1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
|
// 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/meta/type_traits.h"
#include "absl/random/internal/fast_uniform_bits.h"
#include "absl/random/internal/generate_real.h"
#include "absl/random/internal/iostream_state_saver.h"
namespace absl {
// 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::GenerateNegativeTag;
using random_internal::GenerateRealFromBits;
using real_type =
absl::conditional_t<std::is_same<RealType, float>::value, float, double>;
const result_type u = GenerateRealFromBits<real_type, GenerateNegativeTag,
false>(fast_u64_(g)); // U(-1, 0)
// 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;
}
} // namespace absl
#endif // ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_
|
