bug 109: improve the doxygen documentation for RandomVariable
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Mathieu Lacage
@@ -193,20 +193,24 @@ protected:
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/**
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* \brief The uniform distribution RNG for NS-3.
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed uniform distribution. It also supports the generation of
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* single random numbers from various uniform distributions.
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*
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* The low end of the range is always included and the high end
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* of the range is always excluded.
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* \code
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* UniformVariable x(0,10);
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* x.GetValue(); //will always return numbers [0,10]
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* UniformVariable::GetSingleValue(100,1000); //returns a value [100,1000]
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* x.GetValue(); //will always return numbers [0,10)
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* UniformVariable::GetSingleValue(100,1000); //returns a value [100,1000)
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* \endcode
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*/
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class UniformVariable : public RandomVariable {
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public:
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/**
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* Creates a uniform random number generator in the
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* range [0.0 .. 1.0)
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* range [0.0 .. 1.0).
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*/
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UniformVariable();
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@@ -334,9 +338,19 @@ private:
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/**
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* \brief Exponentially Distributed random var
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed exponential distribution. It also supports the generation of
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* single random numbers from various exponential distributions.
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*
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* The probability density function of an exponential variable
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* is defined over the interval [0, +inf) as:
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* \f$ \alpha e^{-\alpha x} \f$
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* where \f$ \alpha = \frac{1}{mean} \f$
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*
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* The bounded version is defined over the internal [0,+inf) as:
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* \f$ \left\{ \begin{array}{cl} \alpha e^{-\alpha x} & x < bound \\ bound & x > bound \end{array}\right. \f$
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*
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* \code
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* ExponentialVariable x(3.14);
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* x.GetValue(); //will always return with mean 3.14
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@@ -394,9 +408,18 @@ private:
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/**
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* \brief ParetoVariable distributed random var
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed pareto distribution. It also supports the generation of
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* single random numbers from various pareto distributions.
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*
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* The probability density function is defined over the range [\f$x_m\f$,+inf) as:
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* \f$ k \frac{x_m^k}{x^{k+1}}\f$ where \f$x_m > 0\f$ is called the location
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* parameter and \f$ k > 0\f$ is called the pareto index or shape.
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*
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* The parameter \f$ x_m \f$ can be infered from the mean and the parameter \f$ k \f$
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* with the equation \f$ x_m = mean \frac{k-1}{k}, k > 1\f$.
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*
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* \code
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* ParetoVariable x(3.14);
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* x.GetValue(); //will always return with mean 3.14
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@@ -468,9 +491,16 @@ private:
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/**
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* \brief WeibullVariable distributed random var
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed weibull distribution. It also supports the generation of
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* single random numbers from various weibull distributions.
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*
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* The probability density function is defined over the interval [0, +inf]
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* as: \f$ \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-\left(\frac{x}{\lambda}\right)^k} \f$
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* where \f$ k > 0\f$ is the shape parameter and \f$ \lambda > 0\f$ is the scale parameter. The
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* specified mean is related to the scale and shape parameters by the following relation:
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* \f$ mean = \lambda\Gamma\left(1+\frac{1}{k}\right) \f$ where \f$ \Gamma \f$ is the Gamma function.
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*/
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class WeibullVariable : public RandomVariable {
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public:
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@@ -533,11 +563,16 @@ private:
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/**
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* \brief Class NormalVariable defines a random variable with a
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* normal (Gaussian) distribution.
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed normal distribution. It also supports the generation of
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* single random numbers from various normal distributions.
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* \ingroup randomvariable
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*
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* The density probability function is defined over the interval (-inf,+inf)
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* as: \f$ \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{s\sigma^2}}\f$
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* where \f$ mean = \mu \f$ and \f$ variance = \sigma^2 \f$
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*
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*/
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class NormalVariable : public RandomVariable { // Normally Distributed random var
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@@ -588,7 +623,7 @@ private:
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*
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* Defines a random variable that has a specified, empirical
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* distribution. The distribution is specified by a
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* series of calls the the CDF member function, specifying a
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* series of calls to the CDF member function, specifying a
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* value and the probability that the function value is less than
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* the specified value. When values are requested,
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* a uniform random variable is used to select a probabililty,
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@@ -693,6 +728,7 @@ private:
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/**
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* \brief Log-normal Distributed random var
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* \ingroup randomvariable
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*
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* LogNormalVariable defines a random variable with log-normal
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* distribution. If one takes the natural logarithm of random
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* variable following the log-normal distribution, the obtained values
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@@ -700,23 +736,22 @@ private:
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* This class supports the creation of objects that return random numbers
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* from a fixed lognormal distribution. It also supports the generation of
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* single random numbers from various lognormal distributions.
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*
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* The probability density function is defined over the interval [0,+inf) as:
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* \f$ \frac{1}{x\sigma\sqrt{2\pi}} e^{-\frac{(ln(x) - \mu)^2}{2\sigma^2}}\f$
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* where \f$ mean = e^{\mu+\frac{\sigma^2}{2}} \f$ and
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* \f$ variance = (e^{\sigma^2}-1)e^{2\mu+\sigma^2}\f$
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*
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* The \f$ \mu \f$ and \f$ \sigma \f$ parameters can be calculated from the mean
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* and standard deviation with the following equations:
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* \f$ \mu = ln(mean) - \frac{1}{2}ln\left(1+\frac{stddev}{mean^2}\right)\f$, and,
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* \f$ \sigma = \sqrt{ln\left(1+\frac{stddev}{mean^2}\right)}\f$
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*/
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class LogNormalVariable : public RandomVariable {
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public:
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/**
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* \param mu Mean value of the underlying normal distribution.
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* \param sigma Standard deviation of the underlying normal distribution.
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*
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* Notice: the parameters mu and sigma are _not_ the mean and standard
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* deviation of the log-normal distribution. To obtain the
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* parameters mu and sigma for a given mean and standard deviation
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* of the log-normal distribution the following convertion can be
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* used:
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* \code
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* double tmp = log (1 + pow (stddev/mean, 2));
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* double sigma = sqrt (tmp);
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* double mu = log (mean) - 0.5*tmp;
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* \endcode
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* \param mu mu parameter of the lognormal distribution
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* \param sigma sigma parameter of the lognormal distribution
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*/
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LogNormalVariable (double mu, double sigma);
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@@ -727,8 +762,8 @@ public:
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virtual RandomVariable* Copy() const;
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public:
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/**
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* \param mu Mean value of the underlying normal distribution.
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* \param sigma Standard deviation of the underlying normal distribution.
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* \param mu mu parameter of the underlying normal distribution
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* \param sigma sigma parameter of the underlying normal distribution
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* \return A random number from the distribution specified by mu and sigma
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*/
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static double GetSingleValue(double mu, double sigma);
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