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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// class bernoulli_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;
}
int main()
{
{
typedef std::bernoulli_distribution D;
typedef std::minstd_rand G;
G g;
D d(.75);
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
u.push_back(d(g));
double mean = std::accumulate(u.begin(), u.end(),
double(0)) / u.size();
double var = 0;
for (int i = 0; i < u.size(); ++i)
var += sqr(u[i] - mean);
var /= u.size();
double x_mean = d.p();
double x_var = d.p()*(1-d.p());
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
}
{
typedef std::bernoulli_distribution D;
typedef std::minstd_rand G;
G g;
D d(.25);
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
u.push_back(d(g));
double mean = std::accumulate(u.begin(), u.end(),
double(0)) / u.size();
double var = 0;
for (int i = 0; i < u.size(); ++i)
var += sqr(u[i] - mean);
var /= u.size();
double x_mean = d.p();
double x_var = d.p()*(1-d.p());
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
}
}
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