f417abe683
git-svn-id: https://llvm.org/svn/llvm-project/libcxx/trunk@103788 91177308-0d34-0410-b5e6-96231b3b80d8
99 lines
3.0 KiB
C++
99 lines
3.0 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// The LLVM Compiler Infrastructure
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//
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// This file is distributed under the University of Illinois Open Source
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// License. See LICENSE.TXT for details.
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//
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//===----------------------------------------------------------------------===//
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// <random>
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// template<class RealType = double>
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// class gamma_distribution
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// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
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#include <random>
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#include <cassert>
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#include <vector>
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#include <numeric>
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template <class T>
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inline
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T
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sqr(T x)
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{
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return x * x;
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}
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int main()
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{
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{
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typedef std::gamma_distribution<> D;
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typedef D::param_type P;
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typedef std::minstd_rand G;
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G g;
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D d(0.5, 2);
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P p(1, .5);
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const int N = 100000;
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std::vector<D::result_type> u;
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for (int i = 0; i < N; ++i)
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u.push_back(d(g, p));
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D::result_type mean = std::accumulate(u.begin(), u.end(),
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D::result_type(0)) / u.size();
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D::result_type var = 0;
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for (int i = 0; i < u.size(); ++i)
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var += sqr(u[i] - mean);
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var /= u.size();
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D::result_type x_mean = p.alpha() * p.beta();
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D::result_type x_var = p.alpha() * sqr(p.beta());
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assert(std::abs(mean - x_mean) / x_mean < 0.02);
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assert(std::abs(var - x_var) / x_var < 0.02);
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}
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{
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typedef std::gamma_distribution<> D;
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typedef D::param_type P;
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typedef std::minstd_rand G;
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G g;
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D d(1, .5);
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P p(2, 3);
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const int N = 100000;
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std::vector<D::result_type> u;
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for (int i = 0; i < N; ++i)
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u.push_back(d(g, p));
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D::result_type mean = std::accumulate(u.begin(), u.end(),
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D::result_type(0)) / u.size();
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D::result_type var = 0;
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for (int i = 0; i < u.size(); ++i)
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var += sqr(u[i] - mean);
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var /= u.size();
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D::result_type x_mean = p.alpha() * p.beta();
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D::result_type x_var = p.alpha() * sqr(p.beta());
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assert(std::abs(mean - x_mean) / x_mean < 0.02);
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assert(std::abs(var - x_var) / x_var < 0.02);
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}
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{
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typedef std::gamma_distribution<> D;
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typedef D::param_type P;
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typedef std::minstd_rand G;
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G g;
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D d(2, 3);
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P p(.5, 2);
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const int N = 100000;
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std::vector<D::result_type> u;
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for (int i = 0; i < N; ++i)
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u.push_back(d(g, p));
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D::result_type mean = std::accumulate(u.begin(), u.end(),
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D::result_type(0)) / u.size();
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D::result_type var = 0;
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for (int i = 0; i < u.size(); ++i)
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var += sqr(u[i] - mean);
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var /= u.size();
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D::result_type x_mean = p.alpha() * p.beta();
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D::result_type x_var = p.alpha() * sqr(p.beta());
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assert(std::abs(mean - x_mean) / x_mean < 0.02);
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assert(std::abs(var - x_var) / x_var < 0.02);
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}
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}
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