476 lines
16 KiB
C++
476 lines
16 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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//
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// REQUIRES: long_tests
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// <random>
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// template<class RealType = double>
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// class uniform_real_distribution
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// template<class _URNG> result_type operator()(_URNG& g);
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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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#include <cstddef>
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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::uniform_real_distribution<> D;
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typedef std::minstd_rand0 G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::minstd_rand G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::mt19937 G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::mt19937_64 G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::ranlux24_base G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.02);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::ranlux48_base G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::ranlux24 G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::ranlux48 G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::knuth_b G;
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G g;
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D d;
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::minstd_rand G;
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G g;
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D d(-1, 1);
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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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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assert(std::abs(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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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{
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typedef std::uniform_real_distribution<> D;
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typedef std::minstd_rand G;
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G g;
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D d(5.5, 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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{
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D::result_type v = d(g);
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assert(d.a() <= v && v < d.b());
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u.push_back(v);
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}
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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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D::result_type skew = 0;
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D::result_type kurtosis = 0;
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for (std::size_t i = 0; i < u.size(); ++i)
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{
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D::result_type dbl = (u[i] - mean);
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D::result_type d2 = sqr(dbl);
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var += d2;
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skew += dbl * d2;
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kurtosis += d2 * d2;
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}
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var /= u.size();
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D::result_type 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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D::result_type x_mean = (d.a() + d.b()) / 2;
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D::result_type x_var = sqr(d.b() - d.a()) / 12;
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D::result_type x_skew = 0;
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D::result_type x_kurtosis = -6./5;
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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) < 0.01);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
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}
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}
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