... | ... |
@@ -734,13 +734,13 @@ |
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 |
{ |
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return -std::log(real<double>())/lambda; |
|
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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 |
/// |
... | ... |
@@ -779,12 +779,39 @@ |
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 |
|
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/// Weibull distribution |
|
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|
|
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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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|
|
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/// Pareto distribution |
|
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|
|
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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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} |
|
785 | 812 |
|
786 | 813 |
///@} |
787 | 814 |
|
788 | 815 |
///\name Two dimensional distributions |
789 | 816 |
/// |
790 | 817 |
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