1726 lines
45 KiB
C++
1726 lines
45 KiB
C++
/*
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* Copyright (c) 2006 Georgia Tech Research Corporation
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* Copyright (c) 2011 Mathieu Lacage
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*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License version 2 as
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* published by the Free Software Foundation;
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*
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* Authors: Rajib Bhattacharjea<raj.b@gatech.edu>
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* Hadi Arbabi<marbabi@cs.odu.edu>
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* Mathieu Lacage <mathieu.lacage@gmail.com>
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*
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* Modified by Mitch Watrous <watrous@u.washington.edu>
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*
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*/
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#include "random-variable-stream.h"
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#include "assert.h"
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#include "boolean.h"
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#include "double.h"
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#include "integer.h"
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#include "log.h"
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#include "pointer.h"
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#include "rng-seed-manager.h"
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#include "rng-stream.h"
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#include "string.h"
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#include <algorithm> // upper_bound
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#include <cmath>
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#include <iostream>
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/**
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* \file
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* \ingroup randomvariable
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* ns3::RandomVariableStream and related implementations
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*/
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namespace ns3
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{
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NS_LOG_COMPONENT_DEFINE("RandomVariableStream");
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NS_OBJECT_ENSURE_REGISTERED(RandomVariableStream);
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TypeId
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RandomVariableStream::GetTypeId()
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{
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static TypeId tid = TypeId("ns3::RandomVariableStream")
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.SetParent<Object>()
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.SetGroupName("Core")
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.AddAttribute("Stream",
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"The stream number for this RNG stream. -1 means "
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"\"allocate a stream automatically\". "
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"Note that if -1 is set, Get will return -1 so that it "
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"is not possible to know which "
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"value was automatically allocated.",
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IntegerValue(-1),
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MakeIntegerAccessor(&RandomVariableStream::SetStream,
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&RandomVariableStream::GetStream),
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MakeIntegerChecker<int64_t>())
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.AddAttribute("Antithetic",
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"Set this RNG stream to generate antithetic values",
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BooleanValue(false),
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MakeBooleanAccessor(&RandomVariableStream::SetAntithetic,
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&RandomVariableStream::IsAntithetic),
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MakeBooleanChecker());
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return tid;
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}
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RandomVariableStream::RandomVariableStream()
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: m_rng(nullptr)
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{
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NS_LOG_FUNCTION(this);
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}
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RandomVariableStream::~RandomVariableStream()
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{
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NS_LOG_FUNCTION(this);
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delete m_rng;
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}
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void
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RandomVariableStream::SetAntithetic(bool isAntithetic)
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{
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NS_LOG_FUNCTION(this << isAntithetic);
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m_isAntithetic = isAntithetic;
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}
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bool
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RandomVariableStream::IsAntithetic() const
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{
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NS_LOG_FUNCTION(this);
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return m_isAntithetic;
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}
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uint32_t
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RandomVariableStream::GetInteger()
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{
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NS_LOG_FUNCTION(this);
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return static_cast<uint32_t>(GetValue());
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}
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void
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RandomVariableStream::SetStream(int64_t stream)
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{
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NS_LOG_FUNCTION(this << stream);
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// negative values are not legal.
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NS_ASSERT(stream >= -1);
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delete m_rng;
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if (stream == -1)
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{
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// The first 2^63 streams are reserved for automatic stream
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// number assignment.
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uint64_t nextStream = RngSeedManager::GetNextStreamIndex();
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NS_ASSERT(nextStream <= ((1ULL) << 63));
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m_rng = new RngStream(RngSeedManager::GetSeed(), nextStream, RngSeedManager::GetRun());
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}
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else
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{
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// The last 2^63 streams are reserved for deterministic stream
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// number assignment.
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uint64_t base = ((1ULL) << 63);
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uint64_t target = base + stream;
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m_rng = new RngStream(RngSeedManager::GetSeed(), target, RngSeedManager::GetRun());
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}
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m_stream = stream;
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}
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int64_t
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RandomVariableStream::GetStream() const
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{
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NS_LOG_FUNCTION(this);
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return m_stream;
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}
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RngStream*
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RandomVariableStream::Peek() const
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{
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NS_LOG_FUNCTION(this);
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return m_rng;
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}
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NS_OBJECT_ENSURE_REGISTERED(UniformRandomVariable);
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TypeId
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UniformRandomVariable::GetTypeId()
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{
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static TypeId tid =
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TypeId("ns3::UniformRandomVariable")
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.SetParent<RandomVariableStream>()
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.SetGroupName("Core")
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.AddConstructor<UniformRandomVariable>()
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.AddAttribute("Min",
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"The lower bound on the values returned by this RNG stream.",
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DoubleValue(0),
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MakeDoubleAccessor(&UniformRandomVariable::m_min),
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MakeDoubleChecker<double>())
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.AddAttribute("Max",
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"The upper bound on the values returned by this RNG stream.",
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DoubleValue(1.0),
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MakeDoubleAccessor(&UniformRandomVariable::m_max),
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MakeDoubleChecker<double>());
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return tid;
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}
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UniformRandomVariable::UniformRandomVariable()
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{
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// m_min and m_max are initialized after constructor by attributes
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NS_LOG_FUNCTION(this);
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}
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double
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UniformRandomVariable::GetMin() const
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{
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NS_LOG_FUNCTION(this);
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return m_min;
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}
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double
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UniformRandomVariable::GetMax() const
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{
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NS_LOG_FUNCTION(this);
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return m_max;
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}
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double
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UniformRandomVariable::GetValue(double min, double max)
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{
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NS_LOG_FUNCTION(this << min << max);
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double v = min + Peek()->RandU01() * (max - min);
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if (IsAntithetic())
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{
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v = min + (max - v);
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}
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return v;
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}
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uint32_t
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UniformRandomVariable::GetInteger(uint32_t min, uint32_t max)
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{
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NS_LOG_FUNCTION(this << min << max);
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NS_ASSERT(min <= max);
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return static_cast<uint32_t>(GetValue((double)(min), (double)(max) + 1.0));
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}
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double
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UniformRandomVariable::GetValue()
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{
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NS_LOG_FUNCTION(this);
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return GetValue(m_min, m_max);
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}
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uint32_t
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UniformRandomVariable::GetInteger()
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{
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NS_LOG_FUNCTION(this);
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return static_cast<uint32_t>(GetValue(m_min, m_max + 1));
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}
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NS_OBJECT_ENSURE_REGISTERED(ConstantRandomVariable);
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TypeId
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ConstantRandomVariable::GetTypeId()
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{
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static TypeId tid = TypeId("ns3::ConstantRandomVariable")
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.SetParent<RandomVariableStream>()
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.SetGroupName("Core")
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.AddConstructor<ConstantRandomVariable>()
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.AddAttribute("Constant",
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"The constant value returned by this RNG stream.",
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DoubleValue(0),
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MakeDoubleAccessor(&ConstantRandomVariable::m_constant),
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MakeDoubleChecker<double>());
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return tid;
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}
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ConstantRandomVariable::ConstantRandomVariable()
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{
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// m_constant is initialized after constructor by attributes
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NS_LOG_FUNCTION(this);
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}
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double
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ConstantRandomVariable::GetConstant() const
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{
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NS_LOG_FUNCTION(this);
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return m_constant;
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}
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double
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ConstantRandomVariable::GetValue(double constant)
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{
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NS_LOG_FUNCTION(this << constant);
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return constant;
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}
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uint32_t
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ConstantRandomVariable::GetInteger(uint32_t constant)
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{
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NS_LOG_FUNCTION(this << constant);
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return constant;
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}
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double
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ConstantRandomVariable::GetValue()
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{
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NS_LOG_FUNCTION(this);
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return GetValue(m_constant);
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}
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NS_OBJECT_ENSURE_REGISTERED(SequentialRandomVariable);
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TypeId
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SequentialRandomVariable::GetTypeId()
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{
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static TypeId tid =
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TypeId("ns3::SequentialRandomVariable")
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.SetParent<RandomVariableStream>()
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.SetGroupName("Core")
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.AddConstructor<SequentialRandomVariable>()
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.AddAttribute("Min",
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"The first value of the sequence.",
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DoubleValue(0),
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MakeDoubleAccessor(&SequentialRandomVariable::m_min),
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MakeDoubleChecker<double>())
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.AddAttribute("Max",
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"One more than the last value of the sequence.",
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DoubleValue(0),
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MakeDoubleAccessor(&SequentialRandomVariable::m_max),
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MakeDoubleChecker<double>())
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.AddAttribute("Increment",
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"The sequence random variable increment.",
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StringValue("ns3::ConstantRandomVariable[Constant=1]"),
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MakePointerAccessor(&SequentialRandomVariable::m_increment),
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MakePointerChecker<RandomVariableStream>())
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.AddAttribute("Consecutive",
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"The number of times each member of the sequence is repeated.",
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IntegerValue(1),
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MakeIntegerAccessor(&SequentialRandomVariable::m_consecutive),
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MakeIntegerChecker<uint32_t>());
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return tid;
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}
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SequentialRandomVariable::SequentialRandomVariable()
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: m_current(0),
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m_currentConsecutive(0),
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m_isCurrentSet(false)
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{
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// m_min, m_max, m_increment, and m_consecutive are initialized
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// after constructor by attributes.
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NS_LOG_FUNCTION(this);
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}
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double
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SequentialRandomVariable::GetMin() const
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{
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NS_LOG_FUNCTION(this);
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return m_min;
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}
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double
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SequentialRandomVariable::GetMax() const
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{
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NS_LOG_FUNCTION(this);
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return m_max;
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}
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Ptr<RandomVariableStream>
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SequentialRandomVariable::GetIncrement() const
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{
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NS_LOG_FUNCTION(this);
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return m_increment;
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}
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uint32_t
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SequentialRandomVariable::GetConsecutive() const
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{
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NS_LOG_FUNCTION(this);
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return m_consecutive;
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}
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double
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SequentialRandomVariable::GetValue()
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{
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// Set the current sequence value if it hasn't been set.
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NS_LOG_FUNCTION(this);
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if (!m_isCurrentSet)
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{
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// Start the sequence at its minimium value.
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m_current = m_min;
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m_isCurrentSet = true;
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}
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// Return a sequential series of values
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double r = m_current;
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if (++m_currentConsecutive == m_consecutive)
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{ // Time to advance to next
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m_currentConsecutive = 0;
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m_current += m_increment->GetValue();
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if (m_current >= m_max)
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{
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m_current = m_min + (m_current - m_max);
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}
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}
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return r;
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}
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NS_OBJECT_ENSURE_REGISTERED(ExponentialRandomVariable);
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TypeId
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ExponentialRandomVariable::GetTypeId()
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{
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static TypeId tid =
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TypeId("ns3::ExponentialRandomVariable")
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.SetParent<RandomVariableStream>()
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.SetGroupName("Core")
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.AddConstructor<ExponentialRandomVariable>()
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.AddAttribute("Mean",
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"The mean of the values returned by this RNG stream.",
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DoubleValue(1.0),
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MakeDoubleAccessor(&ExponentialRandomVariable::m_mean),
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MakeDoubleChecker<double>())
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.AddAttribute("Bound",
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"The upper bound on the values returned by this RNG stream.",
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DoubleValue(0.0),
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MakeDoubleAccessor(&ExponentialRandomVariable::m_bound),
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MakeDoubleChecker<double>());
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return tid;
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}
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ExponentialRandomVariable::ExponentialRandomVariable()
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{
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// m_mean and m_bound are initialized after constructor by attributes
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NS_LOG_FUNCTION(this);
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}
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double
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ExponentialRandomVariable::GetMean() const
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{
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NS_LOG_FUNCTION(this);
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return m_mean;
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}
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double
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ExponentialRandomVariable::GetBound() const
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{
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NS_LOG_FUNCTION(this);
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return m_bound;
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}
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double
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ExponentialRandomVariable::GetValue(double mean, double bound)
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{
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NS_LOG_FUNCTION(this << mean << bound);
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while (1)
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{
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// Get a uniform random variable in [0,1].
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double v = Peek()->RandU01();
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if (IsAntithetic())
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{
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v = (1 - v);
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}
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// Calculate the exponential random variable.
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double r = -mean * std::log(v);
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// Use this value if it's acceptable.
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if (bound == 0 || r <= bound)
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{
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return r;
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}
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}
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}
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uint32_t
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ExponentialRandomVariable::GetInteger(uint32_t mean, uint32_t bound)
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{
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NS_LOG_FUNCTION(this << mean << bound);
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return static_cast<uint32_t>(GetValue(mean, bound));
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}
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double
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ExponentialRandomVariable::GetValue()
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{
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NS_LOG_FUNCTION(this);
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return GetValue(m_mean, m_bound);
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}
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NS_OBJECT_ENSURE_REGISTERED(ParetoRandomVariable);
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TypeId
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ParetoRandomVariable::GetTypeId()
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{
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static TypeId tid =
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TypeId("ns3::ParetoRandomVariable")
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.SetParent<RandomVariableStream>()
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.SetGroupName("Core")
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.AddConstructor<ParetoRandomVariable>()
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.AddAttribute(
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"Scale",
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"The scale parameter for the Pareto distribution returned by this RNG stream.",
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DoubleValue(1.0),
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MakeDoubleAccessor(&ParetoRandomVariable::m_scale),
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MakeDoubleChecker<double>())
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.AddAttribute(
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"Shape",
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"The shape parameter for the Pareto distribution returned by this RNG stream.",
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DoubleValue(2.0),
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MakeDoubleAccessor(&ParetoRandomVariable::m_shape),
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MakeDoubleChecker<double>())
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.AddAttribute(
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"Bound",
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"The upper bound on the values returned by this RNG stream (if non-zero).",
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DoubleValue(0.0),
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MakeDoubleAccessor(&ParetoRandomVariable::m_bound),
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MakeDoubleChecker<double>());
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return tid;
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}
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ParetoRandomVariable::ParetoRandomVariable()
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{
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// m_shape, m_shape, and m_bound are initialized after constructor
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// by attributes
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NS_LOG_FUNCTION(this);
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}
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double
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ParetoRandomVariable::GetScale() const
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{
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NS_LOG_FUNCTION(this);
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return m_scale;
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}
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double
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ParetoRandomVariable::GetShape() const
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{
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NS_LOG_FUNCTION(this);
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return m_shape;
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}
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double
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ParetoRandomVariable::GetBound() const
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{
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NS_LOG_FUNCTION(this);
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return m_bound;
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}
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double
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ParetoRandomVariable::GetValue(double scale, double shape, double bound)
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{
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// Calculate the scale parameter.
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NS_LOG_FUNCTION(this << scale << shape << bound);
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while (1)
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{
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// Get a uniform random variable in [0,1].
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double v = Peek()->RandU01();
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if (IsAntithetic())
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{
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v = (1 - v);
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}
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// Calculate the Pareto random variable.
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double r = (scale * (1.0 / std::pow(v, 1.0 / shape)));
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// Use this value if it's acceptable.
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if (bound == 0 || r <= bound)
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{
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return r;
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}
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}
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}
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uint32_t
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ParetoRandomVariable::GetInteger(uint32_t scale, uint32_t shape, uint32_t bound)
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{
|
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NS_LOG_FUNCTION(this << scale << shape << bound);
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return static_cast<uint32_t>(GetValue(scale, shape, bound));
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}
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double
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ParetoRandomVariable::GetValue()
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{
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NS_LOG_FUNCTION(this);
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return GetValue(m_scale, m_shape, m_bound);
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}
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NS_OBJECT_ENSURE_REGISTERED(WeibullRandomVariable);
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TypeId
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WeibullRandomVariable::GetTypeId()
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{
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static TypeId tid =
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TypeId("ns3::WeibullRandomVariable")
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.SetParent<RandomVariableStream>()
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.SetGroupName("Core")
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.AddConstructor<WeibullRandomVariable>()
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.AddAttribute(
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"Scale",
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"The scale parameter for the Weibull distribution returned by this RNG stream.",
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DoubleValue(1.0),
|
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MakeDoubleAccessor(&WeibullRandomVariable::m_scale),
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MakeDoubleChecker<double>())
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.AddAttribute(
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"Shape",
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"The shape parameter for the Weibull distribution returned by this RNG stream.",
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DoubleValue(1),
|
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MakeDoubleAccessor(&WeibullRandomVariable::m_shape),
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MakeDoubleChecker<double>())
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.AddAttribute("Bound",
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"The upper bound on the values returned by this RNG stream.",
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DoubleValue(0.0),
|
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MakeDoubleAccessor(&WeibullRandomVariable::m_bound),
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MakeDoubleChecker<double>());
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return tid;
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}
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|
|
WeibullRandomVariable::WeibullRandomVariable()
|
|
{
|
|
// m_scale, m_shape, and m_bound are initialized after constructor
|
|
// by attributes
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
double
|
|
WeibullRandomVariable::GetScale() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_scale;
|
|
}
|
|
|
|
double
|
|
WeibullRandomVariable::GetShape() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_shape;
|
|
}
|
|
|
|
double
|
|
WeibullRandomVariable::GetBound() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_bound;
|
|
}
|
|
|
|
double
|
|
WeibullRandomVariable::GetValue(double scale, double shape, double bound)
|
|
{
|
|
NS_LOG_FUNCTION(this << scale << shape << bound);
|
|
double exponent = 1.0 / shape;
|
|
while (1)
|
|
{
|
|
// Get a uniform random variable in [0,1].
|
|
double v = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
v = (1 - v);
|
|
}
|
|
|
|
// Calculate the Weibull random variable.
|
|
double r = scale * std::pow(-std::log(v), exponent);
|
|
|
|
// Use this value if it's acceptable.
|
|
if (bound == 0 || r <= bound)
|
|
{
|
|
return r;
|
|
}
|
|
}
|
|
}
|
|
|
|
uint32_t
|
|
WeibullRandomVariable::GetInteger(uint32_t scale, uint32_t shape, uint32_t bound)
|
|
{
|
|
NS_LOG_FUNCTION(this << scale << shape << bound);
|
|
return static_cast<uint32_t>(GetValue(scale, shape, bound));
|
|
}
|
|
|
|
double
|
|
WeibullRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return GetValue(m_scale, m_shape, m_bound);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(NormalRandomVariable);
|
|
|
|
const double NormalRandomVariable::INFINITE_VALUE = 1e307;
|
|
|
|
TypeId
|
|
NormalRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid =
|
|
TypeId("ns3::NormalRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<NormalRandomVariable>()
|
|
.AddAttribute("Mean",
|
|
"The mean value for the normal distribution returned by this RNG stream.",
|
|
DoubleValue(0.0),
|
|
MakeDoubleAccessor(&NormalRandomVariable::m_mean),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute(
|
|
"Variance",
|
|
"The variance value for the normal distribution returned by this RNG stream.",
|
|
DoubleValue(1.0),
|
|
MakeDoubleAccessor(&NormalRandomVariable::m_variance),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute("Bound",
|
|
"The bound on the values returned by this RNG stream.",
|
|
DoubleValue(INFINITE_VALUE),
|
|
MakeDoubleAccessor(&NormalRandomVariable::m_bound),
|
|
MakeDoubleChecker<double>());
|
|
return tid;
|
|
}
|
|
|
|
NormalRandomVariable::NormalRandomVariable()
|
|
: m_nextValid(false)
|
|
{
|
|
// m_mean, m_variance, and m_bound are initialized after constructor
|
|
// by attributes
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
double
|
|
NormalRandomVariable::GetMean() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_mean;
|
|
}
|
|
|
|
double
|
|
NormalRandomVariable::GetVariance() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_variance;
|
|
}
|
|
|
|
double
|
|
NormalRandomVariable::GetBound() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_bound;
|
|
}
|
|
|
|
double
|
|
NormalRandomVariable::GetValue(double mean, double variance, double bound)
|
|
{
|
|
NS_LOG_FUNCTION(this << mean << variance << bound);
|
|
if (m_nextValid)
|
|
{ // use previously generated
|
|
m_nextValid = false;
|
|
double x2 = mean + m_v2 * m_y * std::sqrt(variance);
|
|
if (std::fabs(x2 - mean) <= bound)
|
|
{
|
|
return x2;
|
|
}
|
|
}
|
|
while (1)
|
|
{ // See Simulation Modeling and Analysis p. 466 (Averill Law)
|
|
// for algorithm; basically a Box-Muller transform:
|
|
// http://en.wikipedia.org/wiki/Box-Muller_transform
|
|
double u1 = Peek()->RandU01();
|
|
double u2 = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
u1 = (1 - u1);
|
|
u2 = (1 - u2);
|
|
}
|
|
double v1 = 2 * u1 - 1;
|
|
double v2 = 2 * u2 - 1;
|
|
double w = v1 * v1 + v2 * v2;
|
|
if (w <= 1.0)
|
|
{ // Got good pair
|
|
double y = std::sqrt((-2 * std::log(w)) / w);
|
|
double x1 = mean + v1 * y * std::sqrt(variance);
|
|
// if x1 is in bounds, return it, cache v2 and y
|
|
if (std::fabs(x1 - mean) <= bound)
|
|
{
|
|
m_nextValid = true;
|
|
m_y = y;
|
|
m_v2 = v2;
|
|
return x1;
|
|
}
|
|
// otherwise try and return the other if it is valid
|
|
double x2 = mean + v2 * y * std::sqrt(variance);
|
|
if (std::fabs(x2 - mean) <= bound)
|
|
{
|
|
m_nextValid = false;
|
|
return x2;
|
|
}
|
|
// otherwise, just run this loop again
|
|
}
|
|
}
|
|
}
|
|
|
|
uint32_t
|
|
NormalRandomVariable::GetInteger(uint32_t mean, uint32_t variance, uint32_t bound)
|
|
{
|
|
NS_LOG_FUNCTION(this << mean << variance << bound);
|
|
return static_cast<uint32_t>(GetValue(mean, variance, bound));
|
|
}
|
|
|
|
double
|
|
NormalRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return GetValue(m_mean, m_variance, m_bound);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(LogNormalRandomVariable);
|
|
|
|
TypeId
|
|
LogNormalRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid =
|
|
TypeId("ns3::LogNormalRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<LogNormalRandomVariable>()
|
|
.AddAttribute(
|
|
"Mu",
|
|
"The mu value for the log-normal distribution returned by this RNG stream.",
|
|
DoubleValue(0.0),
|
|
MakeDoubleAccessor(&LogNormalRandomVariable::m_mu),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute(
|
|
"Sigma",
|
|
"The sigma value for the log-normal distribution returned by this RNG stream.",
|
|
DoubleValue(1.0),
|
|
MakeDoubleAccessor(&LogNormalRandomVariable::m_sigma),
|
|
MakeDoubleChecker<double>());
|
|
return tid;
|
|
}
|
|
|
|
LogNormalRandomVariable::LogNormalRandomVariable()
|
|
{
|
|
// m_mu and m_sigma are initialized after constructor by
|
|
// attributes
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
double
|
|
LogNormalRandomVariable::GetMu() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_mu;
|
|
}
|
|
|
|
double
|
|
LogNormalRandomVariable::GetSigma() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_sigma;
|
|
}
|
|
|
|
// The code from this function was adapted from the GNU Scientific
|
|
// Library 1.8:
|
|
/* randist/lognormal.c
|
|
*
|
|
* Copyright (C) 1996, 1997, 1998, 1999, 2000 James Theiler, Brian Gough
|
|
*
|
|
* This program is free software; you can redistribute it and/or modify
|
|
* it under the terms of the GNU General Public License as published by
|
|
* the Free Software Foundation; either version 2 of the License, or (at
|
|
* your option) any later version.
|
|
*
|
|
* This program is distributed in the hope that it will be useful, but
|
|
* WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
|
* General Public License for more details.
|
|
*
|
|
* You should have received a copy of the GNU General Public License
|
|
* along with this program; if not, write to the Free Software
|
|
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
|
*/
|
|
/* The lognormal distribution has the form
|
|
|
|
p(x) dx = 1/(x * sqrt(2 pi sigma^2)) exp(-(ln(x) - zeta)^2/2 sigma^2) dx
|
|
|
|
for x > 0. Lognormal random numbers are the exponentials of
|
|
gaussian random numbers */
|
|
double
|
|
LogNormalRandomVariable::GetValue(double mu, double sigma)
|
|
{
|
|
double v1;
|
|
double v2;
|
|
double r2;
|
|
double normal;
|
|
double x;
|
|
|
|
NS_LOG_FUNCTION(this << mu << sigma);
|
|
|
|
do
|
|
{
|
|
/* choose x,y in uniform square (-1,-1) to (+1,+1) */
|
|
|
|
double u1 = Peek()->RandU01();
|
|
double u2 = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
u1 = (1 - u1);
|
|
u2 = (1 - u2);
|
|
}
|
|
|
|
v1 = -1 + 2 * u1;
|
|
v2 = -1 + 2 * u2;
|
|
|
|
/* see if it is in the unit circle */
|
|
r2 = v1 * v1 + v2 * v2;
|
|
} while (r2 > 1.0 || r2 == 0);
|
|
|
|
normal = v1 * std::sqrt(-2.0 * std::log(r2) / r2);
|
|
|
|
x = std::exp(sigma * normal + mu);
|
|
|
|
return x;
|
|
}
|
|
|
|
uint32_t
|
|
LogNormalRandomVariable::GetInteger(uint32_t mu, uint32_t sigma)
|
|
{
|
|
NS_LOG_FUNCTION(this << mu << sigma);
|
|
return static_cast<uint32_t>(GetValue(mu, sigma));
|
|
}
|
|
|
|
double
|
|
LogNormalRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return GetValue(m_mu, m_sigma);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(GammaRandomVariable);
|
|
|
|
TypeId
|
|
GammaRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid =
|
|
TypeId("ns3::GammaRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<GammaRandomVariable>()
|
|
.AddAttribute("Alpha",
|
|
"The alpha value for the gamma distribution returned by this RNG stream.",
|
|
DoubleValue(1.0),
|
|
MakeDoubleAccessor(&GammaRandomVariable::m_alpha),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute("Beta",
|
|
"The beta value for the gamma distribution returned by this RNG stream.",
|
|
DoubleValue(1.0),
|
|
MakeDoubleAccessor(&GammaRandomVariable::m_beta),
|
|
MakeDoubleChecker<double>());
|
|
return tid;
|
|
}
|
|
|
|
GammaRandomVariable::GammaRandomVariable()
|
|
: m_nextValid(false)
|
|
{
|
|
// m_alpha and m_beta are initialized after constructor by
|
|
// attributes
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
double
|
|
GammaRandomVariable::GetAlpha() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_alpha;
|
|
}
|
|
|
|
double
|
|
GammaRandomVariable::GetBeta() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_beta;
|
|
}
|
|
|
|
/*
|
|
The code for the following generator functions was adapted from ns-2
|
|
tools/ranvar.cc
|
|
|
|
Originally the algorithm was devised by Marsaglia in 2000:
|
|
G. Marsaglia, W. W. Tsang: A simple method for generating Gamma variables
|
|
ACM Transactions on mathematical software, Vol. 26, No. 3, Sept. 2000
|
|
|
|
The Gamma distribution density function has the form
|
|
|
|
x^(alpha-1) * exp(-x/beta)
|
|
p(x; alpha, beta) = ----------------------------
|
|
beta^alpha * Gamma(alpha)
|
|
|
|
for x > 0.
|
|
*/
|
|
double
|
|
GammaRandomVariable::GetValue(double alpha, double beta)
|
|
{
|
|
NS_LOG_FUNCTION(this << alpha << beta);
|
|
if (alpha < 1)
|
|
{
|
|
double u = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
return GetValue(1.0 + alpha, beta) * std::pow(u, 1.0 / alpha);
|
|
}
|
|
|
|
double x;
|
|
double v;
|
|
double u;
|
|
double d = alpha - 1.0 / 3.0;
|
|
double c = (1.0 / 3.0) / std::sqrt(d);
|
|
|
|
while (1)
|
|
{
|
|
do
|
|
{
|
|
// Get a value from a normal distribution that has mean
|
|
// zero, variance 1, and no bound.
|
|
double mean = 0.0;
|
|
double variance = 1.0;
|
|
double bound = NormalRandomVariable::INFINITE_VALUE;
|
|
x = GetNormalValue(mean, variance, bound);
|
|
|
|
v = 1.0 + c * x;
|
|
} while (v <= 0);
|
|
|
|
v = v * v * v;
|
|
u = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
if (u < 1 - 0.0331 * x * x * x * x)
|
|
{
|
|
break;
|
|
}
|
|
if (std::log(u) < 0.5 * x * x + d * (1 - v + std::log(v)))
|
|
{
|
|
break;
|
|
}
|
|
}
|
|
|
|
return beta * d * v;
|
|
}
|
|
|
|
double
|
|
GammaRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return GetValue(m_alpha, m_beta);
|
|
}
|
|
|
|
double
|
|
GammaRandomVariable::GetNormalValue(double mean, double variance, double bound)
|
|
{
|
|
NS_LOG_FUNCTION(this << mean << variance << bound);
|
|
if (m_nextValid)
|
|
{ // use previously generated
|
|
m_nextValid = false;
|
|
double x2 = mean + m_v2 * m_y * std::sqrt(variance);
|
|
if (std::fabs(x2 - mean) <= bound)
|
|
{
|
|
return x2;
|
|
}
|
|
}
|
|
while (1)
|
|
{ // See Simulation Modeling and Analysis p. 466 (Averill Law)
|
|
// for algorithm; basically a Box-Muller transform:
|
|
// http://en.wikipedia.org/wiki/Box-Muller_transform
|
|
double u1 = Peek()->RandU01();
|
|
double u2 = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
u1 = (1 - u1);
|
|
u2 = (1 - u2);
|
|
}
|
|
double v1 = 2 * u1 - 1;
|
|
double v2 = 2 * u2 - 1;
|
|
double w = v1 * v1 + v2 * v2;
|
|
if (w <= 1.0)
|
|
{ // Got good pair
|
|
double y = std::sqrt((-2 * std::log(w)) / w);
|
|
double x1 = mean + v1 * y * std::sqrt(variance);
|
|
// if x1 is in bounds, return it, cache v2 an y
|
|
if (std::fabs(x1 - mean) <= bound)
|
|
{
|
|
m_nextValid = true;
|
|
m_y = y;
|
|
m_v2 = v2;
|
|
return x1;
|
|
}
|
|
// otherwise try and return the other if it is valid
|
|
double x2 = mean + v2 * y * std::sqrt(variance);
|
|
if (std::fabs(x2 - mean) <= bound)
|
|
{
|
|
m_nextValid = false;
|
|
return x2;
|
|
}
|
|
// otherwise, just run this loop again
|
|
}
|
|
}
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(ErlangRandomVariable);
|
|
|
|
TypeId
|
|
ErlangRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid =
|
|
TypeId("ns3::ErlangRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<ErlangRandomVariable>()
|
|
.AddAttribute("K",
|
|
"The k value for the Erlang distribution returned by this RNG stream.",
|
|
IntegerValue(1),
|
|
MakeIntegerAccessor(&ErlangRandomVariable::m_k),
|
|
MakeIntegerChecker<uint32_t>())
|
|
.AddAttribute(
|
|
"Lambda",
|
|
"The lambda value for the Erlang distribution returned by this RNG stream.",
|
|
DoubleValue(1.0),
|
|
MakeDoubleAccessor(&ErlangRandomVariable::m_lambda),
|
|
MakeDoubleChecker<double>());
|
|
return tid;
|
|
}
|
|
|
|
ErlangRandomVariable::ErlangRandomVariable()
|
|
{
|
|
// m_k and m_lambda are initialized after constructor by attributes
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
uint32_t
|
|
ErlangRandomVariable::GetK() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_k;
|
|
}
|
|
|
|
double
|
|
ErlangRandomVariable::GetLambda() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_lambda;
|
|
}
|
|
|
|
/*
|
|
The code for the following generator functions was adapted from ns-2
|
|
tools/ranvar.cc
|
|
|
|
The Erlang distribution density function has the form
|
|
|
|
x^(k-1) * exp(-x/lambda)
|
|
p(x; k, lambda) = ---------------------------
|
|
lambda^k * (k-1)!
|
|
|
|
for x > 0.
|
|
*/
|
|
double
|
|
ErlangRandomVariable::GetValue(uint32_t k, double lambda)
|
|
{
|
|
NS_LOG_FUNCTION(this << k << lambda);
|
|
double mean = lambda;
|
|
double bound = 0.0;
|
|
|
|
double result = 0;
|
|
for (unsigned int i = 0; i < k; ++i)
|
|
{
|
|
result += GetExponentialValue(mean, bound);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
uint32_t
|
|
ErlangRandomVariable::GetInteger(uint32_t k, uint32_t lambda)
|
|
{
|
|
NS_LOG_FUNCTION(this << k << lambda);
|
|
return static_cast<uint32_t>(GetValue(k, lambda));
|
|
}
|
|
|
|
double
|
|
ErlangRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return GetValue(m_k, m_lambda);
|
|
}
|
|
|
|
double
|
|
ErlangRandomVariable::GetExponentialValue(double mean, double bound)
|
|
{
|
|
NS_LOG_FUNCTION(this << mean << bound);
|
|
while (1)
|
|
{
|
|
// Get a uniform random variable in [0,1].
|
|
double v = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
v = (1 - v);
|
|
}
|
|
|
|
// Calculate the exponential random variable.
|
|
double r = -mean * std::log(v);
|
|
|
|
// Use this value if it's acceptable.
|
|
if (bound == 0 || r <= bound)
|
|
{
|
|
return r;
|
|
}
|
|
}
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(TriangularRandomVariable);
|
|
|
|
TypeId
|
|
TriangularRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid =
|
|
TypeId("ns3::TriangularRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<TriangularRandomVariable>()
|
|
.AddAttribute(
|
|
"Mean",
|
|
"The mean value for the triangular distribution returned by this RNG stream.",
|
|
DoubleValue(0.5),
|
|
MakeDoubleAccessor(&TriangularRandomVariable::m_mean),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute("Min",
|
|
"The lower bound on the values returned by this RNG stream.",
|
|
DoubleValue(0.0),
|
|
MakeDoubleAccessor(&TriangularRandomVariable::m_min),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute("Max",
|
|
"The upper bound on the values returned by this RNG stream.",
|
|
DoubleValue(1.0),
|
|
MakeDoubleAccessor(&TriangularRandomVariable::m_max),
|
|
MakeDoubleChecker<double>());
|
|
return tid;
|
|
}
|
|
|
|
TriangularRandomVariable::TriangularRandomVariable()
|
|
{
|
|
// m_mean, m_min, and m_max are initialized after constructor by
|
|
// attributes
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
double
|
|
TriangularRandomVariable::GetMean() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_mean;
|
|
}
|
|
|
|
double
|
|
TriangularRandomVariable::GetMin() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_min;
|
|
}
|
|
|
|
double
|
|
TriangularRandomVariable::GetMax() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_max;
|
|
}
|
|
|
|
double
|
|
TriangularRandomVariable::GetValue(double mean, double min, double max)
|
|
{
|
|
// Calculate the mode.
|
|
NS_LOG_FUNCTION(this << mean << min << max);
|
|
double mode = 3.0 * mean - min - max;
|
|
|
|
// Get a uniform random variable in [0,1].
|
|
double u = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
|
|
// Calculate the triangular random variable.
|
|
if (u <= (mode - min) / (max - min))
|
|
{
|
|
return min + std::sqrt(u * (max - min) * (mode - min));
|
|
}
|
|
else
|
|
{
|
|
return max - std::sqrt((1 - u) * (max - min) * (max - mode));
|
|
}
|
|
}
|
|
|
|
uint32_t
|
|
TriangularRandomVariable::GetInteger(uint32_t mean, uint32_t min, uint32_t max)
|
|
{
|
|
NS_LOG_FUNCTION(this << mean << min << max);
|
|
return static_cast<uint32_t>(GetValue(mean, min, max));
|
|
}
|
|
|
|
double
|
|
TriangularRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return GetValue(m_mean, m_min, m_max);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(ZipfRandomVariable);
|
|
|
|
TypeId
|
|
ZipfRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid =
|
|
TypeId("ns3::ZipfRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<ZipfRandomVariable>()
|
|
.AddAttribute("N",
|
|
"The n value for the Zipf distribution returned by this RNG stream.",
|
|
IntegerValue(1),
|
|
MakeIntegerAccessor(&ZipfRandomVariable::m_n),
|
|
MakeIntegerChecker<uint32_t>())
|
|
.AddAttribute("Alpha",
|
|
"The alpha value for the Zipf distribution returned by this RNG stream.",
|
|
DoubleValue(0.0),
|
|
MakeDoubleAccessor(&ZipfRandomVariable::m_alpha),
|
|
MakeDoubleChecker<double>());
|
|
return tid;
|
|
}
|
|
|
|
ZipfRandomVariable::ZipfRandomVariable()
|
|
{
|
|
// m_n and m_alpha are initialized after constructor by attributes
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
uint32_t
|
|
ZipfRandomVariable::GetN() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_n;
|
|
}
|
|
|
|
double
|
|
ZipfRandomVariable::GetAlpha() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_alpha;
|
|
}
|
|
|
|
double
|
|
ZipfRandomVariable::GetValue(uint32_t n, double alpha)
|
|
{
|
|
NS_LOG_FUNCTION(this << n << alpha);
|
|
// Calculate the normalization constant c.
|
|
m_c = 0.0;
|
|
for (uint32_t i = 1; i <= n; i++)
|
|
{
|
|
m_c += (1.0 / std::pow((double)i, alpha));
|
|
}
|
|
m_c = 1.0 / m_c;
|
|
|
|
// Get a uniform random variable in [0,1].
|
|
double u = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
|
|
double sum_prob = 0;
|
|
double zipf_value = 0;
|
|
for (uint32_t i = 1; i <= m_n; i++)
|
|
{
|
|
sum_prob += m_c / std::pow((double)i, m_alpha);
|
|
if (sum_prob > u)
|
|
{
|
|
zipf_value = i;
|
|
break;
|
|
}
|
|
}
|
|
return zipf_value;
|
|
}
|
|
|
|
uint32_t
|
|
ZipfRandomVariable::GetInteger(uint32_t n, uint32_t alpha)
|
|
{
|
|
NS_LOG_FUNCTION(this << n << alpha);
|
|
return static_cast<uint32_t>(GetValue(n, alpha));
|
|
}
|
|
|
|
double
|
|
ZipfRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return GetValue(m_n, m_alpha);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(ZetaRandomVariable);
|
|
|
|
TypeId
|
|
ZetaRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid =
|
|
TypeId("ns3::ZetaRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<ZetaRandomVariable>()
|
|
.AddAttribute("Alpha",
|
|
"The alpha value for the zeta distribution returned by this RNG stream.",
|
|
DoubleValue(3.14),
|
|
MakeDoubleAccessor(&ZetaRandomVariable::m_alpha),
|
|
MakeDoubleChecker<double>());
|
|
return tid;
|
|
}
|
|
|
|
ZetaRandomVariable::ZetaRandomVariable()
|
|
{
|
|
// m_alpha is initialized after constructor by attributes
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
double
|
|
ZetaRandomVariable::GetAlpha() const
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return m_alpha;
|
|
}
|
|
|
|
double
|
|
ZetaRandomVariable::GetValue(double alpha)
|
|
{
|
|
NS_LOG_FUNCTION(this << alpha);
|
|
m_b = std::pow(2.0, alpha - 1.0);
|
|
|
|
double u;
|
|
double v;
|
|
double X;
|
|
double T;
|
|
double test;
|
|
|
|
do
|
|
{
|
|
// Get a uniform random variable in [0,1].
|
|
u = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
|
|
// Get a uniform random variable in [0,1].
|
|
v = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
v = (1 - v);
|
|
}
|
|
|
|
X = std::floor(std::pow(u, -1.0 / (m_alpha - 1.0)));
|
|
T = std::pow(1.0 + 1.0 / X, m_alpha - 1.0);
|
|
test = v * X * (T - 1.0) / (m_b - 1.0);
|
|
} while (test > (T / m_b));
|
|
|
|
return X;
|
|
}
|
|
|
|
uint32_t
|
|
ZetaRandomVariable::GetInteger(uint32_t alpha)
|
|
{
|
|
NS_LOG_FUNCTION(this << alpha);
|
|
return static_cast<uint32_t>(GetValue(alpha));
|
|
}
|
|
|
|
double
|
|
ZetaRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
return GetValue(m_alpha);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(DeterministicRandomVariable);
|
|
|
|
TypeId
|
|
DeterministicRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid = TypeId("ns3::DeterministicRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<DeterministicRandomVariable>();
|
|
return tid;
|
|
}
|
|
|
|
DeterministicRandomVariable::DeterministicRandomVariable()
|
|
: m_count(0),
|
|
m_next(0),
|
|
m_data(nullptr)
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
DeterministicRandomVariable::~DeterministicRandomVariable()
|
|
{
|
|
// Delete any values currently set.
|
|
NS_LOG_FUNCTION(this);
|
|
if (m_data != nullptr)
|
|
{
|
|
delete[] m_data;
|
|
}
|
|
}
|
|
|
|
void
|
|
DeterministicRandomVariable::SetValueArray(const std::vector<double>& values)
|
|
{
|
|
SetValueArray(values.data(), values.size());
|
|
}
|
|
|
|
void
|
|
DeterministicRandomVariable::SetValueArray(const double* values, std::size_t length)
|
|
{
|
|
NS_LOG_FUNCTION(this << values << length);
|
|
// Delete any values currently set.
|
|
if (m_data != nullptr)
|
|
{
|
|
delete[] m_data;
|
|
}
|
|
|
|
// Make room for the values being set.
|
|
m_data = new double[length];
|
|
m_count = length;
|
|
m_next = length;
|
|
|
|
// Copy the values.
|
|
for (std::size_t i = 0; i < m_count; i++)
|
|
{
|
|
m_data[i] = values[i];
|
|
}
|
|
}
|
|
|
|
double
|
|
DeterministicRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
// Make sure the array has been set.
|
|
NS_ASSERT(m_count > 0);
|
|
|
|
if (m_next == m_count)
|
|
{
|
|
m_next = 0;
|
|
}
|
|
return m_data[m_next++];
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED(EmpiricalRandomVariable);
|
|
|
|
// ValueCDF methods
|
|
EmpiricalRandomVariable::ValueCDF::ValueCDF()
|
|
: value(0.0),
|
|
cdf(0.0)
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
EmpiricalRandomVariable::ValueCDF::ValueCDF(double v, double c)
|
|
: value(v),
|
|
cdf(c)
|
|
{
|
|
NS_LOG_FUNCTION(this << v << c);
|
|
NS_ASSERT(c >= 0.0 && c <= 1.0);
|
|
}
|
|
|
|
bool
|
|
operator<(EmpiricalRandomVariable::ValueCDF a, EmpiricalRandomVariable::ValueCDF b)
|
|
{
|
|
return a.cdf < b.cdf;
|
|
}
|
|
|
|
TypeId
|
|
EmpiricalRandomVariable::GetTypeId()
|
|
{
|
|
static TypeId tid =
|
|
TypeId("ns3::EmpiricalRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName("Core")
|
|
.AddConstructor<EmpiricalRandomVariable>()
|
|
.AddAttribute("Interpolate",
|
|
"Treat the CDF as a smooth distribution and interpolate, "
|
|
"default is to treat the CDF as a histogram and sample.",
|
|
BooleanValue(false),
|
|
MakeBooleanAccessor(&EmpiricalRandomVariable::m_interpolate),
|
|
MakeBooleanChecker());
|
|
return tid;
|
|
}
|
|
|
|
EmpiricalRandomVariable::EmpiricalRandomVariable()
|
|
: m_validated(false)
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
}
|
|
|
|
bool
|
|
EmpiricalRandomVariable::SetInterpolate(bool interpolate)
|
|
{
|
|
NS_LOG_FUNCTION(this << interpolate);
|
|
bool prev = m_interpolate;
|
|
m_interpolate = interpolate;
|
|
return prev;
|
|
}
|
|
|
|
bool
|
|
EmpiricalRandomVariable::PreSample(double& value)
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
|
|
if (!m_validated)
|
|
{
|
|
Validate();
|
|
}
|
|
|
|
// Get a uniform random variable in [0, 1].
|
|
double r = Peek()->RandU01();
|
|
if (IsAntithetic())
|
|
{
|
|
r = (1 - r);
|
|
}
|
|
|
|
value = r;
|
|
bool valid = false;
|
|
// check extrema
|
|
if (r <= m_emp.front().cdf)
|
|
{
|
|
value = m_emp.front().value; // Less than first
|
|
valid = true;
|
|
}
|
|
else if (r >= m_emp.back().cdf)
|
|
{
|
|
value = m_emp.back().value; // Greater than last
|
|
valid = true;
|
|
}
|
|
return valid;
|
|
}
|
|
|
|
double
|
|
EmpiricalRandomVariable::GetValue()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
|
|
double value;
|
|
if (PreSample(value))
|
|
{
|
|
return value;
|
|
}
|
|
|
|
// value now has the (unused) URNG selector
|
|
if (m_interpolate)
|
|
{
|
|
value = DoInterpolate(value);
|
|
}
|
|
else
|
|
{
|
|
value = DoSampleCDF(value);
|
|
}
|
|
return value;
|
|
}
|
|
|
|
double
|
|
EmpiricalRandomVariable::DoSampleCDF(double r)
|
|
{
|
|
NS_LOG_FUNCTION(this << r);
|
|
|
|
ValueCDF selector(0, r);
|
|
auto bound = std::upper_bound(m_emp.begin(), m_emp.end(), selector);
|
|
|
|
return bound->value;
|
|
}
|
|
|
|
double
|
|
EmpiricalRandomVariable::Interpolate()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
|
|
double value;
|
|
if (PreSample(value))
|
|
{
|
|
return value;
|
|
}
|
|
|
|
// value now has the (unused) URNG selector
|
|
value = DoInterpolate(value);
|
|
return value;
|
|
}
|
|
|
|
double
|
|
EmpiricalRandomVariable::DoInterpolate(double r)
|
|
{
|
|
NS_LOG_FUNCTION(this << r);
|
|
|
|
// Return a value from the empirical distribution
|
|
// This code based (loosely) on code by Bruce Mah (Thanks Bruce!)
|
|
|
|
// search
|
|
ValueCDF selector(0, r);
|
|
auto upper = std::upper_bound(m_emp.begin(), m_emp.end(), selector);
|
|
auto lower = std::prev(upper, 1);
|
|
if (upper == m_emp.begin())
|
|
{
|
|
lower = upper;
|
|
}
|
|
|
|
// Interpolate random value in range [v1..v2) based on [c1 .. r .. c2)
|
|
double c1 = lower->cdf;
|
|
double c2 = upper->cdf;
|
|
double v1 = lower->value;
|
|
double v2 = upper->value;
|
|
|
|
double value = (v1 + ((v2 - v1) / (c2 - c1)) * (r - c1));
|
|
return value;
|
|
}
|
|
|
|
void
|
|
EmpiricalRandomVariable::CDF(double v, double c)
|
|
{
|
|
// Add a new empirical datapoint to the empirical cdf
|
|
// NOTE. These MUST be inserted in non-decreasing order
|
|
NS_LOG_FUNCTION(this << v << c);
|
|
m_emp.emplace_back(v, c);
|
|
}
|
|
|
|
void
|
|
EmpiricalRandomVariable::Validate()
|
|
{
|
|
NS_LOG_FUNCTION(this);
|
|
if (m_emp.empty())
|
|
{
|
|
NS_FATAL_ERROR("CDF is not initialized");
|
|
}
|
|
ValueCDF prior = m_emp[0];
|
|
for (auto current : m_emp)
|
|
{
|
|
if (current.value < prior.value || current.cdf < prior.cdf)
|
|
{ // Error
|
|
std::cerr << "Empirical Dist error,"
|
|
<< " current value " << current.value << " prior value " << prior.value
|
|
<< " current cdf " << current.cdf << " prior cdf " << prior.cdf << std::endl;
|
|
NS_FATAL_ERROR("Empirical Dist error");
|
|
}
|
|
prior = current;
|
|
}
|
|
if (prior.cdf != 1.0)
|
|
{
|
|
NS_FATAL_ERROR("CDF does not cover the whole distribution");
|
|
}
|
|
m_validated = true;
|
|
}
|
|
|
|
} // namespace ns3
|