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SqMod/include/RandomLib/MPFRNormalR.hpp

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/**
* \file MPFRNormalR.hpp
* \brief Header for MPFRNormalR
*
* Sampling exactly from the normal distribution for MPFR using the ratio
* method.
*
* Copyright (c) Charles Karney (2012) <charles@karney.com> and licensed under
* the MIT/X11 License. For more information, see
* http://randomlib.sourceforge.net/
**********************************************************************/
#if !defined(RANDOMLIB_MPFRNORMALR_HPP)
#define RANDOMLIB_MPFRNORMALR_HPP 1
#include <algorithm> // for max/min
#include <cmath> // for pow
#include <mpfr.h>
#define HAVE_MPFR (MPFR_VERSION_MAJOR >= 3)
#if HAVE_MPFR || defined(DOXYGEN)
namespace RandomLib {
/**
* \brief The normal distribution for MPFR (ratio method).
*
* This class is <b>DEPRECATED</b>. It is included for illustrative purposes
* only. The MPFRNormal class provides a much more efficient method for
* sampling from the normal distribution.
*
* This is an adaption of NormalDistribution to MPFR. The changes are
* - Use MPFR's random number generator
* - Use sufficient precision internally to ensure that a correctly rounded
* result is returned.
*
* This class uses a mutable private object. So a single MPFRNormalR
* object cannot safely be used by multiple threads. In a multi-processing
* environment, each thread should use a thread-specific MPFRNormalR
* object.
**********************************************************************/
class MPFRNormalR {
private:
// The number of bits of randomness to add at a time. Require that Leva's
// bounds "work" at a precision of 2^-chunk and that an unsigned long can
// hold this many bits.
static const long chunk_ = 32;
static const unsigned long m = 3684067834; // ceil(2^chunk*sqrt(2/e))
public:
/**
* Initialize the MPFRNormalR object.
**********************************************************************/
MPFRNormalR() {
mpz_init(_ui);
mpz_init(_vi);
mpfr_init2(_eps, chunk_);
mpfr_init2(_u, chunk_);
mpfr_init2(_v, chunk_);
mpfr_init2(_up, chunk_);
mpfr_init2(_vp, chunk_);
mpfr_init2(_vx, chunk_);
mpfr_init2(_x1, chunk_);
mpfr_init2(_x2, chunk_);
}
/**
* Destroy the MPFRNormalR object.
**********************************************************************/
~MPFRNormalR() {
mpfr_clear(_x2);
mpfr_clear(_x1);
mpfr_clear(_vx);
mpfr_clear(_vp);
mpfr_clear(_up);
mpfr_clear(_v);
mpfr_clear(_u);
mpfr_clear(_eps);
mpz_clear(_vi);
mpz_clear(_ui);
}
/**
* Sample from the normal distribution with mean 0 and variance 1.
*
* @param[out] val the sample from the normal distribution
* @param[in,out] r a GMP random generator.
* @param[in] round the rounding direction.
* @return the MPFR ternary result (&plusmn;1 if val is larger/smaller than
* the exact sample).
**********************************************************************/
int operator()(mpfr_t val, gmp_randstate_t r, mpfr_rnd_t round) const {
const double
s = 0.449871, // Constants from Leva
t = -0.386595,
a = 0.19600 ,
b = 0.25472 ,
r1 = 0.27597 ,
r2 = 0.27846 ,
u1 = 0.606530, // sqrt(1/e) rounded down and up
u2 = 0.606531,
scale = std::pow(2.0, -chunk_); // for turning randoms into doubles
while (true) {
mpz_urandomb(_vi, r, chunk_);
if (mpz_cmp_ui(_vi, m) >= 0) continue; // Very early reject
double vf = (mpz_get_ui(_vi) + 0.5) * scale;
mpz_urandomb(_ui, r, chunk_);
double uf = (mpz_get_ui(_ui) + 0.5) * scale;
double
x = uf - s,
y = vf - t,
Q = x*x + y * (a*y - b*x);
if (Q >= r2) continue; // Early reject
mpfr_set_ui_2exp(_eps, 1u, -chunk_, MPFR_RNDN);
mpfr_prec_t prec = chunk_;
mpfr_set_prec(_u, prec);
mpfr_set_prec(_v, prec);
// (u,v) = sw corner of range
mpfr_set_z_2exp(_u, _ui, -prec, MPFR_RNDN);
mpfr_set_z_2exp(_v, _vi, -prec, MPFR_RNDN);
mpfr_set_prec(_up, prec);
mpfr_set_prec(_vp, prec);
// (up,vp) = ne corner of range
mpfr_add(_up, _u, _eps, MPFR_RNDN);
mpfr_add(_vp, _v, _eps, MPFR_RNDN);
// Estimate how many extra bits will be needed to achieve the desired
// precision.
mpfr_prec_t prec_guard = 3 + chunk_ -
(std::max)(mpz_sizeinbase(_ui, 2), mpz_sizeinbase(_vi, 2));
if (Q > r1) {
int reject;
while (true) {
// Rejection curve v^2 + 4 * u^2 * log(u) < 0 has a peak at u =
// exp(-1/2) = 0.60653066. So treat uf in (0.606530, 0.606531) =
// (u1, u2) specially
// Try for rejection first
if (uf <= u1)
reject = Reject(_u, _vp, prec, MPFR_RNDU);
else if (uf >= u2)
reject = Reject(_up, _vp, prec, MPFR_RNDU);
else { // u in (u1, u2)
mpfr_set_prec(_vx, prec);
mpfr_add(_vx, _vp, _eps, MPFR_RNDN);
reject = Reject(_u, _vx, prec, MPFR_RNDU); // Could use _up too
}
if (reject < 0) break; // tried to reject but failed, so accept
// Try for acceptance
if (uf <= u1)
reject = Reject(_up, _v, prec, MPFR_RNDD);
else if (uf >= u2)
reject = Reject(_u, _v, prec, MPFR_RNDD);
else { // u in (u2, u2)
mpfr_sub(_vx, _v, _eps, MPFR_RNDN);
reject = Reject(_u, _vx, prec, MPFR_RNDD); // Could use _up too
}
if (reject > 0) break; // tried to accept but failed, so reject
prec = Refine(r, prec); // still can't decide, to refine
}
if (reject > 0) continue; // reject, back to outer loop
}
// Now evaluate v/u to the necessary precision
mpfr_prec_t prec0 = mpfr_get_prec (val);
// while (prec < prec0 + prec_guard) prec = Refine(r, prec);
if (prec < prec0 + prec_guard)
prec = Refine(r, prec,
(prec0 + prec_guard - prec + chunk_ - 1) / chunk_);
mpfr_set_prec(_x1, prec0);
mpfr_set_prec(_x2, prec0);
int flag;
while (true) {
int
f1 = mpfr_div(_x1, _v, _up, round), // min slope
f2 = mpfr_div(_x2, _vp, _u, round); // max slope
if (f1 == f2 && mpfr_equal_p(_x1, _x2)) {
flag = f1;
break;
}
prec = Refine(r, prec);
}
mpz_urandomb(_ui, r, 1);
if (mpz_tstbit(_ui, 0)) {
flag = -flag;
mpfr_neg(val, _x1, MPFR_RNDN);
} else
mpfr_set(val, _x1, MPFR_RNDN);
// std::cerr << uf << " " << vf << " " << Q << "\n";
return flag;
}
}
private:
// disable copy constructor and assignment operator
MPFRNormalR(const MPFRNormalR&);
MPFRNormalR& operator=(const MPFRNormalR&);
// Refine the random square
mpfr_prec_t Refine(gmp_randstate_t r, mpfr_prec_t prec, long num = 1)
const {
if (num <= 0) return prec;
// Use _vx as scratch
prec += num * chunk_;
mpfr_div_2ui(_eps, _eps, num * chunk_, MPFR_RNDN);
mpz_urandomb(_ui, r, num * chunk_);
mpfr_set_prec(_up, prec);
mpfr_set_z_2exp(_up, _ui, -prec, MPFR_RNDN);
mpfr_set_prec(_vx, prec);
mpfr_add(_vx, _u, _up, MPFR_RNDN);
mpfr_swap(_u, _vx); // u = vx
mpfr_add(_up, _u, _eps, MPFR_RNDN);
mpz_urandomb(_vi, r, num * chunk_);
mpfr_set_prec(_vp, prec);
mpfr_set_z_2exp(_vp, _vi, -prec, MPFR_RNDN);
mpfr_set_prec(_vx, prec);
mpfr_add(_vx, _v, _vp, MPFR_RNDN);
mpfr_swap(_v, _vx); // v = vx
mpfr_add(_vp, _v, _eps, MPFR_RNDN);
return prec;
}
// Evaluate the sign of the rejection condition v^2 + 4*u^2*log(u)
int Reject(mpfr_t u, mpfr_t v, mpfr_prec_t prec, mpfr_rnd_t round) const {
// Use x1, x2 as scratch
mpfr_set_prec(_x1, prec);
mpfr_log(_x1, u, round);
mpfr_mul(_x1, _x1, u, round); // Important to do the multiplications in
mpfr_mul(_x1, _x1, u, round); // this order so that rounding works right.
mpfr_mul_2ui(_x1, _x1, 2u, round); // 4*u^2*log(u)
mpfr_set_prec(_x2, prec);
mpfr_mul(_x2, v, v, round); // v^2
mpfr_add(_x1, _x1, _x2, round); // v^2 + 4*u^2*log(u)
return mpfr_sgn(_x1);
}
mutable mpz_t _ui;
mutable mpz_t _vi;
mutable mpfr_t _eps;
mutable mpfr_t _u;
mutable mpfr_t _v;
mutable mpfr_t _up;
mutable mpfr_t _vp;
mutable mpfr_t _vx;
mutable mpfr_t _x1;
mutable mpfr_t _x2;
};
} // namespace RandomLib
#endif // HAVE_MPFR
#endif // RANDOMLIB_MPFRNORMALR_HPP
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