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479 lines
16 KiB
C++
479 lines
16 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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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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#include "test_macros.h"
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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(int, char**)
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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);
|
|
}
|
|
{
|
|
typedef std::uniform_real_distribution<> D;
|
|
typedef std::minstd_rand G;
|
|
G g;
|
|
D d(5.5, 25);
|
|
const int N = 100000;
|
|
std::vector<D::result_type> u;
|
|
for (int i = 0; i < N; ++i)
|
|
{
|
|
D::result_type v = d(g);
|
|
assert(d.a() <= v && v < d.b());
|
|
u.push_back(v);
|
|
}
|
|
D::result_type mean = std::accumulate(u.begin(), u.end(),
|
|
D::result_type(0)) / u.size();
|
|
D::result_type var = 0;
|
|
D::result_type skew = 0;
|
|
D::result_type kurtosis = 0;
|
|
for (std::size_t i = 0; i < u.size(); ++i)
|
|
{
|
|
D::result_type dbl = (u[i] - mean);
|
|
D::result_type d2 = sqr(dbl);
|
|
var += d2;
|
|
skew += dbl * d2;
|
|
kurtosis += d2 * d2;
|
|
}
|
|
var /= u.size();
|
|
D::result_type dev = std::sqrt(var);
|
|
skew /= u.size() * dev * var;
|
|
kurtosis /= u.size() * var * var;
|
|
kurtosis -= 3;
|
|
D::result_type x_mean = (d.a() + d.b()) / 2;
|
|
D::result_type x_var = sqr(d.b() - d.a()) / 12;
|
|
D::result_type x_skew = 0;
|
|
D::result_type x_kurtosis = -6./5;
|
|
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) < 0.01);
|
|
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
|
|
}
|
|
|
|
return 0;
|
|
}
|