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This commit is contained in:
Mathieu Lacage
2008-03-27 09:13:41 -07:00
parent c61932afc5 473ea28f10
commit ac7ef5158d
1 changed files with 31 additions and 28 deletions
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59
src/core/random-variable.h
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@@ -48,7 +48,7 @@ class RandomVariableBase;
* the University of Montreal.
*
* NS-3 has a rich set of random number generators.
* Class RandomVariableBase defines the base class functionalty
* Class RandomVariable defines the base class functionalty
* required for all random number generators. By default, the underlying
* generator is seeded with the time of day, and then deterministically
* creates a sequence of seeds for each subsequent generator that is created.
@@ -93,7 +93,7 @@ public:
* generator is seeded with data from /dev/random instead of
* being seeded based upon the time of day. For this to be effective,
* it must be called before the creation of the first instance of a
* RandomVariableBase or subclass. Example:
* RandomVariable or subclass. Example:
* \code
* RandomVariable::UseDevRandom();
* UniformVariable x(2,3); //these are seeded randomly
@@ -221,19 +221,19 @@ public:
* \brief A random variable that returns a constant
* \ingroup randomvariable
*
* Class ConstantVariableImpl defines a random number generator that
* Class ConstantVariable defines a random number generator that
* returns the same value every sample.
*/
class ConstantVariable : public RandomVariable {
public:
/**
* Construct a ConstantVariableImpl RNG that returns zero every sample
* Construct a ConstantVariable RNG that returns zero every sample
*/
ConstantVariable();
/**
* Construct a ConstantVariableImpl RNG that returns the specified value
* Construct a ConstantVariable RNG that returns the specified value
* every sample.
* \param c Unchanging value for this RNG.
*/
@@ -260,10 +260,10 @@ class SequentialVariable : public RandomVariable
{
public:
/**
* \brief Constructor for the SequentialVariableImpl RNG.
* \brief Constructor for the SequentialVariable RNG.
*
* The four parameters define the sequence. For example
* SequentialVariableImpl(0,5,1,2) creates a RNG that has the sequence
* SequentialVariable(0,5,1,2) creates a RNG that has the sequence
* 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 0, 0 ...
* \param f First value of the sequence.
* \param l One more than the last value of the sequence.
@@ -273,13 +273,13 @@ public:
SequentialVariable(double f, double l, double i = 1, uint32_t c = 1);
/**
* \brief Constructor for the SequentialVariableImpl RNG.
* \brief Constructor for the SequentialVariable RNG.
*
* Differs from the first only in that the increment parameter is a
* random variable
* \param f First value of the sequence.
* \param l One more than the last value of the sequence.
* \param i Reference to a RandomVariableBase for the sequence increment
* \param i Reference to a RandomVariable for the sequence increment
* \param c Number of times each member of the sequence is repeated
*/
SequentialVariable(double f, double l, const RandomVariable& i, uint32_t c = 1);
@@ -303,10 +303,10 @@ public:
* \f$ \left\{ \begin{array}{cl} \alpha e^{-\alpha x} & x < bound \\ bound & x > bound \end{array}\right. \f$
*
* \code
* ExponentialVariableImpl x(3.14);
* ExponentialVariable x(3.14);
* x.GetValue(); //will always return with mean 3.14
* ExponentialVariableImpl::GetSingleValue(20.1); //returns with mean 20.1
* ExponentialVariableImpl::GetSingleValue(108); //returns with mean 108
* ExponentialVariable::GetSingleValue(20.1); //returns with mean 20.1
* ExponentialVariable::GetSingleValue(108); //returns with mean 108
* \endcode
*
*/
@@ -347,7 +347,7 @@ public:
};
/**
* \brief ParetoVariableImpl distributed random var
* \brief ParetoVariable distributed random var
* \ingroup randomvariable
*
* This class supports the creation of objects that return random numbers
@@ -362,10 +362,10 @@ public:
* with the equation \f$ x_m = mean \frac{k-1}{k}, k > 1\f$.
*
* \code
* ParetoVariableImpl x(3.14);
* ParetoVariable x(3.14);
* x.GetValue(); //will always return with mean 3.14
* ParetoVariableImpl::GetSingleValue(20.1); //returns with mean 20.1
* ParetoVariableImpl::GetSingleValue(108); //returns with mean 108
* ParetoVariable::GetSingleValue(20.1); //returns with mean 20.1
* ParetoVariable::GetSingleValue(108); //returns with mean 108
* \endcode
*/
class ParetoVariable : public RandomVariable
@@ -419,7 +419,7 @@ public:
};
/**
* \brief WeibullVariableImpl distributed random var
* \brief WeibullVariable distributed random var
* \ingroup randomvariable
*
* This class supports the creation of objects that return random numbers
@@ -459,7 +459,7 @@ public:
/**
* \brief Constructs a weibull random variable with the specified mean
* \brief value, shape (alpha), and upper bound.
* Since WeibullVariableImpl distributions can theoretically return unbounded values,
* Since WeibullVariable distributions can theoretically return unbounded values,
* it is sometimes usefull to specify a fixed upper limit. Note however
* that when the upper limit is specified, the true mean of the distribution
* is slightly smaller than the mean value specified.
@@ -478,7 +478,7 @@ public:
};
/**
* \brief Class NormalVariableImpl defines a random variable with a
* \brief Class NormalVariable defines a random variable with a
* normal (Gaussian) distribution.
* \ingroup randomvariable
*
@@ -505,20 +505,20 @@ public:
* \brief Construct a normal random variable with specified mean and variance
* \param m Mean value
* \param v Variance
* \param b Bound. The NormalVariableImpl is bounded within +-bound.
* \param b Bound. The NormalVariable is bounded within +-bound.
*/
NormalVariable(double m, double v, double b = INFINITE_VALUE);
/**
* \param m Mean value
* \param v Variance
* \param b Bound. The NormalVariableImpl is bounded within +-bound.
* \param b Bound. The NormalVariable is bounded within +-bound.
* \return A random number from a distribution specified by m,v, and b.
*/
static double GetSingleValue(double m, double v, double b = INFINITE_VALUE);
};
/**
* \brief EmpiricalVariableImpl distribution random var
* \brief EmpiricalVariable distribution random var
* \ingroup randomvariable
*
* Defines a random variable that has a specified, empirical
@@ -528,12 +528,14 @@ public:
* 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
* two appropriate points in the CDF. The method is known
* as inverse transform sampling:
* (http://en.wikipedia.org/wiki/Inverse_transform_sampling).
*/
class EmpiricalVariable : public RandomVariable {
public:
/**
* Constructor for the EmpiricalVariableImpl random variables.
* Constructor for the EmpiricalVariable random variables.
*/
explicit EmpiricalVariable();
@@ -552,8 +554,9 @@ protected:
* \ingroup randomvariable
*
* Defines an empirical distribution where all values are integers.
* Indentical to EmpiricalVariableImpl, but with slightly different
* interpolation between points.
* Indentical to EmpiricalVariable, except that the inverse transform
* sampling interpolation described in the EmpiricalVariable documentation
* is modified to only return integers.
*/
class IntEmpiricalVariable : public EmpiricalVariable
{
@@ -580,7 +583,7 @@ public:
* on successive calls to ::Value(). Note that the d pointer is copied
* for use by the generator (shallow-copy), not its contents, so the
* contents of the array d points to have to remain unchanged for the use
* of DeterministicVariableImpl to be meaningful.
* of DeterministicVariable to be meaningful.
* \param d Pointer to array of random values to return in sequence
* \param c Number of values in the array
*/
@@ -592,7 +595,7 @@ public:
* \brief Log-normal Distributed random var
* \ingroup randomvariable
*
* LogNormalVariableImpl defines a random variable with log-normal
* 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.