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alpar (Alpar Juttner)
alpar@cs.elte.hu
Pareto and Weibull random distributions
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      double V1,V2,S;
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      do {
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	V1=2*real<double>()-1;
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	V2=2*real<double>()-1;
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	S=V1*V1+V2*V2;
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      } while(S>=1);
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      return std::sqrt(-2*std::log(S)/S)*V1;
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    }
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    /// Gauss distribution with given mean and standard deviation
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    /// \sa gauss()
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    ///
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    double gauss(double mean,double std_dev)
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    {
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      return gauss()*std_dev+mean;
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    }
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    /// Exponential distribution with given mean
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    /// This function generates an exponential distribution random number
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    /// with mean <tt>1/lambda</tt>.
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    ///
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    double exponential(double lambda=1.0)
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    {
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      return -std::log(real<double>())/lambda;
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      return -std::log(1.0-real<double>())/lambda;
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    }
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    /// Gamma distribution with given integer shape
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    /// This function generates a gamma distribution random number.
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    /// 
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    ///\param k shape parameter (<tt>k>0</tt> integer)
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    double gamma(int k) 
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    {
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      double s = 0;
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      for(int i=0;i<k;i++) s-=std::log(1.0-real<double>());
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      return s;
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    }
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    /// Gamma distribution with given shape and scale parameter
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    /// This function generates a gamma distribution random number.
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    /// 
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    ///\param k shape parameter (<tt>k>0</tt>)
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    ///\param theta scale parameter
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    ///
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    double gamma(double k,double theta=1.0)
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    {
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      double xi,nu;
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      const double delta = k-std::floor(k);
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      const double v0=M_E/(M_E-delta);
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      do {
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	double V0=1.0-real<double>();
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	double V1=1.0-real<double>();
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	double V2=1.0-real<double>();
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	if(V2<=v0) 
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	  {
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	    xi=std::pow(V1,1.0/delta);
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	    nu=V0*std::pow(xi,delta-1.0);
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	  }
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	else 
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	  {
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	    xi=1.0-std::log(V1);
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	    nu=V0*std::exp(-xi);
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	  }
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      } while(nu>std::pow(xi,delta-1.0)*std::exp(-xi));
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      return theta*(xi-gamma(int(std::floor(k))));
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    }
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    /// Weibull distribution
786

	
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    /// This function generates a Weibull distribution random number.
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    /// 
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    ///\param k shape parameter (<tt>k>0</tt>)
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    ///\param lambda scale parameter (<tt>lambda>0</tt>)
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    ///
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    double weibull(double k,double lambda)
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    {
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      return lambda*pow(-std::log(1.0-real<double>()),1.0/k);
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    }  
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    /// Pareto distribution
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    /// This function generates a Pareto distribution random number.
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    /// 
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    ///\param x_min location parameter (<tt>x_min>0</tt>)
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    ///\param k shape parameter (<tt>k>0</tt>)
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    ///
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    ///\warning This function used inverse transform sampling, therefore may
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    ///suffer from numerical unstability.
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    ///
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    ///\todo Implement a numerically stable method
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    double pareto(double x_min,double k)
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    {
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      return x_min*pow(1.0-real<double>(),1.0/k);
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    }  
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    ///@}
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    ///\name Two dimensional distributions
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    ///
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    ///@{
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    /// Uniform distribution on the full unit circle.
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    dim2::Point<double> disc() 
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    {
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      double V1,V2;
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      do {
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	V1=2*real<double>()-1;
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	V2=2*real<double>()-1;
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      } while(V1*V1+V2*V2>=1);
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      return dim2::Point<double>(V1,V2);
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    }
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    /// A kind of two dimensional Gauss distribution
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    /// This function provides a turning symmetric two-dimensional distribution.
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    /// Both coordinates are of standard normal distribution, but they are not
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    /// independent.
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