... | ... |
@@ -724,33 +724,33 @@ |
724 | 724 |
/// Gauss distribution with given mean and standard deviation |
725 | 725 |
|
726 | 726 |
/// \sa gauss() |
727 | 727 |
/// |
728 | 728 |
double gauss(double mean,double std_dev) |
729 | 729 |
{ |
730 | 730 |
return gauss()*std_dev+mean; |
731 | 731 |
} |
732 | 732 |
|
733 | 733 |
/// Exponential distribution with given mean |
734 | 734 |
|
735 | 735 |
/// This function generates an exponential distribution random number |
736 | 736 |
/// with mean <tt>1/lambda</tt>. |
737 | 737 |
/// |
738 | 738 |
double exponential(double lambda=1.0) |
739 | 739 |
{ |
740 |
return -std::log(real<double>())/lambda; |
|
740 |
return -std::log(1.0-real<double>())/lambda; |
|
741 | 741 |
} |
742 | 742 |
|
743 | 743 |
/// Gamma distribution with given integer shape |
744 | 744 |
|
745 | 745 |
/// This function generates a gamma distribution random number. |
746 | 746 |
/// |
747 | 747 |
///\param k shape parameter (<tt>k>0</tt> integer) |
748 | 748 |
double gamma(int k) |
749 | 749 |
{ |
750 | 750 |
double s = 0; |
751 | 751 |
for(int i=0;i<k;i++) s-=std::log(1.0-real<double>()); |
752 | 752 |
return s; |
753 | 753 |
} |
754 | 754 |
|
755 | 755 |
/// Gamma distribution with given shape and scale parameter |
756 | 756 |
|
... | ... |
@@ -769,32 +769,59 @@ |
769 | 769 |
double V1=1.0-real<double>(); |
770 | 770 |
double V2=1.0-real<double>(); |
771 | 771 |
if(V2<=v0) |
772 | 772 |
{ |
773 | 773 |
xi=std::pow(V1,1.0/delta); |
774 | 774 |
nu=V0*std::pow(xi,delta-1.0); |
775 | 775 |
} |
776 | 776 |
else |
777 | 777 |
{ |
778 | 778 |
xi=1.0-std::log(V1); |
779 | 779 |
nu=V0*std::exp(-xi); |
780 | 780 |
} |
781 | 781 |
} while(nu>std::pow(xi,delta-1.0)*std::exp(-xi)); |
782 | 782 |
return theta*(xi-gamma(int(std::floor(k)))); |
783 | 783 |
} |
784 | 784 |
|
785 |
/// Weibull distribution |
|
786 |
|
|
787 |
/// This function generates a Weibull distribution random number. |
|
788 |
/// |
|
789 |
///\param k shape parameter (<tt>k>0</tt>) |
|
790 |
///\param lambda scale parameter (<tt>lambda>0</tt>) |
|
791 |
/// |
|
792 |
double weibull(double k,double lambda) |
|
793 |
{ |
|
794 |
return lambda*pow(-std::log(1.0-real<double>()),1.0/k); |
|
795 |
} |
|
796 |
|
|
797 |
/// Pareto distribution |
|
798 |
|
|
799 |
/// This function generates a Pareto distribution random number. |
|
800 |
/// |
|
801 |
///\param x_min location parameter (<tt>x_min>0</tt>) |
|
802 |
///\param k shape parameter (<tt>k>0</tt>) |
|
803 |
/// |
|
804 |
///\warning This function used inverse transform sampling, therefore may |
|
805 |
///suffer from numerical unstability. |
|
806 |
/// |
|
807 |
///\todo Implement a numerically stable method |
|
808 |
double pareto(double x_min,double k) |
|
809 |
{ |
|
810 |
return x_min*pow(1.0-real<double>(),1.0/k); |
|
811 |
} |
|
785 | 812 |
|
786 | 813 |
///@} |
787 | 814 |
|
788 | 815 |
///\name Two dimensional distributions |
789 | 816 |
/// |
790 | 817 |
|
791 | 818 |
///@{ |
792 | 819 |
|
793 | 820 |
/// Uniform distribution on the full unit circle. |
794 | 821 |
dim2::Point<double> disc() |
795 | 822 |
{ |
796 | 823 |
double V1,V2; |
797 | 824 |
do { |
798 | 825 |
V1=2*real<double>()-1; |
799 | 826 |
V2=2*real<double>()-1; |
800 | 827 |
|
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