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Diffstat (limited to 'third_party/abseil_cpp/absl/random/zipf_distribution.h')
| -rw-r--r-- | third_party/abseil_cpp/absl/random/zipf_distribution.h | 271 |
1 files changed, 0 insertions, 271 deletions
diff --git a/third_party/abseil_cpp/absl/random/zipf_distribution.h b/third_party/abseil_cpp/absl/random/zipf_distribution.h deleted file mode 100644 index 22ebc756cf..0000000000 --- a/third_party/abseil_cpp/absl/random/zipf_distribution.h +++ /dev/null @@ -1,271 +0,0 @@ -// 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_ |