eval_param.pass.cpp revision d6d1171f2c3f254582ae1d5b9e14cea0ea8e701b 
1//===----------------------------------------------------------------------===//
2//
3//                     The LLVM Compiler Infrastructure
4//
5// This file is distributed under the University of Illinois Open Source
6// License. See LICENSE.TXT for details.
7//
8//===----------------------------------------------------------------------===//
9
10// <random>
11
12// template<class RealType = double>
13// class extreme_value_distribution
14
15// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
16
17#include <random>
18#include <cassert>
19#include <vector>
20#include <numeric>
21
22template <class T>
23inline
24T
25sqr(T x)
26{
27    return x * x;
28}
29
30int main()
31{
32    {
33        typedef std::extreme_value_distribution<> D;
34        typedef D::param_type P;
35        typedef std::mt19937 G;
36        G g;
37        D d(-0.5, 1);
38        P p(0.5, 2);
39        const int N = 1000000;
40        std::vector<D::result_type> u;
41        for (int i = 0; i < N; ++i)
42        {
43            D::result_type v = d(g, p);
44            u.push_back(v);
45        }
46        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
47        double var = 0;
48        double skew = 0;
49        double kurtosis = 0;
50        for (int i = 0; i < u.size(); ++i)
51        {
52            double d = (u[i] - mean);
53            double d2 = sqr(d);
54            var += d2;
55            skew += d * d2;
56            kurtosis += d2 * d2;
57        }
58        var /= u.size();
59        double dev = std::sqrt(var);
60        skew /= u.size() * dev * var;
61        kurtosis /= u.size() * var * var;
62        kurtosis -= 3;
63        double x_mean = p.a() + p.b() * 0.577215665;
64        double x_var = sqr(p.b()) * 1.644934067;
65        double x_skew = 1.139547;
66        double x_kurtosis = 12./5;
67        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
68        assert(std::abs((var - x_var) / x_var) < 0.01);
69        assert(std::abs((skew - x_skew) / x_skew) < 0.01);
70        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
71    }
72    {
73        typedef std::extreme_value_distribution<> D;
74        typedef D::param_type P;
75        typedef std::mt19937 G;
76        G g;
77        D d(-0.5, 1);
78        P p(1, 2);
79        const int N = 1000000;
80        std::vector<D::result_type> u;
81        for (int i = 0; i < N; ++i)
82        {
83            D::result_type v = d(g, p);
84            u.push_back(v);
85        }
86        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
87        double var = 0;
88        double skew = 0;
89        double kurtosis = 0;
90        for (int i = 0; i < u.size(); ++i)
91        {
92            double d = (u[i] - mean);
93            double d2 = sqr(d);
94            var += d2;
95            skew += d * d2;
96            kurtosis += d2 * d2;
97        }
98        var /= u.size();
99        double dev = std::sqrt(var);
100        skew /= u.size() * dev * var;
101        kurtosis /= u.size() * var * var;
102        kurtosis -= 3;
103        double x_mean = p.a() + p.b() * 0.577215665;
104        double x_var = sqr(p.b()) * 1.644934067;
105        double x_skew = 1.139547;
106        double x_kurtosis = 12./5;
107        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
108        assert(std::abs((var - x_var) / x_var) < 0.01);
109        assert(std::abs((skew - x_skew) / x_skew) < 0.01);
110        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
111    }
112    {
113        typedef std::extreme_value_distribution<> D;
114        typedef D::param_type P;
115        typedef std::mt19937 G;
116        G g;
117        D d(-0.5, 1);
118        P p(1.5, 3);
119        const int N = 1000000;
120        std::vector<D::result_type> u;
121        for (int i = 0; i < N; ++i)
122        {
123            D::result_type v = d(g, p);
124            u.push_back(v);
125        }
126        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
127        double var = 0;
128        double skew = 0;
129        double kurtosis = 0;
130        for (int i = 0; i < u.size(); ++i)
131        {
132            double d = (u[i] - mean);
133            double d2 = sqr(d);
134            var += d2;
135            skew += d * d2;
136            kurtosis += d2 * d2;
137        }
138        var /= u.size();
139        double dev = std::sqrt(var);
140        skew /= u.size() * dev * var;
141        kurtosis /= u.size() * var * var;
142        kurtosis -= 3;
143        double x_mean = p.a() + p.b() * 0.577215665;
144        double x_var = sqr(p.b()) * 1.644934067;
145        double x_skew = 1.139547;
146        double x_kurtosis = 12./5;
147        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
148        assert(std::abs((var - x_var) / x_var) < 0.01);
149        assert(std::abs((skew - x_skew) / x_skew) < 0.01);
150        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
151    }
152    {
153        typedef std::extreme_value_distribution<> D;
154        typedef D::param_type P;
155        typedef std::mt19937 G;
156        G g;
157        D d(-0.5, 1);
158        P p(3, 4);
159        const int N = 1000000;
160        std::vector<D::result_type> u;
161        for (int i = 0; i < N; ++i)
162        {
163            D::result_type v = d(g, p);
164            u.push_back(v);
165        }
166        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
167        double var = 0;
168        double skew = 0;
169        double kurtosis = 0;
170        for (int i = 0; i < u.size(); ++i)
171        {
172            double d = (u[i] - mean);
173            double d2 = sqr(d);
174            var += d2;
175            skew += d * d2;
176            kurtosis += d2 * d2;
177        }
178        var /= u.size();
179        double dev = std::sqrt(var);
180        skew /= u.size() * dev * var;
181        kurtosis /= u.size() * var * var;
182        kurtosis -= 3;
183        double x_mean = p.a() + p.b() * 0.577215665;
184        double x_var = sqr(p.b()) * 1.644934067;
185        double x_skew = 1.139547;
186        double x_kurtosis = 12./5;
187        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
188        assert(std::abs((var - x_var) / x_var) < 0.01);
189        assert(std::abs((skew - x_skew) / x_skew) < 0.01);
190        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
191    }
192}
193