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core: refactor GetInteger() in the random variable streams

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This commit is contained in:
Peter D. Barnes, Jr
2023-01-27 16:42:33 -08:00
parent 3d67f736cd
commit bf8b442aeb
2 changed files with 28 additions and 338 deletions
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114
src/core/model/random-variable-stream.cc
View File

@@ -102,6 +102,13 @@ RandomVariableStream::IsAntithetic() const
return m_isAntithetic;
}
uint32_t
RandomVariableStream::GetInteger()
{
NS_LOG_FUNCTION(this);
return static_cast<uint32_t>(GetValue());
}
void
RandomVariableStream::SetStream(int64_t stream)
{
@@ -216,7 +223,7 @@ uint32_t
UniformRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_min, m_max + 1);
return static_cast<uint32_t>(GetValue(m_min, m_max + 1));
}
NS_OBJECT_ENSURE_REGISTERED(ConstantRandomVariable);
@@ -270,13 +277,6 @@ ConstantRandomVariable::GetValue()
return GetValue(m_constant);
}
uint32_t
ConstantRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_constant);
}
NS_OBJECT_ENSURE_REGISTERED(SequentialRandomVariable);
TypeId
@@ -374,13 +374,6 @@ SequentialRandomVariable::GetValue()
return r;
}
uint32_t
SequentialRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue();
}
NS_OBJECT_ENSURE_REGISTERED(ExponentialRandomVariable);
TypeId
@@ -462,13 +455,6 @@ ExponentialRandomVariable::GetValue()
return GetValue(m_mean, m_bound);
}
uint32_t
ExponentialRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_mean, m_bound);
}
NS_OBJECT_ENSURE_REGISTERED(ParetoRandomVariable);
TypeId
@@ -568,13 +554,6 @@ ParetoRandomVariable::GetValue()
return GetValue(m_scale, m_shape, m_bound);
}
uint32_t
ParetoRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_scale, m_shape, m_bound);
}
NS_OBJECT_ENSURE_REGISTERED(WeibullRandomVariable);
TypeId
@@ -672,13 +651,6 @@ WeibullRandomVariable::GetValue()
return GetValue(m_scale, m_shape, m_bound);
}
uint32_t
WeibullRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_scale, m_shape, m_bound);
}
NS_OBJECT_ENSURE_REGISTERED(NormalRandomVariable);
const double NormalRandomVariable::INFINITE_VALUE = 1e307;
@@ -804,13 +776,6 @@ NormalRandomVariable::GetValue()
return GetValue(m_mean, m_variance, m_bound);
}
uint32_t
NormalRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_mean, m_variance, m_bound);
}
NS_OBJECT_ENSURE_REGISTERED(LogNormalRandomVariable);
TypeId
@@ -934,13 +899,6 @@ LogNormalRandomVariable::GetValue()
return GetValue(m_mu, m_sigma);
}
uint32_t
LogNormalRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_mu, m_sigma);
}
NS_OBJECT_ENSURE_REGISTERED(GammaRandomVariable);
TypeId
@@ -1055,13 +1013,6 @@ GammaRandomVariable::GetValue(double alpha, double beta)
return beta * d * v;
}
uint32_t
GammaRandomVariable::GetInteger(uint32_t alpha, uint32_t beta)
{
NS_LOG_FUNCTION(this << alpha << beta);
return static_cast<uint32_t>(GetValue(alpha, beta));
}
double
GammaRandomVariable::GetValue()
{
@@ -1069,13 +1020,6 @@ GammaRandomVariable::GetValue()
return GetValue(m_alpha, m_beta);
}
uint32_t
GammaRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_alpha, m_beta);
}
double
GammaRandomVariable::GetNormalValue(double mean, double variance, double bound)
{
@@ -1213,13 +1157,6 @@ ErlangRandomVariable::GetValue()
return GetValue(m_k, m_lambda);
}
uint32_t
ErlangRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_k, m_lambda);
}
double
ErlangRandomVariable::GetExponentialValue(double mean, double bound)
{
@@ -1340,13 +1277,6 @@ TriangularRandomVariable::GetValue()
return GetValue(m_mean, m_min, m_max);
}
uint32_t
TriangularRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_mean, m_min, m_max);
}
NS_OBJECT_ENSURE_REGISTERED(ZipfRandomVariable);
TypeId
@@ -1437,13 +1367,6 @@ ZipfRandomVariable::GetValue()
return GetValue(m_n, m_alpha);
}
uint32_t
ZipfRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_n, m_alpha);
}
NS_OBJECT_ENSURE_REGISTERED(ZetaRandomVariable);
TypeId
@@ -1525,13 +1448,6 @@ ZetaRandomVariable::GetValue()
return GetValue(m_alpha);
}
uint32_t
ZetaRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue(m_alpha);
}
NS_OBJECT_ENSURE_REGISTERED(DeterministicRandomVariable);
TypeId
@@ -1598,13 +1514,6 @@ DeterministicRandomVariable::GetValue()
return m_data[m_next++];
}
uint32_t
DeterministicRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return (uint32_t)GetValue();
}
NS_OBJECT_ENSURE_REGISTERED(EmpiricalRandomVariable);
// ValueCDF methods
@@ -1661,13 +1570,6 @@ EmpiricalRandomVariable::SetInterpolate(bool interpolate)
return prev;
}
uint32_t
EmpiricalRandomVariable::GetInteger()
{
NS_LOG_FUNCTION(this);
return static_cast<uint32_t>(GetValue());
}
bool
EmpiricalRandomVariable::PreSample(double& value)
{

252
src/core/model/random-variable-stream.h
View File

@@ -150,9 +150,10 @@ class RandomVariableStream : public Object
/**
* \brief Get the next random value as an integer drawn from the distribution.
* The base implementation returns `(uint32_t)GetValue()`
* \return An integer random value.
*/
virtual uint32_t GetInteger() = 0;
virtual uint32_t GetInteger();
protected:
/**
@@ -271,10 +272,10 @@ class UniformRandomVariable : public RandomVariableStream
* \note The upper limit is excluded from the output range.
*/
double GetValue() override;
/**
* \brief Get the next random value as an integer drawn from the distribution.
* \return An integer random value.
* \note The upper limit is included in the output range.
* \copydoc RandomVariableStream::GetInteger()
* \note The upper limit is included in the output range, unlike GetValue().
*/
uint32_t GetInteger() override;
@@ -334,7 +335,7 @@ class ConstantRandomVariable : public RandomVariableStream
/* \note This RNG always returns the same value. */
double GetValue() override;
/* \note This RNG always returns the same value. */
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The constant value returned by this RNG stream. */
@@ -420,7 +421,7 @@ class SequentialRandomVariable : public RandomVariableStream
// Inherited from RandomVariableStream
double GetValue() override;
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The first value of the sequence. */
@@ -565,7 +566,7 @@ class ExponentialRandomVariable : public RandomVariableStream
// Inherited from RandomVariableStream
double GetValue() override;
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The mean value of the unbounded exponential distribution. */
@@ -742,31 +743,7 @@ class ParetoRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns a random unsigned integer from a Pareto distribution with the current mean,
* shape, and upper bound.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if
* m_isAntithetic is equal to true. If \f$u\f$ is a uniform variable
* over [0,1] and
*
* \f[
* x = \frac{scale}{u^{\frac{1}{shape}}}
* \f]
*
* is a value that would be returned normally.
*
* The value returned in the antithetic case, \f$x'\f$, is
* calculated as
*
* \f[
* x' = \frac{scale}{{(1 - u)}^{\frac{1}{shape}}} ,
* \f]
*
* which now involves the distance \f$u\f$ is from 1 in the denominator.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The scale parameter for the Pareto distribution returned by this RNG stream. */
@@ -955,30 +932,7 @@ class WeibullRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns a random unsigned integer from a Weibull distribution with the current scale,
* shape, and upper bound.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if
* m_isAntithetic is equal to true. If \f$u\f$ is a uniform variable
* over [0,1] and
*
* \f[
* x = scale * {(-\log(u))}^{\frac{1}{shape}}
* \f]
*
* is a value that would be returned normally, then \f$(1 - u\f$) is
* the distance that \f$u\f$ would be from \f$1\f$. The value
* returned in the antithetic case, \f$x'\f$, is calculated as
*
* \f[
* x' = scale * {(-\log(1 - u))}^{\frac{1}{shape}} ,
* \f]
*
* which now involves the log of the distance \f$u\f$ is from 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The scale parameter for the Weibull distribution returned by this RNG stream. */
@@ -1185,42 +1139,7 @@ class NormalRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns a random unsigned integer from a normal distribution with the current mean,
* variance, and bound.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if m_isAntithetic
* is equal to true. If \f$u1\f$ and \f$u2\f$ are uniform variables
* over [0,1], then the values that would be returned normally, \f$x1\f$ and \f$x2\f$, are
* calculated as follows:
*
* \f{eqnarray*}{
* v1 & = & 2 * u1 - 1 \\
* v2 & = & 2 * u2 - 1 \\
* w & = & v1 * v1 + v2 * v2 \\
* y & = & \sqrt{\frac{-2 * \log(w)}{w}} \\
* x1 & = & mean + v1 * y * \sqrt{variance} \\
* x2 & = & mean + v2 * y * \sqrt{variance} .
* \f}
*
* For the antithetic case, \f$(1 - u1\f$) and \f$(1 - u2\f$) are
* the distances that \f$u1\f$ and \f$u2\f$ would be from \f$1\f$.
* The antithetic values returned, \f$x1'\f$ and \f$x2'\f$, are
* calculated as follows:
*
* \f{eqnarray*}{
* v1' & = & 2 * (1 - u1) - 1 \\
* v2' & = & 2 * (1 - u2) - 1 \\
* w' & = & v1' * v1' + v2' * v2' \\
* y' & = & \sqrt{\frac{-2 * \log(w')}{w'}} \\
* x1' & = & mean + v1' * y' * \sqrt{variance} \\
* x2' & = & mean + v2' * y' * \sqrt{variance} ,
* \f}
*
* which now involves the distances \f$u1\f$ and \f$u2\f$ are from 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The mean value for the normal distribution returned by this RNG stream. */
@@ -1425,40 +1344,7 @@ class LogNormalRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns a random unsigned integer from a log-normal distribution with the current mu
* and sigma.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if m_isAntithetic
* is equal to true. If \f$u1\f$ and \f$u2\f$ are uniform variables
* over [0,1], then the value that would be returned normally, \f$x\f$, is calculated as
* follows:
*
* \f{eqnarray*}{
* v1 & = & -1 + 2 * u1 \\
* v2 & = & -1 + 2 * u2 \\
* r2 & = & v1 * v1 + v2 * v2 \\
* normal & = & v1 * \sqrt{\frac{-2.0 * \log{r2}}{r2}} \\
* x & = & \exp{sigma * normal + mu} .
* \f}
*
* For the antithetic case, \f$(1 - u1\f$) and \f$(1 - u2\f$) are
* the distances that \f$u1\f$ and \f$u2\f$ would be from \f$1\f$.
* The antithetic value returned, \f$x'\f$, is calculated as
* follows:
*
* \f{eqnarray*}{
* v1' & = & -1 + 2 * (1 - u1) \\
* v2' & = & -1 + 2 * (1 - u2) \\
* r2' & = & v1' * v1' + v2' * v2' \\
* normal' & = & v1' * \sqrt{\frac{-2.0 * \log{r2'}}{r2'}} \\
* x' & = & \exp{sigma * normal' + mu} .
* \f}
*
* which now involves the distances \f$u1\f$ and \f$u2\f$ are from 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The mu value for the log-normal distribution returned by this RNG stream. */
@@ -1576,19 +1462,7 @@ class GammaRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns a random unsigned integer from a gamma distribution with the current alpha and
* beta.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if m_isAntithetic
* is equal to true. If \f$u\f$ is a uniform variable over [0,1]
* and \f$x\f$ is a value that would be returned normally, then
* \f$(1 - u\f$) is the distance that \f$u\f$ would be from \f$1\f$.
* The value returned in the antithetic case, \f$x'\f$, uses (1-u),
* which is the distance \f$u\f$ is from the 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/**
@@ -1757,20 +1631,9 @@ class ErlangRandomVariable : public RandomVariableStream
* classes.
*/
double GetValue() override;
using RandomVariableStream::GetInteger;
/**
* \brief Returns a random unsigned integer from an Erlang distribution with the current k and
* lambda.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if m_isAntithetic
* is equal to true. If \f$u\f$ is a uniform variable over [0,1]
* and \f$x\f$ is a value that would be returned normally, then
* \f$(1 - u\f$) is the distance that \f$u\f$ would be from \f$1\f$.
* The value returned in the antithetic case, \f$x'\f$, uses (1-u),
* which is the distance \f$u\f$ is from the 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/**
@@ -1991,42 +1854,7 @@ class TriangularRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns a random unsigned integer from a triangular distribution with the current
* mean, min, and max.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if
* m_isAntithetic is equal to true. If \f$u\f$ is a uniform variable
* over [0,1] and
*
* \f[
* x = \left\{ \begin{array}{rl}
* min + \sqrt{u * (max - min) * (mode - min)} &\mbox{ if $u <= (mode - min)/(max -
* min)$} \\ max - \sqrt{ (1 - u) * (max - min) * (max - mode) } &\mbox{ otherwise} \end{array}
* \right. \f]
*
* is a value that would be returned normally, where the mode or
* peak of the triangle is calculated as
*
* \f[
* mode = 3.0 * mean - min - max .
* \f]
*
* Then, \f$(1 - u\f$) is the distance that \f$u\f$ would be from
* \f$1\f$. The value returned in the antithetic case, \f$x'\f$, is
* calculated as
*
* \f[
* x' = \left\{ \begin{array}{rl}
* min + \sqrt{(1 - u) * (max - min) * (mode - min)} &\mbox{ if $(1 - u) <= (mode -
* min)/(max - min)$} \\ max - \sqrt{ u * (max - min) * (max - mode) } &\mbox{ otherwise}
* \end{array} \right.
* \f]
*
* which now involves the distance \f$u\f$ is from the 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The mean value for the triangular distribution returned by this RNG stream. */
@@ -2174,19 +2002,7 @@ class ZipfRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns a random unsigned integer from a Zipf distribution with the current n and
* alpha.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if m_isAntithetic
* is equal to true. If \f$u\f$ is a uniform variable over [0,1]
* and \f$x\f$ is a value that would be returned normally, then
* \f$(1 - u\f$) is the distance that \f$u\f$ would be from \f$1\f$.
* The value returned in the antithetic case, \f$x'\f$, uses (1-u),
* which is the distance \f$u\f$ is from the 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The n value for the Zipf distribution returned by this RNG stream. */
@@ -2307,18 +2123,7 @@ class ZetaRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns a random unsigned integer from a zeta distribution with the current alpha.
* \return A random unsigned integer value.
*
* Note that antithetic values are being generated if m_isAntithetic
* is equal to true. If \f$u\f$ is a uniform variable over [0,1]
* and \f$x\f$ is a value that would be returned normally, then
* \f$(1 - u\f$) is the distance that \f$u\f$ would be from \f$1\f$.
* The value returned in the antithetic case, \f$x'\f$, uses (1-u),
* which is the distance \f$u\f$ is from the 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** The alpha value for the zeta distribution returned by this RNG stream. */
@@ -2391,11 +2196,7 @@ class DeterministicRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns the next value in the sequence.
* \return The integer next value in the sequence.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
private:
/** Size of the array of values. */
@@ -2525,20 +2326,7 @@ class EmpiricalRandomVariable : public RandomVariableStream
*/
double GetValue() override;
/**
* \brief Returns the next value in the empirical distribution.
* \return The integer next value in the empirical distribution.
*
* Note that this does not interpolate the CDF, but treats it as a
* stepwise continuous function.
* Also note that antithetic values are being generated if m_isAntithetic
* is equal to true. If \f$u\f$ is a uniform variable over [0,1]
* and \f$x\f$ is a value that would be returned normally, then
* \f$(1 - u\f$) is the distance that \f$u\f$ would be from \f$1\f$.
* The value returned in the antithetic case, \f$x'\f$, uses (1-u),
* which is the distance \f$u\f$ is from the 1.
*/
uint32_t GetInteger() override;
using RandomVariableStream::GetInteger;
/**
* \brief Returns the next value in the empirical distribution using