tmp
/
pip-install-ghxuqwgs
/numpy_78e94bf2b6094bf9a1f3d92042f9bf46
/numpy
/random
/mtrand
/distributions.h
| /* Copyright 2005 Robert Kern ([email protected]) | |
| * | |
| * Permission is hereby granted, free of charge, to any person obtaining a | |
| * copy of this software and associated documentation files (the | |
| * "Software"), to deal in the Software without restriction, including | |
| * without limitation the rights to use, copy, modify, merge, publish, | |
| * distribute, sublicense, and/or sell copies of the Software, and to | |
| * permit persons to whom the Software is furnished to do so, subject to | |
| * the following conditions: | |
| * | |
| * The above copyright notice and this permission notice shall be included | |
| * in all copies or substantial portions of the Software. | |
| * | |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS | |
| * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF | |
| * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. | |
| * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY | |
| * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, | |
| * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE | |
| * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. | |
| */ | |
| extern "C" { | |
| /* References: | |
| * | |
| * Devroye, Luc. _Non-Uniform Random Variate Generation_. | |
| * Springer-Verlag, New York, 1986. | |
| * http://cgm.cs.mcgill.ca/~luc/rnbookindex.html | |
| * | |
| * Kachitvichyanukul, V. and Schmeiser, B. W. Binomial Random Variate | |
| * Generation. Communications of the ACM, 31, 2 (February, 1988) 216. | |
| * | |
| * Hoermann, W. The Transformed Rejection Method for Generating Poisson Random | |
| * Variables. Insurance: Mathematics and Economics, (to appear) | |
| * http://citeseer.csail.mit.edu/151115.html | |
| * | |
| * Marsaglia, G. and Tsang, W. W. A Simple Method for Generating Gamma | |
| * Variables. ACM Transactions on Mathematical Software, Vol. 26, No. 3, | |
| * September 2000, Pages 363–372. | |
| */ | |
| /* Normal distribution with mean=loc and standard deviation=scale. */ | |
| extern double rk_normal(rk_state *state, double loc, double scale); | |
| /* Standard exponential distribution (mean=1) computed by inversion of the | |
| * CDF. */ | |
| extern double rk_standard_exponential(rk_state *state); | |
| /* Exponential distribution with mean=scale. */ | |
| extern double rk_exponential(rk_state *state, double scale); | |
| /* Uniform distribution on interval [loc, loc+scale). */ | |
| extern double rk_uniform(rk_state *state, double loc, double scale); | |
| /* Standard gamma distribution with shape parameter. | |
| * When shape < 1, the algorithm given by (Devroye p. 304) is used. | |
| * When shape == 1, a Exponential variate is generated. | |
| * When shape > 1, the small and fast method of (Marsaglia and Tsang 2000) | |
| * is used. | |
| */ | |
| extern double rk_standard_gamma(rk_state *state, double shape); | |
| /* Gamma distribution with shape and scale. */ | |
| extern double rk_gamma(rk_state *state, double shape, double scale); | |
| /* Beta distribution computed by combining two gamma variates (Devroye p. 432). | |
| */ | |
| extern double rk_beta(rk_state *state, double a, double b); | |
| /* Chi^2 distribution computed by transforming a gamma variate (it being a | |
| * special case Gamma(df/2, 2)). */ | |
| extern double rk_chisquare(rk_state *state, double df); | |
| /* Noncentral Chi^2 distribution computed by modifying a Chi^2 variate. */ | |
| extern double rk_noncentral_chisquare(rk_state *state, double df, double nonc); | |
| /* F distribution computed by taking the ratio of two Chi^2 variates. */ | |
| extern double rk_f(rk_state *state, double dfnum, double dfden); | |
| /* Noncentral F distribution computed by taking the ratio of a noncentral Chi^2 | |
| * and a Chi^2 variate. */ | |
| extern double rk_noncentral_f(rk_state *state, double dfnum, double dfden, double nonc); | |
| /* Binomial distribution with n Bernoulli trials with success probability p. | |
| * When n*p <= 30, the "Second waiting time method" given by (Devroye p. 525) is | |
| * used. Otherwise, the BTPE algorithm of (Kachitvichyanukul and Schmeiser 1988) | |
| * is used. */ | |
| extern long rk_binomial(rk_state *state, long n, double p); | |
| /* Binomial distribution using BTPE. */ | |
| extern long rk_binomial_btpe(rk_state *state, long n, double p); | |
| /* Binomial distribution using inversion and chop-down */ | |
| extern long rk_binomial_inversion(rk_state *state, long n, double p); | |
| /* Negative binomial distribution computed by generating a Gamma(n, (1-p)/p) | |
| * variate Y and returning a Poisson(Y) variate (Devroye p. 543). */ | |
| extern long rk_negative_binomial(rk_state *state, double n, double p); | |
| /* Poisson distribution with mean=lam. | |
| * When lam < 10, a basic algorithm using repeated multiplications of uniform | |
| * variates is used (Devroye p. 504). | |
| * When lam >= 10, algorithm PTRS from (Hoermann 1992) is used. | |
| */ | |
| extern long rk_poisson(rk_state *state, double lam); | |
| /* Poisson distribution computed by repeated multiplication of uniform variates. | |
| */ | |
| extern long rk_poisson_mult(rk_state *state, double lam); | |
| /* Poisson distribution computer by the PTRS algorithm. */ | |
| extern long rk_poisson_ptrs(rk_state *state, double lam); | |
| /* Standard Cauchy distribution computed by dividing standard gaussians | |
| * (Devroye p. 451). */ | |
| extern double rk_standard_cauchy(rk_state *state); | |
| /* Standard t-distribution with df degrees of freedom (Devroye p. 445 as | |
| * corrected in the Errata). */ | |
| extern double rk_standard_t(rk_state *state, double df); | |
| /* von Mises circular distribution with center mu and shape kappa on [-pi,pi] | |
| * (Devroye p. 476 as corrected in the Errata). */ | |
| extern double rk_vonmises(rk_state *state, double mu, double kappa); | |
| /* Pareto distribution via inversion (Devroye p. 262) */ | |
| extern double rk_pareto(rk_state *state, double a); | |
| /* Weibull distribution via inversion (Devroye p. 262) */ | |
| extern double rk_weibull(rk_state *state, double a); | |
| /* Power distribution via inversion (Devroye p. 262) */ | |
| extern double rk_power(rk_state *state, double a); | |
| /* Laplace distribution */ | |
| extern double rk_laplace(rk_state *state, double loc, double scale); | |
| /* Gumbel distribution */ | |
| extern double rk_gumbel(rk_state *state, double loc, double scale); | |
| /* Logistic distribution */ | |
| extern double rk_logistic(rk_state *state, double loc, double scale); | |
| /* Log-normal distribution */ | |
| extern double rk_lognormal(rk_state *state, double mean, double sigma); | |
| /* Rayleigh distribution */ | |
| extern double rk_rayleigh(rk_state *state, double mode); | |
| /* Wald distribution */ | |
| extern double rk_wald(rk_state *state, double mean, double scale); | |
| /* Zipf distribution */ | |
| extern long rk_zipf(rk_state *state, double a); | |
| /* Geometric distribution */ | |
| extern long rk_geometric(rk_state *state, double p); | |
| extern long rk_geometric_search(rk_state *state, double p); | |
| extern long rk_geometric_inversion(rk_state *state, double p); | |
| /* Hypergeometric distribution */ | |
| extern long rk_hypergeometric(rk_state *state, long good, long bad, long sample); | |
| extern long rk_hypergeometric_hyp(rk_state *state, long good, long bad, long sample); | |
| extern long rk_hypergeometric_hrua(rk_state *state, long good, long bad, long sample); | |
| /* Triangular distribution */ | |
| extern double rk_triangular(rk_state *state, double left, double mode, double right); | |
| /* Logarithmic series distribution */ | |
| extern long rk_logseries(rk_state *state, double p); | |
| } | |