tvl-depot/absl/random/gaussian_distribution.h
Abseil Team e9324d926a Export of internal Abseil changes.
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7a6ff16a85beb730c172d5d25cf1b5e1be885c56 by Laramie Leavitt <lar@google.com>:

Internal change.

PiperOrigin-RevId: 254454546

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ff8f9bafaefc26d451f576ea4a06d150aed63f6f by Andy Soffer <asoffer@google.com>:

Internal changes

PiperOrigin-RevId: 254451562

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deefc5b651b479ce36f0b4ef203e119c0c8936f2 by CJ Johnson <johnsoncj@google.com>:

Account for subtracting unsigned values from the size of InlinedVector

PiperOrigin-RevId: 254450625

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3c677316a27bcadc17e41957c809ca472d5fef14 by Andy Soffer <asoffer@google.com>:

Add C++17's std::make_from_tuple to absl/utility/utility.h

PiperOrigin-RevId: 254411573

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4ee3536a918830eeec402a28fc31a62c7c90b940 by CJ Johnson <johnsoncj@google.com>:

Adds benchmark for the rest of the InlinedVector public API

PiperOrigin-RevId: 254408378

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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

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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

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fa099e02c413a7ffda732415e8105cad26a90337 by Andy Soffer <asoffer@google.com>:

Internal changes

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ce34b7f36933b30cfa35b9c9a5697a792b5666e4 by Andy Soffer <asoffer@google.com>:

Internal changes

PiperOrigin-RevId: 254273059

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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

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8bd3c186bf2fc0c55d8a2dd6f28a5327502c9fba by Andy Soffer <asoffer@google.com>:

Adding short-hand IntervalClosed for IntervalClosedClosed and IntervalOpen for
IntervalOpenOpen.

PiperOrigin-RevId: 254252419

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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

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89ab385cd26b34d64130bce856253aaba96d2345 by Andy Soffer <asoffer@google.com>:

Internal changes

PiperOrigin-RevId: 254242321

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cffc793d93eca6d6bdf7de733847b6ab4a255ae9 by CJ Johnson <johnsoncj@google.com>:

Adds benchmark for InlinedVector::reserve(size_type)

PiperOrigin-RevId: 254199226

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c90c7a9fa3c8f0c9d5114036979548b055ea2f2a by Gennadiy Rozental <rogeeff@google.com>:

Import of CCTZ from GitHub.

PiperOrigin-RevId: 254072387

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c4c388beae016c9570ab54ffa1d52660e4a85b7b by Laramie Leavitt <lar@google.com>:

Internal cleanup.

PiperOrigin-RevId: 254062381

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d3c992e221cc74e5372d0c8fa410170b6a43c062 by Tom Manshreck <shreck@google.com>:

Update distributions.h to Abseil standards

PiperOrigin-RevId: 254054946

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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

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2f37e807efc3a8ef1f4b539bdd379917d4151520 by Andy Soffer <asoffer@google.com>:

Initial release of Abseil Random

PiperOrigin-RevId: 253999861

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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

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5613d95f5a7e34a535cfaeadce801441e990843e by CJ Johnson <johnsoncj@google.com>:

Adds benchmarks for InlinedVector::shrink_to_fit()

PiperOrigin-RevId: 253989647

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2a96ddfdac40bbb8cb6a7f1aeab90917067c6e63 by Abseil Team <absl-team@google.com>:

Initial release of Abseil Random

PiperOrigin-RevId: 253927497

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bf1aff8fc9ffa921ad74643e9525ecf25b0d8dc1 by Andy Soffer <asoffer@google.com>:

Initial release of Abseil Random

PiperOrigin-RevId: 253920512

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bfc03f4a3dcda3cf3a4b84bdb84cda24e3394f41 by Laramie Leavitt <lar@google.com>:

Internal change.

PiperOrigin-RevId: 253886486

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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
2019-06-21 16:18:10 -04:00

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// 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_GAUSSIAN_DISTRIBUTION_H_
#define ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_
// absl::gaussian_distribution implements the Ziggurat algorithm
// for generating random gaussian numbers.
//
// Implementation based on "The Ziggurat Method for Generating Random Variables"
// by George Marsaglia and Wai Wan Tsang: http://www.jstatsoft.org/v05/i08/
//
#include <cmath>
#include <cstdint>
#include <istream>
#include <limits>
#include <type_traits>
#include "absl/random/internal/distribution_impl.h"
#include "absl/random/internal/fast_uniform_bits.h"
#include "absl/random/internal/iostream_state_saver.h"
namespace absl {
namespace random_internal {
// absl::gaussian_distribution_base implements the underlying ziggurat algorithm
// using the ziggurat tables generated by the gaussian_distribution_gentables
// binary.
//
// The specific algorithm has some of the improvements suggested by the
// 2005 paper, "An Improved Ziggurat Method to Generate Normal Random Samples",
// Jurgen A Doornik. (https://www.doornik.com/research/ziggurat.pdf)
class gaussian_distribution_base {
public:
template <typename URBG>
inline double zignor(URBG& g); // NOLINT(runtime/references)
private:
friend class TableGenerator;
template <typename URBG>
inline double zignor_fallback(URBG& g, // NOLINT(runtime/references)
bool neg);
// Constants used for the gaussian distribution.
static constexpr double kR = 3.442619855899; // Start of the tail.
static constexpr double kRInv = 0.29047645161474317; // ~= (1.0 / kR) .
static constexpr double kV = 9.91256303526217e-3;
static constexpr uint64_t kMask = 0x07f;
// The ziggurat tables store the pdf(f) and inverse-pdf(x) for equal-area
// points on one-half of the normal distribution, where the pdf function,
// pdf = e ^ (-1/2 *x^2), assumes that the mean = 0 & stddev = 1.
//
// These tables are just over 2kb in size; larger tables might improve the
// distributions, but also lead to more cache pollution.
//
// x = {3.71308, 3.44261, 3.22308, ..., 0}
// f = {0.00101, 0.00266, 0.00554, ..., 1}
struct Tables {
double x[kMask + 2];
double f[kMask + 2];
};
static const Tables zg_;
random_internal::FastUniformBits<uint64_t> fast_u64_;
};
} // namespace random_internal
// absl::gaussian_distribution:
// Generates a number conforming to a Gaussian distribution.
template <typename RealType = double>
class gaussian_distribution : random_internal::gaussian_distribution_base {
public:
using result_type = RealType;
class param_type {
public:
using distribution_type = gaussian_distribution;
explicit param_type(result_type mean = 0, result_type stddev = 1)
: mean_(mean), stddev_(stddev) {}
// Returns the mean distribution parameter. The mean specifies the location
// of the peak. The default value is 0.0.
result_type mean() const { return mean_; }
// Returns the deviation distribution parameter. The default value is 1.0.
result_type stddev() const { return stddev_; }
friend bool operator==(const param_type& a, const param_type& b) {
return a.mean_ == b.mean_ && a.stddev_ == b.stddev_;
}
friend bool operator!=(const param_type& a, const param_type& b) {
return !(a == b);
}
private:
result_type mean_;
result_type stddev_;
static_assert(
std::is_floating_point<RealType>::value,
"Class-template absl::gaussian_distribution<> must be parameterized "
"using a floating-point type.");
};
gaussian_distribution() : gaussian_distribution(0) {}
explicit gaussian_distribution(result_type mean, result_type stddev = 1)
: param_(mean, stddev) {}
explicit gaussian_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 -std::numeric_limits<result_type>::infinity();
}
result_type(max)() const {
return std::numeric_limits<result_type>::infinity();
}
result_type mean() const { return param_.mean(); }
result_type stddev() const { return param_.stddev(); }
friend bool operator==(const gaussian_distribution& a,
const gaussian_distribution& b) {
return a.param_ == b.param_;
}
friend bool operator!=(const gaussian_distribution& a,
const gaussian_distribution& b) {
return a.param_ != b.param_;
}
private:
param_type param_;
};
// --------------------------------------------------------------------------
// Implementation details only below
// --------------------------------------------------------------------------
template <typename RealType>
template <typename URBG>
typename gaussian_distribution<RealType>::result_type
gaussian_distribution<RealType>::operator()(
URBG& g, // NOLINT(runtime/references)
const param_type& p) {
return p.mean() + p.stddev() * static_cast<result_type>(zignor(g));
}
template <typename CharT, typename Traits, typename RealType>
std::basic_ostream<CharT, Traits>& operator<<(
std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references)
const gaussian_distribution<RealType>& x) {
auto saver = random_internal::make_ostream_state_saver(os);
os.precision(random_internal::stream_precision_helper<RealType>::kPrecision);
os << x.mean() << os.fill() << x.stddev();
return os;
}
template <typename CharT, typename Traits, typename RealType>
std::basic_istream<CharT, Traits>& operator>>(
std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references)
gaussian_distribution<RealType>& x) { // NOLINT(runtime/references)
using result_type = typename gaussian_distribution<RealType>::result_type;
using param_type = typename gaussian_distribution<RealType>::param_type;
auto saver = random_internal::make_istream_state_saver(is);
auto mean = random_internal::read_floating_point<result_type>(is);
if (is.fail()) return is;
auto stddev = random_internal::read_floating_point<result_type>(is);
if (!is.fail()) {
x.param(param_type(mean, stddev));
}
return is;
}
namespace random_internal {
template <typename URBG>
inline double gaussian_distribution_base::zignor_fallback(URBG& g, bool neg) {
// This fallback path happens approximately 0.05% of the time.
double x, y;
do {
// kRInv = 1/r, U(0, 1)
x = kRInv * std::log(RandU64ToDouble<PositiveValueT, false>(fast_u64_(g)));
y = -std::log(RandU64ToDouble<PositiveValueT, false>(fast_u64_(g)));
} while ((y + y) < (x * x));
return neg ? (x - kR) : (kR - x);
}
template <typename URBG>
inline double gaussian_distribution_base::zignor(
URBG& g) { // NOLINT(runtime/references)
while (true) {
// We use a single uint64_t to generate both a double and a strip.
// These bits are unused when the generated double is > 1/2^5.
// This may introduce some bias from the duplicated low bits of small
// values (those smaller than 1/2^5, which all end up on the left tail).
uint64_t bits = fast_u64_(g);
int i = static_cast<int>(bits & kMask); // pick a random strip
double j = RandU64ToDouble<SignedValueT, false>(bits); // U(-1, 1)
const double x = j * zg_.x[i];
// Retangular box. Handles >97% of all cases.
// For any given box, this handles between 75% and 99% of values.
// Equivalent to U(01) < (x[i+1] / x[i]), and when i == 0, ~93.5%
if (std::abs(x) < zg_.x[i + 1]) {
return x;
}
// i == 0: Base box. Sample using a ratio of uniforms.
if (i == 0) {
// This path happens about 0.05% of the time.
return zignor_fallback(g, j < 0);
}
// i > 0: Wedge samples using precomputed values.
double v = RandU64ToDouble<PositiveValueT, false>(fast_u64_(g)); // U(0, 1)
if ((zg_.f[i + 1] + v * (zg_.f[i] - zg_.f[i + 1])) <
std::exp(-0.5 * x * x)) {
return x;
}
// The wedge was missed; reject the value and try again.
}
}
} // namespace random_internal
} // namespace absl
#endif // ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_