104 lines
3.1 KiB
C++
104 lines
3.1 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 dual licensed under the MIT and the University of Illinois Open
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// Source Licenses. See LICENSE.TXT for details.
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//
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//===----------------------------------------------------------------------===//
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// <random>
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// class bernoulli_distribution
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// template<class _URNG> result_type operator()(_URNG& g);
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#include <random>
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#include <numeric>
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#include <vector>
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#include <cassert>
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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::bernoulli_distribution D;
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typedef std::minstd_rand G;
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G g;
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D d(.75);
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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));
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double mean = std::accumulate(u.begin(), u.end(),
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double(0)) / u.size();
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double var = 0;
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double skew = 0;
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double kurtosis = 0;
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for (int i = 0; i < u.size(); ++i)
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{
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double d = (u[i] - mean);
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double d2 = sqr(d);
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var += d2;
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skew += d * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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double dev = std::sqrt(var);
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skew /= u.size() * dev * var;
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kurtosis /= u.size() * var * var;
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kurtosis -= 3;
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double x_mean = d.p();
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double x_var = d.p()*(1-d.p());
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double x_skew = (1 - 2 * d.p())/std::sqrt(x_var);
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double x_kurtosis = (6 * sqr(d.p()) - 6 * d.p() + 1)/x_var;
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assert(std::abs((mean - x_mean) / x_mean) < 0.01);
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assert(std::abs((var - x_var) / x_var) < 0.01);
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assert(std::abs((skew - x_skew) / x_skew) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02);
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}
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{
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typedef std::bernoulli_distribution D;
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typedef std::minstd_rand G;
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G g;
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D d(.25);
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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));
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double mean = std::accumulate(u.begin(), u.end(),
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double(0)) / u.size();
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double var = 0;
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double skew = 0;
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double kurtosis = 0;
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for (int i = 0; i < u.size(); ++i)
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{
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double d = (u[i] - mean);
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double d2 = sqr(d);
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var += d2;
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skew += d * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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double dev = std::sqrt(var);
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skew /= u.size() * dev * var;
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kurtosis /= u.size() * var * var;
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kurtosis -= 3;
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double x_mean = d.p();
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double x_var = d.p()*(1-d.p());
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double x_skew = (1 - 2 * d.p())/std::sqrt(x_var);
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double x_kurtosis = (6 * sqr(d.p()) - 6 * d.p() + 1)/x_var;
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assert(std::abs((mean - x_mean) / x_mean) < 0.01);
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assert(std::abs((var - x_var) / x_var) < 0.01);
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assert(std::abs((skew - x_skew) / x_skew) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02);
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}
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}
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