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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_EXPONENTIAL_DISTRIBUTION_H_
#define ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_
#include <cassert>
#include <cmath>
#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 {
inline namespace lts_2019_08_08 {
// absl::exponential_distribution:
// Generates a number conforming to an exponential distribution and is
// equivalent to the standard [rand.dist.pois.exp] distribution.
template <typename RealType = double>
class exponential_distribution {
public:
using result_type = RealType;
class param_type {
public:
using distribution_type = exponential_distribution;
explicit param_type(result_type lambda = 1) : lambda_(lambda) {
assert(lambda > 0);
neg_inv_lambda_ = -result_type(1) / lambda_;
}
result_type lambda() const { return lambda_; }
friend bool operator==(const param_type& a, const param_type& b) {
return a.lambda_ == b.lambda_;
}
friend bool operator!=(const param_type& a, const param_type& b) {
return !(a == b);
}
private:
friend class exponential_distribution;
result_type lambda_;
result_type neg_inv_lambda_;
static_assert(
std::is_floating_point<RealType>::value,
"Class-template absl::exponential_distribution<> must be parameterized "
"using a floating-point type.");
};
exponential_distribution() : exponential_distribution(1) {}
explicit exponential_distribution(result_type lambda) : param_(lambda) {}
explicit exponential_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 0; }
result_type(max)() const {
return std::numeric_limits<result_type>::infinity();
}
result_type lambda() const { return param_.lambda(); }
friend bool operator==(const exponential_distribution& a,
const exponential_distribution& b) {
return a.param_ == b.param_;
}
friend bool operator!=(const exponential_distribution& a,
const exponential_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 exponential_distribution<RealType>::result_type
exponential_distribution<RealType>::operator()(
URBG& g, // NOLINT(runtime/references)
const param_type& p) {
using random_internal::NegativeValueT;
const result_type u = random_internal::RandU64ToReal<
result_type>::template Value<NegativeValueT, false>(fast_u64_(g));
// log1p(-x) is mathematically equivalent to log(1 - x) but has more
// accuracy for x near zero.
return p.neg_inv_lambda_ * std::log1p(u);
}
template <typename CharT, typename Traits, typename RealType>
std::basic_ostream<CharT, Traits>& operator<<(
std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references)
const exponential_distribution<RealType>& x) {
auto saver = random_internal::make_ostream_state_saver(os);
os.precision(random_internal::stream_precision_helper<RealType>::kPrecision);
os << x.lambda();
return os;
}
template <typename CharT, typename Traits, typename RealType>
std::basic_istream<CharT, Traits>& operator>>(
std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references)
exponential_distribution<RealType>& x) { // NOLINT(runtime/references)
using result_type = typename exponential_distribution<RealType>::result_type;
using param_type = typename exponential_distribution<RealType>::param_type;
result_type lambda;
auto saver = random_internal::make_istream_state_saver(is);
lambda = random_internal::read_floating_point<result_type>(is);
if (!is.fail()) {
x.param(param_type(lambda));
}
return is;
}
} // inline namespace lts_2019_08_08
} // namespace absl
#endif // ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_
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