/* Copyright 2010-2014 NVIDIA Corporation. All rights reserved.
*
* NOTICE TO LICENSEE:
*
* The source code and/or documentation ("Licensed Deliverables") are
* subject to NVIDIA intellectual property rights under U.S. and
* international Copyright laws.
*
* The Licensed Deliverables contained herein are PROPRIETARY and
* CONFIDENTIAL to NVIDIA and are being provided under the terms and
* conditions of a form of NVIDIA software license agreement by and
* between NVIDIA and Licensee ("License Agreement") or electronically
* accepted by Licensee. Notwithstanding any terms or conditions to
* the contrary in the License Agreement, reproduction or disclosure
* of the Licensed Deliverables to any third party without the express
* written consent of NVIDIA is prohibited.
*
* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. THEY ARE
* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
* OF THESE LICENSED DELIVERABLES.
*
* U.S. Government End Users. These Licensed Deliverables are a
* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
* 1995), consisting of "commercial computer software" and "commercial
* computer software documentation" as such terms are used in 48
* C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
* U.S. Government End Users acquire the Licensed Deliverables with
* only those rights set forth herein.
*
* Any use of the Licensed Deliverables in individual and commercial
* software must include, in the user documentation and internal
* comments to the code, the above Disclaimer and U.S. Government End
* Users Notice.
*/
#if !defined(CURAND_POISSON_H_)
#define CURAND_POISSON_H_
/**
* \defgroup DEVICE Device API
*
* @{
*/
#ifndef __CUDACC_RTC__
#include <math.h>
#endif // __CUDACC_RTC__
#include <nv/target>
#include "curand_mrg32k3a.h"
#include "curand_mtgp32_kernel.h"
#include "curand_philox4x32_x.h"
#define CR_CUDART_PI 3.1415926535897931e+0
#define CR_CUDART_TWO_TO_52 4503599627370496.0
QUALIFIERS float __cr_rsqrt(float a)
{
NV_IF_ELSE_TARGET(NV_IS_DEVICE,
asm ("rsqrt.approx.f32.ftz %0, %1;" : "=f"(a) : "f"(a));
,
a = 1.0f / sqrtf (a);
)
return a;
}
QUALIFIERS float __cr_exp (float a)
{
NV_IF_ELSE_TARGET(NV_IS_DEVICE,
a = a * 1.4426950408889634074;
asm ("ex2.approx.f32.ftz %0, %1;" : "=f"(a) : "f"(a));
,
a = expf (a);
)
return a;
}
QUALIFIERS float __cr_log (float a)
{
NV_IF_ELSE_TARGET(NV_IS_DEVICE,
asm ("lg2.approx.f32.ftz %0, %1;" : "=f"(a) : "f"(a));
a = a * 0.69314718055994530942;
,
a = logf (a);
)
return a;
}
QUALIFIERS float __cr_rcp (float a)
{
NV_IF_ELSE_TARGET(NV_IS_DEVICE,
asm ("rcp.approx.f32.ftz %0, %1;" : "=f"(a) : "f"(a));
,
a = 1.0f / a;
)
return a;
}
/* Computes regularized gamma function: gammainc(a,x)/gamma(a) */
QUALIFIERS float __cr_pgammainc (float a, float x)
{
float t, alpha, beta;
/* First level parametrization constants */
float ma1 = 1.43248035075540910f,
ma2 = 0.12400979329415655f,
ma3 = 0.00025361074907033f,
mb1 = 0.21096734870196546f,
mb2 = 1.97381164089999420f,
mb3 = 0.94201734077887530f;
/* Second level parametrization constants (depends only on a) */
alpha = __cr_rsqrt (a - ma2);
alpha = ma1 * alpha + ma3;
beta = __cr_rsqrt (a - mb2);
beta = mb1 * beta + mb3;
/* Final approximation (depends on a and x) */
t = a - x;
t = alpha * t - beta;
t = 1.0f + __cr_exp (t);
t = t * t;
t = __cr_rcp (t);
/* Negative a,x or a,x=NAN requires special handling */
//t = !(x > 0 && a >= 0) ? 0.0 : t;
return t;
}
/* Computes inverse of pgammainc */
QUALIFIERS float __cr_pgammaincinv (float a, float y)
{
float t, alpha, beta;
/* First level parametrization constants */
float ma1 = 1.43248035075540910f,
ma2 = 0.12400979329415655f,
ma3 = 0.00025361074907033f,
mb1 = 0.21096734870196546f,
mb2 = 1.97381164089999420f,
mb3 = 0.94201734077887530f;
/* Second level parametrization constants (depends only on a) */
alpha = __cr_rsqrt (a - ma2);
alpha = ma1 * alpha + ma3;
beta = __cr_rsqrt (a - mb2);
beta = mb1 * beta + mb3;
/* Final approximation (depends on a and y) */
t = __cr_rsqrt (y) - 1.0f;
t = __cr_log (t);
t = beta + t;
t = - t * __cr_rcp (alpha) + a;
/* Negative a,x or a,x=NAN requires special handling */
//t = !(y > 0 && a >= 0) ? 0.0 : t;
return t;
}
#if defined(__CUDACC_RDC__) && (__cplusplus >= 201703L) && defined(__cpp_inline_variables)
inline __constant__ double __cr_lgamma_table [] = {
#else
static __constant__ double __cr_lgamma_table [] = {
#endif
0.000000000000000000e-1,
0.000000000000000000e-1,
6.931471805599453094e-1,
1.791759469228055001e0,
3.178053830347945620e0,
4.787491742782045994e0,
6.579251212010100995e0,
8.525161361065414300e0,
1.060460290274525023e1
};
QUALIFIERS double __cr_lgamma_integer(int a)
{
double s;
double t;
double fa = fabs((float)a);
double sum;
if (a > 8) {
/* Stirling approximation; coefficients from Hart et al, "Computer
* Approximations", Wiley 1968. Approximation 5404.
*/
s = 1.0 / fa;
t = s * s;
sum = -0.1633436431e-2;
sum = sum * t + 0.83645878922e-3;
sum = sum * t - 0.5951896861197e-3;
sum = sum * t + 0.793650576493454e-3;
sum = sum * t - 0.277777777735865004e-2;
sum = sum * t + 0.833333333333331018375e-1;
sum = sum * s + 0.918938533204672;
s = 0.5 * log (fa);
t = fa - 0.5;
s = s * t;
t = s - fa;
s = s + sum;
t = t + s;
return t;
} else {
NV_IF_ELSE_TARGET(NV_IS_DEVICE,
return __cr_lgamma_table [(int) fa-1];
,
switch(a) {
case 1: return 0.000000000000000000e-1;
case 2: return 0.000000000000000000e-1;
case 3: return 6.931471805599453094e-1;
case 4: return 1.791759469228055001e0;
case 5: return 3.178053830347945620e0;
case 6: return 4.787491742782045994e0;
case 7: return 6.579251212010100995e0;
case 8: return 8.525161361065414300e0;
default: return 1.060460290274525023e1;
}
)
}
}
#define KNUTH_FLOAT_CONST 60.0
template <typename T>
// Donald E. Knuth Seminumerical Algorithms. The Art of Computer Programming, Volume 2
QUALIFIERS unsigned int curand_poisson_knuth(T *state, float lambda)
{
unsigned int k = 0;
float p = expf(lambda);
do{
k++;
p *= curand_uniform(state);
}while (p > 1.0);
return k-1;
}
template <typename T>
// Donald E. Knuth Seminumerical Algorithms. The Art of Computer Programming, Volume 2
QUALIFIERS uint4 curand_poisson_knuth4(T *state, float lambda)
{
uint4 k = {0,0,0,0};
float exp_lambda = expf(lambda);
float4 p={ exp_lambda,exp_lambda,exp_lambda,exp_lambda };
do{
k.x++;
p.x *= curand_uniform(state);
}while (p.x > 1.0);
do{
k.y++;
p.y *= curand_uniform(state);
}while (p.y > 1.0);
do{
k.z++;
p.z *= curand_uniform(state);
}while (p.z > 1.0);
do{
k.w++;
p.w *= curand_uniform(state);
}while (p.w > 1.0);
k.x--;
k.y--;
k.z--;
k.w--;
return k;
}
template <typename T>
// Marsaglia, Tsang, Wang Journal of Statistical Software, square histogram.
QUALIFIERS unsigned int _curand_M2_double(T x, curandDistributionM2Shift_t distributionM2)
{
double u = _curand_uniform_double(x);
int j = (int) floor(distributionM2->length*u);
double histogramVj;
unsigned int histogramKj;
NV_IF_ELSE_TARGET(NV_PROVIDES_SM_35,
histogramVj = __ldg( &(distributionM2->histogram->V[j]));
histogramKj = __ldg( &(distributionM2->histogram->K[j]));
,
histogramVj = distributionM2->histogram->V[j];
histogramKj = distributionM2->histogram->K[j];
)
//if (u < distributionM2->histogram->V[j]) return distributionM2->shift + j;
//return distributionM2->shift + distributionM2->histogram->K[j];
if (u < histogramVj) return distributionM2->shift + j;
return distributionM2->shift + histogramKj;
}
template <typename T>
// Marsaglia, Tsang, Wang Journal of Statistical Software, square histogram.
QUALIFIERS uint4 _curand_M2_double4(T x, curandDistributionM2Shift_t distributionM2)
{
double4 u;
uint4 result = {0,0,0,0};
int4 flag = {1,1,1,1};
u.x = _curand_uniform_double(x.x);
u.y = _curand_uniform_double(x.y);
u.z = _curand_uniform_double(x.z);
u.w = _curand_uniform_double(x.w);
int4 j;
j.x = (int) floor(distributionM2->length*u.x);
j.y = (int) floor(distributionM2->length*u.y);
j.z = (int) floor(distributionM2->length*u.z);
j.w = (int) floor(distributionM2->length*u.w);
// int result;
double histogramVjx;
double histogramVjy;
double histogramVjz;
double histogramVjw;
unsigned int histogramKjx;
unsigned int histogramKjy;
unsigned int histogramKjz;
unsigned int histogramKjw;
NV_IF_ELSE_TARGET(NV_PROVIDES_SM_35,
histogramVjx = __ldg( &(distributionM2->histogram->V[j.x]));
histogramVjy = __ldg( &(distributionM2->histogram->V[j.y]));
histogramVjz = __ldg( &(distributionM2->histogram->V[j.z]));
histogramVjw = __ldg( &(distributionM2->histogram->V[j.w]));
histogramKjx = __ldg( &(distributionM2->histogram->K[j.x]));
histogramKjy = __ldg( &(distributionM2->histogram->K[j.y]));
histogramKjz = __ldg( &(distributionM2->histogram->K[j.z]));
histogramKjw = __ldg( &(distributionM2->histogram->K[j.w]));
,
histogramVjx = distributionM2->histogram->V[j.x];
histogramVjy = distributionM2->histogram->V[j.y];
histogramVjz = distributionM2->histogram->V[j.z];
histogramVjw = distributionM2->histogram->V[j.w];
histogramKjx = distributionM2->histogram->K[j.x];
histogramKjy = distributionM2->histogram->K[j.y];
histogramKjz = distributionM2->histogram->K[j.z];
histogramKjw = distributionM2->histogram->K[j.w];
)
if (u.x < histogramVjx){ result.x = distributionM2->shift + j.x; flag.x = 0; }
if (u.y < histogramVjy){ result.y = distributionM2->shift + j.y; flag.y = 0; }
if (u.z < histogramVjz){ result.z = distributionM2->shift + j.z; flag.z = 0; }
if (u.w < histogramVjw){ result.w = distributionM2->shift + j.w; flag.w = 0; }
//return distributionM2->shift + distributionM2->histogram->K[j];
if(flag.x) result.x = distributionM2->shift + histogramKjx;
if(flag.y) result.y = distributionM2->shift + histogramKjy;
if(flag.z) result.z = distributionM2->shift + histogramKjz;
if(flag.w) result.w = distributionM2->shift + histogramKjw;
return result;
}
template <typename STATE>
QUALIFIERS unsigned int curand_M2_double(STATE *state, curandDistributionM2Shift_t distributionM2)
{
return _curand_M2_double(curand(state), distributionM2);
}
template <typename STATE>
QUALIFIERS uint4 curand_M2_double4(STATE *state, curandDistributionM2Shift_t distributionM2)
{
return _curand_M2_double4(curand4(state), distributionM2);
}
template <typename T>
QUALIFIERS unsigned int _curand_binary_search_double(T x, curandDistributionShift_t distribution)
{
double u = _curand_uniform_double(x);
int min = 0;
int max = distribution->length-1;
do{
int mid = (max + min)/2;
double probability_mid;
NV_IF_ELSE_TARGET(NV_PROVIDES_SM_35,
probability_mid = __ldg( &(distribution->probability[mid]));
,
probability_mid = distribution->probability[mid];
)
if (u <= probability_mid){
max = mid;
}else{
min = mid+1;
}
}while (min < max);
return distribution->shift + min;
}
template <typename STATE>
QUALIFIERS unsigned int curand_binary_search_double(STATE *state, curandDistributionShift_t distribution)
{
return _curand_binary_search_double(curand(state), distribution);
}
// Generates uniformly distributed double values in range (0.0; 1.0) from uniformly distributed
// unsigned int. We can't use standard _curand_uniform_double since it can generate 1.0.
// This is required only for _curand_poisson_ITR_double.
QUALIFIERS double _curand_uniform_double_excluding_one(unsigned int x)
{
return x * CURAND_2POW32_INV_DOUBLE + (CURAND_2POW32_INV_DOUBLE/2.0);
}
// Overload for unsigned long long.
// This is required only for _curand_poisson_ITR_double.
QUALIFIERS double _curand_uniform_double_excluding_one(unsigned long long x)
{
return (x >> 11) * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/4.0);
}
#define MAGIC_DOUBLE_CONST 500.0
template <typename T>
//George S. Fishman Discrete-event simulation: modeling, programming, and analysis
QUALIFIERS unsigned int _curand_poisson_ITR_double(T x, double lambda)
{
double L,p = 1.0;
double q = 1.0;
unsigned int k = 0;
int pow=0;
// This algorithm requires u to be in (0;1) range, however, _curand_uniform_double
// returns a number in range (0;1]. If u is 1.0 the inner loop never ends. The
// following operation transforms the range from (0;1] to (0;1).
double u = _curand_uniform_double_excluding_one(x);
do{
if (lambda > (double)(pow+MAGIC_DOUBLE_CONST)){
L = exp(-MAGIC_DOUBLE_CONST);
}else{
L = exp((double)(pow - lambda));
}
p *= L;
q *= L;
pow += (int) MAGIC_DOUBLE_CONST;
while (u > q){
k++;
p *= ((double)lambda / (double) k);
q += p;
}
}while((double)pow < lambda);
return k;
}
template <typename T>
/* Rejection Method for Poisson distribution based on gammainc approximation */
QUALIFIERS unsigned int curand_poisson_gammainc(T state, float lambda){
float y, x, t, z,v;
float logl = __cr_log (lambda);
while (true) {
y = curand_uniform (state);
x = __cr_pgammaincinv (lambda, y);
x = floorf (x);
z = curand_uniform (state);
v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
z = z*v;
t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
if ((z < t) && (v>=1e-20))
break;
}
return (unsigned int)x;
}
template <typename T>
/* Rejection Method for Poisson distribution based on gammainc approximation */
QUALIFIERS uint4 curand_poisson_gammainc4(T state, float lambda){
uint4 result;
float y, x, t, z,v;
float logl = __cr_log (lambda);
while (true) {
y = curand_uniform(state);
x = __cr_pgammaincinv (lambda, y);
x = floorf (x);
z = curand_uniform (state);
v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
z = z*v;
t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
if ((z < t) && (v>=1e-20))
break;
}
result.x = (unsigned int)x;
while (true) {
y = curand_uniform(state);
x = __cr_pgammaincinv (lambda, y);
x = floorf (x);
z = curand_uniform (state);
v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
z = z*v;
t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
if ((z < t) && (v>=1e-20))
break;
}
result.y = (unsigned int)x;
while (true) {
y = curand_uniform(state);
x = __cr_pgammaincinv (lambda, y);
x = floorf (x);
z = curand_uniform (state);
v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
z = z*v;
t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
if ((z < t) && (v>=1e-20))
break;
}
result.z = (unsigned int)x;
while (true) {
y = curand_uniform(state);
x = __cr_pgammaincinv (lambda, y);
x = floorf (x);
z = curand_uniform (state);
v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
z = z*v;
t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
if ((z < t) && (v>=1e-20))
break;
}
result.w = (unsigned int)x;
return result;
}
// Note below that the round to nearest integer, where needed,is done in line with code that
// assumes the range of values is < 2**32
template <typename T>
QUALIFIERS unsigned int _curand_poisson(T x, double lambda)
{
if (lambda < 1000)
return _curand_poisson_ITR_double(x, lambda);
return (unsigned int)((sqrt(lambda) * _curand_normal_icdf_double(x)) + lambda + 0.5); //Round to nearest
}
template <typename T>
QUALIFIERS unsigned int _curand_poisson_from_normal(T x, double lambda)
{
return (unsigned int)((sqrt(lambda) * _curand_normal_icdf(x)) + lambda + 0.5); //Round to nearest
}
template <typename STATE>
QUALIFIERS unsigned int curand_poisson_from_normal(STATE state, double lambda)
{
return (unsigned int)((sqrt(lambda) * curand_normal(state)) + lambda + 0.5); //Round to nearest
}
template <typename STATE>
QUALIFIERS uint4 curand_poisson_from_normal4(STATE state, double lambda)
{
uint4 result;
float4 _res;
_res = curand_normal4(state);
result.x = (unsigned int)((sqrt(lambda) * _res.x) + lambda + 0.5); //Round to nearest
result.y = (unsigned int)((sqrt(lambda) * _res.y) + lambda + 0.5); //Round to nearest
result.z = (unsigned int)((sqrt(lambda) * _res.z) + lambda + 0.5); //Round to nearest
result.w = (unsigned int)((sqrt(lambda) * _res.w) + lambda + 0.5); //Round to nearest
return result; //Round to nearest
}
/**
* \brief Return a Poisson-distributed unsigned int from a XORWOW generator.
*
* Return a single unsigned int from a Poisson
* distribution with lambda \p lambda from the XORWOW generator in \p state,
* increment the position of the generator by a variable amount, depending
* on the algorithm used.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS unsigned int curand_poisson(curandStateXORWOW_t *state, double lambda)
{
if (lambda < 64)
return curand_poisson_knuth(state, (float)lambda);
if (lambda > 4000)
return (unsigned int)((sqrt(lambda) * curand_normal_double(state)) + lambda + 0.5); //Round to nearest
return curand_poisson_gammainc(state, (float)lambda);
}
/**
* \brief Return a Poisson-distributed unsigned int from a Philox4_32_10 generator.
*
* Return a single unsigned int from a Poisson
* distribution with lambda \p lambda from the Philox4_32_10 generator in \p state,
* increment the position of the generator by a variable amount, depending
* on the algorithm used.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS unsigned int curand_poisson(curandStatePhilox4_32_10_t *state, double lambda)
{
if (lambda < 64)
return curand_poisson_knuth(state, (float)lambda);
if (lambda > 4000)
return (unsigned int)((sqrt(lambda) * curand_normal_double(state)) + lambda + 0.5); //Round to nearest
return curand_poisson_gammainc(state, (float)lambda);
}
/**
* \brief Return four Poisson-distributed unsigned ints from a Philox4_32_10 generator.
*
* Return a four unsigned ints from a Poisson
* distribution with lambda \p lambda from the Philox4_32_10 generator in \p state,
* increment the position of the generator by a variable amount, depending
* on the algorithm used.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS uint4 curand_poisson4(curandStatePhilox4_32_10_t *state, double lambda)
{
uint4 result;
double4 _res;
if (lambda < 64)
return curand_poisson_knuth4(state, (float)lambda);
if (lambda > 4000) {
_res = curand_normal4_double(state);
result.x = (unsigned int)((sqrt(lambda) * _res.x) + lambda + 0.5); //Round to nearest
result.y = (unsigned int)((sqrt(lambda) * _res.y) + lambda + 0.5); //Round to nearest
result.z = (unsigned int)((sqrt(lambda) * _res.z) + lambda + 0.5); //Round to nearest
result.w = (unsigned int)((sqrt(lambda) * _res.w) + lambda + 0.5); //Round to nearest
return result;
}
return curand_poisson_gammainc4(state, (float)lambda);
}
/**
* \brief Return a Poisson-distributed unsigned int from a MRG32k3A generator.
*
* Return a single unsigned int from a Poisson
* distribution with lambda \p lambda from the MRG32k3a generator in \p state,
* increment the position of the generator by a variable amount, depending
* on the algorithm used.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS unsigned int curand_poisson(curandStateMRG32k3a_t *state, double lambda)
{
if (lambda < 64)
return curand_poisson_knuth(state, (float)lambda);
if (lambda > 4000)
return (unsigned int)((sqrt(lambda) * curand_normal_double(state)) + lambda + 0.5); //Round to nearest
return curand_poisson_gammainc(state, (float)lambda);
}
/**
* \brief Return a Poisson-distributed unsigned int from a MTGP32 generator.
*
* Return a single int from a Poisson
* distribution with lambda \p lambda from the MTGP32 generator in \p state,
* increment the position of the generator by one.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS unsigned int curand_poisson(curandStateMtgp32_t *state, double lambda)
{
return _curand_poisson(curand(state), lambda);
}
/**
* \brief Return a Poisson-distributed unsigned int from a Sobol32 generator.
*
* Return a single unsigned int from a Poisson
* distribution with lambda \p lambda from the Sobol32 generator in \p state,
* increment the position of the generator by one.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS unsigned int curand_poisson(curandStateSobol32_t *state, double lambda)
{
return _curand_poisson(curand(state), lambda);
}
/**
* \brief Return a Poisson-distributed unsigned int from a scrambled Sobol32 generator.
*
* Return a single unsigned int from a Poisson
* distribution with lambda \p lambda from the scrambled Sobol32 generator in \p state,
* increment the position of the generator by one.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS unsigned int curand_poisson(curandStateScrambledSobol32_t *state, double lambda)
{
return _curand_poisson(curand(state), lambda);
}
/**
* \brief Return a Poisson-distributed unsigned int from a Sobol64 generator.
*
* Return a single unsigned int from a Poisson
* distribution with lambda \p lambda from the Sobol64 generator in \p state,
* increment position of generator by one.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS unsigned int curand_poisson(curandStateSobol64_t *state, double lambda)
{
return _curand_poisson(curand(state), lambda);
}
/**
* \brief Return a Poisson-distributed unsigned int from a scrambled Sobol64 generator.
*
* Return a single unsigned int from a Poisson
* distribution with lambda \p lambda from the scrambled Sobol64 generator in \p state,
* increment position of generator by one.
*
* \param state - Pointer to state to update
* \param lambda - Lambda of the Poisson distribution
*
* \return Poisson-distributed unsigned int with lambda \p lambda
*/
QUALIFIERS unsigned int curand_poisson(curandStateScrambledSobol64_t *state, double lambda)
{
return _curand_poisson(curand(state), lambda);
}
#endif // !defined(CURAND_POISSON_H_)