Creation of LTS branch "lts_2019_08_08"20190808

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GitOrigin-RevId: 9ee91d3e430fb33a4590486573792eb0fa146c2d
Change-Id: Ia06e548bc106cc9d136f6c65714be6645317aced

1 files changed, 444 insertions, 0 deletions

diff --git a/absl/random/distributions.h b/absl/random/distributions.h
new file mode 100644
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--- /dev/null
+++ b/absl/random/distributions.h
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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.
+//
+// -----------------------------------------------------------------------------
+// File: distributions.h
+// -----------------------------------------------------------------------------
+//
+// This header defines functions representing distributions, which you use in
+// combination with an Abseil random bit generator to produce random values
+// according to the rules of that distribution.
+//
+// The Abseil random library defines the following distributions within this
+// file:
+//
+//   * `absl::Uniform` for uniform (constant) distributions having constant
+//     probability
+//   * `absl::Bernoulli` for discrete distributions having exactly two outcomes
+//   * `absl::Beta` for continuous distributions parameterized through two
+//     free parameters
+//   * `absl::Exponential` for discrete distributions of events occurring
+//     continuously and independently at a constant average rate
+//   * `absl::Gaussian` (also known as "normal distributions") for continuous
+//     distributions using an associated quadratic function
+//   * `absl::LogUniform` for continuous uniform distributions where the log
+//     to the given base of all values is uniform
+//   * `absl::Poisson` for discrete probability distributions that express the
+//     probability of a given number of events occurring within a fixed interval
+//   * `absl::Zipf` for discrete probability distributions commonly used for
+//     modelling of rare events
+//
+// Prefer use of these distribution function classes over manual construction of
+// your own distribution classes, as it allows library maintainers greater
+// flexibility to change the underlying implementation in the future.
+
+#ifndef ABSL_RANDOM_DISTRIBUTIONS_H_
+#define ABSL_RANDOM_DISTRIBUTIONS_H_
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <random>
+#include <type_traits>
+
+#include "absl/base/internal/inline_variable.h"
+#include "absl/random/bernoulli_distribution.h"
+#include "absl/random/beta_distribution.h"
+#include "absl/random/distribution_format_traits.h"
+#include "absl/random/exponential_distribution.h"
+#include "absl/random/gaussian_distribution.h"
+#include "absl/random/internal/distributions.h"  // IWYU pragma: export
+#include "absl/random/internal/uniform_helper.h"  // IWYU pragma: export
+#include "absl/random/log_uniform_int_distribution.h"
+#include "absl/random/poisson_distribution.h"
+#include "absl/random/uniform_int_distribution.h"
+#include "absl/random/uniform_real_distribution.h"
+#include "absl/random/zipf_distribution.h"
+
+namespace absl {
+inline namespace lts_2019_08_08 {
+
+ABSL_INTERNAL_INLINE_CONSTEXPR(random_internal::IntervalClosedClosedT,
+                               IntervalClosedClosed, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(random_internal::IntervalClosedClosedT,
+                               IntervalClosed, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(random_internal::IntervalClosedOpenT,
+                               IntervalClosedOpen, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(random_internal::IntervalOpenOpenT,
+                               IntervalOpenOpen, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(random_internal::IntervalOpenOpenT,
+                               IntervalOpen, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(random_internal::IntervalOpenClosedT,
+                               IntervalOpenClosed, {});
+
+// -----------------------------------------------------------------------------
+// absl::Uniform<T>(tag, bitgen, lo, hi)
+// -----------------------------------------------------------------------------
+//
+// `absl::Uniform()` produces random values of type `T` uniformly distributed in
+// a defined interval {lo, hi}. The interval `tag` defines the type of interval
+// which should be one of the following possible values:
+//
+//   * `absl::IntervalOpenOpen`
+//   * `absl::IntervalOpenClosed`
+//   * `absl::IntervalClosedOpen`
+//   * `absl::IntervalClosedClosed`
+//
+// where "open" refers to an exclusive value (excluded) from the output, while
+// "closed" refers to an inclusive value (included) from the output.
+//
+// In the absence of an explicit return type `T`, `absl::Uniform()` will deduce
+// the return type based on the provided endpoint arguments {A lo, B hi}.
+// Given these endpoints, one of {A, B} will be chosen as the return type, if
+// a type can be implicitly converted into the other in a lossless way. The
+// lack of any such implcit conversion between {A, B} will produce a
+// compile-time error
+//
+// See https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//
+//   // Produce a random float value between 0.0 and 1.0, inclusive
+//   auto x = absl::Uniform(absl::IntervalClosedClosed, bitgen, 0.0f, 1.0f);
+//
+//   // The most common interval of `absl::IntervalClosedOpen` is available by
+//   // default:
+//
+//   auto x = absl::Uniform(bitgen, 0.0f, 1.0f);
+//
+//   // Return-types are typically inferred from the arguments, however callers
+//   // can optionally provide an explicit return-type to the template.
+//
+//   auto x = absl::Uniform<float>(bitgen, 0, 1);
+//
+template <typename R = void, typename TagType, typename URBG>
+typename absl::enable_if_t<!std::is_same<R, void>::value, R>  //
+Uniform(TagType tag,
+        URBG&& urbg,  // NOLINT(runtime/references)
+        R lo, R hi) {
+  using gen_t = absl::decay_t<URBG>;
+  return random_internal::UniformImpl<R, TagType, gen_t>(tag, urbg, lo, hi);
+}
+
+// absl::Uniform<T>(bitgen, lo, hi)
+//
+// Overload of `Uniform()` using the default closed-open interval of [lo, hi),
+// and returning values of type `T`
+template <typename R = void, typename URBG>
+typename absl::enable_if_t<!std::is_same<R, void>::value, R>  //
+Uniform(URBG&& urbg,  // NOLINT(runtime/references)
+        R lo, R hi) {
+  constexpr auto tag = absl::IntervalClosedOpen;
+  using tag_t = decltype(tag);
+  using gen_t = absl::decay_t<URBG>;
+
+  return random_internal::UniformImpl<R, tag_t, gen_t>(tag, urbg, lo, hi);
+}
+
+// absl::Uniform(tag, bitgen, lo, hi)
+//
+// Overload of `Uniform()` using different (but compatible) lo, hi types. Note
+// that a compile-error will result if the return type cannot be deduced
+// correctly from the passed types.
+template <typename R = void, typename TagType, typename URBG, typename A,
+          typename B>
+typename absl::enable_if_t<std::is_same<R, void>::value,
+                           random_internal::uniform_inferred_return_t<A, B>>
+Uniform(TagType tag,
+        URBG&& urbg,  // NOLINT(runtime/references)
+        A lo, B hi) {
+  using gen_t = absl::decay_t<URBG>;
+  using return_t = typename random_internal::uniform_inferred_return_t<A, B>;
+
+  return random_internal::UniformImpl<return_t, TagType, gen_t>(tag, urbg, lo,
+                                                                hi);
+}
+
+// absl::Uniform(bitgen, lo, hi)
+//
+// Overload of `Uniform()` using different (but compatible) lo, hi types and the
+// default closed-open interval of [lo, hi). Note that a compile-error will
+// result if the return type cannot be deduced correctly from the passed types.
+template <typename R = void, typename URBG, typename A, typename B>
+typename absl::enable_if_t<std::is_same<R, void>::value,
+                           random_internal::uniform_inferred_return_t<A, B>>
+Uniform(URBG&& urbg,  // NOLINT(runtime/references)
+        A lo, B hi) {
+  constexpr auto tag = absl::IntervalClosedOpen;
+  using tag_t = decltype(tag);
+  using gen_t = absl::decay_t<URBG>;
+  using return_t = typename random_internal::uniform_inferred_return_t<A, B>;
+
+  return random_internal::UniformImpl<return_t, tag_t, gen_t>(tag, urbg, lo,
+                                                              hi);
+}
+
+// absl::Uniform<unsigned T>(bitgen)
+//
+// Overload of Uniform() using the minimum and maximum values of a given type
+// `T` (which must be unsigned), returning a value of type `unsigned T`
+template <typename R, typename URBG>
+typename absl::enable_if_t<!std::is_signed<R>::value, R>  //
+Uniform(URBG&& urbg) {  // NOLINT(runtime/references)
+  constexpr auto tag = absl::IntervalClosedClosed;
+  constexpr auto lo = std::numeric_limits<R>::lowest();
+  constexpr auto hi = (std::numeric_limits<R>::max)();
+  using tag_t = decltype(tag);
+  using gen_t = absl::decay_t<URBG>;
+
+  return random_internal::UniformImpl<R, tag_t, gen_t>(tag, urbg, lo, hi);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Bernoulli(bitgen, p)
+// -----------------------------------------------------------------------------
+//
+// `absl::Bernoulli` produces a random boolean value, with probability `p`
+// (where 0.0 <= p <= 1.0) equaling `true`.
+//
+// Prefer `absl::Bernoulli` to produce boolean values over other alternatives
+// such as comparing an `absl::Uniform()` value to a specific output.
+//
+// See https://en.wikipedia.org/wiki/Bernoulli_distribution
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   if (absl::Bernoulli(bitgen, 1.0/3721.0)) {
+//     std::cout << "Asteroid field navigation successful.";
+//   }
+//
+template <typename URBG>
+bool Bernoulli(URBG&& urbg,  // NOLINT(runtime/references)
+               double p) {
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = absl::bernoulli_distribution;
+  using format_t = random_internal::DistributionFormatTraits<distribution_t>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t, format_t>(&urbg, p);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Beta<T>(bitgen, alpha, beta)
+// -----------------------------------------------------------------------------
+//
+// `absl::Beta` produces a floating point number distributed in the closed
+// interval [0,1] and parameterized by two values `alpha` and `beta` as per a
+// Beta distribution. `T` must be a floating point type, but may be inferred
+// from the types of `alpha` and `beta`.
+//
+// See https://en.wikipedia.org/wiki/Beta_distribution.
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   double sample = absl::Beta(bitgen, 3.0, 2.0);
+//
+template <typename RealType, typename URBG>
+RealType Beta(URBG&& urbg,  // NOLINT(runtime/references)
+              RealType alpha, RealType beta) {
+  static_assert(
+      std::is_floating_point<RealType>::value,
+      "Template-argument 'RealType' must be a floating-point type, in "
+      "absl::Beta<RealType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::beta_distribution<RealType>;
+  using format_t = random_internal::DistributionFormatTraits<distribution_t>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t, format_t>(&urbg, alpha, beta);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Exponential<T>(bitgen, lambda = 1)
+// -----------------------------------------------------------------------------
+//
+// `absl::Exponential` produces a floating point number for discrete
+// distributions of events occurring continuously and independently at a
+// constant average rate. `T` must be a floating point type, but may be inferred
+// from the type of `lambda`.
+//
+// See https://en.wikipedia.org/wiki/Exponential_distribution.
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   double call_length = absl::Exponential(bitgen, 7.0);
+//
+template <typename RealType, typename URBG>
+RealType Exponential(URBG&& urbg,  // NOLINT(runtime/references)
+                     RealType lambda = 1) {
+  static_assert(
+      std::is_floating_point<RealType>::value,
+      "Template-argument 'RealType' must be a floating-point type, in "
+      "absl::Exponential<RealType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::exponential_distribution<RealType>;
+  using format_t = random_internal::DistributionFormatTraits<distribution_t>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t, format_t>(&urbg, lambda);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Gaussian<T>(bitgen, mean = 0, stddev = 1)
+// -----------------------------------------------------------------------------
+//
+// `absl::Gaussian` produces a floating point number selected from the Gaussian
+// (ie. "Normal") distribution. `T` must be a floating point type, but may be
+// inferred from the types of `mean` and `stddev`.
+//
+// See https://en.wikipedia.org/wiki/Normal_distribution
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   double giraffe_height = absl::Gaussian(bitgen, 16.3, 3.3);
+//
+template <typename RealType, typename URBG>
+RealType Gaussian(URBG&& urbg,  // NOLINT(runtime/references)
+                  RealType mean = 0, RealType stddev = 1) {
+  static_assert(
+      std::is_floating_point<RealType>::value,
+      "Template-argument 'RealType' must be a floating-point type, in "
+      "absl::Gaussian<RealType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::gaussian_distribution<RealType>;
+  using format_t = random_internal::DistributionFormatTraits<distribution_t>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t, format_t>(&urbg, mean, stddev);
+}
+
+// -----------------------------------------------------------------------------
+// absl::LogUniform<T>(bitgen, lo, hi, base = 2)
+// -----------------------------------------------------------------------------
+//
+// `absl::LogUniform` produces random values distributed where the log to a
+// given base of all values is uniform in a closed interval [lo, hi]. `T` must
+// be an integral type, but may be inferred from the types of `lo` and `hi`.
+//
+// I.e., `LogUniform(0, n, b)` is uniformly distributed across buckets
+// [0], [1, b-1], [b, b^2-1] .. [b^(k-1), (b^k)-1] .. [b^floor(log(n, b)), n]
+// and is uniformly distributed within each bucket.
+//
+// The resulting probability density is inversely related to bucket size, though
+// values in the final bucket may be more likely than previous values. (In the
+// extreme case where n = b^i the final value will be tied with zero as the most
+// probable result.
+//
+// If `lo` is nonzero then this distribution is shifted to the desired interval,
+// so LogUniform(lo, hi, b) is equivalent to LogUniform(0, hi-lo, b)+lo.
+//
+// See http://ecolego.facilia.se/ecolego/show/Log-Uniform%20Distribution
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   int v = absl::LogUniform(bitgen, 0, 1000);
+//
+template <typename IntType, typename URBG>
+IntType LogUniform(URBG&& urbg,  // NOLINT(runtime/references)
+                   IntType lo, IntType hi, IntType base = 2) {
+  static_assert(std::is_integral<IntType>::value,
+                "Template-argument 'IntType' must be an integral type, in "
+                "absl::LogUniform<IntType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::log_uniform_int_distribution<IntType>;
+  using format_t = random_internal::DistributionFormatTraits<distribution_t>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t, format_t>(&urbg, lo, hi, base);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Poisson<T>(bitgen, mean = 1)
+// -----------------------------------------------------------------------------
+//
+// `absl::Poisson` produces discrete probabilities for a given number of events
+// occurring within a fixed interval within the closed interval [0, max]. `T`
+// must be an integral type.
+//
+// See https://en.wikipedia.org/wiki/Poisson_distribution
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   int requests_per_minute = absl::Poisson<int>(bitgen, 3.2);
+//
+template <typename IntType, typename URBG>
+IntType Poisson(URBG&& urbg,  // NOLINT(runtime/references)
+                double mean = 1.0) {
+  static_assert(std::is_integral<IntType>::value,
+                "Template-argument 'IntType' must be an integral type, in "
+                "absl::Poisson<IntType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::poisson_distribution<IntType>;
+  using format_t = random_internal::DistributionFormatTraits<distribution_t>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t, format_t>(&urbg, mean);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Zipf<T>(bitgen, hi = max, q = 2, v = 1)
+// -----------------------------------------------------------------------------
+//
+// `absl::Zipf` produces discrete probabilities commonly used for modelling of
+// rare events over the closed interval [0, hi]. The parameters `v` and `q`
+// determine the skew of the distribution. `T`  must be an integral type, but
+// may be inferred from the type of `hi`.
+//
+// See http://mathworld.wolfram.com/ZipfDistribution.html
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   int term_rank = absl::Zipf<int>(bitgen);
+//
+template <typename IntType, typename URBG>
+IntType Zipf(URBG&& urbg,  // NOLINT(runtime/references)
+             IntType hi = (std::numeric_limits<IntType>::max)(), double q = 2.0,
+             double v = 1.0) {
+  static_assert(std::is_integral<IntType>::value,
+                "Template-argument 'IntType' must be an integral type, in "
+                "absl::Zipf<IntType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::zipf_distribution<IntType>;
+  using format_t = random_internal::DistributionFormatTraits<distribution_t>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t, format_t>(&urbg, hi, q, v);
+}
+
+}  // inline namespace lts_2019_08_08
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_DISTRIBUTIONS_H_