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Export of internal Abseil changes -- f012012ef78234a6a4585321b67d7b7c92ebc266 by Laramie Leavitt <lar@google.com>: Slight restructuring of absl/random/internal randen implementation. Convert round-keys.inc into randen_round_keys.cc file. Consistently use a 128-bit pointer type for internal method parameters. This allows simpler pointer arithmetic in C++ & permits removal of some constants and casts. Remove some redundancy in comments & constexpr variables. Specifically, all references to Randen algorithm parameters use RandenTraits; duplication in RandenSlow removed. PiperOrigin-RevId: 312190313 -- dc8b42e054046741e9ed65335bfdface997c6063 by Abseil Team <absl-team@google.com>: Internal change. PiperOrigin-RevId: 312167304 -- f13d248fafaf206492c1362c3574031aea3abaf7 by Matthew Brown <matthewbr@google.com>: Cleanup StrFormat extensions a little. PiperOrigin-RevId: 312166336 -- 9d9117589667afe2332bb7ad42bc967ca7c54502 by Derek Mauro <dmauro@google.com>: Internal change PiperOrigin-RevId: 312105213 -- 9a12b9b3aa0e59b8ee6cf9408ed0029045543a9b by Abseil Team <absl-team@google.com>: Complete IGNORE_TYPE macro renaming. PiperOrigin-RevId: 311999699 -- 64756f20d61021d999bd0d4c15e9ad3857382f57 by Gennadiy Rozental <rogeeff@google.com>: Switch to fixed bytes specific default value. This fixes the Abseil Flags for big endian platforms. PiperOrigin-RevId: 311844448 -- bdbe6b5b29791dbc3816ada1828458b3010ff1e9 by Laramie Leavitt <lar@google.com>: Change many distribution tests to use pcg_engine as a deterministic source of entropy. It's reasonable to test that the BitGen itself has good entropy, however when testing the cross product of all random distributions x all the architecture variations x all submitted changes results in a large number of tests. In order to account for these failures while still using good entropy requires that our allowed sigma need to account for all of these independent tests. Our current sigma values are too restrictive, and we see a lot of failures, so we have to either relax the sigma values or convert some of the statistical tests to use deterministic values. This changelist does the latter. PiperOrigin-RevId: 311840096 GitOrigin-RevId: f012012ef78234a6a4585321b67d7b7c92ebc266 Change-Id: Ic84886f38ff30d7d72c126e9b63c9a61eb729a1a
5 years ago
// Copyright 2017 The Abseil Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef ABSL_RANDOM_ZIPF_DISTRIBUTION_H_
#define ABSL_RANDOM_ZIPF_DISTRIBUTION_H_
#include <cassert>
#include <cmath>
#include <istream>
#include <limits>
#include <ostream>
#include <type_traits>
#include "absl/random/internal/iostream_state_saver.h"
#include "absl/random/uniform_real_distribution.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
// absl::zipf_distribution produces random integer-values in the range [0, k],
// distributed according to the discrete probability function:
//
// P(x) = (v + x) ^ -q
//
// The parameter `v` must be greater than 0 and the parameter `q` must be
// greater than 1. If either of these parameters take invalid values then the
// behavior is undefined.
//
// IntType is the result_type generated by the generator. It must be of integral
// type; a static_assert ensures this is the case.
//
// The implementation is based on W.Hormann, G.Derflinger:
//
// "Rejection-Inversion to Generate Variates from Monotone Discrete
// Distributions"
//
// http://eeyore.wu-wien.ac.at/papers/96-04-04.wh-der.ps.gz
//
template <typename IntType = int>
class zipf_distribution {
public:
using result_type = IntType;
class param_type {
public:
using distribution_type = zipf_distribution;
// Preconditions: k > 0, v > 0, q > 1
// The precondidtions are validated when NDEBUG is not defined via
// a pair of assert() directives.
// If NDEBUG is defined and either or both of these parameters take invalid
// values, the behavior of the class is undefined.
explicit param_type(result_type k = (std::numeric_limits<IntType>::max)(),
double q = 2.0, double v = 1.0);
result_type k() const { return k_; }
double q() const { return q_; }
double v() const { return v_; }
friend bool operator==(const param_type& a, const param_type& b) {
return a.k_ == b.k_ && a.q_ == b.q_ && a.v_ == b.v_;
}
friend bool operator!=(const param_type& a, const param_type& b) {
return !(a == b);
}
private:
friend class zipf_distribution;
inline double h(double x) const;
inline double hinv(double x) const;
inline double compute_s() const;
inline double pow_negative_q(double x) const;
// Parameters here are exactly the same as the parameters of Algorithm ZRI
// in the paper.
IntType k_;
double q_;
double v_;
double one_minus_q_; // 1-q
double s_;
double one_minus_q_inv_; // 1 / 1-q
double hxm_; // h(k + 0.5)
double hx0_minus_hxm_; // h(x0) - h(k + 0.5)
static_assert(std::is_integral<IntType>::value,
"Class-template absl::zipf_distribution<> must be "
"parameterized using an integral type.");
};
zipf_distribution()
: zipf_distribution((std::numeric_limits<IntType>::max)()) {}
explicit zipf_distribution(result_type k, double q = 2.0, double v = 1.0)
: param_(k, q, v) {}
explicit zipf_distribution(const param_type& p) : param_(p) {}
void reset() {}
template <typename URBG>
result_type operator()(URBG& g) { // NOLINT(runtime/references)
return (*this)(g, param_);
}
template <typename URBG>
result_type operator()(URBG& g, // NOLINT(runtime/references)
const param_type& p);
result_type k() const { return param_.k(); }
double q() const { return param_.q(); }
double v() const { return param_.v(); }
param_type param() const { return param_; }
void param(const param_type& p) { param_ = p; }
result_type(min)() const { return 0; }
result_type(max)() const { return k(); }
friend bool operator==(const zipf_distribution& a,
const zipf_distribution& b) {
return a.param_ == b.param_;
}
friend bool operator!=(const zipf_distribution& a,
const zipf_distribution& b) {
return a.param_ != b.param_;
}
private:
param_type param_;
};
// --------------------------------------------------------------------------
// Implementation details follow
// --------------------------------------------------------------------------
template <typename IntType>
zipf_distribution<IntType>::param_type::param_type(
typename zipf_distribution<IntType>::result_type k, double q, double v)
: k_(k), q_(q), v_(v), one_minus_q_(1 - q) {
assert(q > 1);
assert(v > 0);
assert(k > 0);
one_minus_q_inv_ = 1 / one_minus_q_;
// Setup for the ZRI algorithm (pg 17 of the paper).
// Compute: h(i max) => h(k + 0.5)
constexpr double kMax = 18446744073709549568.0;
double kd = static_cast<double>(k);
// TODO(absl-team): Determine if this check is needed, and if so, add a test
// that fails for k > kMax
if (kd > kMax) {
// Ensure that our maximum value is capped to a value which will
// round-trip back through double.
kd = kMax;
}
hxm_ = h(kd + 0.5);
// Compute: h(0)
const bool use_precomputed = (v == 1.0 && q == 2.0);
const double h0x5 = use_precomputed ? (-1.0 / 1.5) // exp(-log(1.5))
: h(0.5);
const double elogv_q = (v_ == 1.0) ? 1 : pow_negative_q(v_);
// h(0) = h(0.5) - exp(log(v) * -q)
hx0_minus_hxm_ = (h0x5 - elogv_q) - hxm_;
// And s
s_ = use_precomputed ? 0.46153846153846123 : compute_s();
}
template <typename IntType>
double zipf_distribution<IntType>::param_type::h(double x) const {
// std::exp(one_minus_q_ * std::log(v_ + x)) * one_minus_q_inv_;
x += v_;
return (one_minus_q_ == -1.0)
? (-1.0 / x) // -exp(-log(x))
: (std::exp(std::log(x) * one_minus_q_) * one_minus_q_inv_);
}
template <typename IntType>
double zipf_distribution<IntType>::param_type::hinv(double x) const {
// std::exp(one_minus_q_inv_ * std::log(one_minus_q_ * x)) - v_;
return -v_ + ((one_minus_q_ == -1.0)
? (-1.0 / x) // exp(-log(-x))
: std::exp(one_minus_q_inv_ * std::log(one_minus_q_ * x)));
}
template <typename IntType>
double zipf_distribution<IntType>::param_type::compute_s() const {
// 1 - hinv(h(1.5) - std::exp(std::log(v_ + 1) * -q_));
return 1.0 - hinv(h(1.5) - pow_negative_q(v_ + 1.0));
}
template <typename IntType>
double zipf_distribution<IntType>::param_type::pow_negative_q(double x) const {
// std::exp(std::log(x) * -q_);
return q_ == 2.0 ? (1.0 / (x * x)) : std::exp(std::log(x) * -q_);
}
template <typename IntType>
template <typename URBG>
typename zipf_distribution<IntType>::result_type
zipf_distribution<IntType>::operator()(
URBG& g, const param_type& p) { // NOLINT(runtime/references)
absl::uniform_real_distribution<double> uniform_double;
double k;
for (;;) {
const double v = uniform_double(g);
const double u = p.hxm_ + v * p.hx0_minus_hxm_;
const double x = p.hinv(u);
k = rint(x); // std::floor(x + 0.5);
if (k > p.k()) continue; // reject k > max_k
if (k - x <= p.s_) break;
const double h = p.h(k + 0.5);
const double r = p.pow_negative_q(p.v_ + k);
if (u >= h - r) break;
}
IntType ki = static_cast<IntType>(k);
assert(ki <= p.k_);
return ki;
}
template <typename CharT, typename Traits, typename IntType>
std::basic_ostream<CharT, Traits>& operator<<(
std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references)
const zipf_distribution<IntType>& x) {
using stream_type =
typename random_internal::stream_format_type<IntType>::type;
auto saver = random_internal::make_ostream_state_saver(os);
os.precision(random_internal::stream_precision_helper<double>::kPrecision);
os << static_cast<stream_type>(x.k()) << os.fill() << x.q() << os.fill()
<< x.v();
return os;
}
template <typename CharT, typename Traits, typename IntType>
std::basic_istream<CharT, Traits>& operator>>(
std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references)
zipf_distribution<IntType>& x) { // NOLINT(runtime/references)
using result_type = typename zipf_distribution<IntType>::result_type;
using param_type = typename zipf_distribution<IntType>::param_type;
using stream_type =
typename random_internal::stream_format_type<IntType>::type;
stream_type k;
double q;
double v;
auto saver = random_internal::make_istream_state_saver(is);
is >> k >> q >> v;
if (!is.fail()) {
x.param(param_type(static_cast<result_type>(k), q, v));
}
return is;
}
ABSL_NAMESPACE_END
} // namespace absl
#endif // ABSL_RANDOM_ZIPF_DISTRIBUTION_H_