forked from OSchip/llvm-project
273 lines
9.0 KiB
C++
273 lines
9.0 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 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 IntType = int>
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// class negative_binomial_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::negative_binomial_distribution<> D;
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typedef std::minstd_rand G;
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G g;
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D d(5, .25);
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const int N = 1000000;
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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.min() <= v && v <= d.max());
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u.push_back(v);
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}
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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.k() * (1 - d.p()) / d.p();
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double x_var = x_mean / d.p();
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double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
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double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
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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::negative_binomial_distribution<> D;
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typedef std::mt19937 G;
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G g;
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D d(30, .03125);
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const int N = 1000000;
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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.min() <= v && v <= d.max());
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u.push_back(v);
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}
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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.k() * (1 - d.p()) / d.p();
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double x_var = x_mean / d.p();
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double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
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double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
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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) < 0.01);
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}
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{
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typedef std::negative_binomial_distribution<> D;
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typedef std::mt19937 G;
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G g;
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D d(40, .25);
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const int N = 1000000;
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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.min() <= v && v <= d.max());
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u.push_back(v);
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}
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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.k() * (1 - d.p()) / d.p();
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double x_var = x_mean / d.p();
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double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
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double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
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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) < 0.03);
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}
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{
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typedef std::negative_binomial_distribution<> D;
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typedef std::mt19937 G;
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G g;
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D d(40, 1);
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const int N = 1000;
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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.min() <= v && v <= d.max());
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u.push_back(v);
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}
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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.k() * (1 - d.p()) / d.p();
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double x_var = x_mean / d.p();
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double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
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double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
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assert(mean == x_mean);
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assert(var == x_var);
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}
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{
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typedef std::negative_binomial_distribution<> D;
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typedef std::mt19937 G;
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G g;
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D d(400, 0.5);
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const int N = 1000000;
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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.min() <= v && v <= d.max());
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u.push_back(v);
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}
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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.k() * (1 - d.p()) / d.p();
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double x_var = x_mean / d.p();
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double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
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double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
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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) < 0.04);
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assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.05);
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}
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{
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typedef std::negative_binomial_distribution<> D;
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typedef std::mt19937 G;
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G g;
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D d(1, 0.05);
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const int N = 1000000;
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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.min() <= v && v <= d.max());
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u.push_back(v);
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}
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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.k() * (1 - d.p()) / d.p();
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double x_var = x_mean / d.p();
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double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
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double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
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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) < 0.03);
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}
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}
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