ns-3.24: comparison src/core/random-variable.h

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-:a082b4f3ac92
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 /**
 * \brief The uniform distribution RNG for NS-3.
 * \ingroup randomvariable
+*
 * This class supports the creation of objects that return random numbers
 * from a fixed uniform distribution.  It also supports the generation of
 * single random numbers from various uniform distributions.
+*
+* The low end of the range is always included and the high end
+* of the range is always excluded.
 * \code
 * UniformVariable x(0,10);
-* x.GetValue();  //will always return numbers [0,10]
+* x.GetValue();  //will always return numbers [0,10)
-* UniformVariable::GetSingleValue(100,1000); //returns a value [100,1000]
+* UniformVariable::GetSingleValue(100,1000); //returns a value [100,1000)
 * \endcode
 */
 class UniformVariable : public RandomVariable {
 public:
 /**
 * Creates a uniform random number generator in the
-* range [0.0 .. 1.0)
+* range [0.0 .. 1.0).
 */
 UniformVariable();
 /**
 * Creates a uniform random number generator with the specified range
 };
 /**
 * \brief Exponentially Distributed random var
 * \ingroup randomvariable
+*
 * This class supports the creation of objects that return random numbers
 * from a fixed exponential distribution.  It also supports the generation of
 * single random numbers from various exponential distributions.
+*
+* The probability density function of an exponential variable
+* is defined over the interval [0, +inf) as:
+* \f$ \alpha  e^{-\alpha x} \f$
+* where \f$ \alpha = \frac{1}{mean} \f$
+*
+* The bounded version is defined over the internal [0,+inf) as:
+* \f$ \left\{ \begin{array}{cl} \alpha  e^{-\alpha x} & x < bound \\ bound & x > bound \end{array}\right. \f$
+*
 * \code
 * ExponentialVariable x(3.14);
 * x.GetValue();  //will always return with mean 3.14
 * ExponentialVariable::GetSingleValue(20.1); //returns with mean 20.1
 * ExponentialVariable::GetSingleValue(108); //returns with mean 108
 };
 /**
 * \brief ParetoVariable distributed random var
 * \ingroup randomvariable
+*
 * This class supports the creation of objects that return random numbers
 * from a fixed pareto distribution.  It also supports the generation of
 * single random numbers from various pareto distributions.
+*
+* The probability density function is defined over the range [\f$x_m\f$,+inf) as:
+* \f$ k \frac{x_m^k}{x^{k+1}}\f$ where \f$x_m > 0\f$ is called the location
+* parameter and \f$ k > 0\f$ is called the pareto index or shape.
+*
+* The parameter \f$ x_m \f$ can be infered from the mean and the parameter \f$ k \f$
+* with the equation \f$ x_m = mean \frac{k-1}{k},  k > 1\f$.
+*
 * \code
 * ParetoVariable x(3.14);
 * x.GetValue();  //will always return with mean 3.14
 * ParetoVariable::GetSingleValue(20.1); //returns with mean 20.1
 * ParetoVariable::GetSingleValue(108); //returns with mean 108
 };
 /**
 * \brief WeibullVariable distributed random var
 * \ingroup randomvariable
+*
 * This class supports the creation of objects that return random numbers
 * from a fixed weibull distribution.  It also supports the generation of
 * single random numbers from various weibull distributions.
+*
+* The probability density function is defined over the interval [0, +inf]
+* as: \f$ \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-\left(\frac{x}{\lambda}\right)^k} \f$
+* where \f$ k > 0\f$ is the shape parameter and \f$ \lambda > 0\f$  is the scale parameter. The
+* specified mean is related to the scale and shape parameters by the following relation:
+* \f$ mean = \lambda\Gamma\left(1+\frac{1}{k}\right) \f$ where \f$ \Gamma \f$ is the Gamma function.
 */
 class WeibullVariable : public RandomVariable {
 public:
 /**
 * Constructs a weibull random variable  with a mean
 };
 /**
 * \brief Class NormalVariable defines a random variable with a
 * normal (Gaussian) distribution.
+* \ingroup randomvariable
 *
 * This class supports the creation of objects that return random numbers
 * from a fixed normal distribution.  It also supports the generation of
 * single random numbers from various normal distributions.
-* \ingroup randomvariable
+*
+* The density probability function is defined over the interval (-inf,+inf)
+* as: \f$ \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{s\sigma^2}}\f$
+* where \f$ mean = \mu \f$ and \f$ variance = \sigma^2 \f$
+*
 */
 class NormalVariable : public RandomVariable { // Normally Distributed random var
 public:
 static const double INFINITE_VALUE;
 * \brief EmpiricalVariable distribution random var
 * \ingroup randomvariable
 *
 * Defines a random variable  that has a specified, empirical
 * distribution.  The distribution is specified by a
-* series of calls the the CDF member function, specifying a
+* series of calls to the CDF member function, specifying a
 * value and the probability that the function value is less than
 * the specified value.  When values are requested,
 * a uniform random variable is used to select a probabililty,
 * and the return value is interpreted linerarly between the
 * two appropriate points in the CDF
 /**
 * \brief Log-normal Distributed random var
 * \ingroup randomvariable
+*
 * LogNormalVariable defines a random variable with log-normal
 * distribution.  If one takes the natural logarithm of random
 * variable following the log-normal distribution, the obtained values
 * follow a normal distribution.
 *  This class supports the creation of objects that return random numbers
 * from a fixed lognormal distribution.  It also supports the generation of
 * single random numbers from various lognormal distributions.
+*
+* The probability density function is defined over the interval [0,+inf) as:
+* \f$ \frac{1}{x\sigma\sqrt{2\pi}} e^{-\frac{(ln(x) - \mu)^2}{2\sigma^2}}\f$
+* where \f$ mean = e^{\mu+\frac{\sigma^2}{2}} \f$ and
+* \f$ variance = (e^{\sigma^2}-1)e^{2\mu+\sigma^2}\f$
+*
+* The \f$ \mu \f$ and \f$ \sigma \f$ parameters can be calculated from the mean
+* and standard deviation with the following equations:
+* \f$ \mu = ln(mean) - \frac{1}{2}ln\left(1+\frac{stddev}{mean^2}\right)\f$, and,
+* \f$ \sigma = \sqrt{ln\left(1+\frac{stddev}{mean^2}\right)}\f$
 */
 class LogNormalVariable : public RandomVariable {
 public:
 /**
-* \param mu Mean value of the underlying normal distribution.
+* \param mu mu parameter of the lognormal distribution
-* \param sigma Standard deviation of the underlying normal distribution.
+* \param sigma sigma parameter of the lognormal distribution
-*
-* Notice: the parameters mu and sigma are _not_ the mean and standard
-* deviation of the log-normal distribution.  To obtain the
-* parameters mu and sigma for a given mean and standard deviation
-* of the log-normal distribution the following convertion can be
-* used:
-* \code
-* double tmp = log (1 + pow (stddev/mean, 2));
-* double sigma = sqrt (tmp);
-* double mu = log (mean) - 0.5*tmp;
-* \endcode
 */
 LogNormalVariable (double mu, double sigma);
 /**
 * \return A random value from this distribution
 */
 virtual double GetValue ();
 virtual RandomVariable* Copy() const;
 public:
 /**
-* \param mu Mean value of the underlying normal distribution.
+* \param mu mu parameter of the underlying normal distribution
-* \param sigma Standard deviation of the underlying normal distribution.
+* \param sigma sigma parameter of the underlying normal distribution
 * \return A random number from the distribution specified by mu and sigma
 */
 static double GetSingleValue(double mu, double sigma);
 private:
 double m_mu;

changeset 1872	4dfb2a623722
parent 1806	34e115600ef2
child 2217	0b4567d545de