSchedule(Now, @event, priority);
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144 | }
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145 |
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146 | /// <summary>
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147 | /// Schedules an event to occur after a certain (positive) delay.
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148 | /// </summary>
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149 | /// <exception cref="ArgumentException">
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150 | /// Thrown when <paramref name="delay"/> is negative.
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151 | /// </exception>
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152 | /// <param name="delay">The (positive) delay after which the event should be fired.</param>
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153 | /// <param name="event">The event that should be scheduled.</param>
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154 | /// <param name="priority">The priority to rank events at the same time (smaller value = higher priority).</param>
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155 | public virtual void Schedule(TimeSpan delay, Event @event, int priority = 0) {
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156 | if (delay < TimeSpan.Zero)
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157 | throw new ArgumentException("Negative delays are not allowed in Schedule(TimeSpan, Event).");
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158 | var eventTime = Now + delay;
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159 | DoSchedule(eventTime, @event, priority);
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160 | }
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161 |
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162 | protected virtual EventQueueNode DoSchedule(DateTime date, Event @event, int priority = 0) {
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163 | if (ScheduleQ.MaxSize == ScheduleQ.Count) {
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164 | // the capacity has to be adjusted, there are more events in the queue than anticipated
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165 | var oldSchedule = ScheduleQ;
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166 | ScheduleQ = new EventQueue(ScheduleQ.MaxSize * 2);
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167 | foreach (var e in oldSchedule) ScheduleQ.Enqueue(e.PrimaryPriority, e.Event, e.SecondaryPriority);
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168 | }
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169 | return ScheduleQ.Enqueue(date, @event, priority);
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170 | }
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171 |
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172 | public virtual object RunD(double? until = null) {
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173 | if (!until.HasValue) return Run();
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174 | return Run(Now + TimeSpan.FromSeconds(DefaultTimeStepSeconds * until.Value));
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175 | }
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176 |
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177 | public virtual object Run(TimeSpan span) {
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178 | return Run(Now + span);
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179 | }
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180 |
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181 | public virtual object Run(DateTime until) {
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182 | if (until <= Now) throw new InvalidOperationException("Simulation end date must lie in the future.");
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183 | var stopEvent = new Event(this);
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184 | var node = DoSchedule(until, stopEvent);
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185 | // stop event is always the first to execute at the given time
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186 | node.InsertionIndex = -1;
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187 | ScheduleQ.OnNodeUpdated(node);
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188 | return Run(stopEvent);
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189 | }
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190 |
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191 | protected bool _stopRequested = false;
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192 | /// <summary>
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193 | /// Run until a certain event is processed.
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194 | /// </summary>
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195 | /// <remarks>
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196 | /// This simulation environment is not thread-safe, thus triggering this event outside the environment
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197 | /// leads to potential race conditions. Please use the <see cref="ThreadSafeSimulation"/> environment in case you
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198 | /// require this functionality. Note that the performance of <see cref="ThreadSafeSimulation"/> is lower due to locking.
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199 | ///
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200 | /// For real-time based termination, you can also call <see cref="StopAsync"/> which sets a flag indicating the simulation
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201 | /// to stop before processing the next event.
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202 | /// </remarks>
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203 | /// <param name="stopEvent">The event that stops the simulation.</param>
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204 | /// <returns></returns>
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205 | public virtual object Run(Event stopEvent = null) {
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206 | _stopRequested = false;
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207 | if (stopEvent != null) {
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208 | if (stopEvent.IsProcessed) return stopEvent.Value;
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209 | stopEvent.AddCallback(StopSimulation);
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210 | }
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211 |
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212 | try {
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213 | var stop = ScheduleQ.Count == 0 || _stopRequested;
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214 | while (!stop) {
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215 | Step();
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216 | ProcessedEvents++;
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217 | stop = ScheduleQ.Count == 0 || _stopRequested;
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218 | }
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219 | } catch (StopSimulationException e) { return e.Value; }
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220 | if (stopEvent == null) return null;
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221 | if (!stopEvent.IsTriggered) throw new InvalidOperationException("No scheduled events left but \"until\" event was not triggered.");
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222 | return stopEvent.Value;
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223 | }
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224 |
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225 | public virtual void StopAsync() {
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226 | _stopRequested = true;
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227 | }
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228 |
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229 | /// <summary>
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230 | /// Performs a single step of the simulation, i.e. process a single event
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231 | /// </summary>
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232 | /// <remarks>
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233 | /// This method is not thread-safe
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234 | /// </remarks>
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235 | public virtual void Step() {
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236 | Event evt;
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237 | var next = ScheduleQ.Dequeue();
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238 | Now = next.PrimaryPriority;
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239 | evt = next.Event;
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240 | evt.Process();
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241 | }
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242 |
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243 | /// <summary>
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244 | /// Peeks at the time of the next event in terms of the defined step
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245 | /// </summary>
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246 | /// <remarks>
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247 | /// This method is not thread-safe
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248 | /// </remarks>
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249 | public virtual double PeekD() {
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250 | if (ScheduleQ.Count == 0) return double.MaxValue;
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251 | return (Peek() - StartDate).TotalSeconds / DefaultTimeStepSeconds;
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252 | }
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253 |
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254 | /// <summary>
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255 | /// Peeks at the time of the next event
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256 | /// </summary>
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257 | /// <remarks>
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258 | /// This method is not thread-safe
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259 | /// </remarks>
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260 | public virtual DateTime Peek() {
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261 | return ScheduleQ.Count > 0 ? ScheduleQ.First.PrimaryPriority : DateTime.MaxValue;
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262 | }
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263 |
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264 | protected virtual void StopSimulation(Event @event) {
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265 | throw new StopSimulationException(@event.Value);
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266 | }
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267 |
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268 | public virtual void Log(string message, params object[] args) {
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269 | if (Logger != null)
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270 | Logger.WriteLine(message, args);
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271 | }
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272 |
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273 | #region Random number distributions
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274 | public double RandUniform(IRandom random, double a, double b) {
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275 | return a + (b - a) * random.NextDouble();
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276 | }
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277 | public double RandUniform(double a, double b) {
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278 | return RandUniform(Random, a, b);
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279 | }
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280 |
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281 | public TimeSpan RandUniform(IRandom random, TimeSpan a, TimeSpan b) {
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282 | return TimeSpan.FromSeconds(RandUniform(random, a.TotalSeconds, b.TotalSeconds));
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283 | }
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284 | public TimeSpan RandUniform(TimeSpan a, TimeSpan b) {
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285 | return RandUniform(Random, a, b);
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286 | }
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287 | public double RandTriangular(IRandom random, double low, double high) {
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288 | var u = random.NextDouble();
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289 | if (u > 0.5)
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290 | return high + (low - high) * Math.Sqrt(((1.0 - u) / 2));
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291 | return low + (high - low) * Math.Sqrt(u / 2);
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292 | }
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293 | public double RandTriangular(double low, double high) {
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294 | return RandTriangular(Random, low, high);
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295 | }
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296 |
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297 | public TimeSpan RandTriangular(IRandom random, TimeSpan low, TimeSpan high) {
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298 | return TimeSpan.FromSeconds(RandTriangular(random, low.TotalSeconds, high.TotalSeconds));
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299 | }
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300 | public TimeSpan RandTriangular(TimeSpan low, TimeSpan high) {
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301 | return RandTriangular(Random, low, high);
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302 | }
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303 |
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304 | public double RandTriangular(IRandom random, double low, double high, double mode) {
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305 | var u = random.NextDouble();
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306 | var c = (mode - low) / (high - low);
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307 | if (u > c)
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308 | return high + (low - high) * Math.Sqrt(((1.0 - u) * (1.0 - c)));
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309 | return low + (high - low) * Math.Sqrt(u * c);
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310 | }
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311 | public double RandTriangular(double low, double high, double mode) {
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312 | return RandTriangular(Random, low, high, mode);
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313 | }
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314 |
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315 | public TimeSpan RandTriangular(IRandom random, TimeSpan low, TimeSpan high, TimeSpan mode) {
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316 | return TimeSpan.FromSeconds(RandTriangular(random, low.TotalSeconds, high.TotalSeconds, mode.TotalSeconds));
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317 | }
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318 | public TimeSpan RandTriangular(TimeSpan low, TimeSpan high, TimeSpan mode) {
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319 | return RandTriangular(Random, low, high, mode);
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320 | }
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321 |
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322 | /// <summary>
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323 | /// Returns a number that is exponentially distributed given a certain mean.
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324 | /// </summary>
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325 | /// <remarks>
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326 | /// Unlike in other APIs here the mean should be given and not the lambda parameter.
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327 | /// </remarks>
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328 | /// <param name="random">The random number generator to use.</param>
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329 | /// <param name="mean">The mean(!) of the distribution is 1 / lambda.</param>
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330 | /// <returns>A number that is exponentially distributed</returns>
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331 | public double RandExponential(IRandom random, double mean) {
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332 | return -Math.Log(1 - random.NextDouble()) * mean;
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333 | }
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334 | /// <summary>
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335 | /// Returns a number that is exponentially distributed given a certain mean.
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336 | /// </summary>
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337 | /// <remarks>
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338 | /// Unlike in other APIs here the mean should be given and not the lambda parameter.
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339 | /// </remarks>
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340 | /// <param name="mean">The mean(!) of the distribution is 1 / lambda.</param>
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341 | /// <returns>A number that is exponentially distributed</returns>
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342 | public double RandExponential(double mean) {
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343 | return RandExponential(Random, mean);
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344 | }
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345 |
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346 | /// <summary>
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347 | /// Returns a timespan that is exponentially distributed given a certain mean.
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348 | /// </summary>
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349 | /// <remarks>
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350 | /// Unlike in other APIs here the mean should be given and not the lambda parameter.
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351 | /// </remarks>
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352 | /// <param name="random">The random number generator to use.</param>
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353 | /// <param name="mean">The mean(!) of the distribution is 1 / lambda.</param>
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354 | /// <returns>A number that is exponentially distributed</returns>
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355 | public TimeSpan RandExponential(IRandom random, TimeSpan mean) {
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356 | return TimeSpan.FromSeconds(RandExponential(random, mean.TotalSeconds));
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357 | }
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358 | /// <summary>
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359 | /// Returns a timespan that is exponentially distributed given a certain mean.
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360 | /// </summary>
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361 | /// <remarks>
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362 | /// Unlike in other APIs here the mean should be given and not the lambda parameter.
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363 | /// </remarks>
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364 | /// <param name="mean">The mean(!) of the distribution is 1 / lambda.</param>
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365 | /// <returns>A number that is exponentially distributed</returns>
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366 | public TimeSpan RandExponential(TimeSpan mean) {
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367 | return RandExponential(Random, mean);
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368 | }
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369 |
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370 | private bool useSpareNormal = false;
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371 | private double spareNormal = double.NaN;
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372 | /// <summary>
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373 | /// Uses the Marsaglia polar method to generate a random variable
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374 | /// from two uniform random distributed values.
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375 | /// </summary>
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376 | /// <remarks>
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377 | /// A spare random variable is generated from the second uniformly
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378 | /// distributed value. Thus, the two calls to the uniform random number
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379 | /// generator will be made only every second call.
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380 | /// </remarks>
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381 | /// <param name="random">The random number generator to use.</param>
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382 | /// <param name="mu">The mean of the normal distribution.</param>
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383 | /// <param name="sigma">The standard deviation of the normal distribution.</param>
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384 | /// <returns>A number that is normal distributed.</returns>
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385 | public virtual double RandNormal(IRandom random, double mu, double sigma) {
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386 | if (useSpareNormal) {
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387 | useSpareNormal = false;
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388 | return spareNormal * sigma + mu;
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389 | } else {
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390 | double u, v, s;
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391 | do {
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392 | u = random.NextDouble() * 2 - 1;
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393 | v = random.NextDouble() * 2 - 1;
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394 | s = u * u + v * v;
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395 | } while (s >= 1 || s == 0);
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396 | var mul = Math.Sqrt(-2.0 * Math.Log(s) / s);
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397 | spareNormal = v * mul;
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398 | useSpareNormal = true;
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399 | return mu + sigma * u * mul;
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400 | }
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401 | }
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402 | /// <summary>
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403 | /// Uses the Marsaglia polar method to generate a random variable
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404 | /// from two uniform random distributed values.
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405 | /// </summary>
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406 | /// <remarks>
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407 | /// A spare random variable is generated from the second uniformly
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408 | /// distributed value. Thus, the two calls to the uniform random number
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409 | /// generator will be made only every second call.
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410 | /// </remarks>
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411 | /// <param name="mu">The mean of the normal distribution.</param>
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412 | /// <param name="sigma">The standard deviation of the normal distribution.</param>
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413 | /// <returns>A number that is normal distributed.</returns>
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414 | public double RandNormal(double mu, double sigma) {
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415 | return RandNormal(Random, mu, sigma);
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416 | }
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417 |
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418 | /// <summary>
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419 | /// Uses the Marsaglia polar method to generate a random variable
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420 | /// from two uniform random distributed values.
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421 | /// </summary>
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422 | /// <remarks>
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423 | /// A spare random variable is generated from the second uniformly
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424 | /// distributed value. Thus, the two calls to the uniform random number
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425 | /// generator will be made only every second call.
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426 | /// </remarks>
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427 | /// <param name="random">The random number generator to use.</param>
|
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428 | /// <param name="mu">The mean of the normal distribution.</param>
|
---|
429 | /// <param name="sigma">The standard deviation of the normal distribution.</param>
|
---|
430 | /// <returns>A number that is normal distributed.</returns>
|
---|
431 | public TimeSpan RandNormal(IRandom random, TimeSpan mu, TimeSpan sigma) {
|
---|
432 | return TimeSpan.FromSeconds(RandNormal(random, mu.TotalSeconds, sigma.TotalSeconds));
|
---|
433 | }
|
---|
434 | /// <summary>
|
---|
435 | /// Uses the Marsaglia polar method to generate a random variable
|
---|
436 | /// from two uniform random distributed values.
|
---|
437 | /// </summary>
|
---|
438 | /// <remarks>
|
---|
439 | /// A spare random variable is generated from the second uniformly
|
---|
440 | /// distributed value. Thus, the two calls to the uniform random number
|
---|
441 | /// generator will be made only every second call.
|
---|
442 | /// </remarks>
|
---|
443 | /// <param name="mu">The mean of the normal distribution.</param>
|
---|
444 | /// <param name="sigma">The standard deviation of the normal distribution.</param>
|
---|
445 | /// <returns>A number that is normal distributed.</returns>
|
---|
446 | public TimeSpan RandNormal(TimeSpan mu, TimeSpan sigma) {
|
---|
447 | return RandNormal(Random, mu, sigma);
|
---|
448 | }
|
---|
449 |
|
---|
450 | public double RandNormalPositive(IRandom random, double mu, double sigma) {
|
---|
451 | double val;
|
---|
452 | do {
|
---|
453 | val = RandNormal(random, mu, sigma);
|
---|
454 | } while (val <= 0);
|
---|
455 | return val;
|
---|
456 | }
|
---|
457 | public double RandNormalPositive(double mu, double sigma) {
|
---|
458 | return RandNormalPositive(Random, mu, sigma);
|
---|
459 | }
|
---|
460 |
|
---|
461 | public TimeSpan RandNormalPositive(IRandom random, TimeSpan mu, TimeSpan sigma) {
|
---|
462 | return TimeSpan.FromSeconds(RandNormalPositive(random, mu.TotalSeconds, sigma.TotalSeconds));
|
---|
463 | }
|
---|
464 | public TimeSpan RandNormalPositive(TimeSpan mu, TimeSpan sigma) {
|
---|
465 | return RandNormalPositive(Random, mu, sigma);
|
---|
466 | }
|
---|
467 |
|
---|
468 | public double RandNormalNegative(IRandom random, double mu, double sigma) {
|
---|
469 | double val;
|
---|
470 | do {
|
---|
471 | val = RandNormal(random, mu, sigma);
|
---|
472 | } while (val >= 0);
|
---|
473 | return val;
|
---|
474 | }
|
---|
475 | public double RandNormalNegative(double mu, double sigma) {
|
---|
476 | return RandNormalNegative(Random, mu, sigma);
|
---|
477 | }
|
---|
478 |
|
---|
479 | public TimeSpan RandNormalNegative(IRandom random, TimeSpan mu, TimeSpan sigma) {
|
---|
480 | return TimeSpan.FromSeconds(RandNormalNegative(random, mu.TotalSeconds, sigma.TotalSeconds));
|
---|
481 | }
|
---|
482 | public TimeSpan RandNormalNegative(TimeSpan mu, TimeSpan sigma) {
|
---|
483 | return RandNormalNegative(Random, mu, sigma);
|
---|
484 | }
|
---|
485 |
|
---|
486 | /// <summary>
|
---|
487 | /// Returns values from a log-normal distribution with the mean
|
---|
488 | /// exp(mu + sigma^2 / 2)
|
---|
489 | /// and the standard deviation
|
---|
490 | /// sqrt([exp(sigma^2)-1] * exp(2 * mu + sigma^2))
|
---|
491 | /// </summary>
|
---|
492 | /// <param name="random">The random number generator to use.</param>
|
---|
493 | /// <param name="mu">The mu parameter of the log-normal distribution (not the mean).</param>
|
---|
494 | /// <param name="sigma">The sigma parameter of the log-normal distribution (not the standard deviation).</param>
|
---|
495 | /// <returns>A log-normal distributed random value.</returns>
|
---|
496 | public double RandLogNormal(IRandom random, double mu, double sigma) {
|
---|
497 | return Math.Exp(RandNormal(random, mu, sigma));
|
---|
498 | }
|
---|
499 | /// <summary>
|
---|
500 | /// Returns values from a log-normal distribution with the mean
|
---|
501 | /// exp(mu + sigma^2 / 2)
|
---|
502 | /// and the standard deviation
|
---|
503 | /// sqrt([exp(sigma^2)-1] * exp(2 * mu + sigma^2))
|
---|
504 | /// </summary>
|
---|
505 | /// <param name="mu">The mu parameter of the log-normal distribution (not the mean).</param>
|
---|
506 | /// <param name="sigma">The sigma parameter of the log-normal distribution (not the standard deviation).</param>
|
---|
507 | /// <returns>A log-normal distributed random value.</returns>
|
---|
508 | public double RandLogNormal(double mu, double sigma) {
|
---|
509 | return RandLogNormal(Random, mu, sigma);
|
---|
510 | }
|
---|
511 |
|
---|
512 | /// <summary>
|
---|
513 | /// Returns values from a log-normal distribution with
|
---|
514 | /// the mean <paramref name="mean"/> and standard deviation <paramref name="stdev"/>.
|
---|
515 | /// </summary>
|
---|
516 | /// <param name="random">The random number generator to use.</param>
|
---|
517 | /// <param name="mean">The distribution mean.</param>
|
---|
518 | /// <param name="stdev">The distribution standard deviation.</param>
|
---|
519 | /// <returns>A log-normal distributed random value.</returns>
|
---|
520 | public double RandLogNormal2(IRandom random, double mean, double stdev) {
|
---|
521 | if (stdev == 0) return mean;
|
---|
522 | var alpha = Math.Sqrt(mean * stdev) / mean;
|
---|
523 | var sigma = Math.Sqrt(Math.Log(1 + (alpha * alpha)));
|
---|
524 | var mu = Math.Log(mean) - 0.5 * sigma * sigma;
|
---|
525 | return Math.Exp(RandNormal(random, mu, sigma));
|
---|
526 | }
|
---|
527 | /// <summary>
|
---|
528 | /// Returns values from a log-normal distribution with
|
---|
529 | /// the mean <paramref name="mean"/> and standard deviation <paramref name="stdev"/>.
|
---|
530 | /// </summary>
|
---|
531 | /// <param name="mean">The distribution mean.</param>
|
---|
532 | /// <param name="stdev">The distribution standard deviation.</param>
|
---|
533 | /// <returns>A log-normal distributed random value.</returns>
|
---|
534 | public double RandLogNormal2(double mean, double stdev) {
|
---|
535 | return RandLogNormal2(Random, mean, stdev);
|
---|
536 | }
|
---|
537 |
|
---|
538 | /// <summary>
|
---|
539 | /// Returns a timespan value from a log-normal distribution with the mean
|
---|
540 | /// exp(mu + sigma^2 / 2)
|
---|
541 | /// and the standard deviation
|
---|
542 | /// sqrt([exp(sigma^2)-1] * exp(2 * mu + sigma^2))
|
---|
543 | /// </summary>
|
---|
544 | /// <param name="random">The random number generator to use.</param>
|
---|
545 | /// <param name="mu">The mu parameter of the log-normal distribution (not the mean).</param>
|
---|
546 | /// <param name="sigma">The sigma parameter of the log-normal distribution (not the standard deviation).</param>
|
---|
547 | /// <returns>A log-normal distributed random timespan.</returns>
|
---|
548 | public TimeSpan RandLogNormal(IRandom random, TimeSpan mu, TimeSpan sigma) {
|
---|
549 | return TimeSpan.FromSeconds(RandLogNormal(random, mu.TotalSeconds, sigma.TotalSeconds));
|
---|
550 | }
|
---|
551 | /// <summary>
|
---|
552 | /// Returns a timespan value from a log-normal distribution with the mean
|
---|
553 | /// exp(mu + sigma^2 / 2)
|
---|
554 | /// and the standard deviation
|
---|
555 | /// sqrt([exp(sigma^2)-1] * exp(2 * mu + sigma^2))
|
---|
556 | /// </summary>
|
---|
557 | /// <param name="mu">The mu parameter of the log-normal distribution (not the mean).</param>
|
---|
558 | /// <param name="sigma">The sigma parameter of the log-normal distribution (not the standard deviation).</param>
|
---|
559 | /// <returns>A log-normal distributed random timespan.</returns>
|
---|
560 | public TimeSpan RandLogNormal(TimeSpan mu, TimeSpan sigma) {
|
---|
561 | return RandLogNormal(Random, mu, sigma);
|
---|
562 | }
|
---|
563 |
|
---|
564 | /// <summary>
|
---|
565 | /// Returns a timespan value from a log-normal distribution with
|
---|
566 | /// the mean <paramref name="mean"/> and standard deviation <paramref name="stdev"/>.
|
---|
567 | /// </summary>
|
---|
568 | /// <param name="random">The random number generator to use.</param>
|
---|
569 | /// <param name="mean">The distribution mean.</param>
|
---|
570 | /// <param name="stdev">The distribution standard deviation.</param>
|
---|
571 | /// <returns>A log-normal distributed random timespan.</returns>
|
---|
572 | public TimeSpan RandLogNormal2(IRandom random, TimeSpan mean, TimeSpan stdev) {
|
---|
573 | return TimeSpan.FromSeconds(RandLogNormal2(random, mean.TotalSeconds, stdev.TotalSeconds));
|
---|
574 | }
|
---|
575 | /// <summary>
|
---|
576 | /// Returns a timespan value from a log-normal distribution with
|
---|
577 | /// the mean <paramref name="mean"/> and standard deviation <paramref name="stdev"/>.
|
---|
578 | /// </summary>
|
---|
579 | /// <param name="mean">The distribution mean.</param>
|
---|
580 | /// <param name="stdev">The distribution standard deviation.</param>
|
---|
581 | /// <returns>A log-normal distributed random timespan.</returns>
|
---|
582 | public TimeSpan RandLogNormal2(TimeSpan mean, TimeSpan stdev) {
|
---|
583 | return RandLogNormal2(Random, mean, stdev);
|
---|
584 | }
|
---|
585 |
|
---|
586 | public double RandCauchy(IRandom random, double x0, double gamma) {
|
---|
587 | return x0 + gamma * Math.Tan(Math.PI * (random.NextDouble() - 0.5));
|
---|
588 | }
|
---|
589 | public double RandCauchy(double x0, double gamma) {
|
---|
590 | return RandCauchy(Random, x0, gamma);
|
---|
591 | }
|
---|
592 |
|
---|
593 | public TimeSpan RandCauchy(IRandom random, TimeSpan x0, TimeSpan gamma) {
|
---|
594 | return TimeSpan.FromSeconds(RandCauchy(random, x0.TotalSeconds, gamma.TotalSeconds));
|
---|
595 | }
|
---|
596 | public TimeSpan RandCauchy(TimeSpan x0, TimeSpan gamma) {
|
---|
597 | return RandCauchy(Random, x0, gamma);
|
---|
598 | }
|
---|
599 |
|
---|
600 | public double RandWeibull(IRandom random, double alpha, double beta) {
|
---|
601 | return alpha * Math.Pow(-Math.Log(1 - random.NextDouble()), 1 / beta);
|
---|
602 | }
|
---|
603 | public double RandWeibull(double alpha, double beta) {
|
---|
604 | return RandWeibull(Random, alpha, beta);
|
---|
605 | }
|
---|
606 |
|
---|
607 | public TimeSpan RandWeibull(IRandom random, TimeSpan alpha, TimeSpan beta) {
|
---|
608 | return TimeSpan.FromSeconds(RandWeibull(random, alpha.TotalSeconds, beta.TotalSeconds));
|
---|
609 | }
|
---|
610 | public TimeSpan RandWeibull(TimeSpan alpha, TimeSpan beta) {
|
---|
611 | return RandWeibull(Random, alpha, beta);
|
---|
612 | }
|
---|
613 | #endregion
|
---|
614 |
|
---|
615 | #region Random timeouts
|
---|
616 | public Timeout TimeoutUniformD(IRandom random, double a, double b) {
|
---|
617 | return new Timeout(this, ToTimeSpan(RandUniform(random, a, b)));
|
---|
618 | }
|
---|
619 | public Timeout TimeoutUniformD(double a, double b) {
|
---|
620 | return TimeoutUniformD(Random, a, b);
|
---|
621 | }
|
---|
622 |
|
---|
623 | public Timeout TimeoutUniform(IRandom random, TimeSpan a, TimeSpan b) {
|
---|
624 | return new Timeout(this, RandUniform(random, a, b));
|
---|
625 | }
|
---|
626 | public Timeout TimeoutUniform(TimeSpan a, TimeSpan b) {
|
---|
627 | return TimeoutUniform(Random, a, b);
|
---|
628 | }
|
---|
629 |
|
---|
630 | public Timeout TimeoutTriangularD(IRandom random, double low, double high) {
|
---|
631 | return new Timeout(this, ToTimeSpan(RandTriangular(random, low, high)));
|
---|
632 | }
|
---|
633 | public Timeout TimeoutTriangularD(double low, double high) {
|
---|
634 | return TimeoutTriangularD(Random, low, high);
|
---|
635 | }
|
---|
636 |
|
---|
637 | public Timeout TimeoutTriangular(IRandom random, TimeSpan low, TimeSpan high) {
|
---|
638 | return new Timeout(this, RandTriangular(random, low, high));
|
---|
639 | }
|
---|
640 | public Timeout TimeoutTriangular(TimeSpan low, TimeSpan high) {
|
---|
641 | return TimeoutTriangular(Random, low, high);
|
---|
642 | }
|
---|
643 |
|
---|
644 | public Timeout TimeoutTriangularD(IRandom random, double low, double high, double mode) {
|
---|
645 | return new Timeout(this, ToTimeSpan(RandTriangular(random, low, high, mode)));
|
---|
646 | }
|
---|
647 | public Timeout TimeoutTriangularD(double low, double high, double mode) {
|
---|
648 | return TimeoutTriangularD(Random, low, high, mode);
|
---|
649 | }
|
---|
650 |
|
---|
651 | public Timeout TimeoutTriangular(IRandom random, TimeSpan low, TimeSpan high, TimeSpan mode) {
|
---|
652 | return new Timeout(this, RandTriangular(random, low, high, mode));
|
---|
653 | }
|
---|
654 | public Timeout TimeoutTriangular(TimeSpan low, TimeSpan high, TimeSpan mode) {
|
---|
655 | return TimeoutTriangular(Random, low, high, mode);
|
---|
656 | }
|
---|
657 |
|
---|
658 | public Timeout TimeoutExponentialD(IRandom random, double mean) {
|
---|
659 | return new Timeout(this, ToTimeSpan(RandExponential(random, mean)));
|
---|
660 | }
|
---|
661 | public Timeout TimeoutExponentialD(double mean) {
|
---|
662 | return TimeoutExponentialD(Random, mean);
|
---|
663 | }
|
---|
664 |
|
---|
665 | public Timeout TimeoutExponential(IRandom random, TimeSpan mean) {
|
---|
666 | return new Timeout(this, RandExponential(random, mean));
|
---|
667 | }
|
---|
668 | public Timeout TimeoutExponential(TimeSpan mean) {
|
---|
669 | return TimeoutExponential(Random, mean);
|
---|
670 | }
|
---|
671 |
|
---|
672 | public Timeout TimeoutNormalPositiveD(IRandom random, double mu, double sigma) {
|
---|
673 | return new Timeout(this, ToTimeSpan(RandNormalPositive(random, mu, sigma)));
|
---|
674 | }
|
---|
675 | public Timeout TimeoutNormalPositiveD(double mu, double sigma) {
|
---|
676 | return TimeoutNormalPositiveD(Random, mu, sigma);
|
---|
677 | }
|
---|
678 |
|
---|
679 | public Timeout TimeoutNormalPositive(IRandom random, TimeSpan mu, TimeSpan sigma) {
|
---|
680 | return new Timeout(this, RandNormalPositive(random, mu, sigma));
|
---|
681 | }
|
---|
682 | public Timeout TimeoutNormalPositive(TimeSpan mu, TimeSpan sigma) {
|
---|
683 | return TimeoutNormalPositive(Random, mu, sigma);
|
---|
684 | }
|
---|
685 |
|
---|
686 | public Timeout TimeoutLogNormalD(IRandom random, double mu, double sigma) {
|
---|
687 | return new Timeout(this, ToTimeSpan(RandLogNormal(random, mu, sigma)));
|
---|
688 | }
|
---|
689 | public Timeout TimeoutLogNormalD(double mu, double sigma) {
|
---|
690 | return TimeoutLogNormalD(Random, mu, sigma);
|
---|
691 | }
|
---|
692 |
|
---|
693 | public Timeout TimeoutLogNormal2D(IRandom random, double mean, double stdev) {
|
---|
694 | return new Timeout(this, ToTimeSpan(RandLogNormal2(random, mean, stdev)));
|
---|
695 | }
|
---|
696 | public Timeout TimeoutLogNormal2D(double mean, double stdev) {
|
---|
697 | return TimeoutLogNormal2D(Random, mean, stdev);
|
---|
698 | }
|
---|
699 |
|
---|
700 | public Timeout TimeoutLogNormal(IRandom random, TimeSpan mu, TimeSpan sigma) {
|
---|
701 | return new Timeout(this, RandLogNormal(random, mu, sigma));
|
---|
702 | }
|
---|
703 | public Timeout TimeoutLogNormal(TimeSpan mu, TimeSpan sigma) {
|
---|
704 | return TimeoutLogNormal(Random, mu, sigma);
|
---|
705 | }
|
---|
706 |
|
---|
707 | public Timeout TimeoutLogNormal2(IRandom random, TimeSpan mean, TimeSpan stdev) {
|
---|
708 | return new Timeout(this, RandLogNormal2(random, mean, stdev));
|
---|
709 | }
|
---|
710 | public Timeout TimeoutLogNormal2(TimeSpan mean, TimeSpan stdev) {
|
---|
711 | return TimeoutLogNormal2(Random, mean, stdev);
|
---|
712 | }
|
---|
713 | #endregion
|
---|
714 | }
|
---|
715 |
|
---|
716 | /// <summary>
|
---|
717 | /// Provides a simulation environment that is thread-safe against manipulations of the event queue.
|
---|
718 | /// Its performance is somewhat lower than the non-thread-safe environment (cf. <see cref="Simulation"/>)
|
---|
719 | /// due to the locking involved.
|
---|
720 | /// </summary>
|
---|
721 | /// <remarks>
|
---|
722 | /// Please carefully consider if you must really schedule the stop event in a separate thread. You can also
|
---|
723 | /// call <see cref="Simulation.StopAsync"/> to request termination after the current event has been processed.
|
---|
724 | ///
|
---|
725 | /// The simulation will still run in only one thread and execute all events sequentially.
|
---|
726 | /// </remarks>
|
---|
727 | public class ThreadSafeSimulation : Simulation {
|
---|
728 | protected object _locker;
|
---|
729 |
|
---|
730 | public ThreadSafeSimulation() : this(new DateTime(1970, 1, 1)) { }
|
---|
731 | public ThreadSafeSimulation(TimeSpan? defaultStep) : this(new DateTime(1970, 1, 1), defaultStep) { }
|
---|
732 | public ThreadSafeSimulation(DateTime initialDateTime, TimeSpan? defaultStep = null) : this(new PcgRandom(), initialDateTime, defaultStep) { }
|
---|
733 | public ThreadSafeSimulation(int randomSeed, TimeSpan? defaultStep = null) : this(new DateTime(1970, 1, 1), randomSeed, defaultStep) { }
|
---|
734 | public ThreadSafeSimulation(DateTime initialDateTime, int randomSeed, TimeSpan? defaultStep = null) : this(new PcgRandom(randomSeed), initialDateTime, defaultStep) { }
|
---|
735 | public ThreadSafeSimulation(IRandom random, DateTime initialDateTime, TimeSpan? defaultStep = null) : base(random, initialDateTime, defaultStep) {
|
---|
736 | _locker = new object();
|
---|
737 | }
|
---|
738 |
|
---|
739 |
|
---|
740 | /// <summary>
|
---|
741 | /// Schedules an event to occur at the same simulation time as the call was made.
|
---|
742 | /// </summary>
|
---|
743 | /// <remarks>
|
---|
744 | /// This method is thread-safe against manipulations of the event queue
|
---|
745 | /// </remarks>
|
---|
746 | /// <param name="event">The event that should be scheduled.</param>
|
---|
747 | /// <param name="priority">The priority to rank events at the same time (smaller value = higher priority).</param>
|
---|
748 | public override void Schedule(Event @event, int priority = 0) {
|
---|
749 | lock (_locker) {
|
---|
750 | DoSchedule(Now, @event, priority);
|
---|
751 | }
|
---|
752 | }
|
---|
753 |
|
---|
754 | /// <summary>
|
---|
755 | /// Schedules an event to occur after a certain (positive) delay.
|
---|
756 | /// </summary>
|
---|
757 | /// <remarks>
|
---|
758 | /// This method is thread-safe against manipulations of the event queue
|
---|
759 | /// </remarks>
|
---|
760 | /// <exception cref="ArgumentException">
|
---|
761 | /// Thrown when <paramref name="delay"/> is negative.
|
---|
762 | /// </exception>
|
---|
763 | /// <param name="delay">The (positive) delay after which the event should be fired.</param>
|
---|
764 | /// <param name="event">The event that should be scheduled.</param>
|
---|
765 | /// <param name="priority">The priority to rank events at the same time (smaller value = higher priority).</param>
|
---|
766 | public override void Schedule(TimeSpan delay, Event @event, int priority = 0) {
|
---|
767 | if (delay < TimeSpan.Zero)
|
---|
768 | throw new ArgumentException("Negative delays are not allowed in Schedule(TimeSpan, Event).");
|
---|
769 | lock (_locker) {
|
---|
770 | var eventTime = Now + delay;
|
---|
771 | DoSchedule(eventTime, @event, priority);
|
---|
772 | }
|
---|
773 | }
|
---|
774 |
|
---|
775 | /// <summary>
|
---|
776 | /// Run until a certain event is processed.
|
---|
777 | /// </summary>
|
---|
778 | /// <remarks>
|
---|
779 | /// This method is thread-safe against manipulations of the event queue
|
---|
780 | /// </remarks>
|
---|
781 | /// <param name="stopEvent">The event that stops the simulation.</param>
|
---|
782 | /// <returns></returns>
|
---|
783 | public override object Run(Event stopEvent = null) {
|
---|
784 | _stopRequested = false;
|
---|
785 | if (stopEvent != null) {
|
---|
786 | if (stopEvent.IsProcessed) return stopEvent.Value;
|
---|
787 | stopEvent.AddCallback(StopSimulation);
|
---|
788 | }
|
---|
789 |
|
---|
790 | try {
|
---|
791 | var stop = false;
|
---|
792 | lock (_locker) {
|
---|
793 | stop = ScheduleQ.Count == 0 || _stopRequested;
|
---|
794 | }
|
---|
795 | while (!stop) {
|
---|
796 | Step();
|
---|
797 | ProcessedEvents++;
|
---|
798 | lock (_locker) {
|
---|
799 | stop = ScheduleQ.Count == 0 || _stopRequested;
|
---|
800 | }
|
---|
801 | }
|
---|
802 | } catch (StopSimulationException e) { return e.Value; }
|
---|
803 | if (stopEvent == null) return null;
|
---|
804 | if (!stopEvent.IsTriggered) throw new InvalidOperationException("No scheduled events left but \"until\" event was not triggered.");
|
---|
805 | return stopEvent.Value;
|
---|
806 | }
|
---|
807 |
|
---|
808 | /// <summary>
|
---|
809 | /// Performs a single step of the simulation, i.e. process a single event
|
---|
810 | /// </summary>
|
---|
811 | /// <remarks>
|
---|
812 | /// This method is thread-safe against manipulations of the event queue
|
---|
813 | /// </remarks>
|
---|
814 | public override void Step() {
|
---|
815 | Event evt;
|
---|
816 | lock (_locker) {
|
---|
817 | var next = ScheduleQ.Dequeue();
|
---|
818 | Now = next.PrimaryPriority;
|
---|
819 | evt = next.Event;
|
---|
820 | }
|
---|
821 | evt.Process();
|
---|
822 | }
|
---|
823 |
|
---|
824 | /// <summary>
|
---|
825 | /// Peeks at the time of the next event in terms of the defined step
|
---|
826 | /// </summary>
|
---|
827 | /// <remarks>
|
---|
828 | /// This method is thread-safe against manipulations of the event queue
|
---|
829 | /// </remarks>
|
---|
830 | public override double PeekD() {
|
---|
831 | lock (_locker) {
|
---|
832 | if (ScheduleQ.Count == 0) return double.MaxValue;
|
---|
833 | return (Peek() - StartDate).TotalSeconds / DefaultTimeStepSeconds;
|
---|
834 | }
|
---|
835 | }
|
---|
836 |
|
---|
837 | /// <summary>
|
---|
838 | /// Peeks at the time of the next event
|
---|
839 | /// </summary>
|
---|
840 | /// <remarks>
|
---|
841 | /// This method is thread-safe against manipulations of the event queue
|
---|
842 | /// </remarks>
|
---|
843 | public override DateTime Peek() {
|
---|
844 | lock (_locker) {
|
---|
845 | return ScheduleQ.Count > 0 ? ScheduleQ.First.PrimaryPriority : DateTime.MaxValue;
|
---|
846 | }
|
---|
847 | }
|
---|
848 | }
|
---|
849 |
|
---|
850 | /// <summary>
|
---|
851 | /// Environments hold the event queues, schedule and process events.
|
---|
852 | /// </summary>
|
---|
853 | [Obsolete("Use class Simulation or ThreadSafeSimulation instead. Due to name clashes with System.Environment the class SimSharp.Environment is being outphased.")]
|
---|
854 | public class Environment : ThreadSafeSimulation {
|
---|
855 | public Environment()
|
---|
856 | : base() {
|
---|
857 | Random = new SystemRandom();
|
---|
858 | }
|
---|
859 | public Environment(TimeSpan? defaultStep)
|
---|
860 | : base(defaultStep) {
|
---|
861 | Random = new SystemRandom();
|
---|
862 | }
|
---|
863 | public Environment(int randomSeed, TimeSpan? defaultStep = null)
|
---|
864 | : base(randomSeed, defaultStep) {
|
---|
865 | Random = new SystemRandom(randomSeed);
|
---|
866 | }
|
---|
867 | public Environment(DateTime initialDateTime, TimeSpan? defaultStep = null)
|
---|
868 | : base(initialDateTime, defaultStep) {
|
---|
869 | Random = new SystemRandom();
|
---|
870 | }
|
---|
871 | public Environment(DateTime initialDateTime, int randomSeed, TimeSpan? defaultStep = null)
|
---|
872 | : base(initialDateTime, randomSeed, defaultStep) {
|
---|
873 | Random = new SystemRandom(randomSeed);
|
---|
874 | }
|
---|
875 |
|
---|
876 | protected static readonly double NormalMagicConst = 4 * Math.Exp(-0.5) / Math.Sqrt(2.0);
|
---|
877 | public override double RandNormal(IRandom random, double mu, double sigma) {
|
---|
878 | double z, zz, u1, u2;
|
---|
879 | do {
|
---|
880 | u1 = random.NextDouble();
|
---|
881 | u2 = 1 - random.NextDouble();
|
---|
882 | z = NormalMagicConst * (u1 - 0.5) / u2;
|
---|
883 | zz = z * z / 4.0;
|
---|
884 | } while (zz > -Math.Log(u2));
|
---|
885 | return mu + z * sigma;
|
---|
886 | }
|
---|
887 | }
|
---|
888 | }
|
---|