4x      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvw#Copyright (c) 2009 , Tracy WadleighBSD3%Patrick Perry <patperry@stanford.edu> experimentalNone 0 wAllocate a new quasi-random number generator of the given type, generating points with the given number of dimensions.5Reset the generator to the beginning of its sequence.Stores the next point from the generator in the given buffer. The space available in the buffer must match the dimension of the generator. The components of the sample will each lie in the range (0,1).%Gets the next sample point as a list.Get the name of the generator.The dimension of the sequence. .Get the size of the generator state, in bytes. Get the generator state. ESet the generator state. The input array should have size equal to getSize9 of the generator; otherwise, strange things will happen. copyQRNG dst src_ copies the state from one generator to another. The two generators must have the same type. VAllocate a new quasi-random number generator that is exact copy of another generator.CThe maximum dimension of samples that the given generator supports.(xyz{|}~  &xyz{|}~ #Copyright (c) 2009 , Tracy WadleighBSD3%Patrick Perry <patperry@stanford.edu> experimentalNone 0  5Copyright (c) , Patrick Perry <patperry@stanford.edu>BSD3%Patrick Perry <patperry@stanford.edu> experimentalNone 0bAllocate a new random number generator of the given type and initialize it with the default seed.(Seed the generator with the given value.+Returns a value uniform in [rngMin, rngMax] Returns a value uniform on [0,1) Returns a value uniform on (0,1)(Returns an integer uniform on [0,n-1]. n must be greater than 0.Get the name of the generator.4Get the largest value that the generator can return.5Get the smallest value that the generator can return..Get the size of the generator state, in bytes. Get the generator state.!ESet the generator state. The input array should have size equal to getSize9 of the generator; otherwise, strange things will happen."copyRNG dst src` copies the state from one generator to another. The two generators must have the same type.#RAllocate a new random number generator that is an exact copy of another generator% !"#$% !"#$%# !"#$%5Copyright (c) , Patrick Perry <patperry@stanford.edu>BSD3%Patrick Perry <patperry@stanford.edu> experimentalNone 0 !"#$% !"#$%5Copyright (c) , Patrick Perry <patperry@stanford.edu>BSD3%Patrick Perry <patperry@stanford.edu> experimentalNone 02&gaussianPdf x sigmaO computes the probabililty density p(x) for a Gaussian distribution with mean 0 and standard deviation sigma.'gaussianP x sigma[ computes the cumulative distribution function P(x) for a Gaussian distribution with mean 0 and standard deviation sigma.(gaussianQ x sigma[ computes the cumulative distribution function Q(x) for a Gaussian distribution with mean 0 and standard deviation sigma.)gaussianPInv p sigmad computes the inverse of the cumulative distribution function of a Gaussian distribution with mean 0 and standard deviation sigma . It returns x such that P(x) = p.*gaussianPInv q sigmad computes the inverse of the cumulative distribution function of a Gaussian distribution with mean 0 and standard deviation sigma . It returns x such that Q(x) = q.+getGaussian r sigma* gets a normal random variable with mean 0 and standard deviation sigma'. This uses the Box-Mueller algorithm.,getGaussianZiggurat r sigma* gets a normal random variable with mean 0 and standard deviation sigma4. This uses the Marsaglia-Tsang ziggurat algorithm.-getGaussianRatioMethod r sigma* gets a normal random variable with mean 0 and standard deviation sigma5. This uses the Kinderman-Monahan-Leva ratio method..ugaussianPdf xO computes the probabililty density p(x) for a Gaussian distribution with mean 0 and standard deviation 1./ ugaussianP x[ computes the cumulative distribution function P(x) for a Gaussian distribution with mean 0 and standard deviation 1.0 ugaussianQ x[ computes the cumulative distribution function Q(x) for a Gaussian distribution with mean 0 and standard deviation 1.1ugaussianPInv pd computes the inverse of the cumulative distribution function of a Gaussian distribution with mean 0 and standard deviation 1 . It returns x such that P(x) = p.2ugaussianPInv qd computes the inverse of the cumulative distribution function of a Gaussian distribution with mean 0 and standard deviation 1 . It returns x such that Q(x) = q.3getUGaussian r* gets a normal random variable with mean 0 and standard deviation 1'. This uses the Box-Mueller algorithm.4getUGaussianRatioMethod r* gets a normal random variable with mean 0 and standard deviation 15. This uses the Kinderman-Monahan-Leva ratio method.5 flatPdf x a b" computes the probability density p(x) at x" for a uniform distribution from a to b.6 flatP x a b/ computes the cumulative distribution function P(x).7 flatQ x a b/ computes the cumulative distribution function Q(x).8flatPInv p a bB computes the inverse of the cumulative distribution and returns x so that function P(x) = p.9flatQInv q a bB computes the inverse of the cumulative distribution and returns x so that function Q(x) = q.: getFlat r a b" gets a value uniformly chosen in [a,b).;getExponential r mu% gets a random exponential with mean mu.<exponentialPdf x mu computes the density at x of an exponential with mean mu.AgetLevy r c alphaH gets a variate from the Levy symmetric stable distribution with scale c and exponent alpha!. The algorithm only works for 0 <= alpha <= 2.BgetLevySkew r c alpha betaC gets a variate from the Levy skew stable distribution with scale c , exponent alpha, and skewness parameter beta0. The skewness parameter must lie in the range [-1,1]!. The algorithm only works for 0 <= alpha <= 2.CpoissonPdf k mu# evaluates the probability density p(k) at k' for a Poisson distribution with mean mu.D poissonP k mu0 evaluates the cumulative distribution function P(k) at k& for a Poisson distribution with mean mu.E poissonQ k mu0 evaluates the cumulative distribution function Q(k) at k& for a Poisson distribution with mean mu.FgetPoisson r mu* gets a poisson random variable with mean mu.G cauchyPdf x a# evaluates the probability density p(x) at x1 for a Cauchy distribution with scale parameter a. The density is given by )p(x) dx = { 1 over api (1 + (x/a^2)) } dx.H getCauchy r a! gets a random cauchy with scale a.M betaPdf x a b# evaluates the probability density p(x) at x* for a Beta distribution with parameters a and b. The density is given by Dp(x) dx = {Gamma(a+b) over Gamma(a) Gamma(b)} x^{a-1} (1-x)^{b-1} dx for  0 <= x <= 1.N getBeta r a b$ gets a random beta with parameters a and b.SlogisticPdf x a# evaluates the probability density p(x) at x3 for a logistic distribution with scale parameter a. The density is given by p(x) dx = { exp(-xa) over a (1 + exp(-x a))^2 } dx.TgetLogistic r a# gets a random logistic with scale a.YparetoPdf x a b# evaluates the probability density p(x) at x* for a Pareto distribution with exponent a and scale b. The density is given by  p(x) dx = (ab)  (x/b)^{a+1} dx for x >= b.ZgetPareto r a b$ gets a random Pareto with exponent a and scale b._weibullPdf x a b# evaluates the probability density p(x) at x( for a Weibull distribution with scale a and exponent b. The density is given by 0p(x) dx = {b over a^b} x^{b-1} exp(-(x/a)^b) dx for x >= 0.`getWeibull r a b" gets a random Weibull with scale a and exponent b.egammaPdf x a b# evaluates the probability density p(x) at x+ for a gamma distribution with parameters a and b. The density is given by =p(x) dx = p(x) dx = {1 over Gamma(a) b^a} x^{a-1} e^{-x/b} dx for x > 0.fgetGamma r a b% gets a random gamma with parameters a and b/. Uses the Marsagli-Tsang fast gamma method.ggetGammaKnuth r a b% gets a random gamma with parameters a and b,. Uses the algorithms from Knuth (vol 2).lmultinomialPdf ns ps$ evaluates the probability density p(ns) at ns1 for a multinomial distribution with parameters ps , where all ps are non-negative and sum to 1. Note that xs and alphas should have the same length.m<multinomialLnPdf xs alphas == log (multinomialPdf xs alphas), but more efficient.ngetMultinomial r n psG gets a random sample from a multinomial distribution with parameters ps formed by n trials.odirichletPdf xs alphas$ evaluates the probability density p(xs) at xs/ for a Dirichlet distribution with parameters alphas , where all alphas8 are positive (strictly greater than zero). Note that xs and alphas should have the same length.p8dirichletLnPdf xs alphas == log (dirichletPdf xs alphas), but more efficient.qgetDirichlet r alphasE gets a random sample from a Dirichlet distribution with parameters alphas , where all alphas are positive.rlognormalPdf x zeta sigma& evaluates the probability density p(x) at x/ for a log-normal distribution with parameters zeta and sigma%, given. The density is given by Qp(x) dx = p(x) {1 over x sqrt{2 pi sigma^2} } exp(-(ln(x) - zeta)^2/2 sigma^2) dxsgetLognormal zeta sigma) gets a random lognormal with parameters zeta and sigma.&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijkl ns psm ns psno xs alphasp xs alphasqrstuvwR&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwR&'()*+,-./0123456789:<=>?@;ABCDEFHGIJKLNMOPQRTSUVWXsrtuvwZY[\]^`_abcdfgehijknlmqop&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvw        !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~'gsl-random-0.5.1-KcxqpppNc5x2KkqLN10dleGSL.Random.QuasiGSL.Random.GenGSL.Random.DistGSL.Random.Quasi.InternalGSL.Random.Gen.InternalQRNGTypeQRNGMkQRNGnewQRNG resetQRNG getSample getListSamplegetName getDimensiongetSizegetStatesetStatecopyQRNG cloneQRNG niederreitersobolhalton reverseHalton maxDimensionRNGTypeRNGMkRNGnewRNGsetSeed getUniform getUniformPos getUniformIntgetMaxgetMincopyRNGcloneRNGmt19937rngType gaussianPdf gaussianP gaussianQ gaussianPInv gaussianQInv getGaussiangetGaussianZigguratgetGaussianRatioMethod ugaussianPdf ugaussianP ugaussianQ ugaussianPInv ugaussianQInv getUGaussiangetUGaussianRatioMethodflatPdfflatPflatQflatPInvflatQInvgetFlatgetExponentialexponentialPdf exponentialP exponentialQexponentialPInvexponentialQInvgetLevy getLevySkew poissonPdfpoissonPpoissonQ getPoisson cauchyPdf getCauchycauchyPcauchyQ cauchyPInv cauchyQInvbetaPdfgetBetabetaPbetaQbetaPInvbetaQInv logisticPdf getLogistic logisticP logisticQ logisticPInv logisticQInv paretoPdf getParetoparetoPparetoQ paretoPInv paretoQInv weibullPdf getWeibullweibullPweibullQ weibullPInv weibullQInvgammaPdfgetGamma getGammaKnuthgammaPgammaQ gammaPInv gammaQInvmultinomialPdfmultinomialLnPdfgetMultinomial dirichletPdfdirichletLnPdf getDirichlet lognormalPdf getLognormal lognormalP lognormalQ lognormalPInv lognormalQInv MkQRNGTypegsl_qrng_get_max_dimensiongsl_qrng_get_reversehaltongsl_qrng_get_haltongsl_qrng_get_sobolgsl_qrng_get_niederreiter_2gsl_qrng_clonegsl_qrng_memcpygsl_qrng_state gsl_qrng_sizegsl_qrng_get_dimension gsl_qrng_name gsl_qrng_get gsl_qrng_initp_gsl_qrng_freegsl_qrng_allocgetNiederreitergetSobol getHaltongetReverseHaltongetMaxDimension MkRNGTypegsl_rng_types_setupc_strcmp gsl_rng_clonegsl_rng_memcpy gsl_rng_state gsl_rng_size gsl_rng_min gsl_rng_max gsl_rng_namegsl_rng_uniform_intgsl_rng_uniform_posgsl_rng_uniform gsl_rng_get gsl_rng_setp_gsl_rng_free gsl_rng_alloc getRngTypegsl_cdf_lognormal_Qinvgsl_cdf_lognormal_Pinvgsl_cdf_lognormal_Qgsl_cdf_lognormal_Pgsl_ran_lognormalgsl_ran_lognormal_pdfgsl_ran_dirichletgsl_ran_dirichlet_lnpdfgsl_ran_dirichlet_pdfgsl_ran_multinomialgsl_ran_multinomial_lnpdfgsl_ran_multinomial_pdfgsl_cdf_gamma_Qinvgsl_cdf_gamma_Pinvgsl_cdf_gamma_Qgsl_cdf_gamma_Pgsl_ran_gamma_knuth gsl_ran_gammagsl_ran_gamma_pdfgsl_cdf_weibull_Qinvgsl_cdf_weibull_Pinvgsl_cdf_weibull_Qgsl_cdf_weibull_Pgsl_ran_weibullgsl_ran_weibull_pdfgsl_cdf_pareto_Qinvgsl_cdf_pareto_Pinvgsl_cdf_pareto_Qgsl_cdf_pareto_Pgsl_ran_paretogsl_ran_pareto_pdfgsl_cdf_logistic_Qinvgsl_cdf_logistic_Pinvgsl_cdf_logistic_Qgsl_cdf_logistic_Pgsl_ran_logisticgsl_ran_logistic_pdfgsl_cdf_beta_Qinvgsl_cdf_beta_Pinvgsl_cdf_beta_Qgsl_cdf_beta_P gsl_ran_betagsl_ran_beta_pdfgsl_cdf_cauchy_Qinvgsl_cdf_cauchy_Pinvgsl_cdf_cauchy_Qgsl_cdf_cauchy_Pgsl_ran_cauchygsl_ran_cauchy_pdfgsl_ran_poissongsl_cdf_poisson_Qgsl_cdf_poisson_Pgsl_ran_poisson_pdfgsl_ran_levy_skew gsl_ran_levygsl_cdf_exponential_Qinvgsl_cdf_exponential_Pinvgsl_cdf_exponential_Qgsl_cdf_exponential_Pgsl_ran_exponential_pdfgsl_ran_exponential gsl_ran_flatgsl_cdf_flat_Qinvgsl_cdf_flat_Pinvgsl_cdf_flat_Qgsl_cdf_flat_Pgsl_ran_flat_pdfgsl_ran_ugaussian_ratio_methodgsl_ran_ugaussiangsl_cdf_ugaussian_Qinvgsl_cdf_ugaussian_Pinvgsl_cdf_ugaussian_Qgsl_cdf_ugaussian_Pgsl_ran_ugaussian_pdfgsl_ran_gaussian_ratio_methodgsl_ran_gaussian_zigguratgsl_ran_gaussiangsl_cdf_gaussian_Qinvgsl_cdf_gaussian_Pinvgsl_cdf_gaussian_Qgsl_cdf_gaussian_Pgsl_ran_gaussian_pdf liftDouble liftDouble2 liftDouble3liftRan0liftRan1liftRan2liftRan3vectorPdfHelpervectorGetHelper