Inheritance diagram for ns3::WeibullVariableImpl:

Collaboration diagram for ns3::WeibullVariableImpl:

Public Member Functions
	WeibullVariableImpl ()
	WeibullVariableImpl (double m)
	WeibullVariableImpl (double m, double s)
	WeibullVariableImpl (double m, double s, double b)
	Constructs a weibull random variable with the specified mean.
	WeibullVariableImpl (const WeibullVariableImpl &c)
virtual RandomVariableBase *	Copy (void) const
virtual double	GetValue ()
Public Member Functions inherited from ns3::RandomVariableBase
	RandomVariableBase ()
	RandomVariableBase (const RandomVariableBase &o)
virtual	~RandomVariableBase ()
virtual uint32_t	GetInteger ()
RngStream *	GetStream (void)

Private Attributes
double	m_alpha
double	m_bound
double	m_mean

Detailed Description

Definition at line 752 of file random-variable.cc.

Constructor & Destructor Documentation

ns3::WeibullVariableImpl::WeibullVariableImpl ( )

Constructs a weibull random variable with a mean value of 1.0 and a shape (alpha) parameter of 1

Definition at line 804 of file random-variable.cc.

Referenced by Copy().

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ns3::WeibullVariableImpl::WeibullVariableImpl ( double m )

Constructs a weibull random variable with the specified mean value and a shape (alpha) parameter of 1.5.

Parameters

m	mean value of the distribution

Definition at line 809 of file random-variable.cc.

ns3::WeibullVariableImpl::WeibullVariableImpl	(	double	m,
		double	s
	)

Constructs a weibull random variable with the specified mean value and a shape (alpha).

Parameters

m	Mean value for the distribution.
s	Shape (alpha) parameter for the distribution.

Definition at line 815 of file random-variable.cc.

ns3::WeibullVariableImpl::WeibullVariableImpl	(	double	m,
		double	s,
		double	b
	)

Constructs a weibull random variable with the specified mean.

value, shape (alpha), and upper bound. Since WeibullVariableImpl 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.

Parameters