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Diffstat (limited to 'third_party/abseil_cpp/absl/random/poisson_distribution.h')
| -rw-r--r-- | third_party/abseil_cpp/absl/random/poisson_distribution.h | 258 |
1 files changed, 0 insertions, 258 deletions
diff --git a/third_party/abseil_cpp/absl/random/poisson_distribution.h b/third_party/abseil_cpp/absl/random/poisson_distribution.h deleted file mode 100644 index cb5f5d5d0ff7..000000000000 --- a/third_party/abseil_cpp/absl/random/poisson_distribution.h +++ /dev/null @@ -1,258 +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_POISSON_DISTRIBUTION_H_ -#define ABSL_RANDOM_POISSON_DISTRIBUTION_H_ - -#include <cassert> -#include <cmath> -#include <istream> -#include <limits> -#include <ostream> -#include <type_traits> - -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/fastmath.h" -#include "absl/random/internal/generate_real.h" -#include "absl/random/internal/iostream_state_saver.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::poisson_distribution: -// Generates discrete variates conforming to a Poisson distribution. -// p(n) = (mean^n / n!) exp(-mean) -// -// Depending on the parameter, the distribution selects one of the following -// algorithms: -// * The standard algorithm, attributed to Knuth, extended using a split method -// for larger values -// * The "Ratio of Uniforms as a convenient method for sampling from classical -// discrete distributions", Stadlober, 1989. -// http://www.sciencedirect.com/science/article/pii/0377042790903495 -// -// NOTE: param_type.mean() is a double, which permits values larger than -// poisson_distribution<IntType>::max(), however this should be avoided and -// the distribution results are limited to the max() value. -// -// The goals of this implementation are to provide good performance while still -// beig thread-safe: This limits the implementation to not using lgamma provided -// by <math.h>. -// -template <typename IntType = int> -class poisson_distribution { - public: - using result_type = IntType; - - class param_type { - public: - using distribution_type = poisson_distribution; - explicit param_type(double mean = 1.0); - - double mean() const { return mean_; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.mean_ == b.mean_; - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class poisson_distribution; - - double mean_; - double emu_; // e ^ -mean_ - double lmu_; // ln(mean_) - double s_; - double log_k_; - int split_; - - static_assert(std::is_integral<IntType>::value, - "Class-template absl::poisson_distribution<> must be " - "parameterized using an integral type."); - }; - - poisson_distribution() : poisson_distribution(1.0) {} - - explicit poisson_distribution(double mean) : param_(mean) {} - - explicit poisson_distribution(const param_type& p) : param_(p) {} - - void reset() {} - - // generating functions - 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); - - 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 (std::numeric_limits<result_type>::max)(); } - - double mean() const { return param_.mean(); } - - friend bool operator==(const poisson_distribution& a, - const poisson_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const poisson_distribution& a, - const poisson_distribution& b) { - return a.param_ != b.param_; - } - - private: - param_type param_; - random_internal::FastUniformBits<uint64_t> fast_u64_; -}; - -// ----------------------------------------------------------------------------- -// Implementation details follow -// ----------------------------------------------------------------------------- - -template <typename IntType> -poisson_distribution<IntType>::param_type::param_type(double mean) - : mean_(mean), split_(0) { - assert(mean >= 0); - assert(mean <= (std::numeric_limits<result_type>::max)()); - // As a defensive measure, avoid large values of the mean. The rejection - // algorithm used does not support very large values well. It my be worth - // changing algorithms to better deal with these cases. - assert(mean <= 1e10); - if (mean_ < 10) { - // For small lambda, use the knuth method. - split_ = 1; - emu_ = std::exp(-mean_); - } else if (mean_ <= 50) { - // Use split-knuth method. - split_ = 1 + static_cast<int>(mean_ / 10.0); - emu_ = std::exp(-mean_ / static_cast<double>(split_)); - } else { - // Use ratio of uniforms method. - constexpr double k2E = 0.7357588823428846; - constexpr double kSA = 0.4494580810294493; - - lmu_ = std::log(mean_); - double a = mean_ + 0.5; - s_ = kSA + std::sqrt(k2E * a); - const double mode = std::ceil(mean_) - 1; - log_k_ = lmu_ * mode - absl::random_internal::StirlingLogFactorial(mode); - } -} - -template <typename IntType> -template <typename URBG> -typename poisson_distribution<IntType>::result_type -poisson_distribution<IntType>::operator()( - URBG& g, // NOLINT(runtime/references) - const param_type& p) { - using random_internal::GeneratePositiveTag; - using random_internal::GenerateRealFromBits; - using random_internal::GenerateSignedTag; - - if (p.split_ != 0) { - // Use Knuth's algorithm with range splitting to avoid floating-point - // errors. Knuth's algorithm is: Ui is a sequence of uniform variates on - // (0,1); return the number of variates required for product(Ui) < - // exp(-lambda). - // - // The expected number of variates required for Knuth's method can be - // computed as follows: - // The expected value of U is 0.5, so solving for 0.5^n < exp(-lambda) gives - // the expected number of uniform variates - // required for a given lambda, which is: - // lambda = [2, 5, 9, 10, 11, 12, 13, 14, 15, 16, 17] - // n = [3, 8, 13, 15, 16, 18, 19, 21, 22, 24, 25] - // - result_type n = 0; - for (int split = p.split_; split > 0; --split) { - double r = 1.0; - do { - r *= GenerateRealFromBits<double, GeneratePositiveTag, true>( - fast_u64_(g)); // U(-1, 0) - ++n; - } while (r > p.emu_); - --n; - } - return n; - } - - // Use ratio of uniforms method. - // - // Let u ~ Uniform(0, 1), v ~ Uniform(-1, 1), - // a = lambda + 1/2, - // s = 1.5 - sqrt(3/e) + sqrt(2(lambda + 1/2)/e), - // x = s * v/u + a. - // P(floor(x) = k | u^2 < f(floor(x))/k), where - // f(m) = lambda^m exp(-lambda)/ m!, for 0 <= m, and f(m) = 0 otherwise, - // and k = max(f). - const double a = p.mean_ + 0.5; - for (;;) { - const double u = GenerateRealFromBits<double, GeneratePositiveTag, false>( - fast_u64_(g)); // U(0, 1) - const double v = GenerateRealFromBits<double, GenerateSignedTag, false>( - fast_u64_(g)); // U(-1, 1) - - const double x = std::floor(p.s_ * v / u + a); - if (x < 0) continue; // f(negative) = 0 - const double rhs = x * p.lmu_; - // clang-format off - double s = (x <= 1.0) ? 0.0 - : (x == 2.0) ? 0.693147180559945 - : absl::random_internal::StirlingLogFactorial(x); - // clang-format on - const double lhs = 2.0 * std::log(u) + p.log_k_ + s; - if (lhs < rhs) { - return x > (max)() ? (max)() - : static_cast<result_type>(x); // f(x)/k >= u^2 - } - } -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const poisson_distribution<IntType>& x) { - auto saver = random_internal::make_ostream_state_saver(os); - os.precision(random_internal::stream_precision_helper<double>::kPrecision); - os << x.mean(); - return os; -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - poisson_distribution<IntType>& x) { // NOLINT(runtime/references) - using param_type = typename poisson_distribution<IntType>::param_type; - - auto saver = random_internal::make_istream_state_saver(is); - double mean = random_internal::read_floating_point<double>(is); - if (!is.fail()) { - x.param(param_type(mean)); - } - return is; -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_POISSON_DISTRIBUTION_H_ |