456 lines
15 KiB
C++
456 lines
15 KiB
C++
//===----------------------------------------------------------------------===//
|
|
//
|
|
// The LLVM Compiler Infrastructure
|
|
//
|
|
// This file is dual licensed under the MIT and the University of Illinois Open
|
|
// Source Licenses. See LICENSE.TXT for details.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
//
|
|
// REQUIRES: long_tests
|
|
|
|
// <random>
|
|
|
|
// template<class _IntType = int>
|
|
// class uniform_int_distribution
|
|
|
|
// template<class _URNG> result_type operator()(_URNG& g);
|
|
|
|
#include <random>
|
|
#include <cassert>
|
|
#include <vector>
|
|
#include <numeric>
|
|
|
|
template <class T>
|
|
inline
|
|
T
|
|
sqr(T x)
|
|
{
|
|
return x * x;
|
|
}
|
|
|
|
int main()
|
|
{
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::minstd_rand0 G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::minstd_rand G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::mt19937 G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::mt19937_64 G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::ranlux24_base G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::ranlux48_base G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::ranlux24 G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::ranlux48 G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::knuth_b G;
|
|
G g;
|
|
D d;
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::minstd_rand0 G;
|
|
G g;
|
|
D d(-6, 106);
|
|
for (int i = 0; i < 10000; ++i)
|
|
{
|
|
int u = d(g);
|
|
assert(-6 <= u && u <= 106);
|
|
}
|
|
}
|
|
{
|
|
typedef std::uniform_int_distribution<> D;
|
|
typedef std::minstd_rand G;
|
|
G g;
|
|
D d(5, 100);
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v <= d.b());
|
|
u.push_back(v);
|
|
}
|
|
double mean = std::accumulate(u.begin(), u.end(),
|
|
double(0)) / u.size();
|
|
double var = 0;
|
|
double skew = 0;
|
|
double kurtosis = 0;
|
|
for (int i = 0; i < u.size(); ++i)
|
|
{
|
|
double dbl = (u[i] - mean);
|
|
double d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
double dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
double x_mean = ((double)d.a() + d.b()) / 2;
|
|
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
|
|
double x_skew = 0;
|
|
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
|
|
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
|
|
assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
|
assert(std::abs((var - x_var) / x_var) < 0.01);
|
|
assert(std::abs(skew - x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
}
|