fc8dc48020
git-subtree-dir: third_party/abseil_cpp git-subtree-mainline:ffb2ae54begit-subtree-split:768eb2ca28
247 lines
7.8 KiB
C++
247 lines
7.8 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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#ifndef ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_
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#define ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_
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#include <cassert>
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#include <cmath>
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#include <istream>
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#include <limits>
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#include <numeric>
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#include <type_traits>
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#include <utility>
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#include <vector>
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#include "absl/random/bernoulli_distribution.h"
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#include "absl/random/internal/iostream_state_saver.h"
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#include "absl/random/uniform_int_distribution.h"
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namespace absl {
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ABSL_NAMESPACE_BEGIN
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// absl::discrete_distribution
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//
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// A discrete distribution produces random integers i, where 0 <= i < n
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// distributed according to the discrete probability function:
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//
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// P(i|p0,...,pn−1)=pi
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//
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// This class is an implementation of discrete_distribution (see
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// [rand.dist.samp.discrete]).
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//
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// The algorithm used is Walker's Aliasing algorithm, described in Knuth, Vol 2.
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// absl::discrete_distribution takes O(N) time to precompute the probabilities
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// (where N is the number of possible outcomes in the distribution) at
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// construction, and then takes O(1) time for each variate generation. Many
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// other implementations also take O(N) time to construct an ordered sequence of
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// partial sums, plus O(log N) time per variate to binary search.
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//
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template <typename IntType = int>
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class discrete_distribution {
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public:
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using result_type = IntType;
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class param_type {
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public:
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using distribution_type = discrete_distribution;
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param_type() { init(); }
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template <typename InputIterator>
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explicit param_type(InputIterator begin, InputIterator end)
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: p_(begin, end) {
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init();
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}
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explicit param_type(std::initializer_list<double> weights) : p_(weights) {
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init();
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}
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template <class UnaryOperation>
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explicit param_type(size_t nw, double xmin, double xmax,
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UnaryOperation fw) {
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if (nw > 0) {
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p_.reserve(nw);
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double delta = (xmax - xmin) / static_cast<double>(nw);
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assert(delta > 0);
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double t = delta * 0.5;
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for (size_t i = 0; i < nw; ++i) {
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p_.push_back(fw(xmin + i * delta + t));
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}
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}
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init();
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}
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const std::vector<double>& probabilities() const { return p_; }
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size_t n() const { return p_.size() - 1; }
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friend bool operator==(const param_type& a, const param_type& b) {
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return a.probabilities() == b.probabilities();
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}
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friend bool operator!=(const param_type& a, const param_type& b) {
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return !(a == b);
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}
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private:
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friend class discrete_distribution;
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void init();
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std::vector<double> p_; // normalized probabilities
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std::vector<std::pair<double, size_t>> q_; // (acceptance, alternate) pairs
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static_assert(std::is_integral<result_type>::value,
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"Class-template absl::discrete_distribution<> must be "
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"parameterized using an integral type.");
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};
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discrete_distribution() : param_() {}
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explicit discrete_distribution(const param_type& p) : param_(p) {}
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template <typename InputIterator>
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explicit discrete_distribution(InputIterator begin, InputIterator end)
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: param_(begin, end) {}
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explicit discrete_distribution(std::initializer_list<double> weights)
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: param_(weights) {}
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template <class UnaryOperation>
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explicit discrete_distribution(size_t nw, double xmin, double xmax,
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UnaryOperation fw)
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: param_(nw, xmin, xmax, std::move(fw)) {}
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void reset() {}
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// generating functions
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template <typename URBG>
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result_type operator()(URBG& g) { // NOLINT(runtime/references)
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return (*this)(g, param_);
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}
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template <typename URBG>
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result_type operator()(URBG& g, // NOLINT(runtime/references)
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const param_type& p);
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const param_type& param() const { return param_; }
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void param(const param_type& p) { param_ = p; }
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result_type(min)() const { return 0; }
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result_type(max)() const {
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return static_cast<result_type>(param_.n());
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} // inclusive
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// NOTE [rand.dist.sample.discrete] returns a std::vector<double> not a
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// const std::vector<double>&.
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const std::vector<double>& probabilities() const {
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return param_.probabilities();
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}
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friend bool operator==(const discrete_distribution& a,
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const discrete_distribution& b) {
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return a.param_ == b.param_;
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}
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friend bool operator!=(const discrete_distribution& a,
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const discrete_distribution& b) {
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return a.param_ != b.param_;
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}
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private:
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param_type param_;
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};
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// --------------------------------------------------------------------------
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// Implementation details only below
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// --------------------------------------------------------------------------
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namespace random_internal {
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// Using the vector `*probabilities`, whose values are the weights or
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// probabilities of an element being selected, constructs the proportional
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// probabilities used by the discrete distribution. `*probabilities` will be
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// scaled, if necessary, so that its entries sum to a value sufficiently close
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// to 1.0.
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std::vector<std::pair<double, size_t>> InitDiscreteDistribution(
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std::vector<double>* probabilities);
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} // namespace random_internal
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template <typename IntType>
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void discrete_distribution<IntType>::param_type::init() {
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if (p_.empty()) {
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p_.push_back(1.0);
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q_.emplace_back(1.0, 0);
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} else {
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assert(n() <= (std::numeric_limits<IntType>::max)());
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q_ = random_internal::InitDiscreteDistribution(&p_);
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}
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}
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template <typename IntType>
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template <typename URBG>
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typename discrete_distribution<IntType>::result_type
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discrete_distribution<IntType>::operator()(
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URBG& g, // NOLINT(runtime/references)
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const param_type& p) {
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const auto idx = absl::uniform_int_distribution<result_type>(0, p.n())(g);
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const auto& q = p.q_[idx];
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const bool selected = absl::bernoulli_distribution(q.first)(g);
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return selected ? idx : static_cast<result_type>(q.second);
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}
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template <typename CharT, typename Traits, typename IntType>
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std::basic_ostream<CharT, Traits>& operator<<(
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std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references)
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const discrete_distribution<IntType>& x) {
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auto saver = random_internal::make_ostream_state_saver(os);
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const auto& probabilities = x.param().probabilities();
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os << probabilities.size();
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os.precision(random_internal::stream_precision_helper<double>::kPrecision);
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for (const auto& p : probabilities) {
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os << os.fill() << p;
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}
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return os;
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}
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template <typename CharT, typename Traits, typename IntType>
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std::basic_istream<CharT, Traits>& operator>>(
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std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references)
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discrete_distribution<IntType>& x) { // NOLINT(runtime/references)
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using param_type = typename discrete_distribution<IntType>::param_type;
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auto saver = random_internal::make_istream_state_saver(is);
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size_t n;
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std::vector<double> p;
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is >> n;
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if (is.fail()) return is;
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if (n > 0) {
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p.reserve(n);
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for (IntType i = 0; i < n && !is.fail(); ++i) {
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auto tmp = random_internal::read_floating_point<double>(is);
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if (is.fail()) return is;
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p.push_back(tmp);
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}
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}
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x.param(param_type(p.begin(), p.end()));
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return is;
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}
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ABSL_NAMESPACE_END
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} // namespace absl
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#endif // ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_
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