297 lines
8.2 KiB
C++
297 lines
8.2 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 negative_binomial_distribution
|
|
|
|
// template<class _URNG> result_type operator()(_URNG& g);
|
|
|
|
#include <random>
|
|
#include <numeric>
|
|
#include <vector>
|
|
#include <cassert>
|
|
|
|
template <class T>
|
|
inline
|
|
T
|
|
sqr(T x)
|
|
{
|
|
return x * x;
|
|
}
|
|
|
|
void
|
|
test1()
|
|
{
|
|
typedef std::negative_binomial_distribution<> D;
|
|
typedef std::minstd_rand G;
|
|
G g;
|
|
D d(5, .25);
|
|
const int N = 1000000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.min() <= v && v <= d.max());
|
|
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 = d.k() * (1 - d.p()) / d.p();
|
|
double x_var = x_mean / d.p();
|
|
double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
|
|
double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
|
|
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) / x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02);
|
|
}
|
|
|
|
void
|
|
test2()
|
|
{
|
|
typedef std::negative_binomial_distribution<> D;
|
|
typedef std::mt19937 G;
|
|
G g;
|
|
D d(30, .03125);
|
|
const int N = 1000000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.min() <= v && v <= d.max());
|
|
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 = d.k() * (1 - d.p()) / d.p();
|
|
double x_var = x_mean / d.p();
|
|
double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
|
|
double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
|
|
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) / x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
|
|
void
|
|
test3()
|
|
{
|
|
typedef std::negative_binomial_distribution<> D;
|
|
typedef std::mt19937 G;
|
|
G g;
|
|
D d(40, .25);
|
|
const int N = 1000000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.min() <= v && v <= d.max());
|
|
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 = d.k() * (1 - d.p()) / d.p();
|
|
double x_var = x_mean / d.p();
|
|
double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
|
|
double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
|
|
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) / x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
|
|
}
|
|
|
|
void
|
|
test4()
|
|
{
|
|
typedef std::negative_binomial_distribution<> D;
|
|
typedef std::mt19937 G;
|
|
G g;
|
|
D d(40, 1);
|
|
const int N = 1000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.min() <= v && v <= d.max());
|
|
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 = d.k() * (1 - d.p()) / d.p();
|
|
double x_var = x_mean / d.p();
|
|
double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
|
|
double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
|
|
assert(mean == x_mean);
|
|
assert(var == x_var);
|
|
}
|
|
|
|
void
|
|
test5()
|
|
{
|
|
typedef std::negative_binomial_distribution<> D;
|
|
typedef std::mt19937 G;
|
|
G g;
|
|
D d(400, 0.5);
|
|
const int N = 1000000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.min() <= v && v <= d.max());
|
|
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 = d.k() * (1 - d.p()) / d.p();
|
|
double x_var = x_mean / d.p();
|
|
double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
|
|
double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
|
|
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) / x_skew) < 0.04);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.05);
|
|
}
|
|
|
|
void
|
|
test6()
|
|
{
|
|
typedef std::negative_binomial_distribution<> D;
|
|
typedef std::mt19937 G;
|
|
G g;
|
|
D d(1, 0.05);
|
|
const int N = 1000000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.min() <= v && v <= d.max());
|
|
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 = d.k() * (1 - d.p()) / d.p();
|
|
double x_var = x_mean / d.p();
|
|
double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
|
|
double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
|
|
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) / x_skew) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
|
|
}
|
|
|
|
int main()
|
|
{
|
|
test1();
|
|
test2();
|
|
test3();
|
|
test4();
|
|
test5();
|
|
test6();
|
|
}
|