1 | /* |
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2 | * This generator uses the straightforward transformation |
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3 | * x = - log(y) * m |
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4 | * |
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5 | * to turn a uniform (0,1) y into an exponentially distributed |
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6 | * variable x. x has density function |
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7 | * |
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8 | * f(x) = (1/m) exp(-(1/m)x) (x > 0) |
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9 | * |
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10 | * and mean m. |
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11 | * |
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12 | * NEEDS_WORK: Adapt the method of Ahrens and Dieter. This will |
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13 | * require extending the precision of the constants. |
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14 | * |
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15 | * Ahrens, J.H. and Dieter, U. Computer Methods for Sampling From the |
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16 | * Exponential and Normal Distributions. Comm. ACM, 15,10 (Oct. 1972), p. 873. |
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17 | */ |
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18 | |
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19 | #ifndef BZ_RANDOM_EXPONENTIAL |
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20 | #define BZ_RANDOM_EXPONENTIAL |
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21 | |
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22 | #ifndef BZ_RANDOM_UNIFORM |
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23 | #include <random/uniform.h> |
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24 | #endif |
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25 | |
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26 | BZ_NAMESPACE(ranlib) |
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27 | |
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28 | template<typename T = double, typename IRNG = defaultIRNG, |
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29 | typename stateTag = defaultState> |
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30 | class ExponentialUnit : public UniformOpen<T,IRNG,stateTag> |
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31 | { |
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32 | public: |
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33 | typedef T T_numtype; |
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34 | |
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35 | T random() |
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36 | { |
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37 | return - log(UniformOpen<T,IRNG,stateTag>::random()); |
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38 | } |
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39 | }; |
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40 | |
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41 | template<typename T = double, typename IRNG = defaultIRNG, |
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42 | typename stateTag = defaultState> |
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43 | class Exponential : public ExponentialUnit<T,IRNG,stateTag> { |
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44 | |
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45 | public: |
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46 | typedef T T_numtype; |
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47 | |
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48 | Exponential(T mean) |
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49 | { |
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50 | mean_ = mean; |
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51 | } |
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52 | |
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53 | T random() |
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54 | { |
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55 | return mean_ * ExponentialUnit<T,IRNG,stateTag>::random(); |
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56 | } |
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57 | |
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58 | private: |
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59 | T mean_; |
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60 | }; |
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61 | |
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62 | BZ_NAMESPACE_END |
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63 | |
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64 | #endif // BZ_RANDOM_EXPONENTIAL |
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