[12657] | 1 | #region License Information
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| 2 | /* SimSharp - A .NET port of SimPy, discrete event simulation framework
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[16307] | 3 | Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[12657] | 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 | /// Environments hold the event queues, schedule and process events.
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| 26 | /// </summary>
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| 27 | public class Environment {
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| 28 | private const int InitialMaxEvents = 1024;
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| 29 | private object locker = new object();
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| 30 |
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| 31 | /// <summary>
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| 32 | /// Describes the number of seconds that a logical step of 1 in the *D-API takes.
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| 33 | /// </summary>
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| 34 | protected double DefaultTimeStepSeconds { get; private set; }
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| 35 |
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| 36 | /// <summary>
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| 37 | /// Calculates the logical date of the simulation by the amount of default steps
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| 38 | /// that have passed.
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| 39 | /// </summary>
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| 40 | public double NowD {
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| 41 | get { return (Now - StartDate).TotalSeconds / DefaultTimeStepSeconds; }
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| 42 | }
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| 43 |
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| 44 | /// <summary>
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| 45 | /// The current simulation time as a calendar date.
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| 46 | /// </summary>
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| 47 | public DateTime Now { get; protected set; }
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| 48 |
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| 49 | /// <summary>
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| 50 | /// The calendar date when the simulation started. This defaults to 1970-1-1 if
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| 51 | /// no other date has been specified in the overloaded constructor.
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| 52 | /// </summary>
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| 53 | public DateTime StartDate { get; protected set; }
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| 54 |
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| 55 | /// <summary>
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| 56 | /// The random number generator that is to be used in all events in
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| 57 | /// order to produce reproducible results.
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| 58 | /// </summary>
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| 59 | protected IRandom Random { get; set; }
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| 60 |
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| 61 | protected EventQueue ScheduleQ;
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| 62 | protected Queue<Event> Queue;
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| 63 | public Process ActiveProcess { get; set; }
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| 64 |
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| 65 | public TextWriter Logger { get; set; }
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| 66 | public int ProcessedEvents { get; protected set; }
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| 67 |
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| 68 | public Environment() : this(new DateTime(1970, 1, 1)) { }
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| 69 | public Environment(TimeSpan? defaultStep) : this(new DateTime(1970, 1, 1), defaultStep) { }
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| 70 | public Environment(int randomSeed, TimeSpan? defaultStep = null) : this(new DateTime(1970, 1, 1), randomSeed, defaultStep) { }
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| 71 | public Environment(DateTime initialDateTime, TimeSpan? defaultStep = null) {
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| 72 | DefaultTimeStepSeconds = (defaultStep ?? TimeSpan.FromSeconds(1)).Duration().TotalSeconds;
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| 73 | StartDate = initialDateTime;
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| 74 | Now = initialDateTime;
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| 75 | Random = new SystemRandom();
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| 76 | ScheduleQ = new EventQueue(InitialMaxEvents);
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| 77 | Queue = new Queue<Event>();
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| 78 | Logger = Console.Out;
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| 79 | }
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| 80 | public Environment(DateTime initialDateTime, int randomSeed, TimeSpan? defaultStep = null) {
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| 81 | DefaultTimeStepSeconds = (defaultStep ?? TimeSpan.FromSeconds(1)).Duration().TotalSeconds;
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| 82 | StartDate = initialDateTime;
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| 83 | Now = initialDateTime;
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| 84 | Random = new SystemRandom(randomSeed);
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| 85 | ScheduleQ = new EventQueue(InitialMaxEvents);
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| 86 | Queue = new Queue<Event>();
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| 87 | Logger = Console.Out;
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| 88 | }
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| 89 |
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| 90 | public double ToDouble(TimeSpan span) {
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| 91 | return span.TotalSeconds / DefaultTimeStepSeconds;
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| 92 | }
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| 93 |
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| 94 | public TimeSpan ToTimeSpan(double span) {
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| 95 | return TimeSpan.FromSeconds(DefaultTimeStepSeconds * span);
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| 96 | }
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| 97 |
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| 98 | public Process Process(IEnumerable<Event> generator) {
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| 99 | return new Process(this, generator);
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| 100 | }
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| 101 |
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| 102 | public Timeout TimeoutD(double delay) {
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| 103 | return Timeout(TimeSpan.FromSeconds(DefaultTimeStepSeconds * delay));
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| 104 | }
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| 105 |
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| 106 | public Timeout Timeout(TimeSpan delay) {
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| 107 | return new Timeout(this, delay);
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| 108 | }
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| 109 |
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| 110 | public virtual void Reset(int randomSeed) {
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[14196] | 111 | ProcessedEvents = 0;
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[12657] | 112 | Now = StartDate;
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| 113 | Random = new SystemRandom(randomSeed);
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| 114 | ScheduleQ = new EventQueue(InitialMaxEvents);
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| 115 | Queue = new Queue<Event>();
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| 116 | }
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| 117 |
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| 118 | public virtual void ScheduleD(double delay, Event @event) {
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| 119 | Schedule(TimeSpan.FromSeconds(DefaultTimeStepSeconds * delay), @event);
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| 120 | }
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| 121 |
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| 122 | public virtual void Schedule(Event @event) {
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| 123 | lock (locker) {
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| 124 | Queue.Enqueue(@event);
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| 125 | }
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| 126 | }
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| 127 |
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| 128 | public virtual void Schedule(TimeSpan delay, Event @event) {
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| 129 | if (delay < TimeSpan.Zero)
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| 130 | throw new ArgumentException("Negative delays are not allowed in Schedule(TimeSpan, Event).");
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| 131 | lock (locker) {
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| 132 | if (delay == TimeSpan.Zero) {
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| 133 | Queue.Enqueue(@event);
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| 134 | return;
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| 135 | }
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| 136 | var eventTime = Now + delay;
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| 137 | DoSchedule(eventTime, @event);
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| 138 | }
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| 139 | }
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| 140 |
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| 141 | protected virtual EventQueueNode DoSchedule(DateTime date, Event @event) {
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| 142 | if (ScheduleQ.MaxSize == ScheduleQ.Count) {
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| 143 | // the capacity has to be adjusted, there are more events in the queue than anticipated
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| 144 | var oldSchedule = ScheduleQ;
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| 145 | ScheduleQ = new EventQueue(ScheduleQ.MaxSize * 2);
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| 146 | foreach (var e in oldSchedule) ScheduleQ.Enqueue(e.Priority, e.Event);
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| 147 | }
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| 148 | return ScheduleQ.Enqueue(date, @event);
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| 149 | }
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| 150 |
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| 151 | public virtual object RunD(double? until = null) {
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| 152 | if (!until.HasValue) return Run();
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| 153 | return Run(Now + TimeSpan.FromSeconds(DefaultTimeStepSeconds * until.Value));
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| 154 | }
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| 155 |
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| 156 | public virtual object Run(TimeSpan span) {
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| 157 | return Run(Now + span);
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| 158 | }
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| 159 |
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| 160 | public virtual object Run(DateTime until) {
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| 161 | if (until <= Now) throw new InvalidOperationException("Simulation end date must lie in the future.");
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| 162 | var stopEvent = new Event(this);
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| 163 | var node = DoSchedule(until, stopEvent);
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| 164 | // stop event is always the first to execute at the given time
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| 165 | node.InsertionIndex = -1;
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| 166 | ScheduleQ.OnNodeUpdated(node);
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| 167 | return Run(stopEvent);
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| 168 | }
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| 169 |
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| 170 | public virtual object Run(Event stopEvent = null) {
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| 171 | if (stopEvent != null) {
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| 172 | if (stopEvent.IsProcessed) return stopEvent.Value;
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| 173 | stopEvent.AddCallback(StopSimulation);
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| 174 | }
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| 175 |
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| 176 | try {
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| 177 | var stop = Queue.Count == 0 && ScheduleQ.Count == 0;
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| 178 | while (!stop) {
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| 179 | Step();
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| 180 | ProcessedEvents++;
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| 181 | lock (locker) {
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| 182 | stop = Queue.Count == 0 && ScheduleQ.Count == 0;
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| 183 | }
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| 184 | }
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| 185 | } catch (StopSimulationException e) { return e.Value; }
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| 186 | if (stopEvent == null) return null;
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| 187 | if (!stopEvent.IsTriggered) throw new InvalidOperationException("No scheduled events left but \"until\" event was not triggered.");
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| 188 | return stopEvent.Value;
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| 189 | }
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| 190 |
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| 191 | public virtual void Step() {
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| 192 | Event evt;
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| 193 | lock (locker) {
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| 194 | if (Queue.Count == 0) {
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| 195 | var next = ScheduleQ.Dequeue();
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| 196 | Now = next.Priority;
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| 197 | evt = next.Event;
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| 198 | } else evt = Queue.Dequeue();
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| 199 | }
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| 200 | evt.Process();
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| 201 | }
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| 202 |
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| 203 | public virtual double PeekD() {
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| 204 | lock (locker) {
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| 205 | if (Queue.Count == 0 && ScheduleQ.Count == 0) return double.MaxValue;
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| 206 | return (Peek() - StartDate).TotalSeconds / DefaultTimeStepSeconds;
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| 207 | }
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| 208 | }
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| 209 |
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| 210 | public virtual DateTime Peek() {
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| 211 | lock (locker) {
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| 212 | return Queue.Count > 0 ? Now : (ScheduleQ.Count > 0 ? ScheduleQ.First.Priority : DateTime.MaxValue);
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| 213 | }
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| 214 | }
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| 215 |
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| 216 | protected virtual void StopSimulation(Event @event) {
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| 217 | throw new StopSimulationException(@event.Value);
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| 218 | }
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| 219 |
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| 220 | public virtual void Log(string message, params object[] args) {
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| 221 | if (Logger != null)
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| 222 | Logger.WriteLine(message, args);
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| 223 | }
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| 224 |
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| 225 | #region Random number distributions
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| 226 | protected static readonly double NormalMagicConst = 4 * Math.Exp(-0.5) / Math.Sqrt(2.0);
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| 227 |
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| 228 | public double RandUniform(double a, double b) {
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| 229 | return a + (b - a) * Random.NextDouble();
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| 230 | }
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| 231 |
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| 232 | public TimeSpan RandUniform(TimeSpan a, TimeSpan b) {
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| 233 | return TimeSpan.FromSeconds(RandUniform(a.TotalSeconds, b.TotalSeconds));
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| 234 | }
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| 235 |
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| 236 | public double RandTriangular(double low, double high) {
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| 237 | var u = Random.NextDouble();
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| 238 | if (u > 0.5)
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| 239 | return high + (low - high) * Math.Sqrt(((1.0 - u) / 2));
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| 240 | return low + (high - low) * Math.Sqrt(u / 2);
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| 241 | }
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| 242 |
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| 243 | public TimeSpan RandTriangular(TimeSpan low, TimeSpan high) {
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| 244 | return TimeSpan.FromSeconds(RandTriangular(low.TotalSeconds, high.TotalSeconds));
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| 245 | }
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| 246 |
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| 247 | public double RandTriangular(double low, double high, double mode) {
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| 248 | var u = Random.NextDouble();
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| 249 | var c = (mode - low) / (high - low);
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| 250 | if (u > c)
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| 251 | return high + (low - high) * Math.Sqrt(((1.0 - u) * (1.0 - c)));
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| 252 | return low + (high - low) * Math.Sqrt(u * c);
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| 253 | }
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| 254 |
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| 255 | public TimeSpan RandTriangular(TimeSpan low, TimeSpan high, TimeSpan mode) {
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| 256 | return TimeSpan.FromSeconds(RandTriangular(low.TotalSeconds, high.TotalSeconds, mode.TotalSeconds));
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| 257 | }
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| 258 |
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| 259 | /// <summary>
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| 260 | /// Returns a number that is exponentially distributed given a certain mean.
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| 261 | /// </summary>
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| 262 | /// <remarks>
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| 263 | /// Unlike in other APIs here the mean should be given and not the lambda parameter.
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| 264 | /// </remarks>
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| 265 | /// <param name="mean">The mean(!) of the distribution is 1 / lambda.</param>
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| 266 | /// <returns>A number that is exponentially distributed</returns>
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| 267 | public double RandExponential(double mean) {
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| 268 | return -Math.Log(1 - Random.NextDouble()) * mean;
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| 269 | }
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| 270 |
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| 271 | /// <summary>
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| 272 | /// Returns a timespan that is exponentially distributed given a certain mean.
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| 273 | /// </summary>
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| 274 | /// <remarks>
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| 275 | /// Unlike in other APIs here the mean should be given and not the lambda parameter.
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| 276 | /// </remarks>
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| 277 | /// <param name="mean">The mean(!) of the distribution is 1 / lambda.</param>
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| 278 | /// <returns>A number that is exponentially distributed</returns>
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| 279 | public TimeSpan RandExponential(TimeSpan mean) {
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| 280 | return TimeSpan.FromSeconds(RandExponential(mean.TotalSeconds));
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| 281 | }
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| 282 |
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| 283 | public double RandNormal(double mu, double sigma) {
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| 284 | double z, zz, u1, u2;
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| 285 | do {
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| 286 | u1 = Random.NextDouble();
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| 287 | u2 = 1 - Random.NextDouble();
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| 288 | z = NormalMagicConst * (u1 - 0.5) / u2;
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| 289 | zz = z * z / 4.0;
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| 290 | } while (zz > -Math.Log(u2));
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| 291 | return mu + z * sigma;
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| 292 | }
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| 293 |
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| 294 | public TimeSpan RandNormal(TimeSpan mu, TimeSpan sigma) {
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| 295 | return TimeSpan.FromSeconds(RandNormal(mu.TotalSeconds, sigma.TotalSeconds));
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| 296 | }
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| 297 |
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| 298 | public double RandNormalPositive(double mu, double sigma) {
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| 299 | double val;
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| 300 | do {
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| 301 | val = RandNormal(mu, sigma);
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| 302 | } while (val <= 0);
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| 303 | return val;
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| 304 | }
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| 305 |
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| 306 | public TimeSpan RandNormalPositive(TimeSpan mu, TimeSpan sigma) {
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| 307 | return TimeSpan.FromSeconds(RandNormalPositive(mu.TotalSeconds, sigma.TotalSeconds));
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| 308 | }
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| 309 |
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| 310 | public double RandNormalNegative(double mu, double sigma) {
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| 311 | double val;
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| 312 | do {
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| 313 | val = RandNormal(mu, sigma);
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| 314 | } while (val >= 0);
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| 315 | return val;
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| 316 | }
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| 317 |
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| 318 | public TimeSpan RandNormalNegative(TimeSpan mu, TimeSpan sigma) {
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| 319 | return TimeSpan.FromSeconds(RandNormalNegative(mu.TotalSeconds, sigma.TotalSeconds));
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| 320 | }
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| 321 |
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| 322 | public double RandLogNormal(double mu, double sigma) {
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| 323 | return Math.Exp(RandNormal(mu, sigma));
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| 324 | }
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| 325 |
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| 326 | public TimeSpan RandLogNormal(TimeSpan mu, TimeSpan sigma) {
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| 327 | return TimeSpan.FromSeconds(RandLogNormal(mu.TotalSeconds, sigma.TotalSeconds));
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| 328 | }
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| 329 |
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| 330 | public double RandCauchy(double x0, double gamma) {
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| 331 | return x0 + gamma * Math.Tan(Math.PI * (Random.NextDouble() - 0.5));
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| 332 | }
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| 333 |
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| 334 | public TimeSpan RandCauchy(TimeSpan x0, TimeSpan gamma) {
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| 335 | return TimeSpan.FromSeconds(RandCauchy(x0.TotalSeconds, gamma.TotalSeconds));
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| 336 | }
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| 337 |
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| 338 | public double RandWeibull(double alpha, double beta) {
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| 339 | return alpha * Math.Pow(-Math.Log(1 - Random.NextDouble()), 1 / beta);
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| 340 | }
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| 341 |
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| 342 | public TimeSpan RandWeibull(TimeSpan mu, TimeSpan sigma) {
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| 343 | return TimeSpan.FromSeconds(RandWeibull(mu.TotalSeconds, sigma.TotalSeconds));
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| 344 | }
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| 345 | #endregion
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| 346 |
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| 347 | #region Random timeouts
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| 348 | public Timeout TimeoutUniformD(double a, double b) {
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| 349 | return new Timeout(this, ToTimeSpan(RandUniform(a, b)));
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| 350 | }
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| 351 |
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| 352 | public Timeout TimeoutUniform(TimeSpan a, TimeSpan b) {
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| 353 | return new Timeout(this, RandUniform(a, b));
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| 354 | }
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| 355 |
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| 356 | public Timeout TimeoutTriangularD(double low, double high) {
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| 357 | return new Timeout(this, ToTimeSpan(RandTriangular(low, high)));
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| 358 | }
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| 359 |
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| 360 | public Timeout TimeoutTriangular(TimeSpan low, TimeSpan high) {
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| 361 | return new Timeout(this, RandTriangular(low, high));
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| 362 | }
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| 363 |
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| 364 | public Timeout TimeoutTriangularD(double low, double high, double mode) {
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| 365 | return new Timeout(this, ToTimeSpan(RandTriangular(low, high, mode)));
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| 366 | }
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| 367 |
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| 368 | public Timeout TimeoutTriangular(TimeSpan low, TimeSpan high, TimeSpan mode) {
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| 369 | return new Timeout(this, RandTriangular(low, high, mode));
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| 370 | }
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| 371 |
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| 372 | public Timeout TimeoutExponentialD(double mean) {
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| 373 | return new Timeout(this, ToTimeSpan(RandExponential(mean)));
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| 374 | }
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| 375 |
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| 376 | public Timeout TimeoutExponential(TimeSpan mean) {
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| 377 | return new Timeout(this, RandExponential(mean));
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| 378 | }
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| 379 |
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| 380 | public Timeout TimeoutNormalPositiveD(double mu, double sigma) {
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| 381 | return new Timeout(this, ToTimeSpan(RandNormalPositive(mu, sigma)));
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| 382 | }
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| 383 |
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| 384 | public Timeout TimeoutNormalPositive(TimeSpan mu, TimeSpan sigma) {
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| 385 | return new Timeout(this, RandNormalPositive(mu, sigma));
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| 386 | }
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| 387 |
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| 388 | public Timeout TimeoutLogNormalD(double mu, double sigma) {
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| 389 | return new Timeout(this, ToTimeSpan(RandLogNormal(mu, sigma)));
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| 390 | }
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| 391 |
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| 392 | public Timeout TimeoutLogNormal(TimeSpan mu, TimeSpan sigma) {
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| 393 | return new Timeout(this, RandLogNormal(mu, sigma));
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| 394 | }
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| 395 | #endregion
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| 396 | }
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| 397 | }
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