Abseil Common Libraries (C++) (grcp 依赖) https://abseil.io/
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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.
//
// -----------------------------------------------------------------------------
// File: uniform_real_distribution.h
// -----------------------------------------------------------------------------
//
// This header defines a class for representing a uniform floating-point
// distribution over a half-open interval [a,b). You use this distribution in
// combination with an Abseil random bit generator to produce random values
// according to the rules of the distribution.
//
// `absl::uniform_real_distribution` is a drop-in replacement for the C++11
// `std::uniform_real_distribution` [rand.dist.uni.real] but is considerably
// faster than the libstdc++ implementation.
//
// Note: the standard-library version may occasionally return `1.0` when
// default-initialized. See https://bugs.llvm.org//show_bug.cgi?id=18767
// `absl::uniform_real_distribution` does not exhibit this behavior.
#ifndef ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_
#define ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_
#include <cassert>
#include <cmath>
#include <cstdint>
#include <istream>
#include <limits>
#include <type_traits>
#include "absl/meta/type_traits.h"
#include "absl/random/internal/fast_uniform_bits.h"
#include "absl/random/internal/generate_real.h"
#include "absl/random/internal/iostream_state_saver.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
// absl::uniform_real_distribution<T>
//
// This distribution produces random floating-point values uniformly distributed
// over the half-open interval [a, b).
//
// Example:
//
// absl::BitGen gen;
//
// // Use the distribution to produce a value between 0.0 (inclusive)
// // and 1.0 (exclusive).
// double value = absl::uniform_real_distribution<double>(0, 1)(gen);
//
template <typename RealType = double>
class uniform_real_distribution {
public:
using result_type = RealType;
class param_type {
public:
using distribution_type = uniform_real_distribution;
explicit param_type(result_type lo = 0, result_type hi = 1)
: lo_(lo), hi_(hi), range_(hi - lo) {
// [rand.dist.uni.real] preconditions 2 & 3
assert(lo <= hi);
// NOTE: For integral types, we can promote the range to an unsigned type,
// which gives full width of the range. However for real (fp) types, this
// is not possible, so value generation cannot use the full range of the
// real type.
assert(range_ <= (std::numeric_limits<result_type>::max)());
assert(std::isfinite(range_));
}
result_type a() const { return lo_; }
result_type b() const { return hi_; }
friend bool operator==(const param_type& a, const param_type& b) {
return a.lo_ == b.lo_ && a.hi_ == b.hi_;
}
friend bool operator!=(const param_type& a, const param_type& b) {
return !(a == b);
}
private:
friend class uniform_real_distribution;
result_type lo_, hi_, range_;
static_assert(std::is_floating_point<RealType>::value,
"Class-template absl::uniform_real_distribution<> must be "
"parameterized using a floating-point type.");
};
uniform_real_distribution() : uniform_real_distribution(0) {}
explicit uniform_real_distribution(result_type lo, result_type hi = 1)
: param_(lo, hi) {}
explicit uniform_real_distribution(const param_type& param) : param_(param) {}
// uniform_real_distribution<T>::reset()
//
// Resets the uniform real distribution. Note that this function has no effect
// because the distribution already produces independent values.
void reset() {}
template <typename URBG>
result_type operator()(URBG& gen) { // NOLINT(runtime/references)
return operator()(gen, param_);
}
template <typename URBG>
result_type operator()(URBG& gen, // NOLINT(runtime/references)
const param_type& p);
result_type a() const { return param_.a(); }
result_type b() const { return param_.b(); }
param_type param() const { return param_; }
void param(const param_type& params) { param_ = params; }
result_type(min)() const { return a(); }
result_type(max)() const { return b(); }
friend bool operator==(const uniform_real_distribution& a,
const uniform_real_distribution& b) {
return a.param_ == b.param_;
}
friend bool operator!=(const uniform_real_distribution& a,
const uniform_real_distribution& b) {
return a.param_ != b.param_;
}
private:
param_type param_;
random_internal::FastUniformBits<uint64_t> fast_u64_;
};
// -----------------------------------------------------------------------------
// Implementation details follow
// -----------------------------------------------------------------------------
template <typename RealType>
template <typename URBG>
typename uniform_real_distribution<RealType>::result_type
uniform_real_distribution<RealType>::operator()(
URBG& gen, const param_type& p) { // NOLINT(runtime/references)
using random_internal::GeneratePositiveTag;
using random_internal::GenerateRealFromBits;
using real_type =
absl::conditional_t<std::is_same<RealType, float>::value, float, double>;
while (true) {
const result_type sample =
GenerateRealFromBits<real_type, GeneratePositiveTag, true>(
fast_u64_(gen));
const result_type res = p.a() + (sample * p.range_);
if (res < p.b() || p.range_ <= 0 || !std::isfinite(p.range_)) {
return res;
}
// else sample rejected, try again.
}
}
template <typename CharT, typename Traits, typename RealType>
std::basic_ostream<CharT, Traits>& operator<<(
std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references)
const uniform_real_distribution<RealType>& x) {
auto saver = random_internal::make_ostream_state_saver(os);
os.precision(random_internal::stream_precision_helper<RealType>::kPrecision);
os << x.a() << os.fill() << x.b();
return os;
}
template <typename CharT, typename Traits, typename RealType>
std::basic_istream<CharT, Traits>& operator>>(
std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references)
uniform_real_distribution<RealType>& x) { // NOLINT(runtime/references)
using param_type = typename uniform_real_distribution<RealType>::param_type;
using result_type = typename uniform_real_distribution<RealType>::result_type;
auto saver = random_internal::make_istream_state_saver(is);
auto a = random_internal::read_floating_point<result_type>(is);
if (is.fail()) return is;
auto b = random_internal::read_floating_point<result_type>(is);
if (!is.fail()) {
x.param(param_type(a, b));
}
return is;
}
ABSL_NAMESPACE_END
} // namespace absl
#endif // ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_