452 lines
18 KiB
C++
452 lines
18 KiB
C++
// Copyright 2017 The Abseil Authors.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// https://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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// -----------------------------------------------------------------------------
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// File: distributions.h
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// -----------------------------------------------------------------------------
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//
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// This header defines functions representing distributions, which you use in
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// combination with an Abseil random bit generator to produce random values
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// according to the rules of that distribution.
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//
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// The Abseil random library defines the following distributions within this
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// file:
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//
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// * `absl::Uniform` for uniform (constant) distributions having constant
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// probability
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// * `absl::Bernoulli` for discrete distributions having exactly two outcomes
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// * `absl::Beta` for continuous distributions parameterized through two
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// free parameters
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// * `absl::Exponential` for discrete distributions of events occurring
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// continuously and independently at a constant average rate
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// * `absl::Gaussian` (also known as "normal distributions") for continuous
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// distributions using an associated quadratic function
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// * `absl::LogUniform` for continuous uniform distributions where the log
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// to the given base of all values is uniform
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// * `absl::Poisson` for discrete probability distributions that express the
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// probability of a given number of events occurring within a fixed interval
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// * `absl::Zipf` for discrete probability distributions commonly used for
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// modelling of rare events
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//
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// Prefer use of these distribution function classes over manual construction of
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// your own distribution classes, as it allows library maintainers greater
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// flexibility to change the underlying implementation in the future.
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#ifndef ABSL_RANDOM_DISTRIBUTIONS_H_
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#define ABSL_RANDOM_DISTRIBUTIONS_H_
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#include <algorithm>
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#include <cmath>
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#include <limits>
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#include <random>
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#include <type_traits>
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#include "absl/base/internal/inline_variable.h"
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#include "absl/random/bernoulli_distribution.h"
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#include "absl/random/beta_distribution.h"
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#include "absl/random/exponential_distribution.h"
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#include "absl/random/gaussian_distribution.h"
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#include "absl/random/internal/distribution_caller.h" // IWYU pragma: export
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#include "absl/random/internal/uniform_helper.h" // IWYU pragma: export
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#include "absl/random/log_uniform_int_distribution.h"
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#include "absl/random/poisson_distribution.h"
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#include "absl/random/uniform_int_distribution.h"
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#include "absl/random/uniform_real_distribution.h"
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#include "absl/random/zipf_distribution.h"
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namespace absl {
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ABSL_NAMESPACE_BEGIN
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ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedClosedTag, IntervalClosedClosed,
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{});
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ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedClosedTag, IntervalClosed, {});
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ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedOpenTag, IntervalClosedOpen, {});
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ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenOpenTag, IntervalOpenOpen, {});
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ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenOpenTag, IntervalOpen, {});
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ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenClosedTag, IntervalOpenClosed, {});
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// -----------------------------------------------------------------------------
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// absl::Uniform<T>(tag, bitgen, lo, hi)
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// -----------------------------------------------------------------------------
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//
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// `absl::Uniform()` produces random values of type `T` uniformly distributed in
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// a defined interval {lo, hi}. The interval `tag` defines the type of interval
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// which should be one of the following possible values:
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//
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// * `absl::IntervalOpenOpen`
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// * `absl::IntervalOpenClosed`
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// * `absl::IntervalClosedOpen`
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// * `absl::IntervalClosedClosed`
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//
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// where "open" refers to an exclusive value (excluded) from the output, while
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// "closed" refers to an inclusive value (included) from the output.
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//
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// In the absence of an explicit return type `T`, `absl::Uniform()` will deduce
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// the return type based on the provided endpoint arguments {A lo, B hi}.
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// Given these endpoints, one of {A, B} will be chosen as the return type, if
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// a type can be implicitly converted into the other in a lossless way. The
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// lack of any such implicit conversion between {A, B} will produce a
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// compile-time error
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//
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// See https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)
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//
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// Example:
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//
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// absl::BitGen bitgen;
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//
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// // Produce a random float value between 0.0 and 1.0, inclusive
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// auto x = absl::Uniform(absl::IntervalClosedClosed, bitgen, 0.0f, 1.0f);
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//
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// // The most common interval of `absl::IntervalClosedOpen` is available by
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// // default:
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//
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// auto x = absl::Uniform(bitgen, 0.0f, 1.0f);
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//
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// // Return-types are typically inferred from the arguments, however callers
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// // can optionally provide an explicit return-type to the template.
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//
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// auto x = absl::Uniform<float>(bitgen, 0, 1);
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//
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template <typename R = void, typename TagType, typename URBG>
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typename absl::enable_if_t<!std::is_same<R, void>::value, R> //
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Uniform(TagType tag,
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URBG&& urbg, // NOLINT(runtime/references)
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R lo, R hi) {
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = random_internal::UniformDistributionWrapper<R>;
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auto a = random_internal::uniform_lower_bound(tag, lo, hi);
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auto b = random_internal::uniform_upper_bound(tag, lo, hi);
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if (a > b) return a;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, tag, lo, hi);
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}
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// absl::Uniform<T>(bitgen, lo, hi)
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//
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// Overload of `Uniform()` using the default closed-open interval of [lo, hi),
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// and returning values of type `T`
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template <typename R = void, typename URBG>
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typename absl::enable_if_t<!std::is_same<R, void>::value, R> //
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Uniform(URBG&& urbg, // NOLINT(runtime/references)
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R lo, R hi) {
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = random_internal::UniformDistributionWrapper<R>;
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constexpr auto tag = absl::IntervalClosedOpen;
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auto a = random_internal::uniform_lower_bound(tag, lo, hi);
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auto b = random_internal::uniform_upper_bound(tag, lo, hi);
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if (a > b) return a;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, lo, hi);
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}
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// absl::Uniform(tag, bitgen, lo, hi)
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//
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// Overload of `Uniform()` using different (but compatible) lo, hi types. Note
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// that a compile-error will result if the return type cannot be deduced
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// correctly from the passed types.
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template <typename R = void, typename TagType, typename URBG, typename A,
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typename B>
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typename absl::enable_if_t<std::is_same<R, void>::value,
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random_internal::uniform_inferred_return_t<A, B>>
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Uniform(TagType tag,
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URBG&& urbg, // NOLINT(runtime/references)
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A lo, B hi) {
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using gen_t = absl::decay_t<URBG>;
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using return_t = typename random_internal::uniform_inferred_return_t<A, B>;
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using distribution_t = random_internal::UniformDistributionWrapper<return_t>;
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auto a = random_internal::uniform_lower_bound<return_t>(tag, lo, hi);
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auto b = random_internal::uniform_upper_bound<return_t>(tag, lo, hi);
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if (a > b) return a;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, tag, static_cast<return_t>(lo),
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static_cast<return_t>(hi));
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}
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// absl::Uniform(bitgen, lo, hi)
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//
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// Overload of `Uniform()` using different (but compatible) lo, hi types and the
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// default closed-open interval of [lo, hi). Note that a compile-error will
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// result if the return type cannot be deduced correctly from the passed types.
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template <typename R = void, typename URBG, typename A, typename B>
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typename absl::enable_if_t<std::is_same<R, void>::value,
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random_internal::uniform_inferred_return_t<A, B>>
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Uniform(URBG&& urbg, // NOLINT(runtime/references)
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A lo, B hi) {
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using gen_t = absl::decay_t<URBG>;
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using return_t = typename random_internal::uniform_inferred_return_t<A, B>;
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using distribution_t = random_internal::UniformDistributionWrapper<return_t>;
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constexpr auto tag = absl::IntervalClosedOpen;
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auto a = random_internal::uniform_lower_bound<return_t>(tag, lo, hi);
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auto b = random_internal::uniform_upper_bound<return_t>(tag, lo, hi);
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if (a > b) return a;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, static_cast<return_t>(lo),
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static_cast<return_t>(hi));
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}
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// absl::Uniform<unsigned T>(bitgen)
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//
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// Overload of Uniform() using the minimum and maximum values of a given type
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// `T` (which must be unsigned), returning a value of type `unsigned T`
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template <typename R, typename URBG>
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typename absl::enable_if_t<!std::is_signed<R>::value, R> //
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Uniform(URBG&& urbg) { // NOLINT(runtime/references)
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = random_internal::UniformDistributionWrapper<R>;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg);
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}
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// -----------------------------------------------------------------------------
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// absl::Bernoulli(bitgen, p)
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// -----------------------------------------------------------------------------
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//
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// `absl::Bernoulli` produces a random boolean value, with probability `p`
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// (where 0.0 <= p <= 1.0) equaling `true`.
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//
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// Prefer `absl::Bernoulli` to produce boolean values over other alternatives
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// such as comparing an `absl::Uniform()` value to a specific output.
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//
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// See https://en.wikipedia.org/wiki/Bernoulli_distribution
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//
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// Example:
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//
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// absl::BitGen bitgen;
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// ...
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// if (absl::Bernoulli(bitgen, 1.0/3721.0)) {
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// std::cout << "Asteroid field navigation successful.";
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// }
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//
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template <typename URBG>
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bool Bernoulli(URBG&& urbg, // NOLINT(runtime/references)
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double p) {
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = absl::bernoulli_distribution;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, p);
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}
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// -----------------------------------------------------------------------------
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// absl::Beta<T>(bitgen, alpha, beta)
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// -----------------------------------------------------------------------------
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//
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// `absl::Beta` produces a floating point number distributed in the closed
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// interval [0,1] and parameterized by two values `alpha` and `beta` as per a
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// Beta distribution. `T` must be a floating point type, but may be inferred
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// from the types of `alpha` and `beta`.
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//
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// See https://en.wikipedia.org/wiki/Beta_distribution.
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//
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// Example:
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//
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// absl::BitGen bitgen;
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// ...
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// double sample = absl::Beta(bitgen, 3.0, 2.0);
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//
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template <typename RealType, typename URBG>
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RealType Beta(URBG&& urbg, // NOLINT(runtime/references)
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RealType alpha, RealType beta) {
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static_assert(
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std::is_floating_point<RealType>::value,
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"Template-argument 'RealType' must be a floating-point type, in "
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"absl::Beta<RealType, URBG>(...)");
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = typename absl::beta_distribution<RealType>;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, alpha, beta);
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}
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// -----------------------------------------------------------------------------
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// absl::Exponential<T>(bitgen, lambda = 1)
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// -----------------------------------------------------------------------------
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//
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// `absl::Exponential` produces a floating point number representing the
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// distance (time) between two consecutive events in a point process of events
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// occurring continuously and independently at a constant average rate. `T` must
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// be a floating point type, but may be inferred from the type of `lambda`.
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//
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// See https://en.wikipedia.org/wiki/Exponential_distribution.
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//
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// Example:
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//
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// absl::BitGen bitgen;
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// ...
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// double call_length = absl::Exponential(bitgen, 7.0);
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//
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template <typename RealType, typename URBG>
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RealType Exponential(URBG&& urbg, // NOLINT(runtime/references)
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RealType lambda = 1) {
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static_assert(
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std::is_floating_point<RealType>::value,
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"Template-argument 'RealType' must be a floating-point type, in "
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"absl::Exponential<RealType, URBG>(...)");
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = typename absl::exponential_distribution<RealType>;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, lambda);
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}
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// -----------------------------------------------------------------------------
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// absl::Gaussian<T>(bitgen, mean = 0, stddev = 1)
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// -----------------------------------------------------------------------------
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//
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// `absl::Gaussian` produces a floating point number selected from the Gaussian
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// (ie. "Normal") distribution. `T` must be a floating point type, but may be
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// inferred from the types of `mean` and `stddev`.
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//
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// See https://en.wikipedia.org/wiki/Normal_distribution
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//
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// Example:
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//
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// absl::BitGen bitgen;
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// ...
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// double giraffe_height = absl::Gaussian(bitgen, 16.3, 3.3);
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//
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template <typename RealType, typename URBG>
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RealType Gaussian(URBG&& urbg, // NOLINT(runtime/references)
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RealType mean = 0, RealType stddev = 1) {
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static_assert(
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std::is_floating_point<RealType>::value,
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"Template-argument 'RealType' must be a floating-point type, in "
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"absl::Gaussian<RealType, URBG>(...)");
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = typename absl::gaussian_distribution<RealType>;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, mean, stddev);
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}
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// -----------------------------------------------------------------------------
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// absl::LogUniform<T>(bitgen, lo, hi, base = 2)
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// -----------------------------------------------------------------------------
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//
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// `absl::LogUniform` produces random values distributed where the log to a
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// given base of all values is uniform in a closed interval [lo, hi]. `T` must
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// be an integral type, but may be inferred from the types of `lo` and `hi`.
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//
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// I.e., `LogUniform(0, n, b)` is uniformly distributed across buckets
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// [0], [1, b-1], [b, b^2-1] .. [b^(k-1), (b^k)-1] .. [b^floor(log(n, b)), n]
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// and is uniformly distributed within each bucket.
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//
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// The resulting probability density is inversely related to bucket size, though
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// values in the final bucket may be more likely than previous values. (In the
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// extreme case where n = b^i the final value will be tied with zero as the most
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// probable result.
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//
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// If `lo` is nonzero then this distribution is shifted to the desired interval,
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// so LogUniform(lo, hi, b) is equivalent to LogUniform(0, hi-lo, b)+lo.
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//
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// See http://ecolego.facilia.se/ecolego/show/Log-Uniform%20Distribution
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//
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// Example:
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//
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// absl::BitGen bitgen;
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// ...
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// int v = absl::LogUniform(bitgen, 0, 1000);
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//
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template <typename IntType, typename URBG>
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IntType LogUniform(URBG&& urbg, // NOLINT(runtime/references)
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IntType lo, IntType hi, IntType base = 2) {
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static_assert(std::is_integral<IntType>::value,
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"Template-argument 'IntType' must be an integral type, in "
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"absl::LogUniform<IntType, URBG>(...)");
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = typename absl::log_uniform_int_distribution<IntType>;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, lo, hi, base);
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}
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// -----------------------------------------------------------------------------
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// absl::Poisson<T>(bitgen, mean = 1)
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// -----------------------------------------------------------------------------
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//
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// `absl::Poisson` produces discrete probabilities for a given number of events
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// occurring within a fixed interval within the closed interval [0, max]. `T`
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// must be an integral type.
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//
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// See https://en.wikipedia.org/wiki/Poisson_distribution
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//
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// Example:
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//
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// absl::BitGen bitgen;
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// ...
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// int requests_per_minute = absl::Poisson<int>(bitgen, 3.2);
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//
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template <typename IntType, typename URBG>
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IntType Poisson(URBG&& urbg, // NOLINT(runtime/references)
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double mean = 1.0) {
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static_assert(std::is_integral<IntType>::value,
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"Template-argument 'IntType' must be an integral type, in "
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"absl::Poisson<IntType, URBG>(...)");
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = typename absl::poisson_distribution<IntType>;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, mean);
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}
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// -----------------------------------------------------------------------------
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// absl::Zipf<T>(bitgen, hi = max, q = 2, v = 1)
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// -----------------------------------------------------------------------------
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//
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// `absl::Zipf` produces discrete probabilities commonly used for modelling of
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// rare events over the closed interval [0, hi]. The parameters `v` and `q`
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// determine the skew of the distribution. `T` must be an integral type, but
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// may be inferred from the type of `hi`.
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//
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// See http://mathworld.wolfram.com/ZipfDistribution.html
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//
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// Example:
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//
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// absl::BitGen bitgen;
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// ...
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// int term_rank = absl::Zipf<int>(bitgen);
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//
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template <typename IntType, typename URBG>
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IntType Zipf(URBG&& urbg, // NOLINT(runtime/references)
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IntType hi = (std::numeric_limits<IntType>::max)(), double q = 2.0,
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double v = 1.0) {
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static_assert(std::is_integral<IntType>::value,
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"Template-argument 'IntType' must be an integral type, in "
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"absl::Zipf<IntType, URBG>(...)");
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using gen_t = absl::decay_t<URBG>;
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using distribution_t = typename absl::zipf_distribution<IntType>;
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return random_internal::DistributionCaller<gen_t>::template Call<
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distribution_t>(&urbg, hi, q, v);
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}
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ABSL_NAMESPACE_END
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} // namespace absl
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#endif // ABSL_RANDOM_DISTRIBUTIONS_H_
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