Abseil Common Libraries (C++) (grcp 依赖) https://abseil.io/
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Export of internal Abseil changes. -- 7a6ff16a85beb730c172d5d25cf1b5e1be885c56 by Laramie Leavitt <lar@google.com>: Internal change. PiperOrigin-RevId: 254454546 -- ff8f9bafaefc26d451f576ea4a06d150aed63f6f by Andy Soffer <asoffer@google.com>: Internal changes PiperOrigin-RevId: 254451562 -- deefc5b651b479ce36f0b4ef203e119c0c8936f2 by CJ Johnson <johnsoncj@google.com>: Account for subtracting unsigned values from the size of InlinedVector PiperOrigin-RevId: 254450625 -- 3c677316a27bcadc17e41957c809ca472d5fef14 by Andy Soffer <asoffer@google.com>: Add C++17's std::make_from_tuple to absl/utility/utility.h PiperOrigin-RevId: 254411573 -- 4ee3536a918830eeec402a28fc31a62c7c90b940 by CJ Johnson <johnsoncj@google.com>: Adds benchmark for the rest of the InlinedVector public API PiperOrigin-RevId: 254408378 -- e5a21a00700ee83498ff1efbf649169756463ee4 by CJ Johnson <johnsoncj@google.com>: Updates the definition of InlinedVector::shrink_to_fit() to be exception safe and adds exception safety tests for it. PiperOrigin-RevId: 254401387 -- 2ea82e72b86d82d78b4e4712a63a55981b53c64b by Laramie Leavitt <lar@google.com>: Use absl::InsecureBitGen in place of std::mt19937 in tests absl/random/...distribution_test.cc PiperOrigin-RevId: 254289444 -- fa099e02c413a7ffda732415e8105cad26a90337 by Andy Soffer <asoffer@google.com>: Internal changes PiperOrigin-RevId: 254286334 -- ce34b7f36933b30cfa35b9c9a5697a792b5666e4 by Andy Soffer <asoffer@google.com>: Internal changes PiperOrigin-RevId: 254273059 -- 6f9c473da7c2090c2e85a37c5f00622e8a912a89 by Jorg Brown <jorg@google.com>: Change absl::container_internal::CompressedTuple to instantiate its internal Storage class with the name of the type it's holding, rather than the name of the Tuple. This is not an externally-visible change, other than less compiler memory is used and less debug information is generated. PiperOrigin-RevId: 254269285 -- 8bd3c186bf2fc0c55d8a2dd6f28a5327502c9fba by Andy Soffer <asoffer@google.com>: Adding short-hand IntervalClosed for IntervalClosedClosed and IntervalOpen for IntervalOpenOpen. PiperOrigin-RevId: 254252419 -- ea957f99b6a04fccd42aa05605605f3b44b1ecfd by Abseil Team <absl-team@google.com>: Do not directly use __SIZEOF_INT128__. In order to avoid linker errors when building with clang-cl (__fixunsdfti, __udivti3 and __fixunssfti are undefined), this CL uses ABSL_HAVE_INTRINSIC_INT128 which is not defined for clang-cl. PiperOrigin-RevId: 254250739 -- 89ab385cd26b34d64130bce856253aaba96d2345 by Andy Soffer <asoffer@google.com>: Internal changes PiperOrigin-RevId: 254242321 -- cffc793d93eca6d6bdf7de733847b6ab4a255ae9 by CJ Johnson <johnsoncj@google.com>: Adds benchmark for InlinedVector::reserve(size_type) PiperOrigin-RevId: 254199226 -- c90c7a9fa3c8f0c9d5114036979548b055ea2f2a by Gennadiy Rozental <rogeeff@google.com>: Import of CCTZ from GitHub. PiperOrigin-RevId: 254072387 -- c4c388beae016c9570ab54ffa1d52660e4a85b7b by Laramie Leavitt <lar@google.com>: Internal cleanup. PiperOrigin-RevId: 254062381 -- d3c992e221cc74e5372d0c8fa410170b6a43c062 by Tom Manshreck <shreck@google.com>: Update distributions.h to Abseil standards PiperOrigin-RevId: 254054946 -- d15ad0035c34ef11b14fadc5a4a2d3ec415f5518 by CJ Johnson <johnsoncj@google.com>: Removes functions with only one caller from the implementation details of InlinedVector by manually inlining the definitions PiperOrigin-RevId: 254005427 -- 2f37e807efc3a8ef1f4b539bdd379917d4151520 by Andy Soffer <asoffer@google.com>: Initial release of Abseil Random PiperOrigin-RevId: 253999861 -- 24ed1694b6430791d781ed533a8f8ccf6cac5856 by CJ Johnson <johnsoncj@google.com>: Updates the definition of InlinedVector::assign(...)/InlinedVector::operator=(...) to new, exception-safe implementations with exception safety tests to boot PiperOrigin-RevId: 253993691 -- 5613d95f5a7e34a535cfaeadce801441e990843e by CJ Johnson <johnsoncj@google.com>: Adds benchmarks for InlinedVector::shrink_to_fit() PiperOrigin-RevId: 253989647 -- 2a96ddfdac40bbb8cb6a7f1aeab90917067c6e63 by Abseil Team <absl-team@google.com>: Initial release of Abseil Random PiperOrigin-RevId: 253927497 -- bf1aff8fc9ffa921ad74643e9525ecf25b0d8dc1 by Andy Soffer <asoffer@google.com>: Initial release of Abseil Random PiperOrigin-RevId: 253920512 -- bfc03f4a3dcda3cf3a4b84bdb84cda24e3394f41 by Laramie Leavitt <lar@google.com>: Internal change. PiperOrigin-RevId: 253886486 -- 05036cfcc078ca7c5f581a00dfb0daed568cbb69 by Eric Fiselier <ericwf@google.com>: Don't include `winsock2.h` because it drags in `windows.h` and friends, and they define awful macros like OPAQUE, ERROR, and more. This has the potential to break abseil users. Instead we only forward declare `timeval` and require Windows users include `winsock2.h` themselves. This is both inconsistent and poor QoI, but so including 'windows.h' is bad too. PiperOrigin-RevId: 253852615 GitOrigin-RevId: 7a6ff16a85beb730c172d5d25cf1b5e1be885c56 Change-Id: Icd6aff87da26f29ec8915da856f051129987cef6
6 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_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/distribution_impl.h"
#include "absl/random/internal/fast_uniform_bits.h"
#include "absl/random/internal/fastmath.h"
#include "absl/random/internal/iostream_state_saver.h"
namespace absl {
// 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::PositiveValueT;
using random_internal::RandU64ToDouble;
using random_internal::SignedValueT;
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 *= RandU64ToDouble<PositiveValueT, true>(fast_u64_(g));
++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 =
RandU64ToDouble<PositiveValueT, false>(fast_u64_(g)); // (0, 1)
const double v =
RandU64ToDouble<SignedValueT, false>(fast_u64_(g)); // (-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;
}
} // namespace absl
#endif // ABSL_RANDOM_POISSON_DISTRIBUTION_H_