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Diffstat (limited to 'test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.weibull/eval.pass.cpp')
| -rw-r--r-- | test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.weibull/eval.pass.cpp | 166 |
1 files changed, 166 insertions, 0 deletions
diff --git a/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.weibull/eval.pass.cpp b/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.weibull/eval.pass.cpp new file mode 100644 index 000000000000..e414932dc870 --- /dev/null +++ b/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.weibull/eval.pass.cpp @@ -0,0 +1,166 @@ +//===----------------------------------------------------------------------===// +// +// The LLVM Compiler Infrastructure +// +// This file is dual licensed under the MIT and the University of Illinois Open +// Source Licenses. See LICENSE.TXT for details. +// +//===----------------------------------------------------------------------===// +// +// REQUIRES: long_tests + +// <random> + +// template<class RealType = double> +// class weibull_distribution + +// template<class _URNG> result_type operator()(_URNG& g); + +#include <random> +#include <cassert> +#include <vector> +#include <numeric> + +template <class T> +inline +T +sqr(T x) +{ + return x * x; +} + +int main() +{ + { + typedef std::weibull_distribution<> D; + typedef D::param_type P; + typedef std::mt19937 G; + G g; + D d(0.5, 2); + const int N = 1000000; + std::vector<D::result_type> u; + for (int i = 0; i < N; ++i) + { + D::result_type v = d(g); + assert(d.min() <= v); + u.push_back(v); + } + double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); + double var = 0; + double skew = 0; + double kurtosis = 0; + for (int i = 0; i < u.size(); ++i) + { + double d = (u[i] - mean); + double d2 = sqr(d); + var += d2; + skew += d * d2; + kurtosis += d2 * d2; + } + var /= u.size(); + double dev = std::sqrt(var); + skew /= u.size() * dev * var; + kurtosis /= u.size() * var * var; + kurtosis -= 3; + double x_mean = d.b() * std::tgamma(1 + 1/d.a()); + double x_var = sqr(d.b()) * std::tgamma(1 + 2/d.a()) - sqr(x_mean); + double x_skew = (sqr(d.b())*d.b() * std::tgamma(1 + 3/d.a()) - + 3*x_mean*x_var - sqr(x_mean)*x_mean) / + (std::sqrt(x_var)*x_var); + double x_kurtosis = (sqr(sqr(d.b())) * std::tgamma(1 + 4/d.a()) - + 4*x_skew*x_var*sqrt(x_var)*x_mean - + 6*sqr(x_mean)*x_var - sqr(sqr(x_mean))) / sqr(x_var) - 3; + 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) / x_skew) < 0.01); + assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03); + } + { + typedef std::weibull_distribution<> D; + typedef D::param_type P; + typedef std::mt19937 G; + G g; + D d(1, .5); + const int N = 1000000; + std::vector<D::result_type> u; + for (int i = 0; i < N; ++i) + { + D::result_type v = d(g); + assert(d.min() <= v); + u.push_back(v); + } + double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); + double var = 0; + double skew = 0; + double kurtosis = 0; + for (int i = 0; i < u.size(); ++i) + { + double d = (u[i] - mean); + double d2 = sqr(d); + var += d2; + skew += d * d2; + kurtosis += d2 * d2; + } + var /= u.size(); + double dev = std::sqrt(var); + skew /= u.size() * dev * var; + kurtosis /= u.size() * var * var; + kurtosis -= 3; + double x_mean = d.b() * std::tgamma(1 + 1/d.a()); + double x_var = sqr(d.b()) * std::tgamma(1 + 2/d.a()) - sqr(x_mean); + double x_skew = (sqr(d.b())*d.b() * std::tgamma(1 + 3/d.a()) - + 3*x_mean*x_var - sqr(x_mean)*x_mean) / + (std::sqrt(x_var)*x_var); + double x_kurtosis = (sqr(sqr(d.b())) * std::tgamma(1 + 4/d.a()) - + 4*x_skew*x_var*sqrt(x_var)*x_mean - + 6*sqr(x_mean)*x_var - sqr(sqr(x_mean))) / sqr(x_var) - 3; + 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) / x_skew) < 0.01); + assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); + } + { + typedef std::weibull_distribution<> D; + typedef D::param_type P; + typedef std::mt19937 G; + G g; + D d(2, 3); + const int N = 1000000; + std::vector<D::result_type> u; + for (int i = 0; i < N; ++i) + { + D::result_type v = d(g); + assert(d.min() <= v); + u.push_back(v); + } + double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); + double var = 0; + double skew = 0; + double kurtosis = 0; + for (int i = 0; i < u.size(); ++i) + { + double d = (u[i] - mean); + double d2 = sqr(d); + var += d2; + skew += d * d2; + kurtosis += d2 * d2; + } + var /= u.size(); + double dev = std::sqrt(var); + skew /= u.size() * dev * var; + kurtosis /= u.size() * var * var; + kurtosis -= 3; + double x_mean = d.b() * std::tgamma(1 + 1/d.a()); + double x_var = sqr(d.b()) * std::tgamma(1 + 2/d.a()) - sqr(x_mean); + double x_skew = (sqr(d.b())*d.b() * std::tgamma(1 + 3/d.a()) - + 3*x_mean*x_var - sqr(x_mean)*x_mean) / + (std::sqrt(x_var)*x_var); + double x_kurtosis = (sqr(sqr(d.b())) * std::tgamma(1 + 4/d.a()) - + 4*x_skew*x_var*sqrt(x_var)*x_mean - + 6*sqr(x_mean)*x_var - sqr(sqr(x_mean))) / sqr(x_var) - 3; + 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) / x_skew) < 0.01); + assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03); + } +} |